Sizing Strategy of Distributed Battery Storage System ... - IEEE Xplore

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IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 2, MARCH 2014

Sizing Strategy of Distributed Battery Storage System With High Penetration of Photovoltaic for Voltage Regulation and Peak Load Shaving Ye Yang, Student Member, IEEE, Hui Li, Senior Member, IEEE, Andreas Aichhorn, Student Member, IEEE, Jianping Zheng, Senior Member, IEEE, and Michael Greenleaf, Student Member, IEEE

Abstract—This paper proposes an effective sizing strategy for distributed battery energy storage system (BESS) in the distribution networks under high photovoltaic (PV) penetration level. The main objective of the proposed method is to optimize the size of the distributed BESS and derive the cost-benefit analysis when the distributed BESS is applied for voltage regulation and peak load shaving. In particular, a system model that includes a physical battery model and a voltage regulation and peak load shaving oriented energy management system (EMS) is developed to apply the proposed strategy. The cost-benefit analysis presented in this paper considers factors of BESS influence on the work stress of voltage regulation devices, load shifting and peaking power generation, as well as individual BESS cost with its lifetime estimation. Based on the cost-benefit analysis, the cost-benefit size can be determined for the distributed BESS. Index Terms—Battery storage, cost-benefit analysis, distributed PV system, overvoltage.

I. INTRODUCTION

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N distribution network, grid-tied photovoltaic (PV) installations have grown dramatically [1]. Under high PV penetration levels, large reverse power may lead to voltage rise, which will cause potential issues to the distribution system [2]–[6]. Traditional voltage control devices, such as on-load tap changer (OLTC) transformer and step voltage regulator (SVR), are usually used to regulate the voltage within the normal voltage range. However, this voltage regulation technology will suffer from increased work stress resulting in reduced system lifetime under high penetration PV applications. Therefore, improved voltage regulation methods have been developed to solve the overvoltage issue [7]–[11]. Limiting real power has been verified to be more effective than reactive power to regulate the voltage in the distribution line [12]. Accordingly,

Manuscript received February 06, 2013; revised May 24, 2013; accepted September 03, 2013. This work was supported by National Science Foundation under Grant ECCS-1001415. Date of publication September 25, 2013; date of current version February 14, 2014. Paper no. TSG-00089-2013. Y. Yang, J. Zheng, and M. Greenleaf are with the Center for Advanced Power Systems, Florida State University, Tallahassee, FL 32310 USA. H. Li is with the Center for Advanced Power Systems, Florida State University, Tallahassee, FL 32310 USA, and also with the Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310 USA (e-mail: [email protected]). A. Aichhorn is with the R&D Department, Company Trench, Austria (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSG.2013.2282504

a possible solution for the overvoltage issue is to integrate battery energy storage system (BESS) into PV systems in order to achieve a flexible real power control. Recent research works have demonstrated that the BESS could help OLTC and SVR to prevent the overvoltage caused by high penetration PV in distribution systems [7], [8], and meanwhile reduce or eliminate voltage violation without OLTC or SVR [9]–[11]. However, the optimized BESS size has not been considered in these researches which usually lead to an oversized BESS usage resulting in extra cost. This has hindered the BESS commercial applications in the distribution systems. As a result, a cost-benefit battery size research is important and beneficial. Aiming to obtain an economical and serviceable BESS, a battery sizing strategy has been developed focusing on shaving the peak demand in a residential power distribution feeder with PV system [13]. The cost and size of hybrid PV and battery system have been also analyzed for demand side application by taking the advantages of the on-peak and off-peak electricity charge difference [14]. However, these studies have not considered factors related to BESS sizing on the supply side of the distribution system, such as reduced workload on OLTC and SVR, as well as reduced peaking power generation cost, which has degraded the BESS size planning and evaluating. Furthermore, the lifetime of BESS should also be considered and evaluated in the sizing procedure. Previous studies [13], [14] could not provide precise cost estimations since they are based on general battery lifetime estimation. However, BESS lifetime varies greatly under different usage, which depends on the PV-bus feeder’s location, PV penetration level, local weather and battery type. As a result, these sizing strategies and cost analysis need to be improved due to the lack of the detailed battery lifetime estimation and other operational information of the power system. In this paper, a novel method is presented to solve the above issues. Firstly, a BESS was integrated into each PV bus of the General Electric (GE) distribution power system model. The real annual load information, PV power profile, and temperature data were adopted. The proposed method is applied on the modified GE model. Secondly, a physical model of a lithium-ion phosphate (LiFPO4) battery was developed with aging effect so the lifetime information under different operation conditions can be derived. In addition, several factors that impact the BESS sizing including OLTC/SVR operation positions, load shifting and peaking power generation were recorded during the whole BESS lifetime. The BESS usage, lifetime and system performance in terms of battery size were then analyzed on every bus

