Vehicle to Grid communication Monte Carlo simulations based on Automated Meter Reading reliability

Transcription

1 Vehicle to Grid communication Monte Carlo simulations based on Automated Meter Reading reliability Claes Sandels, Ulrik Franke, and Lars Nordström The Royal Institute of Technology Stockholm, Sweden {claess, ulrikf, Abstract - Previous work has shown that it could be profitable on some control markets to use Plug-in Hybrid Electric Vehicles (PHEV) as control power resources. This concept, where battery driven vehicles such as PHEVs provide ancillary service to the grid is commonly referred to as Vehicle to Grid (V2G). The idea is to sell the capacity and energy of the parked PHEVs on the control market. Due to the fact that cars on average are parked 92% of the day, the availability of this capacity could be very high, even though it will be highly dependent on commuting patterns in peak hours. Further, as each PHEV has a very small capacity from a grid perspective, it is necessary to implement an aggregating control system, managing a large number of vehicles. This paper presents a Monte Carlo simulation model where technical issues such as the reliability in communication for a large fleet of PHEVs is included. The communication in the simulation model is based on data from Automated Meter Reading (AMR) systems, which share important charachteristics with V2G communications. Moreover, the simulation is based on available data on the distributions of commuting departure times and travel distances, as well as average driving power consumption, PHEV battery capacities and the market constraints of the secondary control market in Germany. Keywords - Vehicle to Grid, Aggregator, Control Market, Monte Carlo simulations, Communication, Automated Meter Reading data 1 Introduction Optimistic predictions suggest that there can be as much as 6.7 million Plug-in Hybrid Electric Vehicles (PHEV) in Germany by the year 2020 [20]. Assuming that all the PHEVs are connected to the grid and using a 11.1 kw connection, the German PHEV fleet would thus have a power equaling the short term capacity of 23 nuclear power plants [24]. Since cars, on average, are parked 92% of the day [15], the availability of this enormous capacity will be very high. The government of Germany has declared that 40% of the greenhouse gas emission have to be reduced until This has to be done from a starting-point where Germany is generating 82% of its energy from fossil fuels [7]. Due to the intermittent character of some renewable energy production such as wind generated energy, an increased demand for control power is anticipated. This is where the PHEV capacity comes into play. The idea is to sell the capacity and energy of the parked PHEVs on what is called the control market [14]. The control market, in short, is a marketplace where an actor can be ready to provide backup power (control power) and energy if something unpredicted would happen, e.g. a breakdown in a power plant or that the wind stops blowing at the wind power farms. A competitive factor of using PHEVs on the control market is that there is no cost related to their availability [2], and compared to conventional control power providers they can provide the power extremely fast [11]. The concept of electric vehicles (EVs) that are connected to the grid and can retrieve and inject controlled amounts of electric energy from/to the grid is often referred to as Vehicle to Grid (V2G). Deeper studies of the V2G concept can be found in [15], [11], [6]. Several small scale tests of this concept have been successfully conducted [13]. However, the challenge that lies ahead is to study the technical feasibility, such as the communication, when considering thousands to hundreds of thousands aggregated PHEVs providing the control power. This must be done fulfilling the requirements of an available, reliable and secure power system for the system operators. 1.1 Scope of the paper In previous work, we have derived strategies for an Aggregator to act on the German control markets, i.e. how to control the PHEVs in order to fulfill the bids of the Aggregator [22], [21]. The main scope of this paper is to evaluate the communications reliability of future V2G systems. Clearly, the reliability of communications is crucial to the aggregation approach to V2G. Without reliable communications, there is no guarantee that the Aggregator can fulfill the bids placed on the control market when called upon. Thus, communication failure entails a very substantial business risk to the entire V2G aggregation concept, and may influence the put bid sizes of the Aggregator. Evidently, other issues such as predicting the correct mobility of the PHEV fleet, or vehicle owners forgetting to plug in their PHEVs will also affect the feasible bid sizes. However, these are not further discussed in this paper. The simulation model presented is based on reliability data collected from the AMR system of Nordic electric power utilities. The model is also based on data on the distributions of commuting departure times and travel distances, average driving power consumption, PHEV battery capacities and control market constraints. The simu-

