Energy Management of a DC Microgrid With Hybrid Energy Sources

Transcription

1 Volume 118 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu Energy Management of a DC Microgrid With Hybrid Energy Sources D Anitha 1,R.Uthra 2,R.Uthra 3 1,2,3 Assistant Professor, SRM Institute of Science and Technology, Kattankulatur, Chennai, Tamil nadu , India February 9, 2018 Abstract The shortage in conventional sources of energy has paved the way for power generation using non-conventional energy sources. Currently, the use of non-conventional source of energy for power generation at various levels is the most researched topic. Solar energy is one of the most promising non-conventional sources of energy because of its abundance of availability. However, the intermittent nature of these sources and hence varying output power makes it unreliable when connected to the power system. In order to compensate for the variable power from PV panel and the fluctuating load, the system is equipped with an additional battery, supercapacitor and diesel generator. A DC microgrid is set up to cater the loads in a particular area. The proposed work aims to manage the energy demand of this system efficiently. The effectiveness of the proposed system with varying PV power output, load requirement, battery SOC and supercapacitor SOC have been studied and verified by MATLAB/SIMULINK. Key Words:DC Microgrid, MPPT, energy management

2 1 INTRODUCTION Conventional sources of energy have been dominating energy production until recent years. The sharp increase in power demand, acute shortage of fossil fuels and its harmful effects on the environment has paved the way for inclusion of non-conventional sources of energy into the power system. The power generation from these non-conventional sources of energy may range from few watts to megawatts. Thus active sources catering to a group of loads at a demarcated region forms a microgrid. However, there are challenges in integration, control and protection schemes of microgrids. Currently, researchers are oriented towards the improvement of reliability and stability of the microgrids. Therefore, many efforts have been put in this direction to improve the reliability and one of those techniques is energy management in a DC microgrid. A micro grid is actually a small electrical distribution system which is self-sustaining with its own power source and works across a small limited area. They are one of the most efficient systems ever designed and often tend to have higher control over the components of the system due to the highly efficient control system. In energy management, the given sources of energy are efficiently used partially or totally depending on the demand of the load. In the field of solar energy, this technique is accompanied with another technique maximum power point (MPPT) aimed to increase the output from the solar panels. Energy management s main aim is to improve the efficiency of the system and use the given sources in the most judicious manner possible. Accompanied with MPPT and its different algorithms it makes up a very good system with many of the desirable qualities but still, it is not enough for the present demands, hence still there is increasing research work is going in this field to make it better. There have been almost equal amounts of work on both DC and AC microgrids but the best thing is to work on a hybrid system to offer more versatility and combine the advantages of both systems. Both DC and AC systems have their own advantages like DC systems are more efficient and durable and are easier to integrate them into the nonconventional sources. But due to the requirement of a large number of power electronic components, make this type of system is complicated than AC system. Some of the promising works in this field still has

3 been on standalone systems are discussed here. The most common design that came up was that of a solar panel accompanied with a battery with different control strategies combined with appropriate MPPT algorithms. [8] One of the switching control mechanism involved was multivariable nonlinear model predictive voltage regulation. Another control mechanism known as coordinated variable and multivariable management strategy was also used, here constant voltage and constant current is used to charge the battery for the increased lifespan of the system. [9] In this paper, the author develops a system which uses multiple power sources both AC and DC with multiple loads working on both off the grid and on grid modes. [10] Here both solar and wind energy is used to satisfy the SOC demand of the lead acid with a supervisory control system. [11] In this three different sources are used namely solar, wind and bioethanol. Here all three sources source a fuel cell which in turn supplies the load. This system is governed by an advanced genetic algorithm accompanied by state machine algorithm to control the production of hydrogen and make the system stable. All the abovementioned systems didnt have a proper backup power source and hence are not totally reliable. [12] Here PV source lead acid and diesel generator are used and two very common control schemes are used namely load flow and cycle charging.now many other systems are designed based on the overview of this system but with different control schemes accompanied by predictive algorithms and different switching machine state. But in all the above systems DG is used as a backup so the matter of fuel economy comes up hence in order to control its proper restrained use of the DG in a limited manner is essential. [1] Some of the previous works were more concerned with the power quality and reduced loss and thus the innovative droop control mechanism with the help of supercapacitor was incorporated in this work to supply continuous bursts of good power quality power. This work also used a new concept of virtual impedance and used the SC to find it. [2] In order to improve the quality some of the works focused on the distribution side of the system like this one which proposed a two-layer control scheme in order to reduce losses in the distribution system and have it decentralized and further uses the droop characteristics to get a stable voltage at the output. [3] Another work focused on the power converters used in this type of works. They proposed that