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YANG et al.: SIZING STRATEGY OF DISTRIBUTED BATTERY STORAGE SYSTEM

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Fig. 1. The proposed battery cost-benefit analysis system diagram.

as well as on the total feeder of the developed distribution power system under different PV penetration levels. Finally, the cost of each bus was evaluated based on the above information to derive a cost-benefit BESS size. II. BATTERY COST-BENEFIT ANALYSIS SYSTEM DESCRIPTION Fig. 1 shows the proposed battery cost-benefit analysis system that is applied to investigate battery lifetime, OLTC&SVR work stress, peaking power generation and peak load shifting in a distribution power system with BESS and PV units. It consists of two subsystems. Subsystem 1 includes a distribution power system model, a physical battery model and a voltage regulation and peak load shaving oriented energy management system (EMS); subsystem 2 contains a cost calculation block and a cost-benefit BESS sizing block. In subsystem 1, the battery cost-benefit analysis process starts with a three-step cycle for a given battery size and PV penetration level. Firstly, the distribution power system model reads current input data including annual load power profile, PV power profile and temperature profile, records the SVR/OLTC operations, then delivers the OLTC&SVR work stress, peaking power generation and peak load shifting information to subsystem 2 and generates dynamic buses’ voltages for EMS. EMS is the control center for voltage regulation and battery protection. Secondly, EMS compares the buses’ voltages with the bus voltage references which are the upper and lower voltage limits. EMS is able to control BESS to regulate the bus voltage within normal range by sending charge/discharge requests to the battery. OLTC and SVR will take actions if there is still overvoltage or undervoltage on the buses after BESS operation. Depth of discharge (DOD) and maximum charge/discharge power will be evaluated to protect the battery. The DOD reveals the battery’s operation range in state of charge (SOC) [15]. In this paper, the operation range of SOC is set from 15% to 85%. The output of EMS block is the charge or discharge command to each physical battery that is modeled in this paper.

In addition, the charge/discharge power is dynamically adjusted to protect battery from overcharging/overdischarging which could degrade its lifetime. Finally, the physical battery model updates the SOC and state of health (SOH) after obtaining the new charge/discharge power command. The above three steps will be repeated and new input data will be read again until the battery’s SOH reaches to the end-of-life condition of battery, which means the battery is scraped and the desired battery lifetime evaluation is finished. After obtaining each battery unit’s lifetime, OLTC&SVR work stress, peaking power generation and peak load shifting information from subsystem 1, the economical annual cost calculation is performed considering BESS unit annual cost, the benefit from SVR/OLTC work stress relieve, load shifting and shaved peaking power generation in the subsystem 2. Therefore, the cost of each bus can be calculated under every possible battery size with different PV penetration level. As a result, a desired cost-benefit BESS size can be derived. The detailed distribution power system model, battery model and EMS development in subsystem 1 are firstly discussed in Section III. The cost-benefit analysis and BESS sizing is presented in Section IV. The simulation results are provided in Section V. III. SYSTEM MODELING AND ENERGY MANAGEMENT STRATEGY A. Distribution System Description A distribution power system model developed by the GE [3] is selected and modified in this paper to investigate voltage rise, BESS usage and system performance. Fig. 2 shows that the distributed PV units, BESS and loads are integrated into the GE model. The model includes some fundamental distribution system components: OLTC as the substation transformer, SVR and switched capacitors. This study is focused on Feeder 2. This feeder’s length is about 6 miles and the total peak load

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Fig. 2. The modified GE distribution power system model.

