Journal of Operation and Automation in Power Engineering. Vol. 5, No. 2, Dec. 2017, Pages:

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1 Journal of Operation and Automation in ower Engineering Vol., No. 2, Dec. 27, age: Optimal Sizing of Energy Storage Sytem in A Renewable-Baed Microgrid Under Flexible Demand Side Management Conidering Reliability and Uncertaintie M. Majidi, S. Nojavan *, K. Zare Faculty of Electrical and Computer Engineering, Univerity of Tabriz, Tabriz, Iran Abtract- Utilization of energy torage ytem (ESS) in microgrid ha turned to be neceary in recent year and now wi e improvement of torage technologie, ytem operator are looking for an exact modeling and calculation for optimal izing of ESS. In e propoed paper, optimal ize of ESS i determined in a microgrid conidering demand repone program (DR) and reliability criterion. Bo larger and mall-cale ESS have eir own problem. A largecale ESS reduce microgrid operating cot but it include higher invetment cot while a mall-cale ESS ha le invetment cot. The main goal of e propoed paper i find optimal ize of ESS in which microgrid invetment cot a well a operating cot are minimized. Since e renewable unit may not have table production and alo becaue of e outage at conventional unit may have, ESS i utilized and en a reliability index called reliability criterion i obtained. Furermore, effect of reliability criterion and DR on optimal izing of ESS are evaluated. A mixedinteger programing (MI) i ued to model e propoed tochatic ESS optimal izing problem in a microgrid and GAMS optimization oftware i ued to olve it. Five tudy cae are tudied and e reult are preented for comparion. Keyword: Demand repone program (DR), Energy torage ytem (ESS), Renewable-baed microgrid, Reliability criterion. Indice h n r t arameter C end C C B CIF B DR DT i NOMENCLATURE Hour index Unit index Renewable unit index Day index Scenario index State of charge at e tart of every day State of charge at e end of every day Maximum torage capacity Main invetment cot of ESS Maximum value of DR Minimal down time of ermal unit Received: 2 Feb. 27 Revied: 8 Apr. 27 Accepted: 2 Jun. 27 Correponding auor: ayyad.nojavan@tabrizu.ac.ir (S. Nojavan) Digital object identifier:.2298/joape Univerity of Mohaghegh Ardabili. All right reerved. DRi arameter IC B ICE B k Ramp down rate of ermal unit Intallation cot of power rating for ESS Intallation cot of energy rating for ESS Dep of dicharge Target LOLE redicted value for lo of load NG NR NH NT M min i i W, R B D, UT i expectation Available conventional unit number Available renewable unit number Hour number Day number Maximum limitation of power import (export) Minimum output power of ermal unit Maximum output power of ermal unit roduction of wind turbine Rated power of ESS The load upplied by microgrid Minimal up time of ermal unit

2 M. Majidi, S. Nojavan, K. Zare : Optimal Sizing of Energy Storage Sytem in A Renewable-Baed Microgrid 26 UR i t t Variable C C t F i I i IC I it( h) LS LOLE OC M, r B, i DR, TOU, SUi UX i UY w y i z i M, Ramp up rate of ermal unit Electricity price Time paue State of charge State of charge at e tart of every day Function of generation cot for ermal unit Unit engagement condition Microgrid overall invetment cot Unit engagement condition at hour (h-) Load reduction Lo of load expectation Overall operating cot of microgrid Sold (purchaed) power to (from) e main grid roduced power by renewable unit ESS conumption (production) roduced power by ermal unit Microgrid new load conidering DR Microgrid new load conidering TOU program Startup cot of ermal unit Outage condition of ermal unit Outage condition of line connecting uptream grid to e microgrid Binary variable Binary variable, if e unit i tarted up; oerwie it i Binary variable, if e unit i hut down; oerwie it i. INTRODUCTION Recently, energy torage ytem (ESS) have been widely ued in microgrid to control peak load, optimize operation and manage output fluctuation of available renewable energy ource. In addition to utilization, optimal izing of ESS in microgrid i neceary [-]. Microgrid i created a a mall power ditribution ytem wi local renewable and non-renewable unit to upply local load. A microgrid can