Luis Pieltain Fernández EL DIRECTOR. Tomás Gómez San Román

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1 Autorizada la entrega de la tesis de master del alumno/a: Luis Pieltain Fernández EL DIRECTOR Tomás Gómez San Román Fdo.: Fecha: /Julio/2009 EL TUTOR Tomás Gómez San Román Fdo.: Fecha: /Julio/2009 Vº Bº del Coordinador de Tesis Tomás Gómez San Román Fdo.: Fecha: /Julio/2009

2 UNIVERSIDAD PONTIFICIA COMILLAS ESCUELA TÉCNICA SUPERIOR DE INGENIERÍA (ICAI) MÁSTER OFICIAL EN EL SECTOR ELÉCTRICO TESIS DE MÁSTER IMPACT OF PLUG-IN HYBRID ELECTRIC VEHICLES (PHEVs) ON POWER SYSTEMS AUTOR: Luis Pieltain Fernández MADRID, Julio de 2009

3 IMPACTO DE LOS COCHES HÍBRIDOS ELÉCTRICOS ENCHUFABLES (PHEVs) EN LOS SISTEMAS DE POTENCIA. Autor: Pieltain Fernández, Luis. Director: Gómez San Román, Tomás. Entidad colaboradora: Instituto de Investigación Tecnológica (IIT) de la Universidad Pontificia Comillas. RESUMEN DE LA TESIS Hoy en día una forma rápida y cómoda de transporte es esencial para la vida del día a día, y más importante para la gente que vive en grandes ciudades que necesitan desplazarse largas distancias todos los días. Actualmente el petróleo es la fuente de energía esencial para alimentar a la clase de vehículos que necesitamos y con la calidad requerida. Es verdad que hace años el petróleo era abundante, fácil de encontrar y muy barato, y además no teníamos conocimiento acerca de sus malas consecuencias para el medio ambiente, como su alta concentración de emisiones de CO 2 o NO x, principales componentes de los tan conocidos gases invernaderos (GI), los cuales aceleran el proceso del calentamiento global. Hoy en día sabemos que los actuales derivados del petróleo están deteriorando la calidad del aire en las ciudades, destruyendo ecosistemas y además su precio incrementa día a día ya que es una fuente de energía limitada. Utilizamos esta fuente de energía más rápido de lo que se regenera. Por otro lado, las reservas de petróleo están concentradas en un número de países relativamente pequeño, lo que supone una barrera importante para la diversificación. Éste hecho hace del petróleo una fuente importante de conflicto político y militar. Es necesario la aceleración en el proceso de la comercialización de vehículos con fuentes de energía diversificadas, alta eficiencia y compatibilidad con un futuro energético sostenible y renovable. La electrificación del transporte ofrece un camino prometedor para alcanzar estos objetivos, pero está ocasionando también la necesidad de incurrir en importantes cambios tanto en el sector eléctrico como en el del automóvil.

4 En la presente tesis algunos de estos cambios son estudiados y evaluados. El propósito aquí es el de evaluar el impacto de una introducción masiva de coches eléctricos en el mercado en la red de distribución, mix energético y emisiones de CO 2 y tratar de definir algunas posibles estrategias para mitigar posibles amenazas o incluso beneficiarse de la introducción de coches eléctricos en algunas cuestiones, como por ejemplo el incremento de la cuota de energías renovables en el mix energético. También se realizó el estudio de algunos de los posibles modelos de negocio que puedan surgir alrededor de los coches eléctricos. La tesis aquí presentada se realizó en unas etapas que cronológicamente se han representado aquí en el orden de los capítulos, es decir, una primera parte en donde se hace una pequeña introducción y se explica la motivación de este proyecto. Se sigue con la explicación paso a paso y con detalle de todos los procesos que se llevaron a cabo para la ejecución y cumplimiento de los objetivos marcados, dividiendo el trabajo global en una serie de tareas más sencillas que se han ido llevando a cabo según una cronología pensada y estudiada previamente en el horizonte de 8 meses. El coche eléctrico tiene diferentes modos de estar conectado a la red, algunos de ellos para cargar la batería a diferentes ratios de velocidad y otro para dar la posibilidad de usar el coche como generador distribuido para inyectar energía en la red haciendo uso de su característica de sistema de almacenamiento de energía, por lo que para empezar con la tesis fue necesario en un primer lugar modelar el coche eléctrico como carga eléctrica y generador distribuido conectado a la red para seguir con un estudio de los posibles impactos de los coches eléctricos en la red de distribución. Para este propósito se utilizaron dos modelos, E-GRID Greenfield y E-GRID Expansion, para analizar el incremento en la inversión necesaria para reforzar la red, estimar incrementos en las pérdidas, etc. Y también tratar de definir estrategias para mitigar estas amenazas. El siguiente paso en la tesis fue definir diferentes modelos de negocio que rodean a los coches eléctricos. Aquí se presenta el modelo de negocio del distribuidos o DSO, la comercializadora, las estaciones de recarga y los fabricantes de coches, como asignar los costes calculados previamente al DSO como entidad regulada (i.e. cuales deben ser los costes que se le deben

5 de reconocer al DSO con el fin de diseñar las tarifas), y qué funciones desempeña, valor añadido y plan de negocio de los comercializadores independientes, estaciones de recarga y fabricantes de coches con la introducción de coches eléctricos. Para que todo esto funcione es necesario el desarrollo de una gestión activa de la demanda, y para esto, los contadores inteligentes son una parte muy importante. Finalmente se utilizó un tercer modelo llamado ROM. Con éste lo que se hizo fue evaluar el impacto de una masiva introducción de coches eléctricos en el mix energético español y en las emisiones de CO 2 para el año Con este modelo fue posible evaluar el impacto de una introducción masiva de coches eléctricos en el mix energético, costes de producción y operación y fiabilidad del sistema, y con esto diseñar estrategias para reducir esos costes e incluso incrementar la cuota de energía renovable de carácter intermitente en el mix de energía. Para tal fin se diseñó un escenario equivalente al caso español y su generación y demanda fue simulada, pero en pequeña escala (con menos generación pero en la misma proporción y las mismas tecnologías) y simplificado para el año Tomando como base los resultados anteriores fue posible predecir la reducción en las emisiones de CO 2 así como evaluar la sensibilidad de esta reducción con la introducción de coches eléctricos. Los VEs traen un mundo de posibilidades, como la posible reducción de emisiones de gases de efecto invernadero o la posibilidad de ayudar a la introducción de más RES de carácter intermitente. Para lograr estos objetivos hay que hacer grandes cambios tanto en el sistema eléctrico como en el del automóvil. Los VEs traen muchas posibilidades a los sistemas eléctricos, pero también muchas amenazas que necesitan ser mitigadas. Los VEs representan más carga eléctrica añadida a la red, por lo que hará falta aplicar refuerzos en ésta. Con una gestión activa de la demanda y una buena definición de los modelos de negocio que rodean a los VEs (i.e. Comercializador, DSO, estaciones de carga o fabricantes de coches, entre otros) se pueden reducir estas inversiones significativamente. Los VEs también suponen un posible y rentable negocio para algunos agentes, como las estaciones de recarga o los comercializadores de energía.

6 IMPACT OF PLUG-IN HYBRID ELECTRIC VEHICLES (PHEVs) ON POWER SYSTEMS. Author: Pieltain Fernández, Luis. Director: Gómez San Román, Tomas. Collaborating partner: Instututo de Investigación Tecnológica (IIT) of Universidad Pontificia Comillas. THESIS SUMMARY Nowadays a fast and comfortable way of transport is essential for our day to day lives, and more important for people who live in big cities who need to move long distances every day. Currently crude oil is essential to power the kind of transport vehicles we need and with the transport quality we ask for. It is true that years ago crude oil was abundant, easy to find and quite cheap, and we didn t know about its bad consequences in the environment, like its high concentration of emissions of CO2 or NOx, main components of the so call Green House Gases (GHGs), which are causing a more accelerated increase in the temperature of the earth. Nowadays we know that current fuels are deteriorating the urban air quality, destroying essential ecosystems and also the crude oil is more and more expensive as time passes as a consequence of the limited quantity of this power source in the earth. We use this source faster than it is regenerated. On the other hand, oil reserves are concentrating in relatively few countries, and this erects a significant barrier to diversification. This geological fact makes crude oil a potent source of political and military conflict. We need to accelerate the commercialization of vehicles with diversified primary energy sources, high efficiency and compatibility with a sustainable, renewable energy future. The electrification of automotive transport offers a promising way to achieve this objective, but this is bringing a lot of necessary changes both in the electric and in the automotive sector. In this thesis some of these changes are studied and evaluated. The purpose in here was to evaluate the impact of a massive introduction of PHEVs in the market, in the distribution grid, energy mix and CO 2 emissions. Some possible strategies to mitigate some threats or even to get profit of the Electric Vehicles (EVs) in some fields were defined, such as a possible

7 increment in the share of Renewable Energy Sources (RES) in the energy mix, and also study some of the possible and profitable business models that could emerge around the EVs. This thesis is laid out in the chronological order in which it was created, it means, one first part where a little introduction and the explanation of the motives of this thesis is made. After this introduction, the thesis continues with the step to step and detailed explanation of all the analysis carried out to achieve the desired objectives, breaking the global work into a series of more easy tasks carried out according to a chronology previously thought and studied on the horizon for 8 months. The PHEV has different modes to be connected in the network, some of them for charging at different rates and another one to give the possibility to use the car as a distributed generator to inject energy into the grid using its condition as an energy storage device. In this thesis it was necessary to first model the PHEV as an electric load and a distributed generator connected to the grid. Next step was the study of the possible impacts of the EVs in the distribution grid. For this purpose two models were used, E-GRID Greenfield and E-GRID Expansion, to analyze the increment in the investments to reinforce the network, estimate losses increment, etc. and also try to define strategies to mitigate and to reduce costs. The next step in the thesis was to define different business models surrounding the EVs. Here it is introduced the business model of the distributor, the retailer, the charge stations and the car manufacturers, how to assign the costs calculated before to the distributor as a regulated business (i.e. what are the costs that should be recognized for the distributor in order to design the relative tariffs). What are the functions, added value and business plan of the independent suppliers, charge stations and car manufacturers with the introduction of hybrid electric cars. In order for all this to work, there is the need for an Active Demand (AD) management, and for this purpose, the smart meters are an essential tool. Finally, a third model called ROM was used to evaluate the impact of a massive introduction of PHEVs in the energy mix in Spain and in CO 2 emissions for year With this model it was possible to evaluate the impact of a massive introduction of PHEVs in the energy mix, production

8 and operation costs and reliability of the system, and so, to define strategies to reduce those costs and even to see how PHEVs can make possible the increase of the share of intermittent RES in the generation mix. For this purpose, a study case of Spain and its generation and demand was simulated, but in small scale (with less generation but in the same proportion and the same technologies) and simplified for the year Based in the results of the energy mix obtained it was possible to forecast the reduction in CO 2 emissions and also the sensitivity of the emissions to the introduction of PHEVs. EVs bring a world of possibilities, such as a decrease in the volume of GHG emissions, or a possibility to help the introduction of more intermittent RES, but in order to achieve all these goals successfully, many changes have to be done, both in the electric and in the automotive fields. EVs bring a lot of possibilities for the electric power systems, but also a lot of threats that need to be mitigated someway. They represent more electric load connected to the grid, so reinforcements will need to be done for a large penetration of them. These investments can be mitigated if a smart active demand management is carried out and the different business models related to the EVs are well defined (i.e. Retailer business model, DSO business model, charge station business model, car manufacturer business model, among others). The electric vehicles also mean a new possible and profitable business for some agents, such as charge stations or retailers.

9 Chapter 1 Introduction Motivation for the Thesis Objectives Methodology/Solution developed Work planning Chapter 2 Study / Development Basic PHEV design concepts Operation modes PHEV configuration Characterization of the electric car as an electric load and generator Basic technology considerations Model of the car as a load and as distributed generator Conclusions Business models Charge Stations business model Retailer and DSO business models Vehicle manufacturers business model Conclusions Assessment of the impact of electric cars on distribution networks Scenarios previous study Basic scenarios Modelling and results Impact in the energy mix and CO 2 emissions Renewable Energy Sources Operation Model (ROM) Impact in the energy mix and CO 2 emissions in Spain Chapter 3 Conclusions Chapter 4 Future works Bibliography Part II Anexes Annex 1 Terminology Annex 2 Battery technologies and EV use Li-Ion Prospects Nickel-Metal Hybride (NiMH) prospects

10 Introduction Na/NiCl2 (ZEBRA) prospects PHEV battery goals set by USABC, MIT and EPRI NREL study about car use Annex 3 Active Demand Management (ADM) ADM Smart meters Economic signals Annex 4 Output files from the E-GRID Greenfield model Manheim (Germany) Peak scenario for year Aranjuez (Madrid, Spain) Peak scenario for year Annex 5 Aranjuez Year Year Year Evolution of investments in Aranjuez Vs. PHEV share Annex 6 Manheim Year Year Year Annex 7 Losses evolution Aranjuez Scenario: Valley hours without EVs Manheim Scenario: Valley hours without EVs

11 Introduction 3 Chapter 1 INTRODUCTION 1 Motivation for the Thesis Nowadays a fast and comfortable way of transport is essential for our day to day lives, and more important for people who live in big cities who need to move long distances every day. However, at the same time, this implies some other concerns, like the big expenses due to the increase of the oil price or the so call Green House Gas (GHG) Emissions. Currently crude oil is essential to power the kind of transport vehicles we need and with the transport quality we ask for. It is true that years ago crude oil was abundant, easy to find and quite cheap, and we didn t know about its bad consequences in the environment, like its high concentration of emissions of CO 2 or NO x, main components of the so call GHGs, which are causing a more accelerated increase in the temperature of the earth. Nowadays we know that current fuels are deteriorating the urban air quality, destroying essential ecosystems and also the crude oil is more and more expensive as time passes as a consequence of the limited quantity of this power source in the earth. We use this source faster than it is regenerated. These days transport is 95% dependent on oil. Half of every barrel of crude oil is converted into transportation fuels, and the automotive sector represents ¾ of the total transport sector demand for primary energy (1/4 aviation and shipping).

12 Introduction 4 Figure 1. World s dependency on oil. Data source: IEA World Energy Outlook One of the keys for security of supply comes through diversification of supply. However, remaining oil reserves are concentrating in relatively few countries, and this erects a significant barrier to diversification. More than three-quarters of all remaining reserves are located in the eleven OPEC member states. With the exception of Russia, all the major oil consumers US, EU, China, Japan, and India are significant net importers today. This geological fact makes crude oil a potent source of political and military conflict.

13 Introduction 5 Figure 2. Crude oil geography in 2006: Reserves Vs. Consumption. Data source: BP Statistical Review Unnecessary journeys can be eliminated through smarter urban planning, encouraging behavioral change, and switching from private to public transport modes. Doubling the effective fuel economy of a private car is as easy as carrying a passenger. Vehicle downsizing, lightweighting, aerodynamic improvements, efficient auxiliary components, lower maximum speed limits, reducing the rolling resistance of tires and simple hybridizing are worthwhile in that all will increase the efficiency of the automotive fleet. Yet none of these measures will do anything to reduce the transport sector s dependency on liquid hydrocarbon fuels. Automotive transport is ripe for transformational change. We need to accelerate the commercialization of vehicles with diversified primary energy sources, high efficiency and compatibility with a sustainable, renewable energy future. The electrification of automotive transport offers a promising way to achieve this objective. With respect to the global warming, nowadays the average global temperature is 0.74ºC higher than a century earlier. Average increase in global surface temperatures must stay below 2 C compared with the preindustrial era. Otherwise we are going to face risks like threats of disease, coastal flooding, and food and water shortages problems. The increase in

14 Introduction 6 the temperature is something natural, but emissions are helping to accelerate the process. Next graphs may help to illustrate this problem: Figure 3. CO 2 emission increase since the beginning of the industrial era. Figure 4. NO x emission increase since the beginning of the industrial era. And Figure 5 represents the different scenarios for the forecast of the evolution of global warming according to different authors.

15 Introduction 7 Figure 5. Global Warming evolution according to different authors (Source: Intergovernmental Panel on Climate Change ( IPCC)). As we can see, the best estimate for low scenario (B1) is 1.8 C (likely range is 1.1 C to 2.9 C), and for high scenario (A1FI) is 4.0 C (likely range is 2.4 C to 6.4 C). In order to counteract these effects, it is necessary to implement technological innovations to reduce GHG emissions. Electric vehicles are powered by clean energy, so they don t emit in the atmosphere while you are driving. However, it is true that the technologies used to obtain this electricity to charge the vehicle s batteries do emit. Taking into consideration all the production chain in order to obtain the final electricity to charge the electric vehicles, the Electric Power Research Institute (EPRI) 1 forecasted the reduction of GHG emissions that could be achieved for 2050 and for different possible scenarios. The future of the electric sector may follow different paths, depending on the evolution of environmental policies, electricity demand, and available technologies. Rather than trying to develop a single consensus view, the EPRI team 1 [2] of the bibliography

16 Introduction 8 created three scenarios to span the impact of PHEVs over different possible futures; high, medium and low CO2 intensity scenario: Figure 6. Annual GHG emissions reduction from PHEVs in the year Data source: EPRI Figure 7. Deffinition of the different scenarios. EPRI According to the EPRI study, cumulative GHG emissions reductions from 2010 to 2050 can range from 3.4 to 10.3 billion metric tons. Technology improvement is an important factor for reducing the GHG intensity of the future electric grid. Figure 8 compares total GHG emissions of the Conventional Vehicle (CV), Hybrid Electric Vehicle (HEV), and the PHEV 20 (can drive 20 miles using just electricity), with the PHEV 20 receiving its energy entirely from each of the fourteen distinct power plant considered technologies. The bottom bar (blue) represents all of the GHGs emitted in the process of producing

17 Introduction 9 and delivering gasoline to the vehicle (well-to-tank). The next bar (red) represents GHGs emitted at the vehicle level (tank-to-wheels). The top bar (yellow) represents GHGs emitted during the generation of electricity for the PHEV 20 (12000 miles driven per year). Figure 8. GHG emissions from different car technologies. Data source: EPRI The conclusions taken from this graph are the next ones: Both HEV and PHEV 20 regardless of electricity supply, result in significantly lower GHG emissions than a comparable conventional vehicle (28% to 67% lower). With power provided by current coal generation technologies, the PHEV 20 has somewhat higher GHG emissions than HEV (4.3% higher). With power provided by the assumed advanced coal technologies (Advanced SCPC and IGCC) PHEV 20 GHG emissions are comparable to the HEV (1.4% higher).

18 Introduction 10 With power provided by combined cycle natural gas technologies (current and advanced) show significant GHG reductions compared to HEV (18% to 25% lower). The two peaking technologies (Old 2010 Gas Turbine and New 2010 Gas Turbine) show modest reductions compared to HEV (4% and 13% lower, respectively). The PHEV 20 recharged by low- and non-emitting generation technologies emits the lowest level of GHGs per mile (Notice that the analysis conducted assumes Adv Nuclear and IGCC with carbon capture and storage are not available in 2010). In reality, PHEVs will not be drawing power solely from individual generating technologies but rather from a mix of resources that include fossil, nuclear, hydroelectric and renewable technologies. In addition, if an active demand management of the PHEVs is carried out, it will be possible to increase the share, in the final portfolio of generation, of renewable energy sources and other sources which emit less pollutant gases. This last point will be explained later on in this thesis. In terms of geography, the electrification of automotive transport will make more sense in any country or region which (i) is a net importer of crude oil; (ii) wishes to use indigenous energy resources as efficiently as possible; (iii) has a large, or fast growing, road transport sector; (iv) has a large, or fast growing, automotive industry; (v) possesses, or intends to invest in, widespread electricity infrastructure; and (vi) is committed to tackle rising greenhouse gas emissions. Prime candidates include North America, the EU, Japan, China, and India.