TABLE I FEEDER INFORMATION IN THE DISTRIBUTED POWER SYSTEM

is 11MVA. Seven loads represent a mixture of both residential loads and commercial peak loads ranging from 0.3MW to 5MW with 0.92 power factor. Hybrid PV and BESS units are connected on 201 to 207 buses. The primary feeder base voltage is 12.5 kV. The secondary feeder base voltage is 240 V for residential loads. The service capacity is rated at 1.5 per unit (p.u.) related to the served load. The impedance of transformers is 2.5% and X/R ratio is 1.5. An average length of 200-ft was selected for the distance from the transformer to the load. The secondary feeder impedance is calculated based on the conductor with 200 A of thermal capacity. The detailed feeder information is listed in Table I. B. The Energy Management System (EMS) The EMS is the control hub in the distribution power system. The voltage regulation and peak load shaving oriented EMS controls the power flow among the battery, PV, load and grid.

Although the BESS can achieve more functions, voltage regulation and peak load shaving are two main functions considered in this study. The EMS sends charge command to BESS when overvoltage caused by reverse PV power happens, and conveys discharge command to BESS during peak load period. The bus 205 is selected as an example to describe the power control flow and shown in Fig. 3. The same strategy can be applied to other buses. The initial voltage of bus 205 is obtained based on the load and PV power data. The bus voltage is then compared with the voltage upper and lower limits. If the bus voltage is controlled within the normal range, EMS collects next load and PV power data. Otherwise, one of the following two conditional voltage control strategies is activated: (a) Overvoltage regulation flowchart: On the one hand, when overvoltage occurs on bus 205 and PV is generating power, battery will be charged with the excess PV power until the bus voltage drop to normal range. On the other hand, when overvoltage occurs but PV is not generating power or the battery charge flag shows that battery reaches to its power limit, the closest voltage control device will step down the tap position to reduce the voltage. (b) Undervoltage regulation and peak load shave flowchart: On the one hand, during peak load time or when voltage sag happens, the battery will discharge to support the bus voltage. On the other hand, when the battery discharge flag shows that battery reaches to its limit and voltage sag still exists, the closest voltage control device will step up the tap position to boost the voltage. C. Physical Battery Model With Aging Effect Estimation Physical battery model is critical to investigate the battery lifetime including SOC and SOH. In this paper, a physical battery model is developed based on a commercial LiFePO4 battery from A123 systems (APR18650) due to its good lifetime performance and high power and energy density [16]. The developed battery equivalent circuit model is shown in


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Fig. 3. The control flowchart of the proposed EMS: (a) overvoltage regulation flowchart; and (b) undervoltage regulation and peak load shave flowchart.

Fig. 5. Cyclation based irreversible capacity loss. Fig. 4. Battery equivalent circuit model.

Fig. 4.The detailed parameters may change with temperature and have been described in [16]–[18]. The developed battery circuit model is integrated into the distribution power system model of Fig. 2. The SOC and SOH of the battery can be calculated and updated. The SOC is the feedback to the EMS which indicates the energy can be converted for the next status. The SOH specifies the leftover lifetime of the battery. The manufacturer defines the battery life ends when the capacity reaches 80% or less of the initial one in a fully charged state [16]. In order to obtain the battery lifetime, two major aging effects are considered to estimate the irreversible capacity loss of the battery. One is the cyclation based state of health and the other is calendrical aging . 1) : The cyclation based capacity loss is derived from counting the amount of transferred coulombs. The battery cell (APR18650) is capable of 2,000 cycles at 100% DOD [16]. Accordingly, the can be reflected from the battery capacity change in the available cycles. Fig. 5 shows the battery capacity in terms of the available battery cycles to illustrate the estimated available volume of coulombs before the battery is scraped. The linear function for capacity reduction rate is selected in this