be connected to e uptream grid to exchange power wi uptream grid. Utilization of ESS in microgrid i important. Therefore, optimal ize of ESS hould be determined to minimize e invetment cot a well a operation cot. A model i propoed for ESS izing in which e charge and dicharge power curve are determined [4]. ESS i ued beide wind turbine and photovoltaic ytem in microgrid to often e untable generation of renewable energy ource [- 7]. A practical approach ha been utilized in [8] to improve ytem reliability by employing ESS. The application and future of ESS ha been tudied in [9]. A torage ytem ha been employed to manage and control power in []. ESS ha been integrated wi wind generation unit in [] to olve e intermittent behavior problem of ee unit. Genetic algorim ha been employed to find optimal ize of an energy torage ytem in [2]. Uing e alternative direction meod of multiplier, energy torage ytem i optimally ited and ized in []. Finally, e optimal capacity and location of battery energy torage ytem have been found uing a heuritic meod in [4]. Energy torage ytem ha been employed for coordination of conumption and renewable generation in []. Compreed air energy torage ytem ha been employed to handle intermittent generation of wind turbine in [6]. Virtual energy torage ytem ha been utilized in [7] to control and improve frequency repone. A a flexible tool, energy torage ytem ha been employed in [8] to handle intermittent output of wind turbine conidering economic and technical iue. Uncertainty baed operation of ditribution ytem have been invetigated in [9] ubject to load and generation uncertaintie. Voltage and current harmonic have been tried to be corrected in an on-grid microgrid in [2]. Thi paper i wi line of wory reference [4]. The effect of DR and reliability index on optimal ESS izing problem are not tudied in mentioned work. Therefore, in e propoed paper, e ame problem i tudied in e preence of reliability index and DR. Alo, tochatic model i employed to conider e uncertainty of ytem component outage, microgrid load and output power of renewable energy ource. The objective function of e propoed paper i total cot of microgrid including e invetment cot of ESS a well a operation cot of microgrid. The invetment cot conit of power rating a well a energy rating cot of ESS while e operation cot include e operation cot of generation unit, cot of purchaed power from uptream grid minu e revenue obtained from elling power to e uptream grid. The propoed reliability index i lo of load expectation (LOLE) in i tudy which i determined baed on e expected load curtailment in each cenario. Alo, e TOU rate of DR i propoed to flatten load curve by tranferring ome percentage of load from peak time period to oer time period which lead to reduction of operation cot. Finally, e MI model i ued to formulate e propoed model for optimal ESS izing

3 Journal of Operation and Automation in ower Engineering, Vol., No. 2, Dec problem in e preence of DR and reliability index. Baed on e given explanation above, e novelty and contribution of i paper are preented a follow:. To determine optimal ize of ESS in microgrid conidering reliability index. 2. To evaluate effect of DR on e optimal izing problem of ESS under reliability index.. The cenario tochatic model i ued to model uncertainty of load, output power of renewable unit and ytem component outage. The ret of paper i claified a follow: The tochatic framework model of ESS izing in a microgrid i provided in Section 2. Maematical formulation are preented in Section. The cae tudie are preented in Section 4 in which e effect of DR and reliability index are invetigated and e reult are preented for comparion. Finally, e concluion are preented in Section. 