19 Introduction 11 Another advantage of the electrification of the automotive sector is related with the price of the energy sources: In Spain we pay electricity about 0.14 /kwh in flat hours and /kwh in valley hours and about 1.75 /kw. Electric equivalent of the drive energy in 1 gallon (4 l) of gasoline delivering miles (40-48 km) in a typical midsized car is about 9-10 kwh. The cost of this gas is about 4 while the cost of the equivalent electricity could be during day hours about 1.5 and during the night about 0.6. Furthermore, EVs function as storage devices, so it will be possible to inject power in the grid. For this reason, an EV user could get profit of it, charge in valley hours (which are cheaper) and then go to the balancing market or give ancillary services and get paid for that. Important changes will have to be carried out in the electric power sector (and also in the automotive sector). It will be necessary to create strategies in order to reduce investment needs in the distribution network. It will be possible to use EVs to reduce production and operational costs and CO 2 emissions. The objective of this work is to forecast the impact of EVs and try to define strategies to make the introduction of them and all these possible improvements possible. 2 Objectives The main objective of this thesis is to study the different possible impacts of the electrification of the automotive sector on the power systems and try to define some different strategies in order to mitigate these possible threats. This main objective can be divided in 5 smaller objectives that need to be addressed:

20 Introduction Characterization of the electric car as electric load and distributed generation (DG) a. Medium term ( ) b. Long term ( ) When connected to the network, EVs will be either charging its battery or injecting energy into the network, so in order to be able to study the impact of EVs on distribution networks, the first thing that need to be addressed is to model them as electric loads and DGs (i.e. how much power do they ask from or inject into the grid). This is very much related with the batteries to be used by the PHEVs, its charge and discharge capacity, and also very much related with the characteristics of the connecting points. 2. Assessment of the impact of electric cars on distribution networks (investment needs, losses, etc.) Once the EVs are modelled as electric loads and distributed generators connected to the grid, it is possible to study its impact on distribution networks, such as the possible investment needs or the increment in losses caused by them. Some strategies were defined and studied in order to mitigate or to reduce these threats. For this purpose 2 models were used, E-GRID Greenfield 2 and E-GRID Expansion, which made possible to run different scenarios in the future with penetration of PHEVs in the market as well as an increment in the conventional DG (e.g. CHP or PV units). The scenarios were based in two areas: a. Study case of Aranjuez, Madrid (Spain) b. Study case of Mannheim, Germany 3. Study of different business models 2 [12] of Bibliography

21 Introduction 13 The electrification of the automotive sector brings changes to some businesses in both the electric and automotive sector. A new scheme and new functions have to be defined. Some of these new or modified businesses are studied in this work: a. Retailer business model b. Distribution System Operator (DSO) business model c. Charge stations business models d. Car manufacturers business model 4. Assessment of the impact of electric vehicles on the generation mix In order to complete this objective, it was used a model which is currently being developed by the Research Institute of Technology or Instituto de Investigación Tecnológica (IIT) of the Universidad Pontificia Comillas. With this model, called ROM, it is possible to evaluate the impact of a massive introduction of PHEVs in the generation mix, production and operation costs and reliability of the system. With this it was also possible to define strategies to reduce costs and even to see how PHEVs can make possible the increase of the share of intermittent RES in the generation mix. With this model, the study is done from an operative point of view (i.e. the installed capacity of each technology is defined at the beginning). For this purpose, a study case of Spain was simulated. It was modelled the case of Spain forecasted for year 2030 by Red Electrica de España in small scale (i.e. with less total generation but with the same technologies and in the same proportion) and simplified for the year Assessment of the impact of electric vehicles on CO 2 emissions With the same model called ROM it was studied the impact of the electric vehicles, that can be used as energy storage systems in the generation mix, in the total CO 2 emissions. It was studied the sensibility of the emissions to

22 Introduction 14 the introduction of EVs and also how an active demand management of them can contribute to reduce these emissions. 3 Methodology/Solution developed 3.1 Work planning In order to comply with the objectives defined previously, the whole work was divided in several different and very well defined tasks. Some of them coincide with the definition of the objectives. The tasks that have been carried out are: 1. Study a. Analyze previous studies about PHEV s (technology, environmental effect, economical issues, etc.) and related issues (DG penetration, battery technology trends, smartgrids development, etc.). b. Learn how to work with the E-GRID Greenfield model and E-GRID Expansion model. c. Learn how to work with the ROM model. d. Study of current and future power system characteristics (network - regulatory and technical- characteristics, demand behaviour, generation mix, etc.). 2. Characterization of the PHEV as a load and DG 3. Assessment of the impact of electric cars on distribution networks 4. Development and characterization of different business models 5. Analysis of the impact of EVs on the energy mix 6. Analysis of the impact of EVs on the CO 2 emissions 7. Thesis writing

23 Introduction 15 DIC JAN FEB MAR APR MAY JUN JUL Table 1. Plan schedule. Table 1 presents the followed plan schedule: In December it was necessary to start looking for information to be able to begging with, to learn the basics about PHEV concept, technology and possibilities for the future, to find out previous studies about PHEVs and try to define which other studies could help to develop the important ideas behind this work (e.g. studies about DG penetration and its impact in the power systems, studies about the smartgrids, etc.) and try to think how to apply them to the electric vehicles. Next step was to characterize in January and February the electric car as an electric load and DG connected to the grid. For this purpose the work was focused in the study of the different battery technology trends for PHEVs. At the same time, in January it was necessary to start to learn how to work with the E-GRID Greenfield and the E-GRID Expansion models, define its possibilities and once it was done and the PHEV characteristics as load

24 Introduction 16 and DG were defined, in February it was started the next step, the analysis of connection in distribution grid impact. In March it was necessary to get deeper in the knowledge about the E- GRID Expansion model and try to solve some problems that were found while using it. Many of these problems were because this model was still under development by that time and some things were not programmed yet in a successful way. In March, after some results from the E-GRID models were obtained, it was time to start with the development and characterization of some of the different business models that could be affected by the electric cars. For this purpose a new research process started to find related studies about this. No study was found directly related with the electric cars, but some related to similar fields, such as the development of smartgrids, smartmeters and active demand management or studies about the impact of DG to the retail and DSO business. The work here was to gather all the information together and try to adapt it to the EV concepts together with some original ideas coming from the knowledge of the current business models and regulatory issues around them. In April it was time to start learning how to work with the ROM model and to see which things can be done with this model, and from that point start to build the different scenarios for analyzing the impact of the PHEVs in the energy mix and in the CO 2 emissions. The study about the impact of the PHEVs in the CO 2 emissions actually began in December when the study of some previous works about this field was carried out, and from that point and some knowledge about the possibilities of the PHEV for reducing emissions, it was time to get some numbers and start to define strategies to reduce emissions. At this point there were some problems with the model, because it is actually under development right now, so the

25 Introduction 17 steps were trying to solve these problems together with the model developers. In March it was started the Thesis writing in order to finish with it by July 16 th.

26 Study / Development 18 Chapter 2 STUDY /DEVELOPMENT 1 Basic PHEV design concepts Here the basic characteristics of the plug-in hybrid electric vehicles are explained. It is clarified how it integrates both motors (i.e. combustion engine and electrical motor) in its operation and which are the 2 basic configurations of the PHEVs. 1.1 Operation modes The two basic modes of operation of a PHEV are charge depleting (CD) and charge sustaining (CS) (as it is portrayed in Figure 9). For a distance, the fully charged PHEV is driven in CD mode energy stored in the battery is used to power the vehicle, gradually depleting the battery s state of charge (SOC). Once the battery is depleted to a minimum level, the vehicle switches to CS mode, sustaining the battery SOC by relying primarily on the gasoline engine to drive the vehicle (like a conventional hybrid electric vehicle). CD range is the distance a fully charged PHEV can travel in CD mode before switching to CS mode (without being plugged in). A PHEV with a CD range of 10 miles is referred to as a PHEV-10 (although notation can differ among reports, for this study it is taken the meaning that PHEV-X drives X miles in all electric mode ). In CD mode, a PHEV can be designed to use grid electricity exclusively (all-electric) or electricity and gasoline (blended). All else equal, a PHEV designed for allelectric operation requires a more powerful battery than a PHEV designed for blended operation because the battery (and motor and power electronics) must be capable of providing the full power of the vehicle, not just partial power. The CD range and operation capabilities of a PHEV will depend on the assumed drive cycle, that is, how aggressively and under what conditions the vehicle is driven.

27 Study / Development 19 On the other hand, in Figure 9 it can be seen both in CD mode and CS mode a flicker. These small variations occur because the battery frequently takes in electric energy from the gasoline engine via a generator and from the regenerative braking, and passes energy to the electric motor as needed to power the vehicle In this study both technologies are considered, all-electric and blended operation, depending to the literature referred to. Figure 9. Basic modes of operation of a PHEV. 1.2 PHEV configuration As mentioned before, PHEV has both an electric motor and a heat engine usually an internal combustion engine (ICE). This flexibility also complicates vehicle designs and possible ways of using energy from two different systems. Figure 10 shows two simple schemes of possible PHEV architectures. A series drivetrain architecture powers the vehicle only by an electric motor using electricity from a battery. The battery is charged from an electrical outlet, or by the gasoline engine via a generator. A

28 Study / Development 20 parallel drivetrain adds a direct connection between the engine and the wheels, adding the potential to power the vehicle by electricity and gasoline simultaneously and by gasoline only. While Toyota is currently developing a PHEV with a parallel architecture, i.e. a plug-in version of the Prius, General Motors is working with a series architecture, i.e. the Chevy Volt. Figure 10. Basic PHEV drivetrains. Series Vs. Parallel design. Ultimately, the commercial success of the PHEV depends on the development of appropriate battery technologies. There is much uncertainty about what exact requirements a battery must meet to produce a successful PHEV and where different battery technologies stand in meeting such requirements. Currently there are some commercial PHEVs already in the market, like the REVA from Endesa, the Chevy Volt from Chevrolet, or the Mitsubishi imiev, however, some authors, like Anderman (2008), state that commercialization prior to 2015 would present substantial business risk.

29 Study / Development 21 The difference between the maximum and minimum SOC is known as the usable depth of discharge (DOD). Small cycles, or waves, can be seen in the SOC during CS operation, where the battery takes on energy from the engine driven generator or from regenerative braking and uses the energy in the electric motor to improve the efficiency of engine operation. 2 Characterization of the electric car as an electric load and generator In this point the Plug-in Hybrid Electric Vehicle (PHEV) is modeled as an electric load added to the distribution grid. The PHEV can be also considered as an energy storage device that in certain moments can inject energy into the grid, so it was also modeled as a distributed generator. This is necessary because one of the objectives is to analyze the impact on the distribution grid with the massive introduction of PHEVs in the market, such as investment needs, O&M costs, etc. 2.1 Basic technology considerations To make this characterization, there is a large dependency in the technology chosen for the PHEVs, mostly in the battery technology we choose would fit better for future hybrid cars (much information about battery technologies can be found in Annex 2 Battery technologies and EV use ). The characterization of the EV as load and DG depends on the energy and power capacity of the battery. It also depends on the power charge and discharge capacity which depends on the battery and electric outlet characteristics. To have a first hint in the study of this problem, first, PHEV battery goals vary according to differing assumptions of PHEV design, performance, use patterns and consumer demand. Second, battery development is constrained by inherent tradeoffs among five main battery

30 Study / Development 22 attributes: power, energy, longevity, safety and cost. Third, as it will be seen later on in this paper, Li-Ion battery designs are better suited to meet the demands of more aggressive PHEV goals than the NiMH batteries currently used for HEVs. Fourth, the flexible nature of Li-Ion technology, as well as concerns over safety, has prompted several alternate paths of continued technological development. So for this study three technologies were taken into account, NiMH, Li-Ion and also ZEBRA battery technology, which was considered important for this study. The battery requirements of any given PHEV design are primarily determined by peak power (kw) and energy storage (kwh). Both are dependent on many assumptions, including CD range, CD operation (allelectric vs. blended), drive cycle, vehicle mass, battery mass, and other issues. On the other hand, the energy capacity determines the distance that can be travelled in CD mode and the mass of the battery system. An important distinction should be made between available and total energy. For example, a battery with 10 kwh of total energy operating with a 65 percent DOD would have only 6.5 kwh of available energy. This will be taken into account for later calculations. Figure 11 presents Ragone plots of different chemistries adapted from Kalhammer et al. (2007) 3. The light grey bands present the power and energy capabilities, and tradeoffs, of lead-acid, nickel-cadmium, NiMH, ZEBRA, and Li-Ion chemistries. Onto Kalhammer et al. s Ragone curves it is plotted USABC, MIT, and EPRI goals as dark stars. The grey squares represent the performance of two prototype PHEV batteries tested by Kalhammer et al. (2007): one NiMH (Varta), and one Li-Ion (Johnston Controls Saft JCS). Whereas EPRI s analysis suggests the performance goals for an all-electric PHEV-20 is achievable by current NiMH technology, the goals of the USABC and MIT are beyond even current Li-

31 Study / Development 23 Ion technology capabilities. In any case, Li-Ion battery technologies hold promise for achieving much higher power and energy density goals, due to lightweight material, potential for high voltage, and anticipated lower costs relative to NiMH. NiMH batteries could play an interim role in less demanding blended-mode designs, but it seems likely that falling Li-Ion battery prices may preclude even this role. However, Li-Ion batteries face drawbacks in longevity and safety which still need to be addressed for automotive applications. Anyway, as has occurred in the electronics market, such as laptops or cell phones, it is very provable that lithium-ion batteries will become the dominant chemistry for electrically driven vehicles in the future: the transition already started in some vehicles such as the Chevrolet Volt, that currently use Li-Ion technology, though it might be years before they fully supplant Nickel Metal-Hybride. The values represented by the grey bands in Figure 11 are for an individual battery cell (which is common practice for Ragone plots). The battery pack (or system) designed for a particular PHEV consists of many individual battery cells, plus a cooling system, intercell connectors, cell monitoring devices and safety circuits. The added weight and volume of the additional components reduce energy and power density of the pack relative to the cell. In addition, the inter-cell connectors and safety circuits of a battery pack can significantly increase resistance, decreasing the power rating from that achievable by a single cell. Thus, when applying cell-based ratings to a battery pack, and vice versa, a packaging factor conversion must be applied. The packaging factor for energy density is the ratio between the combined weights of the cells to the weight of the entire battery pack. This factor varies across battery designs in the range of 0.6 to A summary of this report is done by the University of California. [4] in the bibliography.

32 Study / Development 24 Figure 11. Ragone plot for different battery technologies applying packing factor 4. It is assumed an optimistic packaging factor of 0.75 for each conversion. 2.2 Model of the car as a load and as distributed generator From a study carried out by NREL that can be seen in Annex 2 Battery technologies and EV use, it can be assumed that PHEV-40 would be the most probable model to be sold. Taking this into account and the batteries characteristics (that can be seen in the same annex), the different scenarios were modelled with the technologies that can be seen in the shadow cells of Table 30 in the annex, and also the BEV from Table 32 from year Its characteristics correspond with the ones plotted in the Ragone plot of Figure [4] in the bibliography 5 [9] in the bibliography

33 Study / Development 25 With battery and PHEV technologies, now the PHEV was characterized as an electric load, this means, how much active power and reactive power these possible batteries for future PHEV will require from the grid, and also the required voltage at the requested point. It is important to notice that there are 2 possible charging regimes for PHEV, normal charge and rapid charge : a) Normal charge This will take place in any house with the required adjustments, or for example in garages, work places or in a mall. This will last around 6-8 hours to reach a complete charge with a charge rate of 0.2C 0.125C (C/5 C/8). b) Rapid charge This will take place in special charge stations and it will take around 1 hour with a charge rate of 1C maximum due to safe constraints (if the rate of charge is higher, there would be temperature problems). For the long term 2C rate of charge will be also taken into account. NOTE: 1C charge rate means that the battery takes 1 hour to completely charge it, so for example 0.2C means that it takes around 5 hours or 2C 30 minutes. Although power converters that give voltages and currents big enough capable of charge Ni-MH batteries in less than 15 minutes have been developed, there is no a specific and reliable study about the behaviour that this batteries will have under this charge regimes. In Annex 2 Battery technologies and EV use it can be seen a study about temperature evolution of batteries under aggressive charge regimes. As a conclusion of this study it was decided that a faster charge rate than 1C would not be considered in the short term.

34 Study / Development Normal Charge For normal charge, it will be assumed a charge rate of 0.2C. For the PHEV- 60 which has a DOD of 80%, it will take about 4 hours to charge a 90% of the battery 6, or 3 hours to charge an 80%. For the PHEV-30 and PHEV-40, which have a DOD of 70%, it will take about 3.5 hours to charge a 90% of the battery. In Table 2 it is shown some PHEV assumptions with the respective packaged battery goals. It is assumed a packing factor of 0.75: PHEV 30 PHEV 40 PHEV 60 BEV (200 Mi (MIT) (USABC) (EPRI) Range) (MIT) Peak power [kw] Energy capacity [kwh] Charge power at 0.2C [KW] Table 2. Characterization of the PHEV and BEV as electric load at 0.2C rate of charge. The BEV is assumed for the medium-long term, around the 2030 according to MIT estimations. The batteries will be connected in normal outlets of 230 Vac (400 Vac line to line) both in Spain and Germany when modelled in the study cases. These outlets will be located in places like malls, public parking places or houses. 6 (Battery energy * DOD%) / Charge power at 0.2C rate of charge. (e.g. (8 * 0.7)/1.6 for PHEV 30).

35 Study / Development Rapid charge For this charge mode it is considered the same table as before, but with a different charge rate. It was assumed a charge rate of 1C. It will take less than 1 hour to charge a 90% or 80% in 30 minutes with a maximum DOD of 80% for PHEV-60. For PHEV-30 and PHEV-40 it will take about 45 minutes to charge a 90% of the battery because it was assumed a DOD of 70%. PHEV 30 PHEV 40 PHEV 60 BEV (200 Mi (MIT) (USABC) (EPRI) Range) (MIT) Peak power [kw] Energy capacity [kwh] Charge power at 1C [KW] Table 3. Characterization of the PHEV and BEV as electric load at 1C rate of charge (rapid charge mode). The same assumptions as before have been taken for the packing factor of 0.75 and for the BEVs. This type of charge will be done in special stations, as it is explained later. The batteries, when modelled in the study cases, will be connected in special low voltage outlets (230 Vac Vac line to line-) or medium voltage (20 kv in Germany and 15 kv in Spain), depending on the capacity

36 Study / Development 28 of the facility (number of cars to be connected or power demand willing to provide) Very fast charge It is also interesting to include the hypothesis for a 2C charge rate represented in Table 4. There is a perspective that for the medium-long term ( ) there will be the appropriate battery technology to accept this charge capacity. This option would be relevant only for the studies carried out for year PHEV 30 PHEV 40 PHEV 60 BEV (200 Mi (MIT) (USABC) (EPRI) Range) (MIT) Peak power [kw] Energy capacity [kwh] Charge power at 2C [KW] Table 4. Characterization of the PHEV and BEV as electric load at 2C rate of charge (rapid charge mode). This charge will take less than 30 minutes to reach a 90% SOC or 15 minutes to charge an 80% for a PHEV-60, and about 20 minutes to reach a 90% SOC for PHEV-30 and PHEV-40.