4 if the converters are interleaved and connected with the grid and are further given preset reference values pertaining to specific cases accompanied by control theory and power electronics devices a linear model of such an improved model of the converter was made in a specific programming environment.[4]some works like this one compared DC and AC microgrids and brought forward each of its advantages and disadvantages and proved that DC microgrids are better than AC microgrids. They also did an in-depth work on DC microgrid architecture, controlling and other mechanisms. [5] Some other works show that there can be much more application to DC microgrid than the traditional power supply. This work shows that a DC microgrid with battery and supercapacitor can be used to charge electric vehicles in namely two ways. The first way is to install a charging station in a residential area or install similar charging stations in a truck like vehicles for public areas. [6] This work shows that the efficiency of the system can be increased further by increasing the efficiency of the battery pack used. In order to do so, they have carefully designed the mechanical structure to reduce thermal losses and reduce the load pressure from it. They have also used supercapacitors here as a protective device to prevent the battery from quick degradation and reduce the cost of the project. [7] Another work suggested that with extensive use of battery packs we can smoothen the output of solar and wind energy outputs to increase their stability. The proposed system consists of PV, Lead acid battery, supercapacitor and a diesel generator (DG) connected in parallel supplying a DC load. Each of this source is interfaced to the load through a suitable power electronic converter. A systematic way to efficiently manage the sources in order to meet the load has been detailed. Since PV source is weather dependent a lead-acid battery, supercapacitor, and diesel generator are used to complement it. The block diagram of the system considered is shown in Fig.1. Depending on the load and the generation from the PV source, a systematic algorithm has been developed for efficient energy management by appropriate switching between different energy sources. This paper analyzes the working and controls the system considered with MATLAB Simulink. The paper is organized as follows: Section II deals with energy management strategy, followed by modeling of sources. Section IV describes the power electronics interface used for each source. Section V discusses

5 the results obtained followed by the conclusion. 2 ENERGY MANAGEMENT STRAT- EGY It is well known that the load demand and the solar energy generated are continually varying with time. Incorporating a PV source to meet the demand requires some reliable back up for system reliability and a systematic switching between sources to meet the varying demand. Thus the energy management strategy aims at satisfying the load demand and efficient harnessing of solar energy. Hence along with the PV source, other energy sources such as a battery, supercapacitor, and DG are used. Also, the DC voltage at the microgrid is maintained. Thus the energy management equation is given as P (pv(t))+p (sc(t))+p (dg(t))+p (b(t)) = P (l(t))+cdv D Cdt (1) A PI controllers are incorporated with power electronic interface in order satisfy the load and maintain the DC link voltage. The energy management strategy is the main control scheme, governing the system and its switching operations is mainly divided into three main parts which are given below

6 Fig. 1 Block diagram of the system considered CASE 1: When PV power output is less than load demand (P(pv) < P(l)) Depending on the state of charge(soc) of individual source, CASE 1 is further divided into the following subcases (a) Subcase A: When the PV is not able to satisfy the power demand of the load, the battery provides the remaining load power. (P (pv) + P B = P L ) (b) Subcase B: When the state of charge of the battery (SOC LB ) falls below its minimum value (SOC LB1 ), the Supercapacitor starts discharging to maintain the power supply to the load until the state of charge of supercapacitor SOC SC is less than its minimum value SOC SC1. (P (pv) + P SC = P L ) (c) Subcase C: In the worst case as the supercapacitor SOC reaches SOCSCTH1 the DG is started. It is known that the DG needs some time to get to the stable peak value, the supercapacitor helps to maintain the stable load demand until the DG comes online. Now the load is supplied by PV, Supercapacitor, and DG. (P (pv) + P sc + P DG = P L )