2 lations will try to answer the questions whether it is efficient, reliable and economically profitable for PHEVs to provide control power on today s markets in Germany, when communication reliability is taken into consideration. The technical constraints of V2G are discussed only briefly in this paper. 1.2 Outline The remainder of this paper is structured as follows. Section 2 contrasts the present contribution with some related work in the fields of PHEVs and V2G. In section 3, a background of the German control markets is presented. Here, the strategies of the Aggregator are also presented. It is followed, in section 4, by a Monte Carlo simulation model, where the communication in a V2G system is evaluated in a quantitative manner. Section 5 contains a discussion of the results, and some concluding remarks are given in section 6. 2 Related Work The authors of [11] present a conceptual framework for a V2G concept in the US. This framework defines how the EVs are contracted by a central entity, called the Aggregator, to service the power grid. Here, a lot of emphasis is put on a proposed IT system to handle the defined control signals and communication flows between the various actors, e.g. the PHEV owner, the Aggregator and the Transmission System Operator (TSO). Finally, simulations are performed of an EV fleet used for commuting purposes to study the control power capability. The simulations do not take communications reliability or control market constraints into consideration, and no economical results are calculated. Instead, only the physical properties of an aggregated EV fleet are presented, for instance, the stored battery capacity and EV availability. The conclusion is that EVs have great potential to provide control power. Two different V2G architectures are derived for ancillary services in the US in [17]. The first architecture proposes a system where the TSO controls the plugged in PHEV directly. The second one proposes an Aggregator controlling the PHEVs. These two different architectures are later evaluated regarding availability, reliability and profitability. With the input of e.g. travel and control market data into the model, it is shown that the aggregated architecture is more reliable but less profitable than the other one. It is concluded that the aggregated architecture is more probable to be implemented in a near term ancillary services system. No account is made of communications reliability. In [9], simulations are performed regarding the charging of the future EV fleet in Germany with respect to traffic behavior based on data from the travel survey Mobility in Germany [10]. The aim of this simulation is to, based on three different charging strategies, study how this car fleet will affect the power grid. The first conclusion of the simulations is that 90% of the fleet is parked at any given time. Further on, it is concluded that there can be a huge potential for V2G systems. For example, the aggregated battery storage is significant, and, under conservative assumptions more than 1 GW of power is always available. No account is made of communications reliability. Simulations with 500 participating PHEVs based on the control market constraints of Sweden and Germany are performed in [2]. Here, it is shown that the German markets have the potential to be profitable (e.g. 82 e/phev/month on the secondary control market), whereas the Swedish markets have no potential of being profitable in these simulations. No account is made of communications reliability. None of the articles reviewed adequate consider communications reliability. Furthermore, Aggregator strategies and options on the control markets, i.e. aspects such as control market rules, customer needs and technical battery constraints are largely missing. In addition, none of the articles perform simulations of a PHEV fleet considering availability and reliability of control power, nor the economical incentives of providing control power over time, based on human behavior and control market constraints. 3 Required Systems 3.1 The German control markets In Germany, there are four profit driven TSOs responsible for maintaining the balance between generation and consumption of electric energy in the power system. To maintain this balance, the TSO has three different types of control mechanisms to utilize. These control mechanisms generate three different tendering control markets (i.e., the primary, secondary and tertiary control market) where any actor fulfilling TSO requirements can bid. In the simulations described in section 4, the focus has been put on the secondary control market. The reason is that it has been shown to have the potential to be most profitable [2]. In addition, asymmetric bids are feasible, which is considered to be an interesting property for PHEVs. The secondary control market is further defined in Table 1. Evidently, many control market constraints are set by the rules and regulations of the TSOs. A mutual cross TSO rule is that there is a minimum bid capacity. Even the lowest bid (5 MW on the German primary control market) is far too large for a single PHEV to handle. Therefore, an aggregation function needs to emerge, where many PHEVs are pooled as one unit by an actor denoted as the Aggregator [11], [17]. From the viewpoint of the TSO, the Aggregator will be one single actor who delivers the promised control power. However, the physical delivery is done by each PHEV. Any bidder on the German control markets must be a Balance Responsible Party (BRP), i.e. an actor who is financially responsible for maintaining balance between