37 Study / Development PHEV as distributed generator. V2G function. The electric vehicle can be also considered as a dispersed energy storage system as stated previously. For this reason, it will be possible to use the EV to inject energy into the grid (this is the V2G concept) to give ancillary services, decreasing traditional primary and secondary reserve needs. It will be also possible to give complementary services (e.g. voltage regulation, reserves, etc.). To model this, next assumptions are taken into account: A great amount of cars will be giving this service, so 10 kw for each one of them is considered to be enough. Some times the grid will need more power from the cars and some other times less power. The possible output power from the battery depends on the SOC of it. With these assumptions, when modeling this function, the power output is considered randomly with a random input power in the grid from these cars between 3 kw to 10 kw. On the other hand, 3 elements will be required in order to develop further this concept: a smart interface with the grid, communication with the grid operator to receive controls and metering on-board the vehicles. 2.3 Conclusions For the study cases, it was considered that since year 2020 there would be 3 different PHEV models, PHEV-30, PHEV-40 and PHEV-60. They can be modelled in normal charge mode, rapid charge mode or V2G function. In year 2030 the BEV is introduced. Finally in year 2050 a new mode for the EV to be connected to the grid, 2C rate of charge in special stations, is introduced.

38 Study / Development 30 3 Business models The introduction of EVs brings changes to the business models of some of the agents that are related with them. In this section it is explained the business model of the distributor, the retailer, the charge stations and the car manufacturers. It is studied what should be the structural organization and market mechanisms that should be developed by the retailers and the DSOs, and what should be the relation between them. It is also explained what are the changes and new functions, added value and business plan of the charge stations and car manufacturers with the introduction of hybrid electric cars. As it will be explained later on, there is the need for an Active Demand Management (ADM), and for this purpose, the smart meters are an essential tool. Information about smart meters and ADM can be found in Annex 3 Active Demand Management (ADM). The definition of AD that the project ADDRESS 7 gives us is taken for this work: In ADDRESS, Active Demand means the active participation of domestic and small commercial consumers in the power system markets and in the provision of services to the different power system participants. Within ADDRESS, Active Demand involves all types of equipment installed at the consumers premises: electrical appliances ( pure loads), distributed generation (such as PV or microturbines) and thermal or electrical energy storage systems. Under this definition, the PHEVs would be electrical energy storage systems, that sometimes act as pure loads and some other times as DG. 7 [17] from Bibliography

39 Study / Development Charge Stations business model With the plug-in hybrid electric vehicles in our society and in the market, appears a huge change in the automobile sector and also in the electric sector. From now on the automobile sector has to be close related with the planning and operation of the electric sector. The new PHEVs in the market bring the need to build new electrical infrastructures, such as reinforcement in lines, new substations, or even there is the need to rethink about the market mechanisms, as it was explained previously in section 3.2. But, in addition, and also very important is that these cars are not only powered by gas but also by electricity, so they need a new kind of electric power charge stations. So, in this sense, in addition to the domestic or public individual charging/grid interface points for slower charging, there should be another kind of charge stations used to charge fleets of EVs or to charge EVs that require fast charging, including replacement battery stations: 1. Sharing cars: Creation of a charge infrastructure that consists of the use and later substitution of vehicles in these stations. This will mean that the battery charge is going to be done in the most profitable hours (i.e. valley hours and when the energy is cheaper), without taking into account if the car will be used at that moment or not. This concept could work for instance with rent cars. 2. Fast charge stations: fast/very fast charge systems, being able to charge in a rate up to 400 or 500 A. They will be similar to the current petrol stations. It will be necessary to be very careful and adapt the electrical power systems to this kind of stations. 3. Normal charge points: with a similar size as the parking points, they will be located in parking places, malls, houses, etc. These points will work in the range of 3 6 kw. The car user will have to

40 Study / Development 32 pay to the owner of this infrastructure (e.g. to the mall) after the recharge depending on the time parked and energy purchased (not applicable if the car is charging directly from a house where there is a direct contract between PHEV owner retailer.). 4. Battery change stations: option for long journeys (i.e. longer than miles). They will have a small size and totally automatic (i.e. the user does not intervene in the process and the change last about 3 minutes). Here the batteries will be charged when it is more profitable to do it (in valley hours and when the energy is cheaper), so the battery charge is independent of the presence of the vehicles. In order for this concept to work, there is a need for a battery standardisation (so the energy regulator should take care of this matter in relation with the automobile companies, now integrated in the electrical power system). The fast charge stations and normal charge points will have 3 possibilities to charge for the energy purchased: 1. Charge for the price of the energy in the moment of the service, or 2. make some kind of contract with the generators or financial entities to hedge the volatility of the energy price, in order to be able to sell the energy always at the same price, or 3. without any kind of contract with anyone, they are the ones that hedge for the volatility of the prices to the customers. They will charge always the same average price, so sometimes consumers will pay less and other times more that what actually the energy costs. Between all these possibilities, the first one is the most efficient and the one which gives more security to the electric power system, because the users in this case will behave in an active manner, so they will be willing to charge when the energy price is lower and the system is less affected.

41 Study / Development 33 All of them will have to be in close relation with the DSO (through the Technical VPP which will be explained later) so maybe some times they will be asked to stop charging vehicles if it is required for the security of the electrical power system. This means an opportunity cost for the EV users, so they will have to be compensated someway by the DSO (or by the charge station and this one by the DSO). This will also suppose an opportunity cost for the charge station that will need to be compensated by the DSO. Another way to see this from a more general point of view is the next classification for charge stations: 1. Dedicated Infrastructure Model focused on the creation of a new infrastructure specifically for the management and operation of electric vehicles. Based on the option of recharge or change the battery (charge of the battery is not done by the user). In this category could be for instance the fast charge stations. 2. Utility Infrastructure Management and operation of the electric vehicles charges based in the existing infrastructures. It is necessary to have local measurement systems and to develop a communication protocol to inform about the basic variables that will be part of the battery charge. This could be for example in parking stations. It doesn t allow the implementation of the V2G concept. 3. Virtual Infrastructure Based on the Smart Grid paradigm for electric distribution. It is necessary the complete communication development between the different agents to be able to manage several variables (Vehicle, client, retailer, distributor, third parties, etc.). Here it is possible the V2G concept. 3.2 Retailer and DSO business models The large development of Renewable Energy Sources (RES) is expected to increase the need for Active Demand (AD) services. In addition, with the massive introduction of PHEVs, it gets even more important the

42 Study / Development 34 development of an active demand management mechanism since they have intermittent characteristics. In this field, an active demand management has as an objective either a direct (i.e. controlled by the network operator or retailer) or an indirect management of the demand (i.e. let it to the consumer s decision, giving them some incentives trying to move their consumption pattern to the most effective schedule). In this sense it could be possible to have both management systems, depending on the willingness of the consumer (PHEV user). Regarding the services surrounding the new smart meters, if we let the final decision to the retailers, customers and to the market, we will let the decision of the technology to install to the more experts in this field and this will not affect the plans, contracts and investments of the different companies involved, but it has a serious problem: the absence of a standard in the smart meter, in the communications or in the data transfer methods, increases the risks for a new investment by the retailers (possible problems related with the change in clients (change is very easy), etc.) Because of this problem, the best solution would be to let to the DSOs the installation and ownership of the smart meters and open to the competence the maintenance service, lecture and management of the data Virtual Power Plants (VPPs) Now, in line with the FENIX project 8, and focusing on the role of PHEVs in ADM, the concept of Large-Scale Virtual Power Plants (LSVPPs) is defined. They can be described as an aggregation of a large number of Demand and Distributed Energy Resources (DDERs), including different DER technologies, responsive loads and storage devices (e.g. PHEVs)

43 Study / Development 35 which, when integrated, have the flexibility and controllability similar to large conventional power plants. Distribution and Transmission network control system architectures will need to be redesigned, appropriate communication and information infrastructure will need to be developed to enable distributed energy management to be implemented. In all this, smart meters will have an important role. Furthermore an appropriate market and commercial structure will need to be developed to support exchange of services among all actors, including TSOs, DSOs and LSVPPs Definitions Virtual Power Plant (VPP): The VPP is characterised by a set of parameters such as scheduled output, ramp rates, voltage regulation capabilities and reserves. In this case, conventional DG and PHEVs (treated as storage devices, which can be used as loads or DG as needed in each moment) are going to be together to conform the VPPs. The conventional DG (e.g. wind farms, CHP, PV generators, etc.) will be the base generation of these VPPs, and the PHEVs will give to them a more flexible behaviour, faster ramp-ups (PHEVs generating) and ramp-down (PHEVs stopping generating) or even increase the demand when it is needed (start charging). Commercial Virtual Power Plant (CVPP): A CVPP has an aggregated profile and output which represents the cost and operational characteristics for the DDER portfolio. The impact of the distribution network is not considered in the aggregated CVPP profile. Services provided by a CVPP include trading in the wholesale energy market, balancing or trading portfolios and 8 [15] in the Bibliography

44 Study / Development 36 provision of services to the SO. The operator of this kind of VPP will be the retailers, and they will aggregate DG and PHEVs according to different areas of interest for their services. o Functionalities: Builds the day-ahead schedule (based on contracts, PHEV charging pattern, etc.). In this part they will use day-ahead signals (e.g. TOU tariff where the different hours of the day are defined with different prices). Receive the signals of the DDERS (conventional DG and PHEVs) and send signals to them too (all of it through bidirectional smart meters). These signals are real time volume and price signals. Allows the DG owner to set technical parameters and to declare planned outages. Allow the PHEV owner to declare if it is going to be available. Sell energy provided by the DDERs to the wholesale market. Technical Virtual Power Plant (TVPP): It consists of DDER from the same geographic location. The TVPP has an aggregated profile which includes the influence of the local network on DDER portfolio output as well as representing DDER cost and operating characteristics. Services from TVPP include local system management for DSO, as well as providing TSO balancing and ancillary services. The operator of a TVPP requires detailed information on the local network, so this will be the DSO. o Functionalities: Perform the validation of the day-ahead base schedules of the CVPPs.

45 Study / Development 37 Verifies that the grid voltage and current constraints are compatible with the schedules and with the grid operation. Check the feasibility of the base schedules within the distribution network (based on the forecasted situation of the next day). Propose remedial actions to provide a feasible combination of schedules and network configuration, in case of constraints detected. Brings DDER generation visibility to the TSO. These CVPPs will be the responsible ones to make contracts with the different DG operators and PHEV owners from different areas, as well as sending the correct price and volume signals through the communication system (smart meters are an important part of this) to all the agents. They will have a very close relation with the TVPP, and both of them together will be able to manage generation and load in harmony with the needs and technical constraints of the network. The next figure tries to make a summary of all stated before. This scheme was taken from FENIX project and adapted for the use of PHEVs.

46 Study / Development 38 Figure 12. Different agents in the market and their new role and relationships between each other. (Modification from FENIX project). The CVPP server carries the scheduling and energy optimisation functions for DDER units and the TVPP server includes the applications validating the generation schedules and may amend them in order to address voltage and current constraints. For this purpose, the best solution would be to have a communication between agents in real time. In case there will not be a continuous monitoring of the consumers by the retailer, this would mean that there is no clear view on the potential and real response (acceptance and actions) of the consumer on the request or incentives sent by the retailer. Further research should indicate if continuous monitoring is unavoidable and what the consequences will be of both options (continuous monitoring and strategies without continuous monitoring). A period of 20 to 30 minutes was agreed by the European Commission to be enough, but if there is enough time and resources shorter time ranges could be considered. This will then allow considering other type of services such as primary frequency control and so on.

47 Study / Development Specific issues of the Retailer The retailers, using smart meters technology, will be able to do an indirect management of the PHEV consumption and generation through efficient economic signals. 4 types are remarkable: Real-Time Pricing (RTP), Timeof-Use Tariffs (TOU), Critical Peak Pricing (CPP) and The Tempo tariff, from EDF. Its definition and more about economic signals can be seen in Annex 3 Active Demand Management (ADM). Additionally, the signals could be also real-time volume signals (i.e. some contractual promise to deliver a volume minimum, maximum-. Mainly energy, or power, based signals). The retailer, with the smart metering information (with which he will be able to know the charging and discharging pattern from the PHEV owners, and also to give signals in order to change it as they are needed) together with the contracts signed by the DG operators and some of the probable interested PHEV owners, will be able to forecast the capacity and profile of the VPP in advance with enough time to enter in the different markets (e.g. balancing market). But it is possible to go further in order to achieve a better demand side management of the PHEVs. For this a Demand Side Bidding mechanism should be achieved, which allows consumers, directly or through a retailer, to participate in the electricity market or in the system operation, through biddings that modify their normal demand schedule (this is the function of the CVPPs). As an opportunity to improve, it is important to mention that most people prefer to reduce the billing period. The 2-months period was not considered as the best one for the introduction of intelligent meters and TOU tariff according to a survey carried out by the project ADDRESS in different countries.

48 Study / Development 40 A difference should be noticed between communication and intelligence (regarding retailers side). The communication deals with the signals and information that is exchanged between the consumer and the retailer (and other power system participants when the whole playing field is taken into consideration), and the intelligence are the algorithms, optimisation tools, etc. The part for communicating with the consumers has to be located where each one of the consumers are, to have a communication line between consumers and retailers, but the information from (towards) each one of the customers goes (comes) to (from) the retailers level, where the intelligence part should be located, and there this information is processed. In the consumers side there should be another intelligent system in order to manage with the loads and DG according to the signals gathered. In this sense, there should be a close relationship between car manufacturers and electric companies. This relationship will be explained later on. On the other hand, it has to be noticed that, contrary to conventional DG and large industrial consumers, domestic consumers (PHEV users at the household level) are not motivated purely by economic considerations. In order to motivate end consumers to actively participate in demand side management solutions, it must be taken into account that in addition to economic rewards they are also motivated by other considerations such as environmental concerns, etc. On the other hand, those services may depend on the topology of the concerned network, the geographical location (local/regional) and can vary in time. Therefore, different levels and amounts of distributed intelligence will be required. The issue for the retailer (together with the DSO) then is to put the right amount of intelligence at the right place.

49 Study / Development 41 The retailer will be in competition with other retailers. As it is already the case in current energy markets, this will require risk management instruments to hedge against market price risks and especially against non-delivery of forward purchased demand-side flexibility Specific issues of the Distributor System Operator (DSO) Whereas the current system is designed to transfer centrally generated electricity via high voltage electricity networks to local consumers connected to low voltage distribution networks, DG feeds-in electricity at the distribution level. The increasing penetration of conventional DG, together with the massive introduction of EVs (which can work as distributed generation) in distribution networks brings costs and benefits to different electricity system actors, and in particular, to the DSO. In addition, the EVs working as loads mean more power demand from the grid, and for that reason some reinforcements will be needed. According to the DG-GRID project 9 regarding the increasing penetration of distributed generation, the DSO is affected in different ways. On the one hand, the DSO might benefit from DG presence in the distribution network through a deferral of investments and a reduction in the level of distribution losses. Since decentralised generation effectively takes over centralised generation, less investment is needed in upward connections with the higher voltage networks operated by the transmission system operator (TSO). On the other hand, the DSO might be negatively affected by an increase in network reinforcement costs and an increase in the level of distribution losses due to a higher rate of capacity usage for high DG penetration levels. In addition, the presence of electricity generation units in the distribution network creates opportunities for the DSO to enter the market for ancillary services.

50 Study / Development 42 On the other hand, with the increasing penetration of PHEVs and conventional DG, if there is a well developed ADM, the DSO could find a lot of opportunities to deal with the increasing penetration of DG and EVs, playing the role of TVPPs, as it was stated before. The DSO is also responsible for providing the necessary infrastructures for the correct functioning of the distribution grid. The DSO is a natural monopoly and so highly regulated. Figure 13 represents the business model: Figure 13. Business model of the DSO as a natural monopoly. Capital expenditures include investments in distribution network assets such as transformers, switchboards and cables, and the consequential depreciation costs and remuneration of debt. Operational expenditures 9 [19] of the Bibliography

51 Study / Development 43 encompass costs due to use of the transmission network, distribution losses, costs of ancillary services, and operational and maintenance costs of assets. Commercial costs related to energy measurement and billing to final consumers could be also considered as operational expenditures, but as it was stated previously, probably the best solution would be to let retailers do it, and give to the DSO the responsibility to install the smart meters and to be the owner of them. For the purpose of illustrating the business model with an example, some numbers referring to the case of Aranjuez (Madrid, Spain) in 2020 will be presented. This data concerns to next section Assessment of the impact of electric cars on distribution networks in part Modelling and results and numbers can be seen in Annex 5 Aranjuez. These are some reference numbers taken from network reference models and that can be useful for regulatory institutions such as the CNE in Spain in order to estimate the optimal investment costs and to fix the necessary revenue that the DSO should be allowed to receive. Capital expenditures in the run case of Aranjuez 2020 are 1,130,797 (Investment costs + protection equipment costs from Table 37) plus the costs associated with the procurement of all the information infrastructure (i.e. signalling devices, smart metering, etc.) to be able to do an active network management. These are the investments necessary to meet quality standards and to minimize losses. An important investment should be done for protection devices since, for instance, many of them are currently designed to operate in one direction (i.e. current flowing from transformer centres to final consumers), but with the integration of DG, flows can go either downwards or upwards. Regarding operational expenditures, UoS charges to be paid to the TSO are out of the scope of this study. This cost refers to the passing-through of costs of electricity transmission to end-consumers connected at the

52 Study / Development 44 distribution level. DSOs do not control this cost, they are only an administrative agent. On the other hand, the second category of operational expenditures is currently the DSO payment to the TSO and CVPP for the delivery of ancillary services. Ancillary services include reactive power support, voltage control, and frequency control. It is important to notice that currently all services are solely provided by the TSO but in the near future it may be possible that DG (conventional together with PHEV managed from CVPPs) would be asked to provide some of these services. Regarding energy losses, the estimation taken from the network reference models deals with an increment in 10 years (from 2010 to 2020) of 1,514,424 (losses from Table 37). The losses per year are approximately MWh and the cost of the losses were predefined at /kwh. The regulator should fix a standard level for losses (based on the estimations taken from the network reference models) and penalize (give a prime) in case the actual losses are bigger (smaller). The general approach is to let the DSO compensate the losses with the purchasing of electricity on the wholesale market. Hence, distribution losses are valued at market-based rates. And finally, O&M costs are estimated to be 33,925 (preventive maintenance and corrective maintenance from Table 37). The regulator should now take these costs into account in order to define the allowed revenues for the DSO and to define the tariff (according to a Revenue Cap model for the Spanish case). The charges to be applied by the DSO are variable charges based on energy, fixed charges based on capacity and initial connection charges. In this analysis connection charges are assumed to be shallow, i.e. the DG operator (conventional and PHEV) connected to the network does not bear the full cost burden of connection (however, this is not the case of Spain, where deep charges are applied and the payment division between generators and DSO are open to negotiation). This is important to note since, as it was seen before, the impact of conventional DG and PHEV penetration in the distribution network for a significant part goes via capital expenditures (i.e. investments in network upgrades). Therefore, in the case of deep

53 Study / Development 45 connection charges, both the connection costs and the costs of extending the network for the DSO can be passed through to the DG operator and PHEV owner; consequently the DSO in that case has no incentive (or less incentive) to minimize investments. However, in the case of shallow connection charges additional connection costs cannot be passed through, which gives the DSO an incentive to reduce investments in network upgrades as much as possible because avoidable investments are revenues for the DSO. Growth of electricity demand within the distribution network needs to be met by an increase in flow capacity from the transmission network to the distribution network. With the introduction of DG together with EVs, and all this within an active network management framework, the growth of electricity demand can be met by DG produced electricity. This would result in lower investment requirements for the DSO. On the other hand, a DSO applying active network management and using intelligence within the distribution network to optimise system efficiency may under conditions require the DG operator to shed part of his electricity production. This beholds a particular cost for the latest. The DG operator foregoes the opportunity to sell his electricity on the market, so he will demand a form of compensation for his foregone revenues, which is something the DSO can provide. With respect to this, charge and discharge of EVs could be managed in order to mitigate this problem, and sometimes instead of ask DG operators to stop generating it might be more economically efficient to store this excess of energy in the EV batteries (e.g. in the charging stations where a large number of batteries are recharged and kept in inventory so that PHEV drivers could get their empty ones swapped promptly when they come in). As stated before, the regulator in Spain follows a Revenue Cap methodology to fix tariffs, which follows next equations:

