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

Sizing Strategy of Distributed Battery Storage SystemWith High Penetration of Photovoltaic for Voltage

Regulation and Peak Load ShavingYe 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 fordistributed battery energy storage system (BESS) in the distribu-tion networks under high photovoltaic (PV) penetration level. Themain objective of the proposed method is to optimize the size ofthe distributed BESS and derive the cost-benefit analysis when thedistributed BESS is applied for voltage regulation and peak loadshaving. In particular, a systemmodel that includes a physical bat-terymodel and a voltage regulation and peak load shaving orientedenergy management system (EMS) is developed to apply the pro-posed strategy. The cost-benefit analysis presented in this paperconsiders factors of BESS influence on the work stress of voltageregulation devices, load shifting and peaking power generation, aswell as individual BESS cost with its lifetime estimation. Based onthe cost-benefit analysis, the cost-benefit size can be determined forthe distributed BESS.

Index Terms—Battery storage, cost-benefit analysis, distributedPV system, overvoltage.

I. INTRODUCTION

I N distribution network, grid-tied photovoltaic (PV) in-stallations have grown dramatically [1]. Under high PV

penetration levels, large reverse power may lead to voltagerise, which will cause potential issues to the distribution system[2]–[6]. Traditional voltage control devices, such as on-load tapchanger (OLTC) transformer and step voltage regulator (SVR),are usually used to regulate the voltage within the normalvoltage range. However, this voltage regulation technology willsuffer from increased work stress resulting in reduced systemlifetime under high penetration PV applications. Therefore,improved voltage regulation methods have been developedto solve the overvoltage issue [7]–[11]. Limiting real powerhas been verified to be more effective than reactive power toregulate the voltage in the distribution line [12]. Accordingly,

Manuscript received February 06, 2013; revised May 24, 2013; acceptedSeptember 03, 2013. This work was supported by National Science Foundationunder Grant ECCS-1001415. Date of publication September 25, 2013; date ofcurrent version February 14, 2014. Paper no. TSG-00089-2013.Y. Yang, J. Zheng, andM. 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 Univer-

sity, Tallahassee, FL 32310 USA, and also with the Department of Electrical andComputer Engineering, Florida State University, Tallahassee, FL 32310 USA(e-mail: [email protected]).A. Aichhorn is with the R&DDepartment, 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 integratebattery energy storage system (BESS) into PV systems inorder to achieve a flexible real power control. Recent researchworks have demonstrated that the BESS could help OLTC andSVR to prevent the overvoltage caused by high penetrationPV in distribution systems [7], [8], and meanwhile reduce oreliminate voltage violation without OLTC or SVR [9]–[11].However, the optimized BESS size has not been consideredin these researches which usually lead to an oversized BESSusage resulting in extra cost. This has hindered the BESS com-mercial applications in the distribution systems. As a result, acost-benefit battery size research is important and beneficial.Aiming to obtain an economical and serviceable BESS, a bat-

tery sizing strategy has been developed focusing on shaving thepeak demand in a residential power distribution feeder with PVsystem [13]. The cost and size of hybrid PV and battery systemhave been also analyzed for demand side application by takingthe advantages of the on-peak and off-peak electricity chargedifference [14]. However, these studies have not considered fac-tors related to BESS sizing on the supply side of the distributionsystem, such as reduced workload on OLTC and SVR, as well asreduced peaking power generation cost, which has degraded theBESS size planning and evaluating. Furthermore, the lifetimeof BESS should also be considered and evaluated in the sizingprocedure. Previous studies [13], [14] could not provide precisecost estimations since they are based on general battery life-time estimation. However, BESS lifetime varies greatly underdifferent 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 dueto the lack of the detailed battery lifetime estimation and otheroperational 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 theGeneral Electric (GE) distribution power system model. Thereal annual load information, PV power profile, and tempera-ture data were adopted. The proposed method is applied on themodified GEmodel. Secondly, a physical model of a lithium-ionphosphate (LiFPO4) battery was developed with aging effectso the lifetime information under different operation conditionscan be derived. In addition, several factors that impact the BESSsizing including OLTC/SVR operation positions, load shiftingand peaking power generation were recorded during the wholeBESS lifetime. The BESS usage, lifetime and system perfor-mance 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 983

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

as well as on the total feeder of the developed distribution powersystem under different PV penetration levels. Finally, the cost ofeach bus was evaluated based on the above information to de-rive a cost-benefit BESS size.