7 (d) Subcase D: In this case, battery, supercapacitor, and PV supply the load i.e when the diesel generator fails to operate. (P (pv) + P B + P SC = P L ) CASE 2: When PV power is equal to load demand (P (pv) = P L ). Here PV is able to satisfy the load demand; hence no other energy management support is required. CASE 3: When PV power is greater than load demand (P(pv)>PL). (e) Subcase E: When the Battery SOC is less than the maximum battery SOC (SOC LB2 ), the excess PV power is used to recharge the battery till its SOC reaches SOC LB2 SOC LB = SOC LB2 ). (f) Subcase F: When the battery has been fully charged, the excess PV power charges the supercapacitor till its SOC reaches maximum SOC SCT H2 (SOC SC = SOC SCT H2 ). (g) Subcase G: Now whenever Supercapacitor gets self-discharged the excess power from PV is used to recharge it. (h) Subcase H: Now after Battery and Supercapacitor both are recharged then excess PV power is wasted hence afterward the PV is damped. The different cases explained above are depicted in Fig

8 Fig. 2 Flow Chart for Energy Management Strategy 2.1 PV PANEL MODELING An MSX 60 W solar panel is modeled based on equations [13]. A boost converter is used to interface the PV panel with DC loads. The parameters of MSX 60 W solar panel are shown in Table.1 The model is verified by comparing the I-V and P-V curves with the data sheet. The I-V and P-V curves for different irradiation are shown in Fig.3 and Fig.4. Table 1. SPECIFICATIONS OF SOLAREX-60 Characteristics Specifications Typical peak power (Pmpp) 60W Voltage at peak power (Vmpp) 17.1V Current at peak power (Impp) 3.5 A Short circuit current (Isc) 3.8A Open circuit voltage (Voc) 21.1 V Temperature coefficient of open circuit voltage (Kv) -(80 ± 10)mV/C Temperature coefficient of short-circuit current (Ki) -(0.0065±0.01)%/ C Approximate effect of temperature on power -(0.5±0.015)%/ C Nominal operating temperature (Ki) 47 ±2 C

9 Fig. 3. I-V curves with different irradiations of 1000,600,800 W/m 2 for an operating temperature of 25 C Fig. 4. P-V curves with different irradiations of 1000,600,800 W/m 2 for an operating temperature of 25 C 2.2 SUPERCAPACITOR AND LEAD ACID BAT- TERY A supercapacitor is capable of supplying power instantly for a short duration making it suitable for transient load changes. However, a battery can supply steady and continuous energy at considerable duration. Thus supercapacitors and batteries complement each other. In this work, the supercapacitor used serves two purposes namely: for storage and backup input resource for transient load

10 variations. Secondly, it is used to supply power between the start and steady state operation of the diesel generator (DG). Since DG takes some time to reach the desired power rating of the load, the supercapacitor bridges the gap. supplies for the DG and helps the DG to catch up with the whole system. Since supercapacitor has high energy density it can release it very rapidly and thus suitable to supply high energy in very short time. A supercapacitor rating of 55F & 15V is used. The lead acid battery is one of the most commonly used electrochemical storage devices and is very rugged and inexpensive compared to other counterparts. It is also very durable and reliable. The project uses a 12V, 45Ah Lead acid battery. The battery control is managed by controlling and measuring its SOC. 2.3 POWER ELECTRONIC INTERFACE As shown in fig.1 different power converters are used to interface energy sources and the load. These power converters along with their control circuit ensure that the DC voltage is maintained at 24 V. A boost converter is used to link PV source and the load. The output of the PV panel is stepped up by the boost converter to have a constant DC link voltage. The boost converter with Perturb and Observe (P&O) based Maximum Power Point Tracking (MPPT) algorithm is used. The design values for the boost converter are Inductance L= 4.2mH and Capacitance C =1.2 mf [14]. A buck-boost converter is a DC-DC converter which increases the voltage or reduces the voltage level according to the duty cycle fixed in its control circuitry. If the duty cycle is more than 50% then the output voltage will be boosted and if it s less than 50% then output voltage gets bucked. The supercapacitor and battery are interfaced with the load through a buck-boost converter. A DC source replaces a diesel generator and the rectifier for simulation purposes. 3 RESULTS The components of the system have been modeled, integrated and simulated in MATLAB environment using SIMULINK. The efficient management of the energy sources based on energy manage