54 Study / Development 46 i i i Ec. 1 R n1 R n ( 1 RPI X ) Y n1 Q i n D i n, n1 n1 i: Company i The initial remuneration (R i n) will be the initial costs calculated with E- GRID Greenfield (from scratch) RPI: Retail Price Index X: Efficiency factor to incentive the DSO to be more efficient. It is set in order to obtain NPV(revenues) = NPV(efficient costs) along the regulatory period (4 5 years) Y: Remuneration according to an activity increase, i.e. the result obtained with the E-GRID Expansion model Q: Quality standards pre established D: Correction factor due to unpredicted events Ec. 1 is for general terms. Currently in Spain a similar equation in being applied for calculating the allowed revenue: Ec. 2 R i i i i n 1 ( R n Q n1 P n1 ) (1 IAn 1) Y i n Q i n P i n Q: Incentive/Penalty for continuity of supply results P: Incentive/Penalty for energy losses reduction IAn 0.2 ( IPCn 1 x) 0.8 ( IPRI n1 y) ; where x 80; y 40 points o IPC: Like the RPI IPRI: Spanish acronyms to refer to the Industrial Price Index For the scope of this study, the E-GRID Expansion model is used to calculate incremental costs in periods of over 10 or 20 years, but it could be also used with time periods of only 1 year in order to fit with these equations. This kind of analysis assumes that all the investments are done at the beginning of the 10 or 20 years period, but it could be also assumed that it is progressive as PHEV penetration occurs. For this we could consider that, in the case of Aranjuez 2020 that is being analyzed, PHEV penetration is uniform during the 10 years period from 2010 to 2020, so

55 Study / Development 47 (1/10) th of the total investments presented before are assumed each year as PHEVs are introduced in the market. It is assumed that investments need to be undertaken one year ahead of DG connections. This explains the parallel lines in Figure 14. PHEV penetration and investment trajectory % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% (%) Year 3,241,722 2,917,550 2,593,378 2,269,205 1,945,033 1,620,861 1,296, , , ,172 0 Investment [ ] Investment [%] PHEV penetration [%] Investment ( ) Figure 14. PHEV penetration and investment trajectory. As for the allocation of investment costs over time, costs and benefits in operational aspects need to be dealt with as well. For example, an increase in the flexibility of the spinning reserve provided by an increasing penetration of an amount X of PHEVs will not directly materialise if only 1/10th of amount X is installed. 3.3 Vehicle manufacturers business model As it was stated before, a big change in the automobile sector is presumably coming up, so this sector would have to interact together with the electric one because of obvious reasons. New electric vehicles are now directly linked to the electric sector (and, of course, at the same time to the automobile sector), so careful attention has to be put on this issue. The energy Regulator now has to take care of this sector too, so, for instance, deep studies have to be done in order to determine the

56 Study / Development 48 technology necessary to have a functional agreement between the automobile and the electric sectors. For instance, vehicle manufacturers when manufacturing the car have to take care of the necessary smart metering system onboard the vehicle to communicate with the retailer when connected to the network Standardization Competition between vehicle manufacturers should be promoted, and for that purpose this sector should be regulated in order to fix standards and to avoid market power. As it was mentioned before, an agreement should be set between the electric sector and the vehicle manufacturers. Two examples on this issue are: 1. Smart metering: Smart meters in houses have the technology to be able to have a bidirectional communication between the user and with both the retailer and the EV. This last communication link have to be technically standardized, in order to no matter what car company and no matter what distribution company, the user will be able to connect his car in his house (or in a mall, parking station, etc.). Without any standardization, coalitions might be set between distribution companies and EV manufacturers in order to be the owners of the technology. This is a good example of market power, because if this is happening, other EV companies would not be able to connect their cars in the area controlled by this DSO. 2. Batteries: Batteries should be also standardized if the battery change stations exist. If this is not the case, there could be coalitions between battery manufacturers and car companies, so big companies get the technology and battery change stations would specialise to work with these batteries. For this purpose, a battery standard for each PEHV-X model (e.g. PHEV-40, PHEV-60, etc.) should be set.

57 Study / Development Incentives for its growing Economic support for buyers to buy these new kind of hybrid electric cars is a key factor since it is about change from a very extended technology to another totally new for the citizens. Most of the governments are carrying out programs to promote the clean energy sources. In the case of Spain for instance, the Government has given 800 million to help the development of ecologic vehicles. In addition, the Industrial Minister of Spain has created work groups with the car manufacturers, electric companies and the Autonomous Regions to find the way to incentive car companies such as Renault, Nissan and Seat to produce electric cars in the country. In other countries, as in Israel and Denmark, they have offer direct grants of about 5,000 6,000 to help to buy these vehicles. In the case of Spain, the Government has the objective to introduce 1,000,000 electric cars for the year For this purpose, the Government is going to spend around 10,000,000 in a pilot project to introduce in a two years term 2,000 EVs and the installation of 500 recharge points. 3.4 Conclusions The concept of charge stations for vehicles should be redefined. A lot of infrastructures and charge points have to be developed and distributed all along the cities, in public and private places. The EVs must be able to connect its batteries and charge them without any problem and at different rates, from normal to very fast charge rate. Rapid charge stations, including battery change stations, have to be defined and managed in order to distort as less as possible in the normal functioning of the power systems. On the other hand, the retailer and DSO business models have to be redefined in order to be able to manage, individually or in clusters, directly or indirectly, the vehicle charge. They will also be able to manage

58 Study / Development 50 the EVs in order to allow them to enter in the market with the V2G function. For this purpose, the concept of the VPPs must be developed. Finally, the vehicle manufacturer business plan has to be redefined. New electric vehicles are now directly linked to the electric sector (and, of course, at the same time to the automobile sector), so careful attention has to be put on this issue. Regulatory changes have to be done in this field and there is the need for creating incentives to help EVs to enter in the market. 4 Assessment of the impact of electric cars on distribution networks The development of the SmartGrids paradigm is bringing changes to power system planning and operation that will ease the introduction of large scale distributed loads/storage systems associated with electric plug-in vehicles. It may be seen that major congestion problems in already heavily loaded grids, or voltage profile problems in predominantly radial networks, may occur during peak load periods, especially if the peak load times coincide with the EV charging periods. Hence, if no management strategies are defined, significant technical problems will occur as a result of the shift to EV and the impact from these problems may be larger than the expected benefits in both economic and environmental aspects. If a proper management scheme is defined, predominantly valley hours can be used to charge EVs. The state of the art in this domain involves developments in fields like plug-and-play for DER generation, microgrids to investigate how microgeneration units and responsive loads can be integrated and exploited in a common manner, virtual power plants and smart metering.

59 Study / Development 51 The main objective of this section is to assess the impacts on planning the grid infrastructures as well as evaluating the related investments, with a large penetration of EVs and for different years and scenarios in the future, having in mind the simultaneous (and continuously increasing) presence of conventional distributed generation (DG) (i.e. CHP, PV units, etc.). For this purpose simulation of several scenarios in two regions has been carried out, one in Spain and another one in Germany. In both cases some strategies were studied in order to decrease threats. So, for the purpose of this section, two system expansion models for distribution networks are used. The first one is the model called E-GRID Greenfield 10. With this tool it is possible to plan in an optimal way and from scratch a distribution grid that includes DG, and with a little modification it is also possible to work with EVs. This tool does not take into account the actual grid, but it does take into account the same technical constraints and planning principles. E-GRID Greenfield is used to model different scenarios to build an optimal distribution grid for the current situation. It builds the distribution network to meet current demand and with the current DG installed in the areas considered. After using E-GRID Greenfield, another tool is being used. This new model is currently being developed in the Reseach Institute of Technology, or Instituto de Investigación Tecnológica (IIT) of the Universidad Pontificia Comillas, and it is called E-GRID Expansion. So, once the network is built with the 1 st model, EVs and more DG is introduced, and also both a horizontal and vertical increase in the demand (i.e. old clients with more contracted power and also new clients in new places are introduced). E- GRID Expansion model is able to calculate the necessary reinforcements for the network in order to accommodate this new demand and generation. 10 [12] of the Bibliography

60 Study / Development 52 The E-GRID Greenfield model, which is able to design an optimal distribution grid in very large areas containing up to several million of clients, gives at the same time information about efficient distribution costs. Widespread acceptance of PHEVs may require assurance that this technology will neither result in decreased electric system reliability nor require massive new unsightly and unpopular infrastructure. Previous studies of pure electric vehicles (EVs) in the US have demonstrated that existing capacity can meet the overnight charging loads of a modest penetration (up to 20%) of these vehicles. 4.1 Scenarios previous study For the purpose of this section, many different scenarios in the two areas of study were created and analyzed. For this purpose, a previous study about power systems and EVs was done about some issues of interest Scenarios for EV market penetration From EPRI three distinct market adoption scenarios can be seen, each based on PHEVs entering the market in 2010 and achieving maximum new vehicle market share in Next tables give the EV market penetration for 3 different years.

61 Study / Development New Vehicle Market Share by Vehicle Type Scenario Conventional Hybrid PHEV Low PHEV Fleet Penetration 65% 24% 11% PHEV Fleet Penetration Scenario Medium PHEV Fleet Penetration 41% 24% 35% High PHEV Fleet Penetration 40% 15% 45% Table 5. PHEV fleet penetration in the market scenarios for New Vehicle Market Share Vehicle Type by Scenario Conventional Hybrid PHEV Low PHEV Fleet Penetration 60% 24% 16% PHEV Fleet Penetration Scenario Medium PHEV Fleet Penetration 25% 24% 51% High PHEV Fleet Penetration 19% 15% 66% Table 6. PHEV fleet penetration in the market scenarios for [2] in the Bibliography

62 Study / Development New Vehicle Market Share Vehicle Type by Scenario Conventional Hybrid PHEV Low PHEV Fleet Penetration 56% 24% 20% PHEV Fleet Penetration Scenario Medium PHEV Fleet Penetration 14% 24% 62% High PHEV Fleet Penetration 5% 15% 80% Table 7. PHEV fleet penetration in the market scenarios for The medium PHEV fleet penetration scenarios are going to be the ones to be studied. The low PHEV Fleet Penetration case for the year 2020 is not considered because the existing capacity can meet the overnight charging loads of a modest penetration (up to 20%) of EVs. Next figure is also provided by EPRI 2007 and illustrates de evolution of the PHEV share in a medium PHEV penetration scenario:

63 Study / Development 55 Figure 15. Assumed new car market share for the medium PHEV scenario for conventional vehicles, hybrid electric vehicles and plug-in hybrid electric vehicles for each vehicle category Areas of study The two areas to be analyzed are Aranjuez, in Madrid, Spain, and the other in Mannheim area in Germany. ARANJUEZ (SPAIN) This region has a number of 52,224 inhabitants according to the Statistics National Institute and collects both residential and industrial areas, which affects in the consumption pattern and vehicles behaviour. The vehicles per capita in this area of Madrid are about 0.55 cars/person. Currently there are some DG (CHP and Wind farms) installed in this area and connected to the HV distribution network. For this analysis and important for the economic results of it, it is considered a WACC = 5% and a life

64 Study / Development 56 span of the installations (e.g. transformers or lines) of 40 years. In this area there are 4 voltage levels: 400V, 15 kv, 45 kv and 132 kv. For the scope of this analysis the orography is not considered, and there are no forbidden areas for laying out the electrical installations. MANHEIM (GERMANY) The three cities analyzed in this area of Mannheim are Feudenheim, Wallstadt and Vogelstand. The three areas are taken together for the analysis. These 3 cities have an approximate number of 6,127 inhabitants and the three of them are residential areas, which will give a particular distinction from the previous case of Aranjuez. The vehicles per capita are around 0.6 cars/person. Currently there are some DG installed in the LV level (some PV and a few less CHP). For this analysis and important for the economic results of it, it is considered a WACC = 8.5% and a life span of the installations (e.g. transformers or lines) of 30 years. In this area there are 3 voltage levels: 400V, 20 kv and 110 kv. For the scope of this analysis the orography is not considered, and there are no forbidden areas for laying out the electrical installations. Next table summarizes the assumptions that were taken into account for the analysis of the different scenarios.

65 Study / Development 57 Region Population Vehicles per capita Total vehicles Aranjuez 52, ,723 Feudenheim, Wallstadt and Vogelstand 6, Considered a little bit higher than in the global Spanish case because of the region studied. Table 8. Number of vehicles in the Spanish and German cases NOTE Vehicles per capita data have been taken from the Nation Master web site (a massive central data source and a handy way to graphically compare nations, a vast compilation of data from such sources as the CIA World Factbook, UN, and OECD) The number of vehicles was considered unchanged along the years for the scope of this analysis. Just the proportion of PHEVs among the total number of cars changes PHEV charge profile simulation When characterizing the EV as an electric load, an aggregated charge profile was created by EPRI 2007 for the fleet of PHEVs in the model (Figure 16). Charge energy requirements are apportioned to each hour of the day. The analysis assumes that the highest charging loads occur during late night and early morning hours, with modest loads presumably from daytime public or workplace charging -- occurring in the middle of the day. Hours of minimal charging correspond roughly with commute times. This specific charge profile creates a scenario where 74% of the charging energy

66 Study / Development 58 is delivered from 10:00 p.m. to 6:00 a.m. (nominally off-peak). The remaining 26% is provided between 6:00 a.m. to 10:00 p.m. This is simply one of many possible scenarios and represents an initial approximation of aggregate charging behaviour in a fleet of PHEVs. The scenario is supported by the following assumptions: 1. PHEVs are charged primarily, but not exclusively, at each vehicle s home base. 2. Owners are incentivized or otherwise encouraged to use less expensive off-peak electricity. 3. Near-term vehicles are likely to have charge onset delays built into their systems to allow battery system rest and cooling before recharge. 4. Long-term, large PHEV fleets will likely encourage utilities to use demand response or other programs to actively manage the charging load. Figure 16. PHEV charge profile scenario 1 (hour 1 represents from 12:00 am to 1:00 am). On the other hand, data from the U.S. Department of Transportation indicates that only a small fraction of vehicles (fewer than 20%) are on the road at any one time. However, it is less clear what fraction of stationary

67 Study / Development 59 vehicles are in places likely to be plugged in, including home, work, and other locations during times that a utility will require capacity services from PHEVs (a large development of the necessary facilities to be able to charge your vehicle in the current electric system will need to take place) Demand behaviour It was also studied the normal demand behaviour in Spain, and it was taken as a reference, together with previous figure, to be able to build the desired scenarios and study where it is more profitable to charge the EVs, or to use them to generate and inject energy into the grid, and where and how an Active Demand Management makes sense. If a load duration curve (with different assumptions for a PHEV penetration in the market) for one year is drawn, any load below minimum represents power plants that are not fully utilized. The lowdemand hours on the right side of the LDC generally occur during overnight hours and result in underutilized baseload plants. Figure 17. Example of a load duration curve (LDC). Underutilized baseload capacity.

68 Study / Development 60 The very left side of the LDC illustrates the peak demand that occurs during a small number of hours of the year (in Spain the slope of the LDC is specially big). In addition, utilities employ an additional peak reserve margin, which is typically an extra 10-20% of capacity over projected peak demand. For this purpose PHEVs with V2G function may help giving some reserve, but also represents a threat if they want to be charged during these hours (this figure shows how the charge schedule should be in optimal conditions, however in reality there is the possibility of PHEVs charging in peak hours and that s why this scenario is also going to be modelled). In addition to the fixed costs associated with underutilized capacity, the significant cycling that occurs on a daily basis creates additional costs for plants that actually do run. For this reason, PHEVs should be charged at valley hours. The added optimized charging could both increase the minimum overnight load, and flatten the load during this time period. Figure 18, which illustrates the demand curve for one day in November of 2008, represents this last idea: minimum demand occurs in the early morning, from 00:00 to 6:00, and peak hours occur around 18:00 with a peak demand of 38 GW, as it can be seen in Figure 18: Figure 18. Sample of demand curve It is Thursday 11/12/08. Data taken from Red Electrica de España.

69 Study / Development 61 This is only one of the possible scenarios. The peak and off-peak hours change during the days of the week and also during the year. For this reason, smart meters and an ADM will be needed. The best idea would be to charge most of the EVs during VHs and in PHs there is an opportunity for a smart managing of the EVs to reduce consumption or to help generation with the V2G function. 4.2 Basic scenarios Based on Figure 18 and with all the information stated before, the basic scenarios were built. First of all, the EV will be in V2G mode only when they are in their houses or maybe in their work place or parking in a mall for instance, but not when they are in a rapid charge station, and also they will give this service when it is more needed (i.e. in the peak hours). So, for this study it was considered that in the valley hours all the cars are characterized as loads, and in the peak hours some of them will be charging but some others will be giving service to the grid (i.e. they will be functioning in the V2G mode). So, the different scenarios that were analyzed are: 1. Aranjuez: This is a mix of industrial and residential area, so: a. Valley hours (photo between 00:00 6:00 AM): 85% of PHEVs connected and 15% either driving or parked but not plugged in. Among the cars that are connected, a %5 are in mode rapid charge and 95% in mode normal charge b. Peak hours (photo between 16:00-21:00): 40% of PHEVs are connected and 60% are either driving or parked but not plugged in. Among the cars that are connected, a 40% are in mode rapid charge, 50% in normal charge and 10% generating.

70 Study / Development Mannheim: This is a residential area, so: a. Valley hours (photo between 00:00 6:00 AM): 85% of PHEVs connected and 15% either driving or parked but not plugged in. Among the cars that are connected, a %5 are in mode rapid charge and 95% in mode normal charge b. Peak hours (photo between 16:00 21:00): 40% of PHEVs are connected and 60% are either driving or parked but not connected to the grid. Among the cars that are plugged in, a 25% are in mode rapid charge, 65% in normal charge and 10% generating. 4.3 Modelling and results Based on the assumptions stated before and from the basic scenarios, the different study cases were modelled and analyzed. First a distribution grid to fulfil all current needs of demand and distributed generation was built from scratch with the E-GRID Greenfield model, and then increments in the investment for the grid needed for the years 2020, 2030 and 2050 were assessed using the E-GRID Expansion model. In Aranjuez and Manheim it was started from year 2010 to build the network with real data about the customers that are in these areas. The characteristics of these initial clients and DG installed can be found in Annex 4 Output files from the E-GRID Greenfield model. After that, the analysis for years 2020, 2030 and 2050 was done in both places. Since 2020 it was considered that 3 different PHEV models can be found, PHEV-30, PHEV-40 and PHEV-60. In year 2030 the BEV is introduced and in year 2050 a new mode for the EV to be connected to the grid, 2C rate of charge in special stations.