II. BATTERY COST-BENEFIT ANALYSIS SYSTEM DESCRIPTION

Fig. 1 shows the proposed battery cost-benefit anal-ysis system that is applied to investigate battery lifetime,OLTC&SVR work stress, peaking power generation and peakload shifting in a distribution power system with BESS andPV units. It consists of two subsystems. Subsystem 1 includesa distribution power system model, a physical battery modeland a voltage regulation and peak load shaving oriented en-ergy management system (EMS); subsystem 2 contains a costcalculation 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 PVpenetration level. Firstly, the distribution power system modelreads current input data including annual load power profile, PVpower profile and temperature profile, records the SVR/OLTCoperations, then delivers the OLTC&SVR work stress, peakingpower generation and peak load shifting information to sub-system 2 and generates dynamic buses’ voltages for EMS. EMSis the control center for voltage regulation and battery protec-tion. Secondly, EMS compares the buses’ voltages with thebus voltage references which are the upper and lower voltagelimits. EMS is able to control BESS to regulate the bus voltagewithin normal range by sending charge/discharge requests tothe battery. OLTC and SVR will take actions if there is stillovervoltage or undervoltage on the buses after BESS operation.Depth of discharge (DOD) and maximum charge/dischargepower will be evaluated to protect the battery. The DOD revealsthe 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 dischargecommand to each physical battery that is modeled in this paper.

In addition, the charge/discharge power is dynamically adjustedto protect battery from overcharging/overdischarging whichcould degrade its lifetime. Finally, the physical battery modelupdates the SOC and state of health (SOH) after obtaining thenew charge/discharge power command. The above three stepswill be repeated and new input data will be read again until thebattery’s SOH reaches to the end-of-life condition of battery,which means the battery is scraped and the desired batterylifetime evaluation is finished.After obtaining each battery unit’s lifetime, OLTC&SVR

work stress, peaking power generation and peak load shiftinginformation from subsystem 1, the economical annual costcalculation is performed considering BESS unit annual cost,the benefit from SVR/OLTC work stress relieve, load shiftingand shaved peaking power generation in the subsystem 2.Therefore, the cost of each bus can be calculated under everypossible battery size with different PV penetration level. Asa result, a desired cost-benefit BESS size can be derived.The detailed distribution power system model, battery modeland EMS development in subsystem 1 are firstly discussedin Section III. The cost-benefit analysis and BESS sizing ispresented in Section IV. The simulation results are provided inSection V.

III. SYSTEM MODELING AND ENERGY MANAGEMENTSTRATEGY

A. Distribution System Description

A distribution power system model developed by the GE [3]is selected and modified in this paper to investigate voltagerise, BESS usage and system performance. Fig. 2 shows thatthe distributed PV units, BESS and loads are integrated intothe GE model. The model includes some fundamental distribu-tion system components: OLTC as the substation transformer,SVR and switched capacitors. This study is focused on Feeder2. This feeder’s length is about 6 miles and the total peak load


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

TABLE IFEEDER INFORMATION IN THE DISTRIBUTED POWER SYSTEM

is 11MVA. Seven loads represent a mixture of both residentialloads and commercial peak loads ranging from 0.3MW to 5MWwith 0.92 power factor. Hybrid PV and BESS units are con-nected on 201 to 207 buses. The primary feeder base voltage is12.5 kV. The secondary feeder base voltage is 240V for residen-tial loads. The service capacity is rated at 1.5 per unit (p.u.) re-lated 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 selectedfor the distance from the transformer to the load. The secondaryfeeder impedance is calculated based on the conductor with 200A of thermal capacity. The detailed feeder information is listedin 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 EMScontrols the power flow among the battery, PV, load and grid.