2. MODEL OUTLINE Microgrid expected operating cot and ESS invetment cot are e main objective of propoed model for optimal ESS izing problem. In e propoed paper, one of e purpoe i to reduce e invetment cot of ESS a well a operating cot of microgrid which incorporate e cot of local unit generation and e cot of power procurement from e upper grid. In recent year, bo financial and reliability iue have been ignificant factor in power ytem project and e goal of i paper i to conider e remarked iue and help manager and related deciion maker make an appropriate deciion on e planning problem. By conidering reliability criterion in e propoed model, e financial and ecurity iue will be conidered, o a atifactory edge between upply and energy demand will be obtained and a level of inherent exce will be guaranteed. In imple word, reliability criterion mean e amount of unupplied load in a definite period which ha been aeed a far a LOLE in e propoed model. The reliability of power ytem can be exactly evaluated baed on e probabilitic reliability index. To obtain e expected reliability index, meaningful calculation i needed, o a breakdown approach i neceary to dicrete e reliability iue [2]. Monte Carlo imulation (MCS) i ued to compute e microgrid reliability. It i been utilized to imulate variou unknown condition during e tudy period. According to e microgrid component compulory interruption degree and proper probability diperion function, many irregular number are created to define e condition of every ector and production of renewable reource during e imulation period. To tudy how microgrid would operate in a pecific inpected condition, many different condition are imulated. In e utilized technique, each cenario how a probable condition related to ytem operation which lead to a large number of cenario and complicated calculation. Uing a cenario reduction trategy, number of cenario are decreaed. Each reduced cenario ha a pecial meaning which reflect e poibility of an unknown condition. The MCS technique i appropriate for uch application ince e number of cae i independent from ytem extent for a definite preciion degree [22].. ROBLEM FORMULATION The main purpoe of propoed paper i to minimize invetment cot a well a operation cot of microgrid. Maematical form of i entence i propoed in in Eq. (). Min IC OC () IC IC ICE C R B B B B N Nt Nh Ng i i i i t h i OC F I SU () N Nt Nh t M, t h (2) A mentioned before, e main goal i to define an optimal point for ESS in which invetment cot and operation cot are minimized Eq. (). The power rating cot and energy rating invetment cot are conidered in e invetment cot Eq. (2). It hould be noted at e variable and fixed cot are alo added to e power rating cot. The following cot are tandardized on a yearly premie and by finding e optimal ize for ESS intallation, e ESS total cot will be minimized [2]. The econd objective of propoed paper i to minimize e microgrid operation cot. Like e invetment cot, microgrid operating cot ha been alo divided into everal eparate cot a follow: e expene related to fuel procurement for local unit to produce electricity, e expene related to power procurement (export) from (to) e upper grid and e cot at e unit have when tarting up and hutting down... Microgrid and unit limitation Equation (4) expree e power balance between production and energy demand wi conidering DR. It can be undertood from Eq. (4) at e production of unit (bo renewable and ermal) inide e microgrid plu e power at ESS produce (ue) and e power ent or purchaed to (from) e upper grid hould be equal to e energy demand wi conidering DR. If energy demand i more an e microgrid and network production, e variable will be affixed to e Eq. (4) to expre load hortage. When torage i dicharging, ESS power i a poitive value and when charging, it i negative and when not charging or dicharging, it i zero. If power i purchaed from e upper grid, e upper grid power will be conidered poitive and it will be negative if it i ent to e upper grid and finally it will be zero if

4 M. Majidi, S. Nojavan, K. Zare : Optimal Sizing of Energy Storage Sytem in A Renewable-Baed Microgrid 28 microgrid work in ilanding operation mode. The exchanged power between microgrid and uptream grid i limited by equation Eq. (). Equation (6) i ued to provide e load hedding limitation for table operation. N G N R i I i r B, M, LS DR, i r M, M M, (4) UY () LS (6) DR, Limitation on local ermal unit inide e microgrid are preented in Eq. (7)-(). The lower and upper production limit of unit are expreed by Eq. (7). Ramping up and down limit are preented by Eq. (8) and (9), repectively. The Eq. () and () are employed to determine e minimal down and up time limitation of local unit. I UX I UX min i i i i i i i ( ) UR.