71 Study / Development 63 For year 2020 and for Aranjuez, new DG was considered, 6 new PV units connected at 15 kv and 1 CHP unit at 45 kv, with rated power from to 4000 kw. For Manheim and year 2020 it was also considered an increment in DG of 11,690.7 kw connected in LV level. First thing that was done in both areas was to see the necessary reinforcements for 2020 in peak hours with the grid built in 2010 for peak hours and the necessary reinforcements in valley hours with the grid built in 2010 for valley hours. As it can be seen in Annex 5 Aranjuez and Annex 6 Manheim, the total investment needs (initial + increment) are higher in the peak hours scenario, so this is the one chosen to model the grid for 2010 and For the rest of the analysis the scenarios run for valley hours forecast the reinforcements needed for the grid built in the previous scenario for peak hours (e.g. valley hours scenario in 2020 forecast the reinforcement needs for the grid built in 2010 for peak hours when in 2020 is in valley hours). In Aranjuez for year 2020 it was also modelled the peak hours scenario without an increment in demand (but in EVs) and with an increment of 1.57% of demand per year in the LV level from 2010 to As it can be seen in Annex 5 Aranjuez, there is a huge difference in both results. Most of the investment is needed because of the increase in the demand and not in the number of EVs. For the next analysis only an increment in the number of EVs was considered Assessment of the investment needs For each one of the areas it was started from the grid built in 2010 for peak hours, which characteristics can be seen in Annex 4 Output files from the E-GRID Greenfield model. Of course the initial investments in 2010 will not be so big because in real life we don t start from scratch to build

72 Study / Development 64 the network, but the idea is to have a network with these similar characteristics. Starting from these grids in 2010 and for each one of the different years (2020, 2030 and 2050) it was modelled the peak hours scenarios and its results were taken to forecast the necessary reinforcements. For each one of the years it was also modelled the valley hours scenarios. Results can be seen in Annex 5 Aranjuez and Annex 6 Manheim. Next is a summary of the data introduced for each one of the scenarios: Scenario # EVs PHEV 40 PHEV 60 PHEV 30 Not con. Gen. Charge 1C Charge 2C BEV AP ,053 13% 4.20% 2.70% 60% 4% 16% 0% 0% AV ,053 50% 18% 12.20% 15% 0% 6% 0% 0% MP , % 5.60% 6% 60% 4% 10.40% 0% 0% MV ,287 49% 15.50% 16.20% 15% 0% 4.30% 0% 0% AP ,650 11% 4.10% 2.60% 60% 4.10% 16% 0% 1.80% AV , % 17.80% 12.20% 15% 0% 6% 0% 1.70% MP ,875 14% 4.90% 5% 60% 4% 11% 0% 1.10% MV , % 15.30% 14.50% 15% 0% 4.50% 0% 8.50% AP , % 3.30% 3.40% 60% 4.10% 14% 1.70% 4.40% AV ,808 47% 17.40% 11.70% 15% 0% 5.60% 1.40% 1.80% MP , % 5.23% 5.41% 60% 4.10% 9.40% 0.65% 1.20% MV , % 14.70% 14.80% 15% 0% 4.17% 1.10% 11.70% Table 9. Input data for the different scenarios in Aranjuez and Manheim. NOTE: The notation for the scenarios refers to the area (A-Aranjuez and M- Manheim), the time period (P-Peak hours and V-Valley hours) and the year. On the other hand, PHEV-X and BEV are referring to a specific model of EV charging in normal charge mode. The simultaneity factors considered for valley hours was the same for both areas (SF = 0.15), but for peak hours was different. Because the area of Manheim is only residential, the SF = 0.23, but Aranjuez is also industrial,

73 Study / Development 65 so the SF considered was bigger (SF = 0.7). These SFs were considered for the customers, but for the EVs it was always considered a SF = Techniques for reducing the investments In this study 2 techniques for reducing investment needs were studied and analyzed Active Demand Management (ADM) Previous results are a photo in a specific moment between the hours considered for peak and valley hours, and in the most unfavourable situation, where all cars that are considered to be connected (i.e. 40% for peak and 85% for valley hours), are connected and charging or generating at the same time. With an ADM of the EVs not all of them are charging at the same time. The scenarios built here were for peak hours and for the years 2020, 2030 and The charge of the PHEVs is distributed along the 5 hours (i.e. from 16:00 to 21:00) so it is considered the next SFs for the PHEVs: Normal charge: Take around 4 hours to charge a 90% of the battery, 4 so for these PHEVs it will be considered a SF Rapid charge: Take around 0.75 hours (45 minutes) to charge a 90% of the battery, so for these PHEVs it will be considered a 0.75 SF Very fast charge: Take around 0.33 hours (20 minutes) to charge a 90% of the battery, so for these PHEVs it will be considered a 0.33 SF

74 Study / Development 66 The results obtained with this technique can be seen in Annex 5 Aranjuez and Annex 6 Manheim. An analysis of these results will be done later Change the charge schedule If the results are analyzed it can be seen that the investment needs are smaller in the valley hours than in the peak hours scenarios. It is interesting to think that if a smart active demand management is done with the PHEVs and some of them instead of charging in peak hours they do it in valley hours, the increment in the investments in the peak hours scenario could decreases and in the valley hours could increases so both investments get closer till they reach the same value, what means finally that the increment in the investments needed for the different years is smaller. This could be done for instance with the battery change stations where the vehicle in the peak hours what it does in this stations is to change the battery instead of charge it. The battery charge is done in the valley hours and in normal charge mode, when the energy is cheaper and it has less impact in the network.

75 Study / Development Results Table 10 summarizes the results for the different scenarios: 2010 No EVs Peak Valley Peak Valley Peak Valley Peak Valley Inv. (without SSEE) [ ] 124,439,190 1, 727,296 1,554, , ,990 1, 321, ,780 Aranjuez Losses [ ] 15,571,462-7,358,279 1,514,424-6,237,037 1,438,251-5,569,626 1,961,160-4,529,226 Inv. ADM [ ] 792,380 63, ,488 Inv. (without SSEE) [ ] 76,136,928 6,299,835 5,233,049 4,833,522 4,615,643 3,489,888 2,864,273 Manheim Losses ] ] 1,318, ,325 34,863 90,816 34, , , ,939 Inv. ADM [ ] 2,028,330 1,224,605 1,068,697 Table 10. Summary of the results for Aranjuez and Manheim. NOTE: Investments (without SSEE) and Investments with ADM are increments from the previous case considered, while Losses are increments from year 2010 in peak hours (or what is the same, from peak hours and no EVs) Analysis of results Next is done an analysis of the results which summary is in Table 10. The analysis is done first for the investment needs without any technique to reduce them, and then it is analyzed how investments can be reduced with the use of different techniques. Finally, the evolution of losses in valley hours is analyzed.

76 Study / Development Investment needs For assessing the investment needs it was taken the results from the peak hours scenarios. If the investment needs in Aranjuez are analyzed, it can be seen that there is a big investment in substations in There is a big jump in the investments from year 2020 to To analyze the investment needs with more detail and see when exactly this big investment is needed, it was done the modelling of 6 more scenarios, each one with different penetration levels of PHEVs in the market, and the results are showed in Annex 5 Aranjuez in Evolution of investments in Aranjuez Vs. PHEV share. Next graph shows a summary on how investment needs increase while there are more EVs considered to be in the market. Investment Vs. PHEV share Investment increase [ ] 7,000,000 6,000,000 5,000,000 4,000,000 3,000,000 2,000,000 1,000, PHEV share [%] Figure 19. Increment in the investment Vs. PHEV share in the market. Aranjuez, year 2020, peak hours. Here it can be appreciated better the big jump in the investments from 35% to 40% share of PHEVs in the market (this could be for a medium penetration level scenario the change from year 2020 to 2022, and not 2030

77 Study / Development 69 as the result got in the previous analysis). This big investment is because the model considers necessary to install a new Substation (132 kv/15 kv; 20 MVA) because without it there is an overload current of 0.388% (388 kva). The investment needed for this substation is 1,723,222. Then, the change from 40% to 62% is again much smaller. For the next analysis, the investment in the substation is not considered to be able to compare Manheim with Aranjuez. There are no big investment needs in substations in Manheim. Investment needs per PHEV share [%] in the market Investment Vs. PHEV Share Investment needs [ ] 16,000,000 14,000,000 12,000,000 10,000,000 8,000,000 6,000,000 4,000,000 2,000,000 0 y = x y = 64455x PHEV share [%] Aranjuez Manheim Lineal (Aranjuez) Lineal (Manheim ) Figure 20. Increment in the investment Vs. PHEV share in the market. It can be appreciated that the investment needs can be approximated by the less square method to the linear approximations that are in the graph: There is an investment need of 64,455 /PHEV share [%] in Aranjuez and 231,927 /PHEV share [%] in Manheim. Even if 1% of PHEV share means more EVs in the case of Aranjuez, the increment in the investments is bigger and grows faster in Manheim. This is because each element added to reinforce the network in Manheim is more expensive than those ones in Aranjuez (in the order of 10 times more expensive in some elements such

78 Study / Development 70 as conductors) because in Manheim most things that are installed are underground. Even if more PHEVs are added in Aranjuez, the total increment in the investments is bigger in Manheim. The investments along the years is as follows: Increment in the investments from year 2010 Investment [ ] Millions y = x 2 + 4E+07x - 4E+10 y = x 2 + 2E+07x - 2E Aranjuez Manheim Polinómic a (Manheim) Polinómic a (Aranjuez) Year Figure 21. Investment needs along the years. The investment needs in both places can be approximated to a 2 nd order polynomial equation as it is stated in Figure 21. The investment needs are approximately: From 2010 to 2030: 192, /year in Aranjuez and 556, /year in Manheim. From 2030 to 2050: 10,723.9 /year in Aranjuez and 174,494.4 /year in Manheim. Next there is an analysis on how investments are dependent on number of PHEVs added and kw added to the scenarios: # PHEVs PHEV equivalent kw Year Aranjuez Manheim Aranjuez Manheim ,053 1,287 27, , ,395 1,875 40, , ,808 2,279 51, , Table 11. Relation year Vs. # PHEVs Vs. kw added to the scenarios.

79 Study / Development 71 Investment per # PHEVs introduced in the market: Investment Vs. #PHEVs 25% Investment [%] 20% 15% 10% 5% Aranjuez Manheim 0% # PHEVs Figure 22. Investment evolution Vs. PHEVs introduced in the market. NOTE: Investments in % are respect the initial investment done for peak hours with no EVs. The investment needs are 0.16% / thousand PHEVs or /PHEV in Aranjuez and 8.29% / thousand PHEVs or 6,309.1 /PHEV in Manheim. These are PHEVs added to the market. Taking into account that in peak hours it was considered that only a 40% of the EVs were connected, there is an investment of /PHEV connected in Aranjuez and 15,773 /PHEV connected to the network in Manheim. Investment needs per Kw added to the network: Investment Vs. PHEVs [kw] added Acummulated investment [%] 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% PHEVs [kw] Aranjuez Manheim Figure 23. Investment needs per kw added to the network.

80 Study / Development 72 These are increments of 2.44% /thousand PHEV [kw] (or 1,858.3 /PHEV [kw]) in Manheim and 0.056% / thousand PHEV [kw] (or 70.4 /PHEV [kw]) in Aranjuez. The investment needs per kw are much higher in Manheim. It can be seen how each PHEV or kw added to the system implies a bigger investment in the case of Manheim. The investment in SSEE, according to the previous analysis that has been done, will be needed for a 40% share of PHEVs, which means a power increase from 2010 of about 30,986.2 kw by means of PHEVs added to the network (connected) Investment reduction through an ADM The investments with and without ADM are compared in both cases of Aranjuez (A) and Manheim (M): Increment in the investment - Aranjuez Vs. Manheim Acumulated investemnt [%] 20.00% 15.00% 10.00% 5.00% 0.00% PHEV share [%] A without ADM A with ADM M without ADM M with ADM Figure 24. Investment increment with and without ADM. NOTE: The investment [%] is represented with respect initial investment in 2010.

81 Study / Development 73 The difference in the investment between with ADM and without ADM is much higher in Manheim (and also the investments are bigger) because each element installed to reinforce the network means more money than in the case of Aranjuez. Each element installation that can be avoided with an ADM means a higher saving for the case of Manheim. The increment in the investment is: Aranjuez without ADM: 0.049% /PHEV share [%] (or 94,530.7 /year) Aranjuez with ADM: 0.013% /PHEV share [%] (or 24,908.4 /year) o Saving in investments with ADM: 0.036% /PHEV share [%] (or 69,622.3 /year) Manheim without ADM: 0.31% /PHEV share [%] (or 365,581.1 /year) Manheim with ADM: 0.09% /PHEV share [%] (or 108,040.8 /year) o Saving in investments with ADM: 0.22% / PHEV share [%] (or 257,540.3 /year) In 2050 the accumulated saving would be of 2.24% (2,784,892 ) in Aranjuez and 13.53% (10,301,613.2 ) in Manheim. It is important to notice that the investment in SSEE was not taken into account in the case of Aranjuez because there is no such investment in the case of an ADM is carried out.

82 Study / Development Investment reduction with the Change the charge schedule technique Table 12 compares the investment needs in the PHs and VHs scenarios for both cases, Aranjuez and Manheim. The investments are expressed in % respect the initial investment done in 2010: Aranjuez Manheim PH VH Difference PH VH Difference Year respect respect PH VH respect respect PH VH 2010 [%] 2010 [%] [%] 2010 [%] 2010 [%] [%] Table 12. Evolution of investments in Aranjuez and Manheim. Difference between PHs and VHs. Investment evolution 25.00% increment in the investment [%] 20.00% 15.00% 10.00% 5.00% 0.00% Year A PH A VH M PH M VH Figure 25. Evolution investments in Aranjuez and Manheim in peak and valley hours.

83 Study / Development 75 The investments in peak hours represent the real investments in the network. If now the difference in the investments between PHs and VHs is represented, but with respect PHEV share [%]: Evolution in the investment needs between PHs and VHs 3.00 Difference in the investment [%] PHEV share [%] A PH - VH M PH - VH Figure 26. Difference in the investments between PHs and VHs. This difference allows to move the charge of EVs from peak to valley hours in order to decrease the investment needs till the investment in PHs is equal to that one in VHs. Its evolution is 3.93% / PHEV share [%] in Manheim and 5.02% / PHEV share [%] in Aranjuez. In the other hand, the difference is bigger in Manheim, but if the amount of kw that can move to charge to VHs is analyzed, it will be seen that less kw can move to charge in VHs in the case of Manheim: Investment needs in Aranjuez: There is an investment need of IVH=28.10 /PHEV [kw] connected to the network in valley hours, and IPH=70.41 /PHEV [kw] connected.

84 Study / Development 76 Investment needs in Manheim: There is an investment need of IVH=1,145 /PHEV [kw] connected to the grid in valley hours, and IPH=1,858.3 /PHEV [kw] connected. Taking this information into account and next equations, Table 13 was obtained: Ec. 3 Load _ to _ equal _ inv[ kw ] Inv. Peak[ ] InvValley. [ ] ( IVH IPH ) / kw Ec. 4 FinalInv.[ ] Inv. Peak[ ] IPH[ / kw ] Load _ to _ equal _ inv[ kw ] Year Investmen t PH [ ] (1) Investmen t VH [ ] (2) kw from PH to VH to make (1) = (2) Final investment [ ] Inv. Reduction [ ] (3) Inv. Reductio n [%] (3)/(1) ,727,296 1,554,298 1, ,603, , Aranjuez ,459,698 1,730,288 7, ,938, , ,781,227 1,861,068 19, ,408, ,372, ,299,835 5,233, ,639, , Manheim ,133,357 9,848, ,338, , ,623,245 12,712, ,441, ,181, Table 13. Reduction in the investment by moving EVs from charge in peak hours to valley hours. The first and second columns show the investment needs in each year for the scenarios in peak and valley hours. Each one of the increment in the investments are the accumulated needs referred to the network designed for year 2010 and without taking into account the investment in the substation in Aranjuez. The third column shows how much of the demand in peak hours should move to valley hours (PHEVs charging in VHs instead of doing it in PHs) in order to equal the investment needs in valley and peak hours. The 4 th column shows which would be the final investment with this technique.

85 Study / Development 77 The amount of kw (expressed as the % of total kw that the connected PHEVs represent in the peak hours) that can move to charge to VHs is analyzed in Figure 27: kw from PH to VH kw respect total PHEVs connected in PH [%] Aranjuez Manheim PHEV share [%] Figure 27. kw from PH to VH to reduce investment. Here it can be seen that the percentage of EVs that can move to charge to VHs is smaller in Manheim, and this is because each EV added means more increment in the investments in Manheim than in Aranjuez. For instance if this is about moving PHEV 40 to charge in normal charge mode (which represents a power = 3.4 kw each EV) in VHs instead of doing it in PHs, the kw to move from one hour to another means the next number of cars: Year # PHEVs Aranjuez # PHEVs Manheim , , Figure 28. # of PHEVs to move from peak to valley hours.

86 Study / Development 78 If the difference in the investment needs between PH and VH is expressed in : Difference in the investment needs between PH and VH Investment difference [ ] Thousands 2,500 2,000 1,500 1, PHEV share [%] Aranjuez Manheim Figure 29. Difference in the investment needs between PH and VH. The evolution of this difference is 29,966 /PHEV share [%] in Manheim and 62,544 /PHEV share [%] in Aranjuez. In Aranjuez the difference in the investments between PH and VH increases faster than in Manheim as PHEVs are introduced because the difference between the simultaneity factors is bigger in Aranjuez. For this reason, the difference in the demand between peak hours and valley hours is bigger in Aranjuez than in Manheim. On the other hand, if the investment savings are analyzed:

87 Study / Development 79 Investment evolution with and without moving load to VHs Investment increment [%] 20% 15% 10% 5% 0% PHEV share [%] Initial inv. (A) Final inv. (A) Initial inv. (M) Final inv. (M) Figure 30. Investment evolution with and without moving load to VHs. Previous figure shows the reduction in the investment. Next, investment savings are represented: Investment savings Investment saving [%] 40% 35% 30% 25% 20% 15% 10% 5% 0% PHEV share [%] Aranjuez Manheim Figure 31. Investment saving when charging in VHs instead of PHs. The approximated saving in the investments is of 0.54% /PHEV share[%] in Aranjuez and 0.13% /PHEV share[%] in Manheim. The increase in the investment savings is lower in Manheim because the strategy is more limited because each EV that now charges in VHs means more increment in the investments than in the case of Aranjuez.

88 Study / Development 80 So now it is interesting to see what would happen to the grid designed for 2010 if all the PHEVs forecasted for year 2050 are to charge in valley hours (without ADM). For this purpose next assumptions were done: % of PHEVs connected: 17,808 PHEVs are connected to the grid 95% normal charge mode (i.e. 16,918 EVs) 5% rapid charge mode (i.e. 890 EVs) With this assumptions the result was the next: NOTE: Investment increments are from year 2010 Investment Preventive management (annual) Corrective management (annual) Inv. + Manag. Protections Total Increment (%) Increment LV 501, , , , , Increment CCTT 121,558 7, , , Increment MV 314,542 12,450 6, , ,177 1,027, Increment SSEE Increment HV TOTAL 2,099, Table 14. Aranjuez, year All cars charging in valley hours without ADM. This is not a realistic case where all the cars will charge in valley hours, but it shows how the network designed for year 2022 (40% share of PHEVs with a total investment without SSEE equal to 2,488,838 ) can support all the EVs charging in valley hours in year 2050, so there would not be the need to update the network from year 2022 if all the EVs are to charge at these hours. The main reason for this is that there is a huge difference between valley and peak hours in Aranjuez, with a big difference in the simultaneity factors. In peak hours SF=0.7 and in valley hours SF=0.15, so here the demand is much lower. The difference between demand in peak and valley hours is very big. However, here it is studied the impact of the EVs alone, but an increment in demand has to be taken into account too when designing the network.

89 Study / Development Evolution of losses in Valley Hours The study of the evolution of losses is important from an operative point of view. For this purpose, it was simulated a scenario in valley hours but without EVs. The results can be seen in Annex 7 Losses evolution. If losses in Aranjuez and Manheim are compared: in losses respect 0 EVs in VH [%] PHEV share Aranjuez Manheim 35% 14% 20% 51% 22% 27% 62% 34% 41% Table 15. Variation of losses in valley hours with the addition of EVs. Losses evolution in valley hours.00% Losses variation [%] 80.00% 60.00% 40.00% 20.00% 0.00% PEHVs share [%] Aranjuez Manheim Figure 32. Evolution of losses in Aranjuez and Manheim in valley hours. NOTE: the 2 points in 0% PHEVs represent losses in peak hours with no EVs (the one in the top) and losses in valley hours with no EVs (the one in the bottom). In the other hand, losses variation in % is respect losses in VH with no EVs.