paper to calculate the volume of coulombs during battery discharge in order to simplify the analysis. Once the discharged coulombs are equal to the total area highlighted in Fig. 5, the battery is scraped. The challenge case is the battery is always working at 100% DOD. When the battery life ends, the battery cycles will reach to the limit value 2000 cycles. In the real application, 100% DOD is not practical and the capacity reduction rate may be non-linear, therefore, the battery cycles may be more than 2000 cycles when the battery capacity drops to 80%. In addition, the variation of environment temperature (T) also has an effect on calculation. The terminal voltage of the battery changes with [18]. In this case, the current and the amount of used coulombs under the same amount of power change with . Therefore, the temperature influence is also considered in the evaluation of . can be estimated by the division of the used coulombs with the total available coulombs (1-b). 2) : The is the effect of capacity loss occurring at non-operating state of the battery. The three main factors for the calendrical aging effect are T, current SOC, and the non-operating time of the battery. The estimation of this aging effect is shown in Fig. 6 which is based on the measurement results from [19]. In these functions, the capacity loss/day and can be

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where the BESS on bus i is defined as . The power conversion system (PCS) cost is proportional to the battery’s power rating . is the unit cost of the PCS. The cost of the storage is proportional to each battery’s capacity . is the unit cost of the battery. is the system efficiency [21]. The installation cost is proportional to the sum of PCS and storage cost [22], and it can be represented by introducing a installation cost factor . . is the levelized factors related to the ’s lifetime. For generating the same amount of peaking power, gas combustion turbine plant cost is reduced due to the unleashed power from during peak load time. The annual benefit depending on the peaking power generation from can be derived by: (4) Fig. 6. Experimental measurements of the capacity loss per day [19].

determined by the given SOC and T. Finally, the is estimated by the product of capacity loss/day with non-operating time (1-c). The above two factors are combined to calculate the battery aging effect and obtain the battery lifetime. Therefore, the state of health of the battery can be expressed as: (1)

IV. COST-BENEFIT ANALYSIS AND BESS SIZING Fig. 1 shows that the BESS lifetime, OLTC&SVR work stress, peaking power generation and peak load shifting information can be obtained from subsystem 1. Therefore, the cost of each BESS and the whole feeder can be calculated based on the BESS lifetime. The cost evaluation will determine a cost-benefit BESS size. In order to design a battery size more accurately, the cost calculation will consider multiple factors including the battery annual cost, annual benefit from reduced OLTC/SVR work stress, annual benefit from shaved peaking power generation and annual benefit from load shifting. In view of the BESS lifetime varies a lot under different scenarios, the lifetime levelized annual cost analysis method [20] is adopted in this paper. In general, the levelized cost over its lifetime is given by: (2) where is the current cost; is its lifetime (years) and is the discount rate [20]For simplification, the levelized factor over n years is denoted as . Therefore, the levelized annual cost of BESS on bus can be expressed as:

(3)

is the annual average peaking power generation where from and is the unit levelized annual cost of gas combustion turbine [23]. The annual benefit for bus from load shifting can be calculated by:

(5) where is the annual stored energy on during overvoltage regulation and the energy is used to shave peak load during on-peak time. and are the electricity rate at peak load and off-peak load, respectively. The annual benefit as of the annual saved OLTC&SVR operation is reflected in the reduced operation and maintenance (O&M) cost. Since all 7 BESS units contribute to the work load mitigation of OLTC&SVR, this benefit from can be calculated as:

(6) where

is the capital cost for both OLCT and SVR. is the O&M cost factor [24]. According to [25], the OLTC transformer requires one major maintenance after every 150,000 operation times . is the annual saved operation times when BESS is applied. is the annual operation times of BESSi and it is used to assign the benefit to each. Based on (3) to (6), the annual cost of installing to regulate voltage and shave peak load can be obtained by:

(7) The size can minimize the annual cost is defined as the cost-benefit size. Therefore, the cost-benefit BESS size on bus can be obtained by minimizing . Equation (7) can be applied to obtain the annual cost and the cost-benefit size for every in the distribution network.


