EFFECTS OF BATTERY ENERGY STORAGE SYSTEM ON THE OPERATING SCHEDULE OF A RENEWABLE ENERGY BASED TOU RATE INDUSTRIAL USER UNDER COMPETITIVE ENVIRONMENT

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1 Journal of Marne Scence and Technology, Vol. 23, No. 4, pp (2015) 541 DOI: /JMST EFFECTS OF BATTERY ENERGY STORAGE SYSTEM ON THE OERATING SCHEDULE OF A RENEWABLE ENERGY BASED TOU RATE IUSTRIAL USER UER COMETITIVE ENVIRONMENT Chun-Lung Chen 1, Yu-Lang Ln 2, and Wen-Yu Fu 1 Key words: smart grd, tme-of-use, renewable energy sources, battery energy storage system, partcle swarm optmzaton, ntellgent partcle swarm optmzaton. ABSTRACT Wth ncreasng development of smart grd and restructurng of the power ndustry, the problem of operatng schedule for a tme-of-use (TOU) rate ndustral customer may become a more mportant ssue due to the ncluson of the varatons n the TOU rate structures. Ths paper develops a new algorthm, named ntellgent partcle swarm optmzaton (INSO), to solve the operatng schedule of a renewable energy based TOU rate ndustral user wth battery energy storage system (BESS). Addng another partcle best (best ap ) tem wth a dversty based judgment mechansm, proposed INSO algorthm can gve a good drecton to enhance ts search capacty that leads to a hgher probablty of obtanng the global optmal soluton. A TOU rate ndustral customer of Tawan ower Company (TC) s used as an example to valdate the feasblty of the INSO algorthm for the applcaton consdered. Numercal experments are ncluded to understand how varatons n the rate structures on the optmal operaton of the TOU rate customer system. The computer program developed n ths paper can also be a power tool for TOU rate ndustral users to evaluate the economc benefts of the renewable energy sources (RES) and BESS. I. INTRODUCTION In the comng decades, global envronmental ssues could aper submtted 03/17/14; revsed 05/01/15; accepted 05/21/15. Author for correspondence: Chun-Lung Chen (e-mal: cclung@mal.ntou.edu.tw). 1 Department of Marne Engneerng, Natonal Tawan Ocean Unversty, Keelung, Tawan, R.O.C. 2 Engneerng Dvson, Tape Broadcastng Staton, Tape Cty Government. sgnfcantly affect energy usage patterns around the world. Renewable energy sources wll become more mportant due to a lack of fossl fuels, the need to control atmospherc emssons, and envronmental concerns. The government n Tawan has commssoned research on renewable energy applcatons under the consderaton of dversfyng energy sources (Chen et al., 2008; Chen et al., 2010). Among varous renewable energy sources n Tawan, wnd energy systems (WES) and photovoltac generaton systems (VGS) are promsng alternatves for power generaton because of ther tremendous envronmental and socal benefts (Chang et al., 2003; Lee and Chen, 2009; Wu et al., 2011). Varous WES and VGS based hybrd power systems have receved wdespread attentons and applcatons, and are wdely nstalled or undergong testng n the tme-of-use (TOU) rate ndustral users. However, more and more renewable energy sources wll penetrate nto the grd, whch brngs challenges to operatng schedule problem for a hybrd power system due to the ntermttency and unpredctablty of renewable power generaton (avlos, 2006; Chen, 2007; Jabr and al, 2009). In the future market-based mcro grd system, t can be expected that many problems wll also arse n the renewable energy based TOU rate ndustral user due to the ncluson of the varatons n the TOU rate structures, partcularly n system operaton and plannng (Lee and Chen, 2009; Hopkns et al., 2012; Huang et al., 2012; Mozafar et al., 2012). Recently, both customers and electrc utlty companes have experenced ncreasng costs n energy and electrc power due to the escalatng cost of burnng fuels and captal costs for buldng new generaton unts. Load management changes the shape of the load curve so that generaton of costly peak unts or capacty addtons are avoded or deferred and s an effectve soluton to the above problem. The TOU rate s a load management polcy desgned to shft electrcty use from peak load perods to lght load perods. Ths shft abates the reurements of new constructon projects and rases the overall effcency of the power system. For some heavy load TOU