Although the BESS can achieve more functions, voltage regu-lation and peak load shaving are two main functions consideredin this study. The EMS sends charge command to BESS whenovervoltage caused by reverse PV power happens, and conveysdischarge command to BESS during peak load period. The bus205 is selected as an example to describe the power control flowand shown in Fig. 3. The same strategy can be applied to otherbuses. The initial voltage of bus 205 is obtained based on theload and PV power data. The bus voltage is then compared withthe voltage upper and lower limits. If the bus voltage is con-trolled within the normal range, EMS collects next load and PVpower data. Otherwise, one of the following two conditionalvoltage control strategies is activated:(a) Overvoltage regulation flowchart: On the one hand,

when overvoltage occurs on bus 205 and PV is gener-ating power, battery will be charged with the excess PVpower until the bus voltage drop to normal range. Onthe other hand, when overvoltage occurs but PV is notgenerating power or the battery charge flag shows thatbattery reaches to its power limit, the closest voltagecontrol device will step down the tap position to reducethe voltage.

(b) Undervoltage regulation and peak load shave flowchart:On the one hand, during peak load time or when voltagesag happens, the battery will discharge to support the busvoltage. On the other hand, when the battery dischargeflag shows that battery reaches to its limit and voltage sagstill exists, the closest voltage control device will step upthe tap position to boost the voltage.

C. Physical Battery Model With Aging Effect Estimation

Physical battery model is critical to investigate the batterylifetime including SOC and SOH. In this paper, a physicalbattery model is developed based on a commercial LiFePO4battery from A123 systems (APR18650) due to its good life-time performance and high power and energy density [16].The developed battery equivalent circuit model is shown in


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

Fig. 4. Battery equivalent circuit model.

Fig. 4.The detailed parameters may change with temperatureand have been described in [16]–[18].The developed battery circuit model is integrated into the dis-

tribution power system model of Fig. 2. The SOC and SOH ofthe battery can be calculated and updated. The SOC is the feed-back to the EMS which indicates the energy can be convertedfor the next status. The SOH specifies the leftover lifetime ofthe battery. The manufacturer defines the battery life ends whenthe capacity reaches 80% or less of the initial one in a fullycharged state [16]. In order to obtain the battery lifetime, twomajor aging effects are considered to estimate the irreversiblecapacity loss of the battery. One is the cyclation based state ofhealth 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]. Ac-cordingly, the can be reflected from the battery capacitychange in the available cycles. Fig. 5 shows the battery capacityin terms of the available battery cycles to illustrate the estimatedavailable volume of coulombs before the battery is scraped. Thelinear function for capacity reduction rate is selected in this

Fig. 5. Cyclation based irreversible capacity loss.

paper to calculate the volume of coulombs during battery dis-charge in order to simplify the analysis. Once the dischargedcoulombs are equal to the total area highlighted in Fig. 5, thebattery is scraped. The challenge case is the battery is alwaysworking at 100% DOD. When the battery life ends, the batterycycles will reach to the limit value 2000 cycles. In the real ap-plication, 100% DOD is not practical and the capacity reduc-tion rate may be non-linear, therefore, the battery cycles may bemore 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 voltageof the battery changes with [18]. In this case, the current andthe amount of used coulombs under the same amount of powerchange with . Therefore, the temperature influence is alsoconsidered in the evaluation of . can be estimatedby the division of the used coulombs with the total availablecoulombs (1-b).2) : The is the effect of capacity loss occurring

at non-operating state of the battery. The three main factors forthe calendrical aging effect are T, current SOC, and the non-op-erating time of the battery. The estimation of this aging effectis shown in Fig. 6 which is based on the measurement resultsfrom [19]. In these functions, the capacity loss/day and can be


Fig. 6. Experimental measurements of the capacity loss per day [19].