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V. CASE STUDY AND SIMULATION RESULTS The proposed method of cost-effect BESS size evaluation has been applied on the distribution system model and simulated using Matlab simulation. Four cases are selected to evaluate the proposed method. Case A compares the system performance with and without BESS in one day. Case B shows the BESS lifetime and usage. Case C highlights the OLTC/SVR work stress and peaking power generation with BESS. Case D presents the BESS cost analysis results under different scenarios. The simulation results were derived based on the strategy of Fig. 1 where the local temperature profile is from [26]. The load data is obtained from [27] and the peak load is rated to the 7 buses’ peak load on feeder 2 according to [3]. The PV power profile is provided by [28]. The local PV penetration level is defined as: (8) where is annual PV peak power, and is the annual local peak load. Then the data from [28] is linearly rated to the desired value on each bus. The size in the simulation study can be expressed as:

Fig. 7. 24 hour results without BESS.

(9) where capacity of BESSi. is defined as the number of desired operation hours of BESSi when it is providing local peak load . A. Case Study A: System Performance With and Without BESS in One Day In order to investigate the effect of BESS integration with proposed EMS on system operation, the system performance with BESS and without BESS in one particular day under are firstly shown in Figs. 7 and 8. The BESS size applied for this case study is 2-hour peak load and the PV, Load and BESS power profile is based on bus 205. The same power profiles in 24 hours of PV and load are shown in (a) of both Figs. 7 and 8. The action for voltage regulation is illustrated in Fig. 8(a) with the stored energy in BESS from 8:00 am to 12:00 am, which contributes to eliminate the bus overvoltage caused by large PV output power. During the peak load time, BESS provides energy to load in order to shave the peak load. Figs. 7 and 8(b) show the primary and service voltage profile. The voltage limits of primary voltage and the service voltage are adopted as 0.97 p.u.–1.05 p.u. and 0.94 p.u.–1.04 p.u., respectively [3], [29]. In Fig. 7(b) the service voltage is higher than primary voltage due to the reverse power flow. With the BESS operation, these two voltages are very close as shown in Fig. 8(b). The OLTC&SVR tap positions in one day are shown in (c). Fig. 7(c) shows the tap positions has to be changed frequently in order to regulate the bus voltage within normal range if BESS is not applied. But the tap position can be kept to be constant with the help from BESS in Fig. 8(c). Therefore the work stress of OLTC/SVR can be relieved with BESS by reducing the tap change operations.

Fig. 8. 24 hour results with BESS.

B. Case Study B: BESS Lifetime and Peak Load Shifting In this paper, bus 205 is selected as an example to investigate the BESS lifetime and usage. The similar analysis can be applied to other buses. Fig. 9 shows lifetime under different BESS sizes and PV penetration levels for the applications of voltage regulation and peak load shaving. The BESS lifetime can be estimated based on (1). The PV penetration level and BESS size are defined by (8) and (9) respectively. It can be observed that under low PV penetration, lifetime increases with the size but the increasing rate becomes slow after 2-hour peak load BESS size. On the other hand, under high PV penetration the battery will be used more frequently, therefore, the lifetime increases with the size growth nearly linearly. Moreover, for a chosen size of , its lifetime decreases significantly with the growing of PV penetration .

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Fig. 9. BESS lifetime on bus 205.


Fig. 11. Operation time with and without BESS.

Fig. 12. Peaking power generation on bus 205. Fig. 10. Annual stored energy on bus 205.