( y ) y min i it h i i i i ( ) DR.( z ) z min it h i i i i i hut i Ii UTi. yi kh hdt i kh ( I ) DT. z i i i (7) (8) (9) () () According to e indexe y and z which are ued a unit tartup and hut down indexe, e limitation of Eq. (8)- () are determined. According to e unit commitment in Eq. (2)-(), e following indexe are determined. If e unit i tart up, y will be equal to, or ele it will be zero. If e unit i hut down, z will be equal to, or ele it will be zero. y z I I y i i i it( h ) i z i (2) () In e propoed model for optimal ESS izing problem, bo renewable and ermal unit are conidered. Each renewable unit ha a unique production pattern which will be defined by a long-tanding etimation. We can employ a definite approach or imulation meod to predict e input performance of e production unit. The power curve of wind turbine will be integrated wi input performance of e production unit to create e production pattern according to wory reference [24]. For intance, by utilizing e Weibull probability ditribution function, we can model e wind peed diemination [2]. There are Different approache for predicting Weibull parameter [26, 27]. The power produced by a wind turbine can be calculated a follow: W, (4) Since e renewable ource ize i proportional wi e microgrid ize, e reliability of microgrid would be quetioned by e combination of poradic renewable ource, o e extra reource and ESS hould be able to provide e energy demand [28]..2. ESS contraint The Eq. ()-(2) can be ued to deign ESS. k () R R B B, B C C t (6) S S t ( h ) B, S C C (7) C S t C b (8) S end C C ; ( h N ) (9) V VCI V V V V V V V V V VCO CI W CI R V R VCI W R CO R B B B B B H IC ICE C CIF (2) Evaluating ESS from operational tatu point of view, ree tatue will be obtained: iland, charging and dicharging. The charging/dicharging power limit of ESS are contrained by Eq. (). Equation (6) i ued to determine tate of charge of ESS which i limited by Eq. (7). Total amount of energy at e preent time plu e amount in former hour i equal to e exiting energy inide e torage. The time paue i hour, o i conidered to be. When ESS i charging, ha negative value and e amount of exiting energy increae. When ESS i in dicharge mode, would be conidered negative and e amount of exiting energy decreae. In order to obtain how much energy we have at e beginning and end of every day, e Eq. (8)-(9) are utilized. We need a fundamental fund to et up and exploit e ESS in power ytem and i fund i contrained by Eq. (2) and a a reult, e microgrid ize i limited [29]... Reliability contraint The reliability i determined in term of LOLE. Equation (2) expree e amount of load curtailment in each time and cenario. In e event of load decreae, i equal to. Uing e load curtailment index, poibility of load curtailment cenario in LOLE i conidered in Eq. (22). A conidered in Eq. (2), LOLE predicated value need to be more an e acquired LOLE value at every year.

5 Journal of Operation and Automation in ower Engineering, Vol., No. 2, Dec LS M. w (2) N S NT N h LOLE w LOLE (22) t h T arget (2) LOLE.4. Demand repone program DR include many variou feature and program inide itelf and time-of-ue (TOU) rate of DR ha been ued for optimal ESS izing problem in e propoed paper []. The main reaon of DR employment i to hift ome amount of load from expenive period to e cheaper period to flatten e load curve and reduce e operation cot. Baed on TOU, ome percentage of load can be hifted from peak period off-peak period. Thi entence i maematically expreed by Eq. (24). DR, D, TOU, (24) In e Eq. (24), new load wi conidering TOU i equal to e primary load plu e variable. Thi variable can be eier poitive or negative which mean load increae and load decreae, repectively. In oer word, due to appearance of intelligent network technology, ome amount of load can be tranferred from peak period to off-peak period which i modeled in Eq. (24). DR include ome technical limitation which are preented by Eq. (2) and (26). TOU, DR D, (2) Nh TOU, (26) h It can be undertood from Eq. (2) at e amount of increaing or decreaing load in TOU program hould not exceed e bae load. In e propoed paper, e imum amount of increaed and decreaed load ha been conidered to be 