determined by the given SOC and T. Finally, the is es-timated by the product of capacity loss/day with non-operatingtime (1-c).The above two factors are combined to calculate the battery

aging effect and obtain the battery lifetime. Therefore, the stateof 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 workstress, peaking power generation and peak load shifting infor-mation can be obtained from subsystem 1. Therefore, the costof each BESS and the whole feeder can be calculated basedon the BESS lifetime. The cost evaluation will determine acost-benefit BESS size. In order to design a battery size moreaccurately, the cost calculation will consider multiple factorsincluding the battery annual cost, annual benefit from reducedOLTC/SVR work stress, annual benefit from shaved peakingpower generation and annual benefit from load shifting.In view of the BESS lifetime varies a lot under different sce-

narios, the lifetime levelized annual cost analysis method [20]is adopted in this paper. In general, the levelized cost over itslifetime is given by:

(2)

where is the current cost; is its lifetime (years) and is thediscount rate [20]For simplification, the levelized factor over nyears is denoted as .Therefore, the levelized annual cost of BESS on bus can be

expressed as:

(3)

where the BESS on bus i is defined as . The power con-version system (PCS) cost is proportional to the battery’s powerrating . is the unit cost of the PCS. The cost ofthe 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 andstorage cost [22], and it can be represented by introducing a in-stallation cost factor . . is the levelized factors re-lated to the ’s lifetime.For generating the same amount of peaking power, gas

combustion turbine plant cost is reduced due to the unleashedpower from during peak load time. The annual benefitdepending on the peaking power generation from canbe derived by:

(4)

where is the annual average peaking power generationfrom and is the unit levelized annual cost of gascombustion turbine [23].The annual benefit for bus from load shifting can be calcu-

lated by:

(5)

where is the annual stored energy on during over-voltage regulation and the energy is used to shave peak loadduring on-peak time. and are the elec-tricity rate at peak load and off-peak load, respectively.The annual benefit as of the annual saved OLTC&SVR op-

eration is reflected in the reduced operation and maintenance(O&M) cost. Since all 7 BESS units contribute to the work loadmitigation of OLTC&SVR, this benefit from can be cal-culated 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 every150,000 operation times . isthe annual saved operation times when BESS is applied.is the annual operation times of BESSi and it is used to assignthe 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 thecost-benefit size. Therefore, the cost-benefit BESS size on buscan be obtained by minimizing . Equation (7) canbe applied to obtain the annual cost and the cost-benefit size forevery in the distribution network.


V. CASE STUDY AND SIMULATION RESULTS

The proposed method of cost-effect BESS size evaluation hasbeen applied on the distribution system model and simulatedusing Matlab simulation. Four cases are selected to evaluate theproposed method. Case A compares the system performancewith and without BESS in one day. Case B shows the BESS life-time and usage. Case C highlights the OLTC/SVR work stressand peaking power generation with BESS. Case D presents theBESS 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 loaddata is obtained from [27] and the peak load is rated to the 7buses’ peak load on feeder 2 according to [3]. The PV powerprofile is provided by [28]. The local PV penetration level isdefined as:

(8)

where is annual PV peak power, and is the annuallocal peak load. Then the data from [28] is linearly rated to thedesired value on each bus.The size in the simulation study can be expressed as:

(9)

where capacity of BESSi. is defined as the number ofdesired operation hours of BESSi when it is providing local peakload .

A. Case Study A: System Performance With and Without BESSin One Day

In order to investigate the effect of BESS integration withproposed EMS on system operation, the system performancewith BESS and without BESS in one particular day underare 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 BESSpower 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 voltageregulation is illustrated in Fig. 8(a) with the stored energy inBESS from 8:00 am to 12:00 am, which contributes to eliminatethe bus overvoltage caused by large PV output power. Duringthe peak load time, BESS provides energy to load in order toshave the peak load. Figs. 7 and 8(b) show the primary andservice voltage profile. The voltage limits of primary voltageand the service voltage are adopted as 0.97 p.u.–1.05 p.u. and0.94 p.u.–1.04 p.u., respectively [3], [29]. In Fig. 7(b) the ser-vice voltage is higher than primary voltage due to the reversepower flow. With the BESS operation, these two voltages arevery close as shown in Fig. 8(b). The OLTC&SVR tap posi-tions in one day are shown in (c). Fig. 7(c) shows the tap posi-tions has to be changed frequently in order to regulate the busvoltage within normal range if BESS is not applied. But the tapposition can be kept to be constant with the help from BESS inFig. 8(c). Therefore the work stress of OLTC/SVR can be re-lieved with BESS by reducing the tap change operations.