2 542 Journal of Marne Scence and Technology, Vol. 23, No. 4 (2015) Table 1. Three-secton TOU electrcty rate structures of TC for hgh-voltage power servce. Energy Charge (NT$/kWh) Classfcaton eak load hours (LH) (10:00-12:00 and 13:00-17:00) Medum load hours (MLH) (The hours other than LH and LLH) Lght load hours (LLH) (00:00-07:30 and 22:00-24:00) Summer Season Non-summer Season rate users, electrcty charges play a promnent role n producton costs. In order to mnmze the total electrcty charge of a TOU rates customer, many dfferent energy storage devces, such as refrgeraton storage (RS), compressed ar energy storage (CAES), battery energy storage system (BESS), etc., have been nvestgated. Among them, the BESS s one of the most promsng technologes to reduce the cost of electrcty for TOU rate users (Lee and Chen, 1994; Lee, 2007). The mportance of the schedulng problem of BESS s, thus, lke to ncrease, and more advanced algorthms are worth developng to reach the mnmum electrcty charge of a renewable based TOU rate ndustral customer. Wth ncreasng of the development of smart grd and restructurng of the power ndustry, the effcent operaton schedule for a TOU rate ndustral user may become a more mportant problem. Several new mplcatons wll arse wth respect to the tradtonal concepts of the TOU rate structure of the TC system under compettve envronment. Table 1 shows the prcng structure for hgh-voltage three-secton TOU rates customers n the TC system (Lee and Chen, 2009). The TOU rate for electrcal power s the practce of mplementng dfferent prces for dfferent tmes of use. The TC confrms power peak and power valley tmes, and then adopts hgher prces durng the peak tme and lower prces durng the valley tme. But n a market-based mcro grd system, the TOU rate structure may be changed day-to-day bass by the TC to further rase the overall effcency of the power system. The TOU rate ndustral user can adjust ther operaton polcy on usng electrcty power wth proft motves. In addton, the sold/purchased power from the utlty grd s also another characterstc n a compettve electrcal market. Ths mechansm can sgnfcantly reduce the electrcty charges, ncrease the economc benefts of energy generated by BESS and RES. Therefore, the problem of effcent operaton of BESS for a renewable based TOU rate ndustral customer under compettve envronment may lead to be more complex than the conventonal dspatch. Several optmzaton algorthms based on classcal calculus based technues or stochastc searchng technues (Lee and Chen, 1994; Bakrtzs and etrds, 1996; Juste et al., 1999; Wood and Wollenberg, 1996; Lee, 2007; Gang, 2013), ncludng prorty lst (L), mult-pass dynamc programmng (MD), evolutonary programmng (E), genetc algorthm (GA), and partcle swarm optmzaton (SO), could be used to solve the extended generaton schedulng problem. Among all, the SO approach s of partcular nterest because of ts ablty to generate a hgh-ualty soluton wthn shorter computaton tme and exhbt a more stable convergence characterstc than other stochastc methods. Ths paper proposes an mproved algorthm, modfed from SO (Kennedy and Eberhart, 1995; Sh and Eberhart, 1998), to solve the operatng schedule of BESS for a renewable energy based TOU rate ndustral user. A new ndex called another partcle best (best ap ) s ncorporated nto SO to provde parts of nformaton gudng to the global soluton and gve addtonal exploraton capacty to swarm. A novel dversty based judgment mechansm for the evaluaton of best ap behavor s also proposed to enhance ts search capacty that leads to a hgher probablty of obtanng the global optmal soluton. Test results are provded to llustrate the merts of the proposed method