As described in (5), the benefit from load shifting depends on the stored energy in . Fig. 10 presents the annual stored energy by under different PV penetration level and BESS size. Under higher PV penetration level, BESS is required to absorb more energy to solve overvoltage issue, therefore, the annual stored energy increases rapidly if the BESS size is expanded. However with lower penetration level, there may be only less reverse PV power resulting in bus overvoltage. Therefore, there is less energy needing to be stored in BESS for voltage regulation. As a result, a certain BESS size is enough to meet the requirement of stored energy. Expanded BESS size will not contribute to the saved energy. The overall trends of lifetime and annual stored energy of other buses are similar to the results of . C. Case Study C: OLTC/SVR Work Stress and Peaking Power Generation As shown in Fig. 7, the BESS is helpful to reduce the work stress on OLTC/SVR and achieve the peaking power generation. The integrated BESS on all 7 buses cooperatively regulate the buses’ voltages and reduce the OLTC/SVR operation times. Fig. 11 shows the annual operation times with and without BESS action under different PV penetration level and BESS size. The top line shows the annual operation times

of OLTC and SVR without BESS action. The operation times increase significantly with the growth of PV penetration level. The number under is almost doubled the one under . The lines below show the annual operation times with different selected BESS sizes. The difference between these lines and the top one represents the saved operation times. Under higher and larger BESS size, the difference is more apparent. This means the saved operation time is more. This result is used to evaluate the benefit from mitigating the work stress of OLTC/SVR in (6). It also can be seen from Fig. 11 that the operation times under small BESS size increase quickly with . But the number is still much smaller than the one without BESS action. In addition, it is obvious that large size BESS helps to reduce operation times rapidly under higher . However, a certain BESS size even small size is enough to achieve the desired operation times under lower . Consequently, the optimized BESS size should be decided based on and desired operation times. The peaking power generation due to integration dominates the benefit derived in (4). Fig. 12 shows the annual average peaking power generated by different sizes under different . Since the power supplied by BESS during peak load time is determined by the energy stored in , the trend of the shaved peaking power is similar as the annual saved energy in Fig. 10. It is clearly shown that the peaking


Fig. 13. Annual cost-benefit analysis under

: (a) quantitative annual cost on all buses; and (b) normalized annual cost on all buses.

TABLE II KEY PARAMETERS RELATED TO COST EVALUATION IN (3)–(7)

power generation increases with the PV penetration level and BESS size under higher . But the peaking power generation will be constant under lower when the BESS size reaches to a certain value. Accordingly, BESS size can be selected based on the and peaking power generation requirement. D. Case Study D:

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Cost-Benefit Analysis Results

In order to illustrate the cost evaluation, the BESS annual costs on all 7 buses of feeder 2 are analyzed and compared according to BESS sizes. The size will change from half-hour peak load power provision to 5-hour peak load power support. The annual costs are also explored under different as shown in Figs. 13–15. Table II shows some key parameters applied in this analysis to derive the results of Fig. 13–16 from (7). These parameters can be changed based on the user’s applications. Fig. 13(a) shows the quantitative annual costs of BESSi under . Due to different peak load on these buses, the BESS actual capacity is different especially for bus 207 which has the largest peak load [3]. It can be observed that the annual costs of all 7 BESS are positive with all the possible BESS sizes. Therefore, there is no economic profit of every BESS. But, the

lowest annual cost can be determined from this result. In order to clearly reveal the relationship between size of and its annual cost, the quantitative annual cost results are normalized in the range from to 1 based on the peak-peak annual cost of . Fig. 13(b) shows the normalized cost results. It is much clearer to illustrate the variation trend of annual cost with growth of size. Then, the lowest cost can be found and the cost-benefit size can be obtained. In the demonstrated system, the 3-hour peak load BESS size can bring the minimum cost for ; 2.5-hour peak load for ; 2-hour peak load for ; 1.5-hour peak load for . The cost keeps increasing with the BESS size for others. The normalized results facilitate to find the cost-effect BESS size of all the buses, but the real value of the annual cost should be obtained from Fig. 13(a). The quantitative annual costs under are presented in Fig. 14(a). Similarly, the normalized annual cost results are shown in Fig. 14(b). For , the cost-benefit size is around 2-hour peak load. 1.5-hour peak load is the cost-benefit BESS size for and . The cost keeps increasing with the BESS size for others. It is can be seen from Figs. 13 and 14 that the cost-benefit size of the same BESS reduces with the PV penetration level. Fig. 15 shows the annual cost of changes with BESS size under lower PV penetration level . The quantitative annual costs are always increasing with the BESS size. Therefore, it is difficult to find the cost-benefit size for BESS when the PV penetration level is 50% or lower. The proposed method can be applied to other distribution systems that include PV and BESS units. If there is no OLTC/SVR, the benefit in (6) can be ignored. In addition, the system parameters of Table I and some key parameters related to cost evaluation of Table II need to be changed according to different applications. If the applied battery’s life cycle and information are available, the battery aging effect could also be estimated based on (1). EMS can be modified flexibly to provide other functions of BESS besides voltage regulation in the applied distribution system.