Fig. 7. 24 hour results without BESS.

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 investi-gate the BESS lifetime and usage. The similar analysis can beapplied to other buses. Fig. 9 shows lifetime underdifferent BESS sizes and PV penetration levels for the appli-cations of voltage regulation and peak load shaving. The BESSlifetime can be estimated based on (1). The PV penetration leveland BESS size are defined by (8) and (9) respectively. It can beobserved that under low PV penetration, lifetime in-creases with the size but the increasing rate becomes slow after2-hour peak load BESS size. On the other hand, under high PVpenetration the battery will be used more frequently, therefore,the lifetime increases with the size growth nearly linearly.More-over, for a chosen size of , its lifetime decreases sig-nificantly with the growing of PV penetration .


Fig. 9. BESS lifetime on bus 205.

Fig. 10. Annual stored energy on bus 205.

As described in (5), the benefit from load shifting dependson the stored energy in . Fig. 10 presents the annualstored energy by under different PV penetration leveland BESS size. Under higher PV penetration level, BESS is re-quired to absorb more energy to solve overvoltage issue, there-fore, the annual stored energy increases rapidly if the BESSsize is expanded. However with lower penetration level, theremay be only less reverse PV power resulting in bus overvoltage.Therefore, there is less energy needing to be stored in BESS forvoltage regulation. As a result, a certain BESS size is enoughto meet the requirement of stored energy. Expanded BESS sizewill not contribute to the saved energy.The overall trends of lifetime and annual stored en-

ergy of other buses are similar to the results of .

C. Case Study C: OLTC/SVR Work Stress and Peaking PowerGeneration

As shown in Fig. 7, the BESS is helpful to reduce thework stress on OLTC/SVR and achieve the peaking powergeneration. The integrated BESS on all 7 buses cooperativelyregulate the buses’ voltages and reduce the OLTC/SVR op-eration times. Fig. 11 shows the annual operation times withand without BESS action under different PV penetration leveland BESS size. The top line shows the annual operation times

Fig. 11. Operation time with and without BESS.

Fig. 12. Peaking power generation on bus 205.

of OLTC and SVR without BESS action. The operation timesincrease 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 withdifferent selected BESS sizes. The difference between theselines and the top one represents the saved operation times.Under higher and larger BESS size, the difference is moreapparent. 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. 11that the operation times under small BESS size increase quicklywith . But the number is still much smaller than the one withoutBESS action. In addition, it is obvious that large size BESShelps to reduce operation times rapidly under higher . How-ever, a certain BESS size even small size is enough to achievethe desired operation times under lower . Consequently, theoptimized BESS size should be decided based on and desiredoperation times.The peaking power generation due to integration

dominates the benefit derived in (4). Fig. 12 shows the annualaverage peaking power generated by different sizesunder different . Since the power supplied by BESS duringpeak load time is determined by the energy stored in ,the trend of the shaved peaking power is similar as the annualsaved 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 IIKEY PARAMETERS RELATED TO COST EVALUATION IN (3)–(7)

power generation increases with the PV penetration level andBESS size under higher . But the peaking power generationwill be constant under lower when the BESS size reaches toa certain value. Accordingly, BESS size can be selected basedon the and peaking power generation requirement.

D. Case Study D: Cost-Benefit Analysis Results

In order to illustrate the cost evaluation, the BESS an-nual costs on all 7 buses of feeder 2 are analyzed and comparedaccording to BESS sizes. The size will change fromhalf-hour peak load power provision to 5-hour peak load powersupport. The annual costs are also explored under differentas shown in Figs. 13–15. Table II shows some key parametersapplied in this analysis to derive the results of Fig. 13–16 from(7). These parameters can be changed based on the user’s appli-cations.Fig. 13(a) shows the quantitative annual costs of BESSi under