and to gve a good ndcator to nstall a BESS n a renewable energy based TOU rate ndustral user. II. ROBLEM FORMULATION 1. Notaton The followng notaton s used throughout the paper. a, b, C f : cost coeffcents of desel unt C E (t) : tarff of the purchased power at hour t (NT$/kWh) C SE (t) : tarff of the sold power at hour t (NT$/ kwh) CS 1,t : penalty functon for E. (8) at hour t CS 2,t : penalty functon for E. (10) at hour t CS 3,t : penalty functon for E. (18) at hour t CS 4,t : penalty functon for E. (26) at hour t d % : percentage of mum desel unt capacty FC(t) : desel fuel cost at hour t F () : operaton cost functon of desel unt F E (t) : cost of the purchased power at hour t (NT$/h) F SE (t) : ncome of the sold power at hour t (NT$/h) : ndex for desel unts j : ndex for non-dspatchable unts : number of desel unts n system D (t) : system load demand at hour t (t) : power output of desel unt at hour t : mum generaton lmt of desel unt mn : mnmum generaton lmt of desel unt bat (t) : power output of battery at hour t (postve for dschargng and negatve for chargng) bat : mum power output of battery grd (t) : power output of utlty grd at hour t (pos-

3 C.-L. Chen et al.: Operatng Schedule of BESS n TOU Rate Industral Users 543 tve for purchased and negatve for sold) grd : mum power output of utlty grd SB (t) : the nverter output at hour t Wj : upper generaton lmt of wnd unt j * Wj () t : avalable generaton of wnd unt j at hour t Wj (t) : actual generaton of wnd unt j at hour t WT (t) : total actual wnd power generaton at hour t Vj : upper generaton lmt of solar unt j * Vj () t : avalable generaton of solar unt j at hour t Vj (t) : actual generaton of solar unt j at hour t V (t) : total actual solar power generaton at hour t r 1 % : percentage of load demand r 2 % : percentage of actual renewable power generaton r b : penalty factor rand(-1,1) : unform random value n the range [-1,1]. rand(0,1) : unform random value n the range [0,1]. SD : mnmum solar radaton ntensty SU : mum solar radaton ntensty S r (t) : solar radaton ntensty at hour t SOC(t) : energy left n the battery at hour t SOC mn : mnmum energy left n the battery SOC : mum energy left n the battery T : number of tme ntervals (hours) t : ndex for tme ntervals (hours) TC : total electrcty charge T r (t) US (t), DS (t) : ambent temperature at solar n hour t : up/down reserve contrbuton of desel unt at hour t US, DS : mum up/down reserve contrbuton of desel unt US bat (t), DS bat (t) : up/down reserve contrbuton of battery at hour t USbat, DS bat : mum up/down reserve contrbuton of battery US grd (t), DS grd (t) : up/down reserve contrbuton of utlty grd at hour t USgrd, DS grd : mum up down reserve contrbuton of utlty grd USR(t), DSR(t) : system up/down reserve reurement at hour t v(t) : wnd speed at hour t v Ij : cut n wnd speed of wnd unt j v Rj : rated wnd speed of wnd unt j v Oj : cut out wnd speed of wnd unt j j () : wnd power curve of wnd unt j j () : solar radaton/ ambent temperature power curve of solar unt j B : battery effcency durng the chargng/ dschargng perod t : tme nterval Solar V V BESS V DC/DC Converter bat Desel Generators Wnd ark DC Bus D A DC/AC Converter WT SB D AC Bus grd Fg. 1. TOU rate customer system. Load Utlty Grd 2. Formulaton The objectve of the algorthm presented n ths paper s to reach the mnmum electrcty charge whle satsfyng all operatonal constrants n a renewable energy based TOU rates ndustral customer wth a BESS. The operaton polcy on usng electrcty power for the TOU rate ndustral user can be adjusted properly accordng to the modfed TC rate structures n the future market-based mcro grd system. Fg. 1 shows a typcal ndustral customer n the TC system. The components of the system nclude desel generators, wnd farm, solar V array, BESS and utlty grd. Utlzed extra spnnng reserves for the ntermttency and unpredctablty of renewable power generaton are consdered to ensure the securty and relablty of a power system (Chen, 2007). The mathematcal model of the generatng schedulng problem can be stated as follows. Objectve functon: F E T Mnmze TC { FC( t) FE ( t) FSE ( t)} (1) 1 FC() t F( t ()) (2) 1 ( ( )) f ( ( ) ) F t C a t b (3) ( D( t) ( t) WT( t) SB( t)) CE( t), f grd( t) 0 () t 1 0, otherwse ( ( t () WT() t SB() td()) t CSE(), t fgrd() t 0 FSE () t 1 0, otherwse (4) (5)