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Fig. 14. Annual cost-benefit analysis under

: (a) quantitative annual cost on all buses; and (b) normalized annual cost on all buses .

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Fig. 15. Quantitative annual cost on all buses under

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VI. CONCLUSIONS This paper has proposed a new method to discover a cost-benefit BESS size when distributed BESS is integrated into the distribution system under high PV penetration. The proposed method is able to obtain the BESS cost and benefits of each bus under different PV penetration level for any selected BESS size. Therefore the tradeoff between economic profits and operational benefits can be evaluated quantitatively. This method has been demonstrated on a modified GE distribution system model in this paper. But it can be applied for other distribution systems with distributed BESS and PV units. When the BESS is used to achieve other functions including spinning reserve, frequency regulation, etc. [33], the same method can be applied to determine the cost-benefit battery size with necessary modifications. Although the economic profits cannot be achieved in this paper based on current battery price, it is possible to gain economic revenue when BESS is applied to achieve multiple functions or the price of BESS is reduced in the future.

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Hui Li (S’97–M’00–SM’01) received the B.S. and M.S. degrees in electrical engineering from Huazhong University of Science and Technology, Huazhong, China, in 1992 and 1995, respectively. She received the Ph.D. degree in Electrical Engineering from the University of Tennessee, Knoxville, TN, USA, in 2000. She is currently a Professor in the Electrical and Computer Engineering Department at the Florida A&M University—Florida State University College of Engineering, Tallahassee, FL, USA. Her research interests include PV converters, energy storage applications and smart grid.

Ye Yang (S’12) received the B.S. degree in information engineering from Tianjin University, Tianjin, China, in 2008, and the M.S. degree in electrical engineering from Texas Tech University, Lubbock, TX, USA, in 2010. He is currently working toward the Ph.D. degree in the Center for Advanced Power Systems, Florida State University, Tallahassee, FL, USA. His research interests include integration of renewable energy sources and large scale battery energy storage system optimization.

Andreas Aichhorn (S’11) received the B.Sc. and M.Sc. degrees from the Upper Austria University of Applied Sciences/Campus Wels, Austria, in automation engineering, in 2009 and 2011, respectively. He worked on the M.Sc. thesis in the Center for Advanced Power Systems, Florida State University, Tallahassee, FL, USA, as a visiting research scholar. He is currently working in the R&D department of the Company Trench Austria as a software developer.

Jim P. Zheng (M’87–SM’13) received the Ph.D. degree in electrical engineering from the State University of New York at Buffalo, NY, USA, in 1990. He has worked at US Army Research Laboratory, Fort Monmouth, NJ, USA. He is currently Sprint Eminent Scholar Chair Professor at the Department of Electrical and Computer Engineering at FAMU-FSU College of Engineering. Dr. Zheng is a member of JECS and MRS.

Michael Greenleaf (S’06) received the B.S. and M.S. degree in electrical engineering at Florida State University, Tallahassee, FL, USA, in 2008 and 2010, respectively, where he is currently pursuing the Ph.D. degree in electrical engineering under Dr. J. Zheng. His research interests are in energy storage devices and his current research topic is “Modeling and Simulation of Energy Storage Devices.“