. Due to different peak load on these buses, the BESSactual capacity is different especially for bus 207 which has thelargest peak load [3]. It can be observed that the annual costsof 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 orderto clearly reveal the relationship between size of and itsannual cost, the quantitative annual cost results are normalizedin the range from to 1 based on the peak-peak annual cost of

. Fig. 13(b) shows the normalized cost results. It is muchclearer to illustrate the variation trend of annual costwith growth of size. Then, the lowest cost can be found and thecost-benefit size can be obtained. In the demonstratedsystem, the 3-hour peak load BESS size can bring the minimumcost for ; 2.5-hour peak load for ; 2-hour peakload for ; 1.5-hour peak load for . The costkeeps increasing with the BESS size for others. The normalizedresults facilitate to find the cost-effect BESS size of all the buses,but the real value of the annual cost should be obtained fromFig. 13(a).The quantitative annual costs under are pre-

sented in Fig. 14(a). Similarly, the normalized annual cost re-sults are shown in Fig. 14(b). For , the cost-benefit sizeis around 2-hour peak load. 1.5-hour peak load is the cost-ben-efit BESS size for and . The cost keeps in-creasing with the BESS size for others. It is can be seen fromFigs. 13 and 14 that the cost-benefit size of the same BESS re-duces with the PV penetration level.Fig. 15 shows the annual cost of changes with BESS

size under lower PV penetration level . Thequantitative annual costs are always increasing with the BESSsize. Therefore, it is difficult to find the cost-benefit size forBESS when the PV penetration level is 50% or lower.The proposed method can be applied to other distribution sys-

tems that include PV and BESS units. If there is no OLTC/SVR,the benefit in (6) can be ignored. In addition, the system param-eters of Table I and some key parameters related to cost evalua-tion of Table II need to be changed according to different appli-cations. If the applied battery’s life cycle and informationare available, the battery aging effect could also be estimatedbased on (1). EMS can be modified flexibly to provide otherfunctions of BESS besides voltage regulation in the applied dis-tribution system.


Fig. 14. Annual cost-benefit analysis under : (a) quantitative annual cost on all buses; and (b) normalized annual cost on all buses .

Fig. 15. Quantitative annual cost on all buses under .

VI. CONCLUSIONS

This paper has proposed a newmethod to discover a cost-ben-efit BESS size when distributed BESS is integrated into thedistribution system under high PV penetration. The proposedmethod is able to obtain the BESS cost and benefits of each busunder different PV penetration level for any selected BESS size.Therefore the tradeoff between economic profits and operationalbenefits can be evaluated quantitatively. This method has beendemonstrated on a modified GE distribution system model inthis paper. But it can be applied for other distribution systemswith distributed BESS and PV units. When the BESS is used toachieve other functions including spinning reserve, frequencyregulation, etc. [33], the same method can be applied to deter-mine the cost-benefit battery size with necessary modifications.Although the economic profits cannot be achieved in this paperbased on current battery price, it is possible to gain economicrevenue when BESS is applied to achieve multiple functions orthe price of BESS is reduced in the future.

REFERENCES

[1] C. Whitaker, J. Newmiller, M. Ropp, and B. Norris, Renewable Sys-tems Interconnection Study: Distributed Photovoltaic Systems Designand Technology Requirements. Albuquerque, NM: Sandia NationalLaboratories, 2008.

[2] R. Tonkoski, D. Turcotte, and T. H. M. EL-Fouly, “Impact of highPV penetration on voltage profiles in residential neighborhoods,” IEEETrans. Sustain. Energy, vol. 3, no. 3, pp. 518–527, Jul. 2012.

[3] E. Liu and J. Bebic, Distribution System Voltage Performance Anal-ysis for High-Penetration Photovoltaic. Niskayuna, NY, USA: GEGlobal Research, 2008.

[4] F. Katiraei and J. R. Agüero, “Solar PV integration challenges,” IEEEP&E Mag., vol. 9, no. 3, pp. 62–71, May-Jun. 2011.