3 Table 1: The secondary control market of Germany [3], [8]. Activation Dispatch time requirements Semi automatic - frequency Start activation after 30 deviation but with respect seconds. Complete activation within five min- to affected control zone (50±0.2 Hz). utes (at most). Bid requirements Procured Payments At least 10 MW asymmetric. Delivery is one month, and is split into peak and off peak hours. Monthly Capacity and energy. Pay as bid. production and consumption. This is not possible for a single PHEV owner. In addition, the establishment of an Aggregator makes sure that the PHEV owner does not have to be engaged in administration processes, e.g. procurement, bidding and price settings with the TSO. MinSOC PHEVs in standby for up control. SOC > 99 % A UC B 1 B 2 Active PHEVs for up control. Charging PHEVs in standby for up control. 3.2 PHEVs participating on the control markets PHEVs can provide symmetric control power by just decreasing and increasing the load on the grid, i.e. start to charge a not charging PHEV to deliver down control, and stop the charging of an already charging PHEV to deliver up control. But in addition to this, a PHEV can discharge their batteries and feed the electric energy into the grid as an extra feature to provide up control. This fact, results in two different configurations for delivering symmetric control power. These two configurations are denoted as part Generation (with a discharging function) and part Controllable Load (without a discharging function). However, in [21], it is shown that part Controllable Load cannot fulfill symmetric control bids, and is therefore, excluded in the simulations of this paper. When a customer enters the V2G service, certain information must be available for the Aggregator. This is required for the placement of correct control bids. Such information includes: The battery capacity and battery type of the PHEV Minimum Wanted SOC (MinSOC), see Table 2 Information about connection capacity Estimated mobility behavior (e.g., private commuter, taxi, service vehicle, etc.) If a symmetric bid by the Aggregator is won on the control market, his task is to ensure that promised control power is available and can be delivered if needed. The responsibility to know which PHEVs that can, and will be deployed for control power, is the central function of the Aggregator. Therefore, a process must be defined for how this can be accomplished. This process will be denoted as the grouping process of the Aggregator. MinSOC MinSOC + - SOC > MinSOC SOC < MinSOC Active PHEVs for down control. DC C PHEVs in this area will always charge. D Non charging PHEVs in standby for down control. PHEVs not connected to grid Figure 1: The grouping process performed by the Aggregator for part Generation. Each area represents a set of PHEVs. In Figure 1, the graphical representation of this grouping process for part Generation is displayed. With an aggregated PHEV fleet of size N, combined with data regarding battery size, State of Charge (SOC), MinSOC (see Table 2), connection capacity and a triggered signal as soon as a PHEV plugs in/plugs out, the Aggregator can calculate the aggregated control power up and down, by placing the PHEVs into different sets. Note that the connection capacity is a function of the SOC when it comes to charging. A high SOC, implies less capacity, and this is denoted as the actual transfer capacity (κ bat in Table 2). The Aggregator then makes sure that the up control bid (UC) fits within the sets A, B 1 and B 2, and, that the down control bid (DC) fits within B 1. The sizes of UC and DC vary. For example, if a PHEV owner active in set B 1 disconnects, and no new PHEVs connect, the DC bubble will increase in size. However, if UC is smaller than DC, the Aggregator can move PHEVs located in B 2 to B 1, and increase the down control capability, i.e. decrease the DC bubble. Evidently, decisions made by the PHEV owner and the Aggregator, will result in that the sizes UC and DC will vary over time, and, his goal is to fit them within the respective sets at all times, therefore, fulfilling his control bids to the TSO. Moreover, there is a time constraint on how fast the grouping process must be executed by the Aggregator. In Figure 2, an illustration of this is displayed for the primary markets in Sweden and Germany. As see, frequency deviations in the power system will require response times in the matter of seconds. Therefore, a lot requirements will be put on a reliable and fast communication infrastructure between the Aggregator and each PHEV on the control markets.