2 %. Furermore, Eq. (26) expree at e load i fixed and it i only tranferred from peak period to off-peak period. It mean at e amount of increaing and decreaing load during a day hould be equal... rice and demand uncertainty model In order to model uncertainty of demand and price, e forecat error ditribution curve are divided into ome interval wi e wid of one tandard deviation. In uncertainty modeling e input are e value ued for price and demand in determinitic olution. The percentage of increae or decreae for price and demand i conidered to be %. Fig. how a ample dicrete form of e predication error probability ditribution function. It i eential at for every available cenario 2 value be computed:. By integrating e area below e probability ditribution curve in every period, each cenario probability can be achieved. 2. The realized prediction error in each relevant cenario i conidered to be e average amount of period. Table how e amount and it probability in each relevant cenario..6. Scenario reduction Utilizing cenario production technique, many variou cenario are acquired. Due to larger ize of obtained cenario, e problem will be complicated and it will take much more time to be olved. So, e number of cenario hould be decreaed. In i paper, e mot common probability ditance ued in tochatic optimization i e Kantorovich ditance [], DK(.),defined between two probability ditribution Q and Q by Eq. (28), where c(, )i a non-negative, continuou, ymmetric cot function and e infimum i taken over all joint probability ditribution defined on Ώ Ώ. Fig.. robability ditribution function for uncertainty parameter. Table. The caption mut be hown before e Table. Scenario number Value of each relevant cenario probability of each relevant cenario S S2. S S4. S Dk ( Q, Q ) inf c(, ) ( d, d ) : (., d ) Q, ( d,.) Q (27) T c(, ) (28) The utilized meod to reduce e number of cenario i e fat-forward algorim []. It can be een from reported reult in [] at e utilized technique i a popular and particle approach. 4. ROBLEM FORMULATION In order to how e efficiency of e propoed model,

6 M. Majidi, S. Nojavan, K. Zare : Optimal Sizing of Energy Storage Sytem in A Renewable-Baed Microgrid 2 different cae are evaluated to how e effect of reliability criterion and DR on optimal izing of ESS in a ample microgrid. 4.. Input data A microgrid i invetigated to how e effectivene of propoed approach. A expreed in Table 2, i microgrid include one wind turbine a well a four ermal generation unit. ESS include power invetment cot of 4 $/MW/year and energy invetment cot of $/MWh/year. It i been conidered at a MW line connect microgrid to e main grid and contrain e power exchange between e uptream grid and microgrid. In order to imulate component interruption, wind peed and e microgrid load, cenario are created. To reduce e time ued for calculation becaue of e problem complication and it coniderable ize, by applying e cenario reduction technique, number of cenario are reduced to which probabilitie are hown in Table. It hould be noted at e microgrid peak load i equal to 7 MW and e microgrid load for ample day in pring, ummer, autumn, and winter i preented in Figure 2. Alo e electricity price in upper grid to which e microgrid i connected i preented in Figure. Finally e wind peed in e related cenario i hown in Figure 4. We can ue e cenario and equation (4) to calculate e output power of wind turbine which i hown in Figure. The microgrid load i conidered contant for e upcoming year and en e whole cheme are jut conidered for one year. The reliability criterion i conidered. day/year by microgrid operator which hould be atified. The propoed approach wa carried on a 2.4-GHz C utilizing CLEX. in GAMS optimization package [2]. Fig. 2. Load profile for a day in pring, ummer, autumn and winter. Fig.. Forecated electricity price in upper market. Unit no. 2 4 Unit no. 2 4 Table 2. Characteritic of generating unit. Bu no. Cot coefficient ($/MWh) Min capacity (MW) Max capacity (MW) Ga 27.7 Ga 9. Ga Ga Wind Min. up time(h) Min. down time(h) Ramp up (MW/h) Ramp down (MW/h) Fig. 4. Wind peed in ree cenario. Table. robabilitie of reduced cenario. Scenario 2 4 robability Fig.. Output power of wind turbine in ree cenario.