[5] Y. Ueda, K. Kurokawa, T. Tanabe, K. Kitamura, and H. Sugihara,“Analysis results of output power loss due to the grid voltage rise ingrid-connected photovoltaic power generation systems,” IEEE Trans.Ind. Electron., vol. 55, no. 7, pp. 2744–2751, Jul. 2008.

[6] J. Matsuki, S. Kawasaki, S. Hosokawa, and N. Kobayashi, “Optimalgrid voltage control in distribution feeder connected with PV systems,”in Proc. Photovoltaic Energy Conversion, Conf. Record of the 2006IEEE 4th World Conf., May 2006, vol. 2, pp. 2327–2330.

[7] H. Ravindra,M. O. Faruque, K. Schoder,M. Steurer, P.Mclaren, and R.Meeker, “Dynamic interactions between distribution network voltageregulators for large and distributed PV plants,” in Proc. Transm. Dis-tribution Conf. Exposition (T&D), May 2012, pp. 1–8.

[8] X. Liu, A. Aichhorn, L. Liu , and H. Li, “Coordinated control ofdistributed energy storage system with tap changer transformers forvoltage rise mitigation under high photovoltaic penetration,” IEEETrans. Smart Grid, vol. 3, no. 2, pp. 897–906, Jun. 2012.

[9] M. Zillmann, R. Yan, and T. K. Saha, “Regulation of distribution net-work voltage using dispersed battery storage systems: A case study of arural network,” in Proc. 2011 Power Energy Society General Meeting,Jul. 2011, pp. 1–8.

[10] J. Cappelle, J. Vanalme, S. Vispoel, T. V. Maerhem, B. Verhelst, C.Debruyne, and J. Desmet, “Introducing small storage capacity at res-idential PV installations to prevent overvoltage,” in Proc. Smart GridCommun., Oct. 2011, pp. 534–539.

[11] K. H. Chua, Y. S. Lim, P. Taylor, S. Morris, and J. Wong, “Energystorage system for mitigating voltage unbalance on low-voltage net-works with photovoltaic systems,” IEEE Trans. Power Delivery, vol.27, no. 4, pp. 1783–1790, Oct. 2012.

[12] R. Tonkoski and L. A. C. Lopes, “Voltage regulation and radial distri-bution feeders with high penetration of photovoltaic,” in Proc. Energy2030 Conf., Nov. 2008, pp. 1–7.

[13] C. Venu, Y. Riffonneau, S. Bacha, and Y. Baghzouz, “Battery storagesystem sizing in distribution feeders with distributed photovoltaic sys-tems,” in Proc. 2009 IEEE PwerTech Bucharest, Jul. 2009.

[14] W. F. Su, C. E. Lin, and S. J. Huang, “Economic analysis for demand-side hybrid photovoltaic and battery energy storage system,” in Proc.Ind. Appl. Conf. 34th IAS AnnualMeeting, 1999, vol. 3, pp. 2051–2057.


[15] T. Guena and P. Leblanc, “How depth of discharge affects the cyclelife of lithium-metal-polymer batteries,” in Proc. Telecommun. EnergyConf., Sep. 2006, pp. 1,8–1,8.

[16] [Online]. Available: http://www.a123systems.com[17] A. Aichhorn, M. Greeleaf, and H. L. Zheng, “A cost effective battery

sizing strategy based on a detailed battery lifetime model and an eco-nomic energy management strategy,” in Proc. 2012 Power Energy So-ciety General Meeting, Jul. 2012.

[18] M. Greenleaf, Physical Based Modeling and Simulation of LiFepo4Secondary Batteries. Tallahassee, FL, USA: Dept.ECE., FloridaState Univ., 2010.

[19] A. Farman, Erstellen Eines Messtechnisch Gestützten Modells ZurBerechnung der Kalendarischen Alterung von LiFePO4-Batte-rien. Germany: Hochschule München, 2010.

[20] H. L. Willis and W. G. Scott, Distributed Power Generation Planningand Evaluation. NewYork, NY, USA:Marcel Dekker, Inc., 2000, pp.141–144.