4 544 Journal of Marne Scence and Technology, Vol. 23, No. 4 (2015) subject to the followng constrants. 1) System Constrants ower balance constrant t () WT() tgrd() tsb() t D() t (6) 1 0 S ( t) SD r * Vj j r r r Vj r () () t ( S (), t T ()) t SD S () t SU 4) Battery Constrants Charge/dscharge power lmts S t SU (17) System up spnnng reserve constrants USR() t r % () t r % [ () t ()] t (7) 1 D 2 V WT State of charge lmts () t (18) bat bat bat US () t USgrd () t USbat () t USR() t (8) 1 System down spnnng reserve constrants DSR() t r % () t r % [ () t ()] t (9) 1 1 D 2 V WT DS() t DSgrd() t DSbat() t DSR() t (10) 2) Desel Unt Constrants Unt generaton lmts () t (11) mn Unt s mum up/down reserve contrbuton constrants US d% (12) DS d% (13) Unt s up/down reserve contrbuton constrants US t US t (14) () mn, () DS t DS t (15) mn () mn, () 3) Non-Dspatchable Unt Constrants Wnd power curve constrants 0 vt ( ) v orvt ( ) v Ij * Wj () j ( ()) Ij () Rj Wj vrj v() t voj t v t v v t v Oj (16) Solar radaton/ambent temperature power curve constrants SOC SOC() t SOC (19) mn SOC(0) SOCS ntal state of charge (20) SOC( T ) SOCE fnal state of charge (21) State of charge balance euaton SOC() t SOC( t 1) () t t f () t 0 (22) bat B bat t SOC() t SOC( t 1) bat () t f bat () t 0 (23) Battery s up/down spnnng reserve contrbuton constrants SOC() t SOCmn USbat () t mn bat bat (), t bat, B t (24) DS t t SOC SOC() t bat () mn bat bat (), bat, B t 5) Utlty Grd Constrants Sold/purchased power lmts grd grd grd B (25) () t (26) Grd s up/down spnnng reserve contrbuton constrants US t US t (27) grd () mn grd, grd grd () DS t DS t (28) grd () mn grd, grd () grd III. INTELLIGENT ARTICLE SWARM OTIMIZATION (INSO) SO s a populaton-based optmzaton approach frst

5 C.-L. Chen et al.: Operatng Schedule of BESS n TOU Rate Industral Users 545 proposed by Kennedy and Eberhart n 1995 (Kennedy and Eberhart, 1995). In a physcal N-dmensonal search space, the poston and velocty of partcle are represented as the X x, x,..., x V v, v,..., v n vectors 1 2 N and 1 2 N best best best the SO algorthm. Let best x1, x2,..., xn Gbest x1, Gbest, x2, Gbest,..., xn, Gbest and be the best poston of partcle and the best poston that has been acheved so far by any partcles, respectvely. By trackng two best values,.e. best and Gbest, the global optmal mght be reached by ths optmzaton technue. In the tradtonal SO, t should be noted that the socal behavor models the memory of the partcle (fsh) about the best poston among the partcles (the experence of ts neghbors; Gbest). However, t s not reasonable for socal behavor to only employ the Gbest whch s not normally the global optmal soluton, contanng parts of non-optmal nformaton. The nfluence of socal behavor to the next movement of the fsh (partcle) often s affected not only by the locaton of the fsh (partcle) whch s n the best poston of all, but also by the locaton of the fsh (partcle) whch t randomly looked at when fsh schools start lookng for food. To ncrease the possblty of explorng the search space where the global optmal soluton exsts, we follow a slghtly dfferent approach about the socal behavor to further provde a selecton of the global best gude of the partcle swarm. The socal behavor conssts of two phases, the best partcle poston ever obtaned (Gbest) and the random another partcle best poston (best ap ), namely, another behavor. After ncreasng another behavor to the socal behavor, the best ap provdes parts of nformaton gudng to the global soluton and gves addtonal exploraton capacty to