7 Journal of Operation and Automation in ower Engineering, Vol., No. 2, Dec Reult of imulation in different cae In order to how e efficiency of propoed model, e following item have been conidered and e reult are preented to compare different cae: Cae : bae cae wiout ESS. Cae 2: e cae to which an ESS, MWh wi rated power of MW i added. Cae : evaluating e optimal ize for ESS to be intalled in cae 2. Cae 4: evaluating e impact of reliability criterion on cae, 2,. Cae : evaluating e impact of DR on cae, 2, Cae : bae cae wiout ESS. In e cae, 2 and, it i aumed at e expected reliability criterion from point of microgrid operator i equal to. day/year. Alo in cae, it i aumed at e microgrid i planned wiout ESS conideration. The reult related to i cae are ummarized in Table 4. The total cot of microgrid i 2,48,2 $. The total cot of production i 4,6,79 $ and e cot of purchaed power i,844 $. When e generation of ESS i more an e microgrid demand, e produced power can be old to e upper grid which reult in economic benefit of 2,26,4 $ for e microgrid. Unit i e bae unit which operate a full-time unit but whenever unit i not able to upply e microgrid load and it i not able to import power from e upper grid due to high price at e power purchaed from upper grid ha, oer unit will be committed. Microgrid will import power from upper grid if e offered electricity by e main network ha low price and if e electricity price i high, e power produced by e local unit inide e microgrid will upply e load and e extra produced energy will be exported to e upper grid. In i cae e amount of LOLE i equal to. day/year and e amount of unupplied energy i equal to 8.46 MWh Cae 2: adding an ESS, MWh wi rated power of MW to cae In cae 2, an ESS wi predefined characteritic i added to e microgrid. The added ESS i a MWh ESS wi rated power of MW. It take hour for ESS to be completely charged and attain e imum SOC. The reult of i cae are ummarized in Table 4. After ESS intallation, e expene related to energy procurement from e upper grid would be equal to 2,8 $ and alo e cot related to e production of local unit would be 4,6,79 $. The ESS can ave 2,67,26 $ by exporting power to e upper grid. It hould be mentioned at e cot of ESS intallation i equal to 9, $. Comparing e obtained reult, we can conclude at e total cot related to e microgrid operation i equal to 2,,777 $. So it can be concluded at total cot ha.68% reduction in comparion wi cae which i mainly becaue of power export to e upper grid. ESS i charged at e time electricity price i low like off-peak hour and it i dicharged at e time electricity price i high like peak hour. In peak hour, ESS can ue e aved energy to upply e load and gain profit by exporting it to e upper grid. In i cae e amount of expected LOLE i equal to. day/year and e amount of unupplied energy expected to have i equal to 7.2 MWh. Table 4. Comparion of ummarized reult related to cae, 2 and. Different parameter Cae Cae 2 Cae ESS rated power (MW) ESS rated energy (MWh) Expected unupplied energy (MWh) ESS invetment cot ($) Microgrid generation cot ($) Import cot ($) Benefit from export to grid ($) Total cot ($) Total cot reduction (%) ,6,79,844 2,26,4 2,48, , 4,6,79 2,8 2,67,26 2,, % , 4,6,79 49,9 2,84,4 2,2,. % Cae : evaluating e optimal ize for ESS to be intalled in cae 2. In i cae, optimal ize of ESS i determined baed on e propoed model. So, e calculated optimal ize for ESS to be intalled in e microgrid i a MWh ESS wi rated power of 2.6 MW. The total cot related to e microgrid operation i equal to 2,2, $, which incorporate 4,6,79 $ total production cot, 49,9 $ overall expene related to power purchae, 247, $ primary fund to be inveted for ESS intallation and 2,84,4 $ gained from elling energy to e upper grid. Comparing e reult obtained in cae wi e one obtained in cae, it can be concluded at e total cot in cae ha.% reduction. ESS i charged at e time electricity price i low (off-peak hour) and it i dicharged at e time electricity price i high (peak hour). In i cae, e amount of expected LOLE i equal to. day/year and e amount of expected unupplied energy i equal to 7.2 MWh. The reult related to e cae, 2 and are ummarized in Table Cae 4: evaluating e impact of reliability criterion on cae, 2 and. The calculated optimal ize of ESS in cae can atify e microgrid reliability criterion contraint. Conidering e LOLE. day/year and.2 day/year compared to. day/year in cae, 2 and, ESS planning will have a little change to atify e microgrid reliability criterion.