4 Synchronous interconnected 100 A ~ 30 A 100 A ~ 30 A 100 A ~ 30 A 100 A ~ 30 A Generation Aggregator In the previous sections, the information layer of a proposed V2G system has been analyzed. Evidently, for this layer layer to work (e.g., to transfer grouping process related information, such as SOC and availability between the PHEVs and Aggregator), there must exist a hardware layer at the premises of the V2G customers as well. The design of such a hardware layer is investigated and presented in [22]. The purpose of this paper is to derive V2G system designs utilizing a home location (a dispatched house with a garage) as well as a work location (office building with an attached parking lot) for the control markets in Sweden and Germany. Based on the toughest constraints regarding, e.g., control market rules, technical limitations of the PHEV, customer convenience and electricity safety issues, it is shown that the V2G system can be designed in two separate ways for both the home and work locations. In short, the two designs differ on if the communication between PHEV and Aggregator is located in the charging point or in the PHEV itself. For instance, if the communication device is placed in the PHEV, it must rely on some form of mobile communication. But if the communication device is placed in the charging point instead, it can also rely on, e.g., a fixed Internet connection. Evidently, these differences in location, leads to big distinctions in system properties (e.g., mobile communication can be considered to be less reliable than a communication based on a fixed connection, etc) and designs. In Figure 3, a reference architecture of the V2G system design at the home location, where the intelligence is placed in the charging point, a.k.a. the Homebox, is displayed. This reference architecture also presents the involved stakeholders, and some additional technical devices. Primary control Germany Consumption Frequency deviates from nominal value Time = 0 Read frequency value Select PHEV s from Set UC & Set DC Send signal Recieve acknowledgem ent After 15 seconds 50 % of the promised power. Figure 2: Process of activating PHEVs from a frequency deviation. Own Local Grid Actor TSO Grid owner Electricity flow Hardware Communication path Read Consumer unit Measures Bi directional meter Aggregator Homebox Customer Figure 3: Homebox system design - Home location Part 1 Generation System concept: Homebox/Workbox Location: Home Control unit 3.3 Reference architectures of V2G systems 4 Simulation Model To be able to study the properties of the suggested grouping process of the Aggregator on the control market, a mathematical model is derived. This model will be used in a limited scenario in Matlab. The purpose of the model is to see how well the part Generation can provide control power when communication reliability is included (see section 4.2 for more information). Later, a sensitivity analysis is performed in order to see how robust part Generation is when faced with communication reliability changes. Moreover, the chosen scenario is to study PHEVs used for commuting purposes. Commuting purposes imply that the PHEVs will be driven twice a day, i.e. from home to work in the morning, and from work to home in the afternoon/evening. As mentioned in section 3, these PHEVs will participate on the secondary control market. 4.1 Data and assumptions Evidently, in order to model a PHEV fleet and a control market, some data and assumptions are needed. In Table 2, the parameters of the model are presented with the corresponding numerical value and comment. These parameter settings will be denoted as the Basic Case. Note, the value of φ Agg is lower than the minimum bid size on the German secondary control market (i.e., 10 MW). φ Agg is assigned the value of 4 MW to reduce the needed amount of PHEVs in the Aggregation. By doing so, the computing time for the simulations are reduced. Further, it is assumed that there is a linear relationship between the fleet size and the feasible put bid sizes. 4.2 Automated Meter Reading data While the communications reliability in V2G systems is thus very interesting, no large scale V2G systems are presently available for study. However, the increased adoption of Automated Meter Reading (AMR) systems offers an interesting opportunity for study: the AMR systems are distributed and designed to communicate information on electric power consumption, thus sharing some important characteristics with potential future large-scale V2G systems. Furthermore, just like proposed V2G systems, present AMR systems use a variety of communication solutions, ranging from telephony (fixed and mobile) to PLC and radio communications. Due to these facts, it is reasonable to approximate the communication in V2G systems with characteristics from AMR communication. The next step, therefore, is to model the AMR communication. In other words, we want to find a probability, and a related probability distribution, for successful communication between two or more nodes. These are determined by distributing a short survey to a handful of utility companies using AMR systems in Sweden. The survey