11 ment strategy are shown in Fig.5. The different subcases are clearly indicated and discussed. Fig.5 shows PV power Vs time, load power Vs time and Battery and Supercapacitor SOC Vs time. The first curve shows the varying PV power with respect to varying irradiance. Whenever the PV power is sufficient to meet the load demand the other sources have no role to play. Hence the charge in the battery and supercapacitor remains the same. This condition is shown as case 2 in Fig.5. When solar irradiation decreases, the PV source along with battery meets the load demand (subcase A). When the battery SOC has reduced below the threshold the supercapacitor comes into action and the DG is switched ON. This is observed by decreasing value of supercapacitor SOC (subcase B &C). Subcase D shows PV, battery, and supercapacitor supplying the load. When PV power is sufficient to supply the load demand and still has excess power left it starts charging the battery and the supercapacitor respectively, which can be seen by observing their respective SOC curves (subcase E and F). Now as both of them gets totally recharged and there is still excess power available the PV is operated in a damped manner (subcase G and H). Fig.6 shows the DC bus voltage for the variations in PV power shown in Fig

12 Fig.5. PV power, Load power, Battery SOC, Super capacitor SOC at different time instants based on energy strategy Fig.6. DC bus voltage of the system 4 CONCLUSION The Energy management system has been studied to satisfy the demand of the load while maintaining the bus voltage constant. Considering the current power shortages using depletion of fossil fuel

13 for conventional sources, a solution has been proposed to use nonconventional energy sources (major player) and conventional energy sources (used only in cases non-conventional sources fail) which increases the efficiency and reliability. Thus the system utilizes four power sources namely PV, Battery, SC, and DG.The system was simulated and the intermittency in solar irradiation was simulated and the behavior of the system was studied. The results with different case and subcases have proved to be satisfactory. The system has shown the merits of using a microgrid with non-conventional resources such as solar to increase their productivity. The system has high scope in domestic and commercial areas. References [1] Hossain E, Kabalci E, Bayindir R, Perez R. Micro grid testbeds around the world, state of art. Energy Conversion and Management 2014; vol 86, pg [2] Farhangi H. The path of the smart grid,. IEEE Power and Energy Magazine 2010; vol8, pg1828. [3] Planas E, Andreu J, Grate JI, Martnez de Alegra I, Ibarra E. AC and DC technology in micro grids: a review,. Renewable and Sustainable Energy Reviews 2015; vol.43: pg [4] Dragicevic T, Lu X, Vasquez JC Guerrero JM. DC micro grids Part I: a review of control strategies and stabilization techniques. IEEE Transactions on Power Electronics, 2016; vol 31:pg [5] Dragicevic T, Lu X, Vasquez JC, Guerrero JM. DC micro grids Part II: a review of power architectures, applications, and standardization issues,. IEEE Journals and magazines, 2016; vol 31: pg [6] Elsayed AT, Mohamed AA, Mohammed OA. DC micro grids and distribution systems: an overview,. Electric Power System Research, 2015; vol.119: pg [7] Sechilariu M, Locment F. Urban DC micro grid: intelligent control and power ow optimization, chapter connecting and

14 integrating variable renewable electricity in utility grid, p [8] Dizqah AM, Maheri A, Busawon K, Fritzson P. Standalone DC micro grids as complementarity dynamical systems: modeling and applications,. Control Engineering Practice. 2015; vol. 35: pg [9] Kumar M, Srivastava SC, Singh SN. Control strategies of a DC micro grid for grid connected and islanded operations,. IEEE Transactions Smart Grid 2015; vol. 6: pg [10] Valenciaga F, Puleston PF. Supervisor control for a standalone hybrid generation system using wind and photovoltaic energy,. IEEE Transactions Energy Conversions 2005; vol. 20: pg [11] Feroldi D, Degliuomini LN, Basualdo M. Energy management of a hybrid system based on windsolar power sources and bioethanol,. Chemical Engineering Research Design 2013; vol.91: pg [12] Ameen AM, Pasupuleti J, Khatib T.Simplied performance models of photovoltaic/diesel generator/battery system considering typical control strategies,. Energy Conversion Management 2015; vol.99: pg [13] Hany M. Hasanien, Shufed Frog Leaping Algorithm for Photovoltaic Model Identication, IEEE Transactions on sustainable energy,2015, vol.6, pg [14] Pooja Sahu, Deepak Verma, Dr. S Nema, Physical Design and Modelling of Boost Converter for Maximum Power Point Tracking in Solar PV systems, 2016 International Conference on Electrical Power and Energy Systems (ICEPES), pg

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