90 Study / Development 82 The evolution of losses in Aranjuez is 0.52% / PHEV share [%] (or 2.1% / thousand PHEVs) and in Manheim 0.62% / PHEV share [%] (or 19.7% / thousand PHEVs). In both cases if no EV is introduced, there is a decrement in the losses in VHs, but then when EVs are introduced, the variation of losses keeps negative in Aranjuez while it gets positive in Manheim. This is because in Aranjuez there is a huge difference in the demand between peak and valley hours (SF in PH = 0.7 and SF in PH = 0.15) while in Manheim the difference is not so big (SF in PH = 0.23 and SF in PH = 0.15). The final increment in losses is quite similar in both cases, but the variation respect peak hours with no EVs is much higher in Aranjuez. The losses in Aranjuez in valley hours always remain smaller than those of peak hours with no EVs. On the other hand, each vehicle represents a bigger increment in losses in Manheim because it represents a higher % of the total vehicles.

91 Study / Development 83 5 Impact in the energy mix and CO2 emissions The impact of a massive introduction of PHEVs in the energy mix in Spain and in the CO 2 emissions for year 2030 is evaluated in this section. For this purpose the model ROM 12 was used. This model is under development in the Research Institute of Technology, or Instituto de Investigación Tecnológica (IIT) of the Universidad Pontificia Comillas. 5.1 Renewable Energy Sources Operation Model (ROM) Impacts of the deployment of EVs on market functioning will be computed using a medium term operation model named ROM. The model objective is determining the technical and economic impact of EVs into the medium-term system operation. Given that large shares of intermittent generation are expected in the study horizon, this tool also model this kind of generation and its impact on the functioning of the system. Results of the ROM model include generation output including wind waste, pumped storage hydro usage, market prices, fuel consumption and CO2 emissions, among others. This tool optimizes the medium term operation planning of a generation system with different technologies. Its characteristics are: Unit commitment and economic dispatch: This is vital to obtain short-term system operation and evaluate IG (Intermittent Generation) integration. It uses a combined approach to model the IG integration: a previous daily optimization followed by a sequential hourly simulation all along one year. Detailed operation constraints such as minimum load, ramp rate, minimum up-time and down-time of thermal units and tertiary reserve provision are included into the daily optimization model. The hourly simulation 12 [11] of the Bibliography

92 Study / Development 84 is run for the same day to account for wind production errors and therefore revising the previous schedule. Differences among optimization and simulation decisions are due to prediction errors and represent the value of the perfect IG forecast. It also includes EV data, such as different uses (i.e. in which moment of the day they are driving or connected), technical characteristics of the EVs, number of EVs, etc. A smart management of the EVs is taken into account for the unit commitment and economic dispatch. Monte Carlo Simulation with different scenarios (different years), which takes into account the stochastic nature of the intermittent RES. Reliability evaluation: Contribution of each generating unit to the system reliability. It is used for the following purposes: Quantify how much intermittent RES can be connected in a safe way and evaluate its technical and economical impact. Evaluate the impact of the integration of EVs in the energy mix. Evaluate the impact of the integration of EVs in the production and operational costs and in the system reliability. With all this it is possible to define strategies to reduce these costs and even to help to increase the share of intermittent RES with help of the EVs. With the previous results it is also possible to estimate the variation in the CO 2 emission thanks to the introduction of EVs and a smart management of them, and how sensible is this to the different strategies and penetration scenarios of the EVs. Based on that, it is possible to define strategies in order to reduce emissions.

93 Study / Development Impact in the energy mix and CO 2 emissions in Spain Te ROM model was used for evaluating a possible future scenario of the generation mix and penetration of PHEVs in Spain in year The generation mix stimation for year 2030 by Red Eléctrica de España, this scenario was simplified by designing a generation mix in a small scale, with similar technology capacity proportions but with less total installed capacity. It was also simplified by considering that demand would follow the same pattern every day. The same assumption was made for wind and distributed generation profiles. The distributed generation is considered to be always giving the same power of 6,500 MW. These simplifications facilitate the assessment of influence on the energy production mix and CO 2 emissions. In Table 16 the considered generation mix for year 2030 is presented: Technology Power Installed (MW) % Power modeled (MW) % Nuclear 7, , Coal 7, , Fuel/Gas 3, , CCGT 40, , Tot. Thermal 57,310 27,200 Hydraulic 16, , Tot. ord. reg. 73, , Wind 40, , Others 14, , Tot. esp. reg. 54, , TOTAL 128,467 56,700 Table 16. Generation mix for 2030 in Spain. Actual data forecasted by REE and data taken for the model. The estimated demand for 2030 varies between 80,000 MW and 30,000 MW. In the model the demand varies between numbers that change depending on which study is being done.

94 Study / Development 86 On the other hand, the number of PHEVs forecasted for year 2030 is 3,000,000 for the Spanish region, but in the model this number will also vary depending on which study is being done Scenarios of PHEVs penetration Several scenarios of PHEVs penetration are defined to influence in the power systems and GHG emissions Data 1. Scenarios: 10 different scenarios were designed, each one of them with a different number of PHEVs: Scenario # PHEVs 6 [ 10 ] Table 17. # of PHEVs for each scenario. 2. Demand: The demand behaviour was, as stated previously, unchanged along the year with a minimum of 15,000 MW and a maximum of 27,000 MW. The daily pattern is represented in Figure 33: Demand Demand [MW] Time [hour] Figure 33. Demand daily pattern.

95 Study / Development Wind Power: The wind power was introduced first as a forecast for every day (each day follows the same pattern). Then a deterministic correction to the previous forecast at hour 14:00 and 24:00 of the day before is done. It has to be added also an stochastic correction of this forecast which is modelled with Monte Carlo simulation and represents the corrections at real time, but is not shown in next figure: Wind Power Wind power [MW] Wind forecast Wind after correction time [hour] Figure 34. Wind power forecast. The blue line represents the wind forecast for each hour of the day, and the green line the final forecast with the correction at hour 24:00 of the day before. The wind pattern of Figure 34 repeats every day. Because there is a lot of wind and the demand is not that big, in the results will be possible to see that there are no energy non served. This is not important anyway because in this simulation the intention was focused on the study of other results, like the evolution of the intermittent RES share in the generation mix with the increment in the number of PHEVs.

96 Study / Development Hydro inflow: It is considered that the hydro inflow each day is the same, and it is 750 MWh. 5. PHEV data: It was defined as common for all PHEVs the energy capacity of 25 kwh and power of 7 kw (i.e. if it is charging, is represented as an electric load of 7 kw, and if it is generating (V2G function) it is represented as a DG unit of 7 kw of rated power). Its specific consumption is of 0.25 kwh/km. next data was also defined for all the scenarios: First, 5 different PHEV uses were defined: Use Distance driven each day [km] Initial load [kwh] Table 18. Different car uses. Distance driven each day and initial load. For each type of use of the car it was defined a different use % of the car in each hour (i.e. which % of the previous km defined is driven in each hour):

97 Study / Development 89 Use Use % in each hour Table 19. PHEV use % in each hour. And it was also defined the number of cars connected in each hour. Use Car % connected in each hour Table 20. % of cars connected in each hour. In Table 20 it can be seen that a lot of cars were considered to be connected every time. This is because, for instance, it is also considered that there will be batteries connected in the battery change stations (not necessary with the presence of the EV). With this assumption it was easier to study how cars influence in the energy mix and CO 2 emissions.

98 Study / Development 90 Next it was defined, for each one of the 27 car models considered (all of them for this study with the same power and energy characteristics), the use that is made with them during the week days and in the weekends. Car use Car model 1 weekday X 2 weekday X 3 weekday X 4 weekday X X 5 weekday X X 6 weekday X X 7 weekday X X 8 weekday X X 9 weekday X X 10 weekday X 11 weekday X 12 weekday X 13 weekday X X 14 weekday X X 15 weekday X X 16 weekday X X 17 weekday X X 18 weekday X X 19 weekday X 20 weekday X 21 weekday X 22 weekday X X 23 weekday X X 24 weekday X X 25 weekday X X 26 weekday X X 27 weekday X X Table 21. Use associated to each car model in the weekdays.

99 Study / Development 91 Car use Car model 1 weekend X 2 weekend X 3 weekend X 4 weekend X 5 weekend X 6 weekend X 7 weekend X 8 weekend X 9 weekend X 10 weekend X X 11 weekend X X 12 weekend X X 13 weekend X X 14 weekend X X 15 weekend X X 16 weekend X X 17 weekend X X 18 weekend X X 19 weekend X X 20 weekend X X 21 weekend X X 22 weekend X X 23 weekend X X 24 weekend X X 25 weekend X X 26 weekend X X 27 weekend X X Table 22. Use associated to each car model in the weekends. NOTE: There is the same number of PHEVs per car model Given this input data for each one of the 10 scenarios considered, the results observed were the next ones:

100 Study / Development Results Energy mix in different scenarios with different number of PHEVs o Scenario 1 (Total generation = 186,000 GWh) Energy produced by technologies year 2030 BEV_Gen, 0.00% PS_Hydro_Consumptio n, 0.06% BEV_Consumption, 0.00% Nuclear, 4.51% Coal, 0.01% CombinedCycle, 11.79% Unscheduled_Hydro, 0.00% PSHydro_Generation, 0.03% GasTurbine, 0.18% Hydro, 0.11% Intermittent_Gen, 18.07% Demand, 49.94% ENS, 0.00% Distributed_Gen, 15.31% Figure 35. Energy mix scenario 1. o Scenario 2 (Total generation = 189,260 GWh) Energy produced by technologies year 2030 PS_Hydro_Consumptio n, 0.05% BEV_Consumption, 0.86% BEV_Gen, 0.63% Nuclear, 5.65% Coal, 0.01% CombinedCycle, 10.40% Unscheduled_Hydro, 0.00% PSHydro_Generation, 0.03% GasTurbine, 0.20% Hydro, 0.11% Intermittent_Gen, 17.93% Demand, 49.08% ENS, 0.00% Distributed_Gen, 15.04% Figure 36. Energy mix scenario 2.

101 Study / Development 93 o Scenario 7 (Total generation = 200,892 GWh) Energy produced by technologies year 2030 BEV_Consumption, 3.72% Nuclear, 5.79% Coal, 0.00% CombinedCycle, 8.60% Unscheduled_Hydro, 0.00% PSHydro_Generation, 0.02% Hydro, 0.11% BEV_Gen, 2.64% PS_Hydro_Consumptio n, 0.03% GasTurbine, 0.30% Intermittent_Gen, 18.37% Demand, 46.24% ENS, 0.00% Distributed_Gen, 14.17% Figure 37. Energy mix scenario 7. o Scenario 10 (Total generation = 211,416 GWh) Energy produced by technology year 2030 Unscheduled_Hydro, 0.00% BEV_Gen, 3.87% BEV_Consumption, 6.03% Nuclear, 5.56% Coal, 0.00% CombinedCycle, 8.53% PSHydro_Generation, 0.01% Hydro, 0.11% GasTurbine, 0.15% PS_Hydro_Consumptio n, 0.02% Intermittent_Gen, 18.31% Demand, 43.94% ENS, 0.00% Distributed_Gen, 13.47% Figure 38. Energy mix scenario 10.

102 Study / Development 94 Evolution of generation mix Generation share [%] % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Scenario BEV Gen Unscheduled Hydro PSHydro Generation Coal Nuclear GasTurbine Combined Cycle Distributed Gen Hydro Wind Figure 39. Evolution of generation mix. Evolution of generation mix (without share of EVs) Generation share [%] % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Scenario Unscheduled Hydro PSHydro Generation Coal Nuclear Gas Turbine Combined Cycle Distributed Gen Hydro Wind Figure 40. Evolution of generation mix without taking into account the EVs generation. From the previous figures it can be observed that the share of intermittent RES (Wind) is increasing, while the share of CCGT is reducing. This could be explained because, as it will be showed later on, the wind spillages get smaller. Most of the wind spillages were in the valley hours, but with electric vehicles the demand in valley hours increases. Now the wind energy in valley hours is used to charge the batteries and in peak hours this energy stored in the batteries is used instead of CCGTs, which are more expensive.

103 Study / Development 95 On the other hand, as it can be seen there is no energy non served and the share of the wind generation is very big. This is because it was introduced a lot of wind every day and the demand is not so big. This was modelled this way because in this study the intention was focused in results like the evolution of the intermittent RES share in the energy mix with the increment in the number of PHEVs. Evolution of share of the intermittent RES (wind) in the energy mix: Wind share in generation production mix Wind share [%] 40.50% 40.00% 39.50% 39.00% 38.50% 38.00% 37.50% 37.00% 36.50% 36.00% 35.50% y = x # PHEVs [millions] Wind share in generati on mix Linear aproxim ation Figure 41. Wind share evolution in the generation mix. The blue curve represents the actual data taken by the model and green one the linear approximation. In the equation in green it can be seen that, for this scenarios which does not fairly represent the reality, there is an increase in the wind share of 0.018%/million of PHEV, but this number must be taken only as a reference.

104 Study / Development 96 Evolution of wind spillages: Wind Spillages [GWh] Wind Spillage evolution y = x # PHEVs [million] Wind Spillage reduction Lineal (Wind Spillage reduction) Figure 42. Evolution of wind spillages. Here it can be appreciated how the wind spillages reduce at a rate of 4,865 GWh/million of PHEV as the number of PHEVs in the energy mix increases. The demand increases in valley hours (the EVs charge in VH) as the number of EVs increases because there are more energy storage devices to store the energy from the wind. This figure explains why there is an increase in the wind generation observed in Figure 41. Evolution of share of the CCGT in the energy production mix: CCGT share in generation mix CCGT share [%] 25.00% 23.00% 21.00% 19.00% 17.00% 15.00% y = x # PHEVs [millions] Figure 43. CCGT share evolution in the generation mix. To support the hypothesis stated before, here

105 Study / Development 97 It can be seen that the reduction of the share of CCGTs is of %/million of PHEVs. The increment in Wind generation share was %/million of PHEVs. There is a relation between the increment in the share of Wind generation and the reduction in the share of CCGTs. What is happening is that the energy from the wind stored in the batteries in the valley hours is used in peak hours instead of CCGTs, which are more expensive. For instance, if scenario 8 and scenario 10 are compared looking at the hourly operation, it can be extracted the next numbers: Hour 18:00 CCGT generation BEV generation SUM Scenario 8 11,790 MW 5,631 MW 17,421 MW Scenario 10 10,127 MW 7,293 MW 17,420 MW Table 23. CCGT and BEV generation in a peak hour (18:00) in scenarios 8 and 10. It can be appreciated that in total both of them together produce the same amount of energy, but in scenario 10, because there are more EVs, some more of this energy is generated by the EVs. At this hour it can be also appreciated (not in this table) that no car is being charged, because the energy is more expensive and it is more profitable to charge in valley hours. Another thing that can be observed is that from 1.7 million EVs the CCGT share starts to increase again. This is because more EVs mean more demand, and the wind share at this point keeps increasing but slower. The wind generation does not increase so much as before, so the increment in demand in covered by the CCGT plants in more proportion than before.

106 Study / Development 98 Evolution of total costs: Production cost evolution Cost [M ] # PHEVs [millions] Cost reduction evolution Linear aprox Figure 44. Production cost evolution. Although more EVs means more demand, a smart management of them allows to reduce the generating costs, getting profit of the EVs as energy storage devices that they are and reduce the generation from the most expensive generating units. Some of it is explained with what was demonstrated before, more wind generation and less CCGT generation. Anyway, from a given number of PHEVs it can be appreciated that the costs start to increase. Now the increment in demand is not compensated by the PHEVs treated as DG. This could be explained as follows: the cost starts to increase again more or less with 1.7 million of PHEVs. Introducing EVs what is being done is to flat the demand curve, the EVs charge in valley hours (and generate in peak hours) and the demand in these hours starts to get closer to how it is in the peak hours. Of these 1.7 million EVs only million are connected in valley hours (this number can be deducted from Table 20, Table 21 and Table 22) million EVs (each one of them with a rated power of 7 kw) represent a demand in valley hours of 9,123 MW more or less, so if this demand is added to the normal one in these hours (15,000 MW) there are a total demand in valley hours of 24,123 MW more or less, which starts to be similar to the demand in the peak hours. Once the demand in the valley hours is the same (or very close) as in the peak hours, if more EVs are introduced the whole

107 Study / Development 99 demand curve rises, and the total costs increase. It can be also observed that from 1.7 million EVs also the CCGT generation starts to increase, which can explain also the increment in total costs. The red line in previous figure is the linear approximation of the actual data taken from the model from 0 EVs to 1.08 million of EVs. It shows a reduction in the production costs of more or less 311 M$/million of PHEVs introduced in the market. Evolution of marginal costs MC [$/MWh] Marginal costs evolution # PHEVs [million] from h1 to h7 from h14 to h17 from h18 to h21 from h8 to h13 from h22 to h24 Figure 45. Evolution of marginal costs. In the peak hours (from h8 to h13 and from h18 to h24) the MC do not change very much because as more EVs are introduced, there is not a big change in the number of PHEVs that are to charge at this time, so the generation mix remains unchanged. However, in the valley hours (from h1 to h7) the marginal costs increase because EVs charge at this time, when demand is smaller and so energy is cheaper. As EVs are introduced in the market, most of them charge at this time, and so some of the most expensive generation units must start generating at this time in order to meet demand requests. On the other hand, in hours from h14 to h17, the MC decrease because as more EVs are introduced, there is not a big

108 Study / Development change in the number of vehicles that charge at this time but more EVs generate at this time and so some of the most expensive power plants (such as coal, CCGT or gas) reduce its generation between h14 and h17. Hydro spillage evolution: Hydro Spillages [GWh] Hydro Spillage evolution y = x # PHEVs [million] Hydro Spillage reduction Lineal (Hydro Spillage reduction ) Figure 46. Hydro spillage evolution. Here something similar than with the wind spillages is happening. When there is too much rain, this hydro energy is stored in the PHEV batteries, so the hydro generation also increases as it can be seen in next result. From the previous figure it is observed an approximate reduction in the spillages of 21.4 GWh/million on PHEVs introduced in the market. Hydro generation evolution: Hydro generation [GWh] Hydro generation evolution y = x # PHEVs [million] Hydro generation evolution Linear aprox. Figure 47. Hydro generation evolution.

109 Study / Development 101 The increment in the hydro generation is of GWh/million of PHEVs introduced in the market, and the reduction in the hydro spillages was GWh/million of PHEVs, so as stated before, most of the increment in the hydro generation is due to the reduction in the spillages because of the use of the EVs as energy storage devices. CO 2 emissions and total generation evolution: Figure 48 shows how the total CO 2 emissions evolve as more EVs are introduced in the market. CO2 Emissions evolution Emissions [MtCO2] y = x # PHEVs [millions] Emissions reduction Linear aprox. Figure 48. CO 2 emissions evolution. Evolution of total generation Generation [GWh] 215, , , , , , , , y = 11224x # PHEVs [million] Generation Lineal (Generation) Figure 49. Evolution of total generation.

110 Study / Development 102 It is interesting to see how, even that more EVs means more demand and so more total generation, there is a reduction in the CO 2 emissions because the EVs allow to substitute some generation units for other ones which emit less gases but have the characteristic of being intermittent. For the characteristics of these scenarios, the reduction in CO 2 emissions is of 3.43 million tones of CO 2 /million of PHEVs introduced in the market Scenarios to manage of the PHEVs Here 2 scenarios were modelled. These 2 scenarios have the same number of PHEVs. The number of EVs for year 2030 is 3,000,000 PHEVs, but because in the model there is less generation than in the actual data, the number of PHEVs modelled is also smaller: 1,420,200 PHEVs were modelled for these 2 scenarios. This number of vehicles corresponds to a scenario between Scenario 7 and Scenario 8 of the previous study. The rest is the same as in the previous study, except the next changes: Data Demand: The demand behaviour was unchanged along the whole year with a minimum of 19,500 MW and a maximum of 35,000 MW, and its characteristic every day was the following one: Demand Demand [MW] Time [hours] Figure 50. Demand evolution during the day.