5 Table 2: The parameters in the model and corresponding basic case value. Parameter Description Numerical value Comment N Number of PHEVs 5000 PHEVs Considered to be a reasonable aggregation in a larger city of Germany. M insoc Minimum wanted SOC 20% of N have 30% M insoc 60% of N have 50% M insoc 20% of N have 70% MinSOC The MinSOC is the minimum level of the SOC that the PHEV should have at all times. The level is determined by the customer, and if MinSOC > SOC the Aggregator must charge the PHEV. And, therefore, not available to provide control power. Customers tend to choose mid alternatives when it comes to these kinds of choices [23]. The values of the MinSOC distrubution are assumed. bat Battery capacity 11.3 kwh Battery capacity equivalent to the one in the Volvo V70 PHEV [12]. The depth of discharge is 80% of the battery capacity (i.e., 9 kwh). ρ home Connection capacity home ρ work Connection capacity work ν work Opportunity to charge at work 3.5 kw Standard connections in German homes [3]. 3.5 kw 80% Not likely that all companies can offer charging to their employees. Numerical value taken from [9]. Note that all PHEVs can charge at home. SOC 0 Initial SOC SOC 0 U(0.5, 1.0) Assumed to be rather high. T home Departing time from T home N(08:00, 2.0) Departure times are assumed to be normally distributed home hours throughout the aggregation. Collected from [19] and swiftly verified with [9]. T work Departing time from T work N(17:00, 2.0) work hours T dist Travel distance Mean value: 24 km, median: 8 km Log normally distributed for customers living in urban areas. Collected from [19] and verified with [11] and [4]. υ P HEV Average speed 30 km/h An assumed average speed in urban areas [2]. δ P HEV Electric energy consumption when driving kwh/km This number is derived from the fact that a typical mid size sedan will require this amount of energy for all electric operation [16]. Φ Agg Bids on capacity and energy prices Mean values of historical accepted bids The actors bids are collected from [18] for 9 consecutive months (July March 2010). φ Agg Bid size 4 MW symmetric Derived from the size of aggregation, connection capacity and expected availability. α fixed Fixed charging price 85.1 e/mwh 20% discount on the Berlin klassik privatstrom contract from 2008 [2]. β bat Battery degeneration cost κ bat The actual transfer capacity p com t int Probability for successful communication The time interval for when the Aggregator executes the grouping process 30 e/mwh The cost of battery degeneration due to an extra cycle caused by discharging the battery for control power. Only considered for part Generation. Numerical value taken from [2]. Linear if SOC < 80%. Follows a logarithmic curve otherwise. 96.0% See section 4.2. Every 5 minutes Approximated from a charging curve taken from [19]. Considered to be reasonable due to the secondary control market constraints.

6 consisted of two questions: 1) How large a part (percentage) of the data from the contacted meters is successfully received after the first communication attempt? 2) Is your answer on question 1 based on experience or logged data? Four utility companies replied. The response stated that all the AMR communication data is continuously logged by the utility companies, i.e. the probabilities are based on real data. However, the replies consisted of explicit probabilities only, not the actual logged data itself. The mean of the responses is a 96% probability. A probability distribution could not be inferred without accsss to the log files. However, a binomial distribution corresponds well to the problem studied, i.e. a number of independent communication attempts are executed, and each attempt has a probablity of p com to be successful, and, therefore, 1 p com to fail [5]. Furthermore, the communication is repeated at a time interval t int (5 minutes). Since the utilities use many different communication solutions and no log data is available, we simply assume that there is a mix of all possible solutions in the V2G case as well, e.g. power line communication, telephony, etc. To summarize, the probability of successful communication between PHEV i and the Aggregator is 96.0%, and is assumed to be binomially distributed throughtout the aggregation of PHEVs, i.e. the communication is Bin(N, p com ). 4.3 Results Basic Case In this section, the results and analysis of the performed Monte Carlo simulations for the Basic Case will be presented. In Figure 4, we can see the results from the simulations displayed in two subplots. Each subplot displays two separate curves and one line. The ordinary curve in the upper subplot shows the aggregated down control power from the PHEVs over 24 hours. The crossed curve in the same subplot shows the actual delivered down control power by activation from the TSO. The straight line in the upper subplot states the down control bid of the Aggregator. The lower subplot is composed in the same way, but for up control power instead. The simulations are performed for a time period of three days, and repeated 100 times. The actual plot shows the mean values for the third and last day. Figure 4: The ability to deliver control power for part Generation As seen, the aggregated down control power is larger than the bid, except between the time 04:00 and 08:00. Therefore, a conclusion is that down control will be critical in the morning. This result is reasonable because most PHEVs will arrive at home in the afternoon/evening and plug in their vehicles. Due to the fact that down control is called more often than up control, PHEVs in B 1 and B 2 will charge continuously and retrieve high SOCs. This SOC will not be reduced until the PHEVs are driven to work in the morning. Further, due to the fact that the bid is not fulfilled, it should be decreased in order to be met at all times, plus a safety margin (by glimpsing at Figure 4, the minimum feasible bid size should be around 3.5 MW). The aggregated up control power however, is always larger than the aggregated down control power, although the Aggregator places symmetric bids, i.e. better up control power capability is obtained. This result is reasonable due to three reasons. 