8 M. Majidi, S. Nojavan, K. Zare : Optimal Sizing of Energy Storage Sytem in A Renewable-Baed Microgrid 22 Conidering ree different value for e microgrid expected reliability criterion, it impact on cae, 2 and ha been preented in Table. It can be undertood from Table at le reliability criterion (le outage) can increae microgrid total cot. Thi i becaue of at due to le outage, e microgrid load epecially in peak load hour hould be upplied from market wi higher price or from e unit inide e microgrid which generation cot i high evaluating e impact of DR on Cae, 2 and. In i cae e effect of DR on microgrid development planning and microgrid total cot i tudied. It hould be noted at in order to only tudy e effect of DR, e reliability criterion i fixed to be / day/year. Some percentage of load can be tranferred from peak time period to oer time period during day. Microgrid operator i reponible for imum adjutment of i percentage of load.then comparion reult of cae for different condition are preented in Table 6. It can be een from Table 6 at if e imum ability of load hifting increae, total microgrid cot during planning period will be reduced. The reaon of microgrid total cot reduction i hifting ome percentage of load from peak period to off-peak period which lead to load profile flattening during e day and minimization of operating cot of microgrid. 4.. Comparion of reult and dicuion and it evaluation By decreaing e load hedding, ESS increae e microgrid reliability criterion and improve e microgrid economic performance by producing power in high price hour and toring it at e time in which e electricity price i low. Furermore, implementation of DR in e microgrid lead to microgrid total cot reduction and control e microgrid extra invetment cot to provide peak load.. CONCLUSION In order to find e optimal ize of ESS, an exact and efficient olution i preented in i paper conidering reliability criterion and DR. To conider e cot and expene related to microgrid operation and ESS invetment, development planning problem wa utilized. The reliability criterion i computed in a way at dependable operation of microgrid would be guaranteed by fulfilling reliability criterion. Alo e impact of DR on total microgrid cot and optimal izing of ESS i invetigated. In order to compute e reliability criterion in e propoed problem which lead to efficient reliability evaluation of e microgrid, an MI formulation i preented. The obtained reult uncovered at having an appropriate reliability criterion can lead to.77 % reduction of total cot. Alo, it can be concluded at due to utilization of DR can lead to 6.9 % reduction of total cot. REFERENCES [] X. Zhang, J. Bao, R. Wang, C. Zheng, and M. Skylla- Kazaco, Diipativity baed ditributed economic model predictive control for reidential microgrid wi renewable energy generation and battery energy torage," Renewable Energy, vol., pp. 8-4,27. Table. Comparion of ummarized reult related to cae 4. Reliability criterion. day/year. day/year.2 day/year Different parameter Cae Cae 2 Cae Cae Cae 2 Cae Cae Cae 2 Cae ESS rated power (MW) ESS rated energy (MWh) Expected unupplied energy(mwh) ESS invetment cot($) 9, 247, 9, 247, 9, 247, Generation cot($) ,6,79 4,6,79 4,6, Import cot($) ,844 2,8 49, Benefit from export($) ,26,4 2,67,26 2,84, Total cot($) ,48,2 2,,777 2,2, Reduction cot (%) -.26 %.7 %.8 %.68 %. %.2 %..77 % DR Different cae ESS rated power (MW) ESS rated energy (MWh) Expected unupplied energy(mwh) ESS invetment Cot ($) Microgrid generation Cot ($) Import cot ($) Benefit from export ($) Total cot ($) Reduction cot (%) Table 6. Comparion of ummarized reult related to cae. DR DR. Cae Cae 2 Cae Cae Cae 2 Cae , 247, 9, 247, 4,6,79 4,6,79 4,6, ,844 2,8 49, ,26,4 2,67,26 2,84, ,48,2 2,,777 2,2, %. % 8. % 8.67 % 9.2 % DR.2 Cae Cae , % 6.6 % Cae , %

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