111 Study / Development 103 Wind power: Wind power Wind power [MW] time [hour] Wind power Actual wind Figure 51. Wind power evolution during the day. From previous 2 graphs it can be appreciated that there is an increment in the demand but also a reduction in the wind power. This was done in order to be able to see the influence of the PHEVs management in the Energy Non Served (ENS), among other things related with the reliability of the system. PHEV data: Here almost everything was the same as in the previous study, but some changes were done: o Scenario 1: Use Car % connected in each hour Table 24. % of cars connected in each hour for scenario 1.

112 Study / Development 104 o Scenario 2: Use Car % connected in each hour Table 25. % of cars connected in each hour for scenario 2. In scenario 2 it is supposed that more cars (or only the batteries) are connected more time. It could be for instance the case in which there are battery change stations and many batteries are connected without the car needed to be present, or if more infrastructures are built and people get enough incentives to be connected as long as possible. In this model it is supposed that PHEV users get enough incentives so when they are connected they are directly controlled by the aggregators so they can either start generating or charging as it is needed by the system. In scenario 2 the connectivity increased a 20.3% respect that one in scenario 1. The idea was to see how power system behaviour improves if there are more cars available at each moment in order to be dispatched in the most economical way, how a smart active demand management can reduce costs in the operation planning, reduce generation costs and so reduce energy prices.

113 Study / Development Results Cost Emission XLOL LOLP ENS [M$] [MtCO 2 ] [MW] [h/year] [GWh] Scenario 0 11, ,360 2,005 3,012 Scenario 1 (1) 10, Scenario 2 (2) 10, [1-(2)/(1)]% 2.20% 0.89% 2.36% 10.02% 10.37% Table 26. Power system improvement with a smart active demand management. NOTE: Scenario 0 is the same as Scenario 1 and Scenario 2 but without any EV. Just the fact of having EVs in the energy mix gives an improvement in terms of cost and emission reduction and in terms of reliability improvement, but it can be also observed that with a smart active demand management and with enough infrastructures and incentives so the users are more concern about power system behaviour and are willing to help more often (this is what scenario 2 represents), there is a reduction in total costs, a reduction in CO 2 emissions and also an improvement of the reliability of the system. Loss of Load Expectation (XLOL) and Loss of Load Probability (LOLP) decreases, and that explains why the Energy Non Served (ENS) also gets smaller. The numbers must be taken only for reference, but the actual important thing here is to see how power systems can improve thanks to the smart use of PHEVs.

114 Conclusions 106 Chapter 3 CONCLUSIONS Electric cars are the future, but the future is now. They bring a world of possibilities, many of them stated in the part Motivation for the Thesis and along all the content of this thesis, such as a decrease in the volume of GHG emissions, thing that the entire world is very concern about right now, or a possibility to help the introduction of more intermittent RES, but in order to achieve all these goals successfully, many changes have to be done, both in the electric and in the automotive fields, in order to be able to manage with its introduction in the market. Some of these changes were analyzed in this work, so the objectives proposed at the beginning of this thesis were: Characterization of the electric car as electric load and distributed generation (DG), both in the medium term and in the long term. Analysis of the connection in distribution grid impact (investment, looses, etc.). Definition of different business models related with the introduction of PHEVs in the market. Analysis of the impact in the energy mix. Analysis of the impact in the CO 2 emissions. The pathway followed to achieve these objectives successfully is described schematically in next figure:

115 Conclusions 107 Figure 52. Work pathway followed to achieve the objectives. With this methodology all the objectives were achieved successfully. The general conclusion taken from this study is quite clear. The integration of PHEVs in the market brings a lot of possibilities for the future of the electric power systems, but also a lot of threats that need to be mitigated someway. They represent more electric load connected to the grid, so reinforcements will need to be done for a large penetration of them. These investments can be mitigated if a smart active demand management is carried out and the different business models related to the EVs are well defined (i.e. Retailer business model, DSO business model, charge station business model, car manufacturer business model, among others). The electric vehicles also mean a new possible and profitable business for some agents, such as charge stations or retailers.

116 Conclusions 108 Some particular conclusions extracted from the analysis of the impact of EVs in the distribution network: First of all, 2 scenarios were analyzed: Aranjuez: o Inhabitants: 52,224 o Cars: 0.55 cars/person o Simultaneity factors: valley hours= 0.15 and peak hours= 0.7 o Total contracted power without EVs: MW Manheim o Inhabitants: 6,127 o o o Cars: 0.6 cars/person Simultaneity factors: valley hours= 0.15 and peak hours=0.23 Total contracted power without EVs: MW On the other hand, due to the fact that in Manheim most of the cables and other network elements are underground, the elements installed to reinforce the network are more expensive (in the order of 10 times more) than those ones with the same characteristics used in Aranjuez. The main conclusions extracted from the study were: Much careful must be taken when planning the investments in order to be aware of the installation of MV Substations, because they are the most expensive elements to install in the distribution networks and they could be required from one year to another. This investment must be know beforehand in order to start with an amortization strategy.

117 Conclusions 109 The investment needs are about 0.18% /PHEV share [%] (0.31% /PHEV share [%] in the case of Manheim, and 0.049% /PHEV share [%] in the case of Aranjuez). The difference between Manheim and Aranjuez is because the elements used in the last one are cheaper. The increment in losses in valley hours is between 0.52% /PHEV share[%] (the case observed in Aranjuez) and 0.62% /PHEV share[%] (the case of Manheim). Figure 53. Losses evolution in valley hours. With an active demand management of the EVs (i.e. if it is known that an amount of EVs is going to be connected for a period of time, its charge is distributed along all the time they are connected in order to avoid that all vehicles are charging at the same time) there can be large savings in the investments and bigger if the elements installed to reinforce the network are more expensive. In Aranjuez there is a saving of 0.036% /PHEV share [%] and in Manheim, with elements 10 times more expensive than those used in Aranjuez, 0.22% /PHEV share [%].

118 Conclusions 110 Increment in the investment - Aranjuez Vs. Manheim Acumulated investemnt [%] 20.00% 15.00% 10.00% 5.00% 0.00% PHEV share [%] A without ADM A with ADM M without ADM M with ADM Figure 54. Investment safe with an ADM. If battery change stations are used so some of the EVs that want to charge in peak hours, what they do is to change the battery for another one that was charged during the valley hours when the impact in the network is not so big, there can be savings in the investments of about 0.33% /PHEV share [%] (0.13% /PHEV share [%] in the case of Manheim, and 0.54% /PHEV share [%] in the case of Aranjuez). The difference between the case of Aranjuez and Manheim is because in the first one the elements are cheaper and the difference in the SFs between PHs and VHs is also bigger. Investment evolution with and without moving load to VHs Investment increment [%] 20% 15% 10% 5% 0% Initial inv. (A) Final inv. (A) Initial inv. (M) Final inv. (M) PHEV share [%] Figure 55. Investment evolution with and without moving load to VHs.

119 Conclusions 111 Investment savings Investment saving [%] 40% 35% 30% 25% 20% 15% 10% 5% 0% Aranjuez Manheim PHEV share [%] Figure 56. Investment saving with battery change stations. On the other hand, with the introduction of EVs in the energy mix, and doing a smart active demand management of them, it was seen in previous section Impact in the energy mix and CO 2 emissions that numbers say that there is a good possibility to increase the intermittent RES in the energy mix, while the share of CCGT is reducing. This could be explained because the wind spillages get smaller (i.e. when there is too much wind, this is used to charge the batteries) and in peak hours this energy stored in the batteries is used instead of CCGTs, which are more expensive. Evolution of generation mix (without share of EVs) Generation share [%] % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Scenario Unscheduled Hydro PSHydro Generation Coal Nuclear Gas Turbine Combined Cycle Distributed Gen Hydro Wind Figure 57. Evolution of generation mix without taking into account the EVs generation.

120 Conclusions 112 Here it can be appreciated an increment in the share of wind generation and a reduction in the CCGTs generation. Wind share in generation mix Wind share [%] 40.50% 40.00% 39.50% 39.00% 38.50% 38.00% 37.50% 37.00% 36.50% 36.00% 35.50% y = x # PHEVs [millions] Wind share in generati on mix Lineal (Wind share in generati on mix) Figure 58. Evolution of Wind generation. CCGT share in generation mix 25.00% CCGT share [%] 23.00% 21.00% 19.00% 17.00% 15.00% y = x # PHEVs [millions] Figure 59. CCGT generation. Here it can be seen how the increment in share of wind generation coincides with the reduction in the share of CCGTs. This also brings a reduction in total costs:

121 Conclusions 113 Cost evolution Cost [M ] # PHEVs [millions] Cost reduction evolution Linear aprox Figure 60. Cost evolution. But from the figure it can be appreciated that at a certain number of EVs the operation costs start to increase again. For the same reason, hydro spillages can be reduced and so the hydro power dispatched increases. As more EVs are introduced, the evolution of the hydro spillages taken from the example was GWh/million on PHEVs and in the power generation was GWh/million of PHEVs. Most of the increase in its generation is due to the fact that spillages were reduced. All this means less CO 2 emissions, even when more cars mean more total generation, but more coming from cleaner energy sources.

122 Conclusions 114 Evolution of total generation Generation [GWh] 215, , , , , , , , y = 11224x # PHEVs [million] Generation Lineal (Generation) Figure 61. Evolution of total generation. CO2 Emissions evolution Emissions [MtCO2] y = x # PHEVs [millions] Emissions reduction Linear aprox. Figure 62. CO 2 emissions evolution. It is also possible to improve the power system reliability, both with the introduction of more cars and with a smarter active demand management of the existing PHEVs. Good incentives and more infrastructures distributed everywhere to be able to manage the EVs properly and at any time is necessary. With this it was observed a reduction in the ENS, XLOL and LOLP. In relation with the business models, the main conclusions are the next ones:

123 Conclusions 115 Smart meters are an essential tool in order to well develop an Active Demand Management to be able to control the impact of EVs in the network and energy mix, and in particular the Automatic Meter Management (AMM) are the ones that have to be installed because they allow bidirectional communication between the customer and the retailer. In parallel with the previous idea, it is very important to design good and effective price and volume signals. With this, it is possible to, indirectly, make demand active in the market. Short and very short term signals should be designed and sent through smart meters. Regarding the services surrounding the new smart meters, if the final decision is let to the retailers, customers and to the market, we will let the decision of the technology to install to the more experts in this field and this will not affect the plans, contracts and investments of the different companies involved, but on the other hand, the absence of a standard in the smart meter, in the communications or in the data transfer methods, increases the risks for a new investment by the retailers (possible problems related with the change in clients, etc.). The best solution would be to let to the DSOs the installation and property of the smart meters and open to the competence the maintenance service, lecture and management of the data. The part for communicating with the consumers has to be located where each one of the consumers are, to have a communication line between consumers and retailers, but the information from (towards) each one of the customers goes (comes) to (from) the retailers level, where the intelligence part should be located, and there this information is processed. In the consumers side there should be another intelligent system in order to manage with the loads and DG according to the signals gathered.

124 Conclusions 116 As an opportunity to improve, it is important to mention that most people prefer to reduce the period for billing issue. The 2-months period was not considered as the best one for the introduction of intelligent meters and TOU tariff. Domestic consumers (PHEV users in the household level) are not motivated purely by economic considerations. In order to motivate end consumers to actively participate in demand side management solutions, it must be taken into account that in addition to economic rewards they are also motivated by other considerations such as environmental concerns, etc. Connection charges for the DSO should be shallow (and no deep). With this, additional connection costs cannot be passed through to the DG operator and PHEV owner, which gives the DSO an incentive to reduce investments in network upgrades as much as possible because avoidable investments are revenues for the DSO. The best option for the charge stations to charge for the energy purchased is to do it for the price of the energy in the moment of the service because the PHEV users in this case will behave in an active manner, so they will be willing to charge when the energy price is lower and the system is less affected. The competence between vehicle manufacturers should be promoted, and for that purpose this sector should be regulated in order to fix standards and to avoid market power. Helps for buying these new kind of hybrid electric cars is a key factor since it is about change from a very extended technology to another totally new for the citizens. Electric vehicles bring a lot of challenges for the future but also a lot of opportunities, opportunities for the environment, for creating new business and for improving the reliability and security of supply of power systems.

125 Future works 117 Chapter 4 FUTURE WORKS In this thesis it was studied in general terms the business models of different agents, the impact of the PHEVs in the energy mix and in the CO 2 emissions in Spain for year 2030 in a simplified modelled case, the impact of PHEVs in the distribution network, and some strategies were defined for a smart active demand management in order to reduce threats seen in the simulations and to get profit from the smart use of the EVs. For future works more realistic cases should be run for the analysis of the impact in the energy mix and CO 2 emissions where demand changes along the days of the year in the same way as forecasted for the case of Spain for year 2030, the same for the wind and a more realistic distributed generation, where the power output is not constant along all the year. In general it will be necessary to adapt the energy mix with more accuracy to the actual Spanish case forecasted for year 2030 and generator characteristics to be more similar to the real ones. About the data introduced for the PHEVs, the definition of the different uses that could be done of them can be more similar to real ones, and the same for the data related with its energy capacity and power. The number of cars also needs to be more real, giving a total number of them closer to the one forecasted for 2030, and also differing more among different PHEV models. On the other hand, the ROM model is still under development, so much more improvements can still be done in this field. In particular, some improvements could be done for the PHEV data, differentiating among different charge modes that the same car can be involved in, for instance. It has to do also some improvement with respect to the hydro output, because it was observed that the share in the energy mix is not so big as it would have to be (gas is more expensive than water and it generates more

126 Future works 118 often than hydro plants). The same thing for the E-GRID Expansion model, which is currently under development. It is still failing sometimes, so it needs to be more robust. It could be also interesting to be able to forecast with the E-GRID Expansion the annual losses, year by year and in power units, instead of giving the price translated to the present value. If this thing is achieved, a better study about the operational issues of the distributed network could be studied. Once the analysis have been done, some more strategies for an active demand management should be defined in more detail both for the analysis of the impact in the distribution network and the impact in the energy mix and CO2 emissions for trying to reduce threats and to get profit from the smart use of the EVs. A more detailed study to see the usage that people would do of their EVs (when they drive them, when they are connected, etc.) must be done in order to be able to define good and effective incentives and strategies for an active demand management. Each one of the issues treated in this thesis need to be studied in more detail, as for instance the need to develop the Smartgrids paradigm, with its technical problems, or to study the impact in the distribution networks by voltage levels, or to define with more detail which are the regulatory changes that need to be done in some fields, as for instance those ones related with the commercial and technical virtual power plants. Plug-in Hybrid Electric Vehicles is a very hot topic nowadays, with constant changes and fast innovation, so in general the ideas showed in this study should adapt to these changes.

127 Future works 119

128 Bibliography 120 BIBLIOGRAPHY [1] Matthew A Kromer. Electric Powertrains: Opportunities and Challenges in the US Light-Duty Vehicle Fleet, Massachusetts Institute of Technology (MIT), Boston, Massachusetts, June [2] EPRI, Environmental Assessment of Plug-in Hybrid Electric Vehicles, Final Report, July [3] WWF (World Wide Fund for Nature), Plugged in. The end of the oil age, March [4] Jonn Axsen, Andrew Burke and Ken Kurani, Batteries for Plug-in Hybrid Electric Vehicles (PHEVs): Goals and the State of Technology circa 2008, Institute of transportation Studies, University of California, May [5] Juan Carlos Viera Pérez, Carga rápida de baterías de Ni-Cd y Ni-MH de media y gran capacidad. Análisis, síntesis y comparación de nuevos métodos, Universidad de Oviedo, Abril [6] Andrés Ramos, Luis Olmos, Jesús M. Latorre and Ignacio Pérez-Arriaga, Modeling medium term hydroelectric system operation with large-scale penetration of intermittent generation, Instituto de Investigación Tecnológica (IIT), Universidad Pontificia Comillas, Madrid, Spain, [7] Fredrik Carlsson and Olof johansson-stenman, Costs and Benefits of Electric Vehicles. A 2010 Perspective, Journal of Transport Economics and Policy, Volume 37, Part 1, January [8] Improgres, Improgres Newsletter, Newsletter No. 1, May [9] P. Denholm and W. Short, An Evaluation of Utility System Impacts and Benefits of Optimally Dispatched Plug-in Hybrid Electric Vehicles, NREL (National Renewable Energy Laboratory), Technical Report, October [10] 72, document for ZEBRA batteries [11] A. Ramos, L. Olmos, J.M. Latorre, I.J. Perez-Arriaga, Modeling medium term hydroelectric system operation with large-scale penetration of

129 Bibliography 121 intermittent generation, XIV Latin and Iberian Conference in Operations Research (CLAIO 2008). ISBN Latin-American Association of Operations Research. Cartagena de Indias, Colombia, 9-12 Septiembre [12] J. Peco, T. Gómez, A Model for Planning Large Scale MV Networks considering Uncertainty in Reliability Modeling, 6th International Conference on Probabilistic Methods Applied to Power Systems, Funchal, Madeira Portugal, Septiembre, 2000 [13] SmartCity project, presented by Endesa to CDIT, February 2009 [14] Michael Caramanis, Member IEEE and Justin Foster, Management of Electric Vehicle Charging to Mitigate Renewable Generation Intermittency and Distribution Network Congestion, Boston University, Working Paper, [15] D.Pudjianto, C.Ramsay, G.Strbac, The FENIX vision: the virtual power plant and system integration of distributed energy resources, FENIX Deliverable 1.4.0, available on [16] Lizhi Wang, Anhua Lin and Yihsu Chen, Potential Impacts of Recharging Plug-in Hybrid Electric Vehicles on Locational Marginal Prices and Emissions, Naval Research Logistics, [17] D. Six, E. Peeters, M. Hommelberg, C. Batlle, J. Su, F. Bouffard, R. Belhomme, M. Sebastian, P. Lang, C. Yuen, J. Jimeno, A. Vicino, W. Fritz, R. Cerero, G. Valtorta and D. Hirst, ADDERSS Internal Report Task 1.1 Analysis of ADDRESS concepts and proposed architecture; first contextual scenarios, ADDRESS, January 30 th, [18] Instituto de Investigación Tecnológica (IIT), Análisis económico y desarrollo regulatorio. La experiencia internacional, Proyecto gestión activa de la demanda. Proyecto CENIT GAD, April [19] J. de Joode, A.J. van der Welle and J.J. Jansen, Bussiness models for DSOs under alternative regulatory regimes, DG-GRID, August [20] Instituto de Investigación Tecnológica (IIT), Estado del arte de la evaluación económica, Proyecto gestión activa de la demanda. Proyecto CENIT GAD, [21] Solid-DER Project:

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131 Part II ANEXES

132 Anexes Annex 1 Terminology GHG Green House Gas CV Conventional Vehicle AER All Electric Range EV Electric Vehicle EPRI Electric Power Research Institute USABC US Advanced Battery Consortium PHEV Plug-in Hybrid Electric Vehicle CV Conventional Vehicle HEV Hybrid Electric Vehicle DG Distributed Generation CHP Combined Heat and Power PV Photo-Voltaic CD Charge Depleting CS Charge Sustaining ICE Internal Combustion Engine SOC State of Charge ICE Internal Combustion Engine DOD Depth of Discharge BEV Battery Electric Vehicle SF Simultaneity Factor V2G Vehicle to Grid ADM Active Demand Management

133 Anexes 125 RES Renewable Energy Sources VPP Virtual Power Plant LSVPP Large-Scale Virtual Power Plant DER Distributed Energy Resources DDER Demand and Distributed Energy Resources RTP Real-Time Pricing TOU Time-of-Use Tariff CPP Critical Peak Pricing DSO Distribution System Operator TSO Transmission System Operator LDC Load Duration Curve IG Intermittent Generation ENS Energy Non Served XLOL Loss of Load Expectation LOLP Loss of Load Probability PH Peak Hours VH Valley Hours ROM Renewable Energy Sources Operation Model LV Low Voltage CCTT LV Transformer Centres MV Medium Voltage SSEE Substations HV High Voltage

134 Anexes Annex 2 Battery technologies and EV use 2.1 Li-Ion Prospects Li-Ion batteries can be constructed from a wide variety of materials, allowing battery developers to pursue several different paths. The main Li-Ion cathode material used for consumer applications (e.g. laptop computers and cell phones) is lithium cobalt oxide (LCO). However, due to safety concerns with using this chemistry for automotive applications, several alternative chemistries are being testing for PHEVs, including: lithium nickel, cobalt and aluminum (NCA), lithium iron phosphate (LFP), lithium nickel, cobalt and manganese (NCM), lithium manganese spinel (LMS), lithium titanium (LTO), and manganese titanium (MNS and MS). Table 27 presents an illustrative snapshot of several key Li-Ion technologies according to USABC goals. A rating of poor is far from reaching USABC goals in that category; a moderate rating shows some promise of meeting goals with further development; a good rating has shown evidence of being a good candidate to meet goals; and an excellent holds very strong promise of meeting USABC goals. This table further demonstrates the many inherent tradeoffs in battery development; a single battery has yet to meet power, energy, life, safety, and cost goals.