1) The Aggregator can deliver up control power from three sets, but only from one set when it comes to down control power. 2) The transfer capacity is a function of SOC when it comes to charging a battery, while not when discharging. 3) Down control is activated more often than up control by the TSO, because of the bid structure of the competitors, and that there is historically a bigger down control demand (see ω T SO in Table 2). Evidently, this results in frequent charging of PHEVs in B 1. So, when down control has times when shortages arise, there is always a lot of surplus in up control power in comparison to the bid. However, having a lot of surplus is not wise. If the Aggregator has an up control bid of, e.g. 5 MW, but most of the time there are at least 10 MW available from his PHEVs, then the potential up capacity payment for each PHEV will be reduced by a factor two. Still, to ensure the down control capability, this is a necessary decision by the Aggregator. In Table 3 the mean incomes from the Basic Case simulations are displayed. As seen, the total income is on the level of 17.3 e/month/phev, and the biggest earnings are made from charging the vehicles with cheap down control energy. In comparison with the result of 81.7 e/month/phev in [2], this result is small. However, [2] is an optimal study, assuming that the Aggregator has the best bids, and that the bid is precisely met at all times.

7 Type Up capacity payments Down capacity payments Up energy payments Down energy payments Total Income Table 3: The mean incomes for the simulated Basic Case in e/month/phev. Evidently, this is a part of an optimal approach, but it is not realistic. The result in this paper is reasonable because the bids are not perfectly matched, not the best bids, and that the communication is not ideal. Note that no costs for the needed V2G infrastructure are included in the model (slightly discussed in section 3.3), except for the cost of battery degeneration. This means that the result can be lower in reality when these costs are included for the Aggregator. 4.4 Sensitivity analysis As noted, no conclusions regarding the importance of the communication parameter can be drawn from the Basic Case. For that reason, a sensitivity analysis is performed. The idea is to change the communication parameter p com, and see how the control capability changes for the Aggregator. The values tested for p com can be observed in Table 4. Case 1 Case 2 Case 3 Case 4 Value 100% 90% 80% 70% Table 4: Showing the new values for the parameter p com Firstly, in this sensitivity analysis, the absolute difference in control power capability over time is compared between Case 1 (perfect communication) and Basic Case (realistic communication). In Figure 5, this difference is displayed for the third day of the simulations, i.e. a period of 24 hours. As seen, the down control power capability (subplot to the left) is less sensitive to communication reliability than the up control power capability (subplot to the right). In fact, there is no difference in down control power capability during mid-day. The reason might be that many PHEVs are already unplugged due to rather low work charging opportunities, and that the Aggregator will distribute more PHEVs to the set B 1 in order to maintain equal control power capabilities for up and down regulation. Evidently, this leads to a loss in up control power, e.g. almost a decrease of 0.9 MW at 08:00 p.m. A general conclusion that can be drawn, is that the larger the number of PHEVs plugged into grid, the greater the impact of the communication parameter on the control power capabilities. This is a reasonable result, because unplugged vehicles are insensitive to communication errors. Figure 5: The absolute difference in control power capability between perfect communication and more realistic communication In Figure 6, two box plots are displayed. These box plots show statistics for the up and down control control power capabilities for the different cases over a simulated period of 24 hours. Furthermore, the plots state the smallest and largest values, the lower and upper quartiles, and the medians for the control power capability. The results for the Basic Case are also included as a reference case. Here, it can be seen that the down control power capability has a low sensitivity to poor communications reliability, even at very low p com values. In fact, from the appearance of the plot, it can be hypothesized that the down control capability converges to a stable level as p com decreases. For instance, the relative difference in control power capability between Case 3 and Case 4 is rather insignificant, although the difference in the parameter value is quite large. However, the up control power capability is very sensitive, and loses a lot of its capability as the reliability decreases. Figure 6: Box plots of the results of the sensitivity analysis for p com 5 Discussion Generally, there will be a high average SOC in the aggregation over the day for part Generation. This is because charging opportunity is offered both at home and at work for most of the PHEV owners. However, a low average SOC gives a good effect on both the up and down control power capabilities. This is because the Aggregator can distribute more PHEVs in the B 1 and B 2 sets, i.e., the PHEVs can be distributed so that the up and down con-