[21] Schoenung,M. Susan, andW.V.Hassenzahl, Long-vs. Short-Term En-ergy Storage Technologies Analysis. A Life-Cycle Cost Study. A Studyfor the doe Energy Storage Systems Program. Albuquerque, NM,USA: Sandia National Laboratories, 2003.

[22] D. Raslter, A. Akhil, D. Gauntlett, and E. Cutter, “Energy StorageSystem Costs 2011 Update Executive Summary,” EPRI, 2012 [On-line]. Available: http://www.eosenergystorage.com/documents/EPRI-Energy-Storage-Webcast-to-Suppliers.pdf

[23] Cost of New Entry Estimates for Combustion-Turbine and Combined-Cycle Plants in PJM. HILL: CH2M, 2011.

[24] Gomatom, Phanikrishna, andW. Jewell, “Feasibility evaluation of dis-tributed energy generation and storage for cost and reliability using the”worth factor” criterion,” in Proc. 2012 Frontiers Power Conf., 2002.

[25] [Online]. Available: http://www05.abb.com/global/scot/scot252.nsf/veritydisplay/f741130363e7c6de85256d6f007aade1/$file/030129_On-load.pdf

[26] [Online]. Available: http://wunderground.com[27] [Online]. Available: https://www.sce.com/wps/portal/home/regulatory/

load-profiles/!ut/p/b0/04_Sj9CPykssy0xPLMnMz0vMAfGjzOINLd-wdPTyDDTwtfAKNDTydnDz9zdxMjA28jfQLsh0VAY010s4!/

[28] [Online]. Available: http://egauge699.d.egauge.net/index.html[29] Willis and H. Lee, Power Distribution Planning Reference Book.

Boca Raton, FL, USA: CRC Press, 2004.[30] [Online]. Available: http://www.nrel.gov/analysis/tech_lcoe.html[31] [Online]. Available: http://evcargo.com/2012/05/23/121/[32] [Online]. Available: http://www.talgov.com/you/you-customer-

helpful-rates-res-elec.aspx[33] D. Ton, C. Hanley, G. Peek, and J. D. Boyes, “Solar Energy Grid Inte-

gration Systems-Energy Storage (SEGIS-ES),” Sandia National Labo-ratories, 2008, Report# SAND2008–4247.

Ye Yang (S’12) received the B.S. degree in infor-mation engineering from Tianjin University, Tianjin,China, in 2008, and the M.S. degree in electrical en-gineering from Texas Tech University, Lubbock, TX,USA, in 2010. He is currently working toward thePh.D. degree in the Center for Advanced Power Sys-tems, Florida State University, Tallahassee, FL, USA.His research interests include integration of renew-

able energy sources and large scale battery energystorage system optimization.

Hui Li (S’97–M’00–SM’01) received the B.S.and M.S. degrees in electrical engineering fromHuazhong University of Science and Technology,Huazhong, China, in 1992 and 1995, respectively.She received the Ph.D. degree in Electrical Engi-neering from the University of Tennessee, Knoxville,TN, USA, in 2000.She is currently a Professor in the Electrical and

Computer Engineering Department at the FloridaA&M University—Florida State University Collegeof Engineering, Tallahassee, FL, USA. Her research

interests include PV converters, energy storage applications and smart grid.

Andreas Aichhorn (S’11) received the B.Sc. andM.Sc. degrees from the Upper Austria University ofApplied Sciences/Campus Wels, Austria, in automa-tion engineering, in 2009 and 2011, respectively.He worked on the M.Sc. thesis in the Center forAdvanced 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. de-gree in electrical engineering from the State Univer-sity of New York at Buffalo, NY, USA, in 1990.He has worked at US Army Research Laboratory,

FortMonmouth, NJ, USA. He is currently Sprint Em-inent Scholar Chair Professor at the Department ofElectrical and Computer Engineering at FAMU-FSUCollege of Engineering.Dr. Zheng is a member of JECS and MRS.

Michael Greenleaf (S’06) received the B.S. andM.S. degree in electrical engineering at Florida StateUniversity, Tallahassee, FL, USA, in 2008 and 2010,respectively, where he is currently pursuing thePh.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 Sim-ulation of Energy Storage Devices.“

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