135 Anexes 127 Name Description Automotive Status Power Energy Safety Life Cost LCO Lithium cobalt oxide Limited auto applications (due to safety) Good 4 Good 4 Low 2,4, Low 2,4 Poor 2,3 Mod 3. NCA NCM LFP Lithium iron phosphate Pilot 1 Good 1,3 Good 1,3 Mod 1. Good 1 Mod 1,3. Mod 3., Good 7 Pilot 3 Mod 3. Mod 3. Poor 3 Mod 3. Pilot 1 Good 1 Mod 2,6. Mod 1,2,4. Good 1,4 Mod 1., Good 2,3 LTO Lithium titanium Developing 3 Poor 3, Mod 7. Poor 3 Good 3 Good 3 Poor 3 LMS MN MNS Lithium nickel, cobalt and aluminium Lithium nickel, cobalt and manganese Lithium manganese spinel Manganese titanium Manganese titanium Excel 1., Excel 1., Developing 1 Mod 2. Poor 1,2,3 Good 2 Mod 6 Mod 2 Research 1 Excel 1. Excel 1. Excel 1. Unknown Mod 1. Research 1 Good 1 Mod 1. Excel 1. Unknown Mod 1. Sources: 1 Nelson, Amine and Yomoto (2007, p2) 5 Kohler (2007) 2 Kromer and Heywood (2007, p37) 6 Anderman (2007) 3 Kalhamer et al. (2007) 7 UC Davis Testing 4 Chu (2007) Table 27. Characteristics of different PHEV battery chemistries In the Table 28 it can be seen the main drawbacks for Li-Ion technology and the mitigation strategies for addressing these shortcomings over the long-term:

136 Anexes 128 Table 28. Long term Lithium-ion challenges and mitigation strategies 13. Li-Ion prospects for the long term are expected to achieve better results. Li-Ion battery costs are predicted to fall as low as $395/kWh for a PHEV- 10 and $260/kWh for a PHEV-40, with,000 units of production (Kalhammer et al., 2007). But no everybody is so optimistic, for example, Anderman (2008) expects Li-Ion batteries to maintain costs around $600/kWh even with increased production. 2.2 Nickel-Metal Hybride (NiMH) prospects NiMH batteries are used for most HEVs currently sold in the US. The primary advantage of this chemistry is its proven longevity in calendar and cycle life, and overall history of safety. However, the primary drawbacks of NiMH are limitations in energy and power density, and low prospects for future cost reductions. 13 [1] of the bibliography

137 Anexes 129 Kalhammer et al. (2007) estimate that at,000 units of production per year, NiMH battery prices may fall as low as $530/kWh for a PHEV-10 and $350/kWh for a PHEV-40. These forecasts are far from reaching USABC s goals of $300/kWh and $200/kWh, respectively. No battery currently meets all of the USABC s PHEV goals for these attributes listed in Table 28. Of the chemistries currently being considered for PHEV application, Li-Ion is best suited for the power and energy density goals of the USABC. Although NiMH batteries may be suitable for a less ambitious PHEV design, Li-Ion technologies are still superior to NiMH in potential for lower cost in the future. However, Li- Ion is not yet firmly established for automotive applications, and development must overcome issues of longevity and safety and the resulting tradeoffs with performance in order to achieve commercial success. While still falling short of the ambitious power targets of the USABC s PHEV-10, and the energy targets of the PHEV-40, the JCS Li-Ion battery has more than double the power density and more than 50 percent greater energy density than the Varta NiMH battery. The NiMH battery is nearing fundamental practical limits (estimated at ~75 Wh/kg on a pack level). Over the next several decades, lithium-ion chemistries have been predicted to be capable of achieving specific energies as high as 300 Wh/kg on a cell basis. In Figure 63 it can be seen the historical change in lithium-ion battery specific energy since its introduction in 1991 (~7%/year):

138 Anexes 130 Figure 63. Historical evolution of Li-Ion Batteries Na/NiCl2 (ZEBRA) prospects In addition, it is also important to mention about the good perspectives for ZEBRA batteries (Na/NiCl2) for Vehicle to Grid (V2G) purposes. Comparing with current Li-ion technology, ZEBRA appears to be showing some advantage with a lower price (costs roughly four times less than a Li-ion with similar characteristics). ZEBRA batteries have been demonstrated with more than 2000 nameplate cycles and need 6 hours to reach full charge in normal mode. In fast charge mode they require about 1 hour to reach a state of charge (SOC) of 80%. Even if not a lot of documents talk about this technology, it is important to take it into consideration since currently this technology has a good development for PHEV cars. 14 [1] in the bibliography

139 Anexes 131 A possible summary of its characteristics could be the ones in the next table: Specific energy [Wh/kg] Specific power [W/kg] Typical capacity [Ah/cell] 120 > Table 29. ZEBRA battery characteristics. Battery capacity goes from 32 Ah to 152 Ah with current technologies, peak power from 24 kw to 64 kw and rated energy from 15.5 kwh to 42.3 kwh. The cells are normal charged with 2.67 V/cell within 6-8 h and fast charged with a 1h-rate and 2.85 V/cell up to 80% SOC. The regenerative charge voltage is 3.1 V/cell. Any geometrically reasonable number of ZEBRA cells can be connected in series and in parallel in order to generate the desired voltage and capacity. The ZEBRA Battery has passed all safety tests defined by the European Automotive Industry and USABC as there are the crash tests with 50 km/h, overdischarge test, short circuit test, vibration test, external fire test and submersion under water. The ZEBRA Battery technology has shown in laboratory tests that it provides a calendar life of more than 10 years and a cycle life of nameplate cycles.

140 Anexes 132 ZEBRA Batteries are well suited for pure electric cars, vans and buses as well as for range extender type hybrid cars, hybrid vans and hybrid buses with ZEV range. 2.4 PHEV battery goals set by USABC, MIT and EPRI Now that the possibilities for the different battery technologies are known, is time to see the goals set for the different types of PHEVs. In this section it is presented PHEV battery goals set by the US Advanced Battery Consortium (USABC), as summarized by Pesaran et al. (2007), the Sloan Automotive Laboratory at the Massachusetts Institute of Technology (MIT) and the Electric Power Research Institute (EPRI). As it was stated before, it is used the CARB s definition of PHEV-X, where X is the number of miles the vehicle can drive in all-electric mode during a particular drive cycle, before the gasoline engine turns on. Next table summarizes the differing assumptions and goals of each:

141 Anexes 133 Vehicle assumptions CD Range Units USABC 1 MIT 2 EPRI 3 Miles CD Operation - All-electric All-electric Blended All-electric All-electric Electricity Use 4 kwh/mile Depth of Discharge % 70% 70% 70% 80% 80% Body Type - Cross. SUV Mid. Car Mid. Car Mid. Car Mid. Car Battery Mass, Total kg (Cells Only) 5 (45) (90) (45) (121) (252) Total Vehicle Mass kg Battery Goals Peak Power kw Peak Power Density W/kg Total Energy Capacity kwh Total Energy Density Wh/kg Calendar Life years CD Cycle Life cycles 5,000 5,000 2,500 2,400 1,400 CS Cycle Life cycles 300, , ,000 <200,000 <200,000 1 Pesaren et al. (2007) 2 Kromer and Heywood (2007) 3 Graham et al. (2001) 4 Grid electricity only. Calculated as total available energy divided by CD range 5 Packaging factor of 0.75 assumed for cells only mass. Table 30. Different PHEV battery goals. Cells in colour are the ones that were taken for the study. Power requirements are not typically related to CD range; the PHEV-10 requires slightly more power due to the increased weight (+350 kg), rolling resistance, and frontal area (drag) of the crossover SUV compared to the sedan used for the PHEV-40 analysis. For comparison, Kromer and

142 Anexes 134 Heywood (2007) demonstrate how different types of operation in CD mode can influence power requirements for a PHEV-30. While different levels of blended operation require only 23 to 40 kw of power, a PHEV with all-electric operation requires a battery that can deliver 60 kw (Kromer and Heywood, 2007). The latter value is higher than USABC goals due to Kromer and Heywood s use of more ambitious drive cycles. About battery life, with use and over time, battery performance can substantially degrade, including power, energy capacity, and safety. Calendar life is the ability of the battery to withstand degradation over time, which may be independent of how much or how hard the battery is used. The power and energy goals described in Table 30 must apply after calendar life regardless of use. If these attributes are expected to degrade over time and/or use, initial values will have to be even higher than the stated goals. CD cycle life is the number of discharge-recharge cycles the battery can perform in CD mode. This goal assumes one complete deep cycle each day, 330 days of the year. CS cycle life refer to SOC variations of only a few percent. These smaller variations occur throughout CD and CS mode, as it can be seen in Figure 9. Battery cost is thought to be one of the most crucial factors affecting the commercial deployment of electric drive technologies. The cost perspectives are the following:

143 Anexes 135 Units PHEV-10 1 PHEV-30 1 PHEV-60 1 PHEV-10 2 PHEV-40 2 Battery size Specific cost Battery cost kwh $/kwh $420 $320 $270 $300 $200 $ $1,450 $2,700 $4,500 $1,700 $3,400 1 MIT perspectives 2 USABC perspectives Table 31. PHEV battery cost perspectives. These cost perspectives had been calculated under a scenario where battery production has reached,000 units per year. The MIT also consider the possible introduction of pure plug-in electric cars in the long future. Next table summarizes MIT proposed characteristics of projected for the long term for pure plug-in electric cars: Unit BEV (200 Mi Range) Specific Energy Wh/kg 150 Specific Power W/kg 300 Energy kwh 48 Power kw 80 Cycle Life Cycles 1,000 Calendar Life Years 15 SOC Envelope % 0-% Cost-baseline $/kwh $250 (Optimistic) ($200) Table 32. MIT assumptions for pure plug-in electric cars [1] in the bibliography

144 Anexes 136 According to the MIT, this batteries will start to be commercial around the year NREL study about car use An study carried out by NREL 2006 using a PHEV load tool to examine the potential impacts of large-scale deployment of PHEVs on a given electric power system with different possible scenarios which are shown in Table 33 shows the next results in Table 34. Table 33. List of regions used for PHEV analysis (year 2003 data). Table 34. Regional Vehicle Characteristics and PHEV Electricity Demand for a PHEV with 40% Electric VMTs. From these results it can be extracted some useful data to characterize the PHEV as load, such as the Avg. PHEV daily electric demand (kwh), with an average from the different scenarios of 4.76 kwh or that in average,

145 Anexes 137 people drive less than 40 miles a day. This result reflects that a PHEV-40 would be the most probable model to be sold Voltage and temperature evolution of Ni-MH batteries as a function of charge rate and with an outside temperature of 23ºC There is a study carried out by Universidad de Oviedo 16 which shows some interesting results about different charge modes for this technology with charge rates of 0.1C, 0.2C, 0.5C and 1C. Next a summary of the results is shown: 0.1C 0.2C 0.5C 1C Charge time (min) Capacity supplied (%) Temperature (ºC) Table 35. Different charge regimes for Ni-MH batteries. In the 1C charge rate, the charge was stopped when hydrogen was detected. NOTE: These are some experiences obtained with current Ni-MH battery technologies, without considering future expectations. In next figure it can be appreciated how rapid charges can do a lot of damage to the battery and why a faster charge rate than 1C is not 16 [5] in the bibliography

146 Anexes 138 considered in the short term. Maybe, in the medium-long term and with the technological evolution of the Li-Ion batteries, it will be possible to charge the batteries at a charge rate bigger than 1C. Figure 64. Increment in temperature in Ni-MH battery technology when using charge rates of 0.5C and 1C at 23ºC. 3 Annex 3 Active Demand Management (ADM) 3.1 ADM For an effective integration of electric vehicles on the system, smart control architectures should be adopted for energy management purposes, involving innovative interface, local controllers based on advanced technologies, decentralized/hierarchical control techniques and intelligent autonomous agents. This involves:

147 Anexes 139 Identification of adequate metering schemes, embedded in smart metering approaches, capable of dealing with smart charging (using special contracts or price responsive mechanisms) and delivery of ancillary services to the system, taking into account the required needs of energy of the vehicle s drivers. Consumers behaviour contribution will be assessed in terms of the possibility of defining a combined management procedure to optimize the use of local consumption, microgeneration and storage availability, to be managed by smart meters in a smart home environment. Identification of several EV smart control approaches to be embedded in the smart grid concept to manage EV individually or in clusters (the best idea), exploiting voltage sources inverter like control logics responsive to frequency changes and/or voltage changes. Hierarchical and autonomous agents based approaches must be defined to deal with reserve delivery and control for network management purposes; the main technical specifications for smart charging schemes can then be derived. On the other hand, as a first benefit to mention about an AD management, it is important to say that it brings a benefit for the environment and a reduction in the transport and distribution networks design costs due to a reduction in the final consumption. A survey carried out by the project ADDRESS in different countries demonstrated the following: The large development of Renewable Energy Sources (RES) is expected to increase the need for Active Demand (AD) services. There is a vast

148 Anexes 140 multiplicity of renewable energy sources. Among others, wind power is currently one of the most extensive generating renewable source, being remarkable the proportions that has been reached in Spain, Denmark and Germany. In this sense, retailers can be a key factor in the development of AD, and also AD business can be a key factor for retailers development. Smart meters might be one of the key enablers for AD business development. Three groups of countries can be distinguished: the ones in which the regulator has already opted for defining minimum standards, the ones in which the initiative to install new advanced metering equipment has explicitly been left to the private sector and finally those ones that still have not taken any decision about it. Spain is in the first group. 3.2 Smart meters Smart meters are an essential part for a well development of an active demand management. In some countries, big consumers had been given the choice to install a smart meter with hour discrimination, and now the next step is to install them to household customers. This is a very ambitious objective, but it is essential in order to be able to have an optimal AD response. There are two types of intelligent meters: Automatic Meter Reading (AMR) They allow to do a remote lecture and to have an hourly discrimination. For the communication from the meter to the data centre, there are several possibilities: SMS text messages, internet, radio or PLC. None of them can be applied in every situation (e.g. SMS where there is no signal).

149 Anexes 141 Automatic Meter Management (AMM) They allow a kind of bidirectional communication. This open to a new world of possibilities, like for example sending price signals to the consumer (hourly, half hourly, etc.) or a distance management. They are more expensive than the previous ones, but these are the ones that will allow a real AD management. From now on, when talking about smart meters, it will refer to these ones. With the smart meters, it is possible, for instance, to get a consumption profile of each client, so with them, the retailers will be able to give a bigger variety of services to the clients, thing that will allow improving the efficiency of the market signals and also it will increment supply competition. They will also allow developing the concept of V2G with EVs in a household level. Intelligent meters are necessary for this issue, because it is essential to measure the electricity consumed and generated in every single moment in order to send signals or to directly control its generation and consumption as it is needed by the system in each moment. Te smart meters will also simplify the fault detection process and will reduce the repair time. The smart metering systems could be integrated with the new domotic technologies, making possible the efficient programming of some of the consumptions. For example EDF in France gives the possibility to programme the consumption of your house, so it is function of the tariff applied in each moment [20] in the bibliography

150 Anexes 142 In the EU it has been set the necessity of substitute the old electromechanical meters for the new intelligent ones. In the Spanish case it can be seen in the Royal Decree 809/2006 that since 1 st July 2007 all meter equipment to be installed for new consumers up to a contracted power of 15 kw, and for those old meters to be substituted by new ones, these will be smart meters AMM. The distance management system (system to be able to manage in a direct or indirect - through incentives - way loads and DG such as EVs - from the distance) should be developed by each distribution company, but with minimum requirements in order not to create entry barriers in the retail market. The services regarding the new smart meters includes to buy them, the installation and measurement, data storage and management and the provision of that data to the different agents. All these tasks don t have to be done by the same agent. On the other hand, the smart meters could give also the possibility to use the energy of the car to provide energy to the house, with a possible reduction in demand in peak hours when the energy price is higher, or to use it as a generator to give energy to basic loads in the house, such as the refrigerator and lights, when a black out occurs. 3.3 Economic signals The retailers, using smart meters technology, will be able to do an indirect management of the PHEV consumption and generation through efficient economic signals. 4 types are remarkable: 1. Real-Time Pricing (RTP) The objective is to give very short term economic signals. Some trials have been already carried out in USA. There are different types:

151 Anexes 143 a. Day-Ahead Real-Time Pricing: Each day the consumer is informed about the prices for each one of the hours for the next day. b. Two-Part Real-Time Pricing: In this case, there is a part that is not subject to the risk that comes from the market price. For this purpose, based on historical data, a base load consumption pattern of the consumer is calculated, and consumption above or below this line is charged or remunerated at market price. Figure 65.Two-Part Real-Time Pricing mechanism. 2. Time-of-Use Tariffs (TOU) Different hours of the day are defined with different prices. The most remarkable case in Europe is the Offerta bioraria or binary tariff of ENEL, in which two block hours are defined. 3. Critical Peak Pricing (CPP) A quite expensive energy price is established for those hours defined as critical. A difference from the TOU, is that this critical points are not defined in the tariff, but they are announced when it happen.

152 Anexes 144 a. Fixed-period CPP: The moment and duration of the critical interval are predefined, but not the days when this occurs. Usually there are a maximum number of days per year. b. Variable-period CPP: The moment, duration and day are not predefined. c. Variable Peak Pricing: The critical price will be establish as the mean of the prices of the area considered (Load zone locational Marginal Prices or LMPs). This price will be adjusted to include the losses and other costs that use to be considered as energy charges. d. Critical Peak Rebates: The clients remain with a fixed tariff, but a discount is offered to them if they reduce their consumption during critical periods. 4. The Tempo tariff, from EDF It is a combination from TOU and CPP. This tariff establish 6 price levels, depending on day type (blue, white and red, from lowest to highest price) and hour of the day (2 blocks, biggest price from 6:00 to 22:00). The colour assigned to the day is notified by EDF the day before. Currently, the prices are as follow: Figure 66. Tempo tariff ( With this a considerable reduction was achieved (15% in white days and 45% in red ones). With this, an average client had saved around 10% in his tariffs. From January 2007, the EJP started functioning,

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