8 trol power capabilities become more equal. Also, a low SOC increases the actual transfer capacity, which results in more contributed control power per PHEV. So, in order to reduce the average SOC, it could be wise to only offer charging opportunity at home, and concentrate on providing control power for fewer hours at, e.g. night. An additional advantage by delivering at night, is the high expected availability of PHEVs at these hours. However, the simulations showed that a hight PHEV fleet availability leads to a larger loss in control power. These issues should be anticipated by the Aggregator when he bids on the control markets, i.e. a safety margin is necessary to compensate the loss in control power due to non-perfect communication. Also, it can be wise for the Aggregator to invest in more reliable communication infrastructure at the home location (i.e., the location where most PHEV owners are plugged in at night). 6 Conclusions This paper has presented a simulation model of a commuting PHEV fleet used on the secondary control market in Germany. This model was designed to answer the question of how poor reliability in communication affects the control power capability of the Aggregator. A Basic Case was built upon justifiable data and assumptions. Here, AMR communication systems were used to model V2G communications, due to their evident shared characteristics. Data for the AMR communication was collected using a survey distributed to a handful of utility companies in Sweden. From the replies, we could find an explicit probability of successful communication. A Binomial distribution can reasonably be assumed. The results of the simulations showed that part Generation had very good up control power capabilities, but that the down control power capabilities fluctuated more and had a critical period in the morning. So, it was concluded that it could be disadvantageous to provide symmetric control power due to the differences in up and down control power capabilities. Note that no conclusions regarding the importance of the communication parameter could be drawn from the Basic Case. Instead a sensitivity analysis for the communication parameter was done. The simulations showed that down control power capability was quite insensitive to decreased reliability, whereas up control power capability was not. In general, it was concluded that the more available the PHEV fleet was, the more significant the communication parameter was. This fact should be anticipated by an Aggregator when he puts bids on the control market for times of high expected PHEV fleet availability, e.g. at night. 6.1 Future work More fine-grained AMR data would contribute to the simulation model. Here, it would be interesting to collect data in order to approximate a probability distribution. In addition, a mapping between communication solutions and different probabilities (p com ) would also be valuable. In that way conclusions could be drawn regarding the feasibility of e.g. mobile communication on todays control markets. REFERENCES [1] Amprion Gmbh. en, January [2] S.-L. Andersson, A.K. Elofsson, M.D. Galus, L. Gransson, S. Karlsson, F. Johnsson, and G. Andersson. Plug-in hybrid electric vehicles as regulating power providers: Case studies of sweden and germany. Energy Policy, 38(6): , The Role of Trust in Managing Uncertainties in the Transition to a Sustainable Energy Economy, Special Section with Regular Papers. [3] Sarah-Linnea Andersson and Anna Elofsson. Plugin Hybrid Electric Vehicles as Control Power. Master s thesis, Chalmers Technical University, [4] S Bergman. Plug-in hybrider elhybridsfordon för framtiden. Technical report, Elforsk, [5] Gunnar Blom. Sannolikhetsteori och statistikteori med tillämpningar. Studentlitteratur, [6] A. Brooks. Integration of electric drive vehicles with the power grid-a new application for vehicle batteries. In Battery Conference on Applications and Advances, The Seventeenth Annual, page 239, [7] CEJournal. May [8] Marlene Dupuy. Electricity Markets Balancing Mechanisms and Congestion Management. Master s thesis, Royal Institute of Technology, [9] J. Fluhr, K.-H. Ahlert, and C. Weinhardt. A stochastic model for simulating the availability of electric vehicles for services to the power grid. pages 1 10, jan [10] R Follmer and U Kunert. Mobility in Germany (MiG) Setting standards for Travel surveys. 19th Dresden Conference of Traffic and Transportation Sciences, [11] Christophe Guille and George Gross. A conceptual framework for the vehicle-to-grid (v2g) implementation. Energy Policy, 37(11): , [12] Hybridmile. news/volvo-vattenfall-ener1/, June 2010.

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