swarm. The new velocty update euaton s gven by: V V c1 rand ( best X ) c2 k 1 k k k k k rand ( Gbest X ) c3 rand k k ( besta p X ) 1, 2,..., Q; ap (29) k where c1 and c2 represent the weghtng of the stochastc acceleraton terms that pull each partcle toward best and Gbest postons, rand means a random varable between 0.0 to 1.0, and s the nerta weght factor. best ap = x best 1, best 2,..., best ap xap x apn s the best poston of a random another partcle, called partcle ap. c3 = {c3 1, c3 2,..., c3 N } s the weght factor of another behavor. In general, the ntal canddate solutons are usually far from the global optmum and hence the larger c3 may be proved to be benefcal to explore globally. However, the dfference of global best gude between Gbest and best ap s gradually decreasng durng successve teratons. Therefore, the value of c3 wll be employed the lnearly decreasng and s calculated usng the followng expresson. ter c3 c3 ( c3 c3 mn ) ter = 1, 2, Q; = 1, 2, N (30) where c3 and c3 mn are the ntal and fnal weghts, respectvely, ter s the mum teraton count, and ter s the current number of teratons. Addng the best ap tem n SO, t makes the search space much effectvely and has a good robustness. However, the nformaton gudng to the global soluton from the random another partcle best poston (best ap ) may contan n the best partcle poston ever obtaned, Gbest. The random another partcle best poston cannot normally present a postve gudance. For mantanng populaton dversty, an ntellgent judgment mechansm for the evaluaton of the best ap behavor s developed to gve a good drecton to dentfy the near global regon. The new velocty and poston of each partcle can be calculated as shown n the followng formulas. V V c1 rand ( best X ) c2 k 1 k k k k k k rand ( Gbest X ) c3 rand k k k k k k ( bestap X ), f ( xgbe st x ) ( xap x ) 0 (31) V V c1 rand ( best X ) c2 k 1 k k k rand Gbest X c rand best X k k k k k ( ) 3 ( ap ), k k k k f ( x x ) ( x x ) 0 (32) Gbest ap X X V 1, 2,..., Q; ap (33) k1 k k1 As shown n (31) and (32), the novel dversty-based judgment mechansm for evaluatng best ap behavor s used to mantan populaton dversty, whch facltates dentfcaton of the near-global regon. The weght factor c3 mantans a wde spread of nondomnated solutons. From (31), f (x k ap x k ) and (x k Gbest x k ) move n the same drecton, the nformaton gudng to the global soluton from best ap and Gbest s smlar. Compared wth Gbest, x k ap s a bad poston, and the nfluence of partcle ap to the movement of partcle s negatve. Conversely, the nformaton gudng to the global soluton from best ap and Gbest dffers largely f (x k Gbest x k ) and (x k ap x k ) do not move n the same drecton. As shown n (32), the nfluence of partcle ap on the movement of partcle s postve. The most attractve feature of the ntellgent judgment mechansm for evaluatng the aforementoned best ap

6 546 Journal of Marne Scence and Technology, Vol. 23, No. 4 (2015) behavor s ts ablty to mantan populaton dversty, whch ncreases the possblty of escapng local optmal soluton traps. IV. SOLUTION METHOD A IMLEMENTATION OF INSO The man computatonal processes of the algorthm presented n ths paper to solve the operatng schedule problem of a TOU rate ndustral user are dscussed n the followng steps. Ths algorthm s an mplementaton of INSO. Step 1: Intalze the INSO parameters. Set up the set of parameters of INSO, such as number of partcles Q, weghtng factors, c1, c2, c3, c3 mn, and mum number of teratons ter. Step 2: Calculate the avalable output of RES. The avalable renewable power generaton can be obtaned from the wnd speed, solar radaton ntensty, and ambent temperature by applyng Es. (16) and (17). Once the amount of actual renewable power generaton s determned, the system up/down spnnng reserve reurements can be calculated properly by applyng Es. (7) and (9). Step 3: Generate the energy stored n a BESS randomly. Electrcal energy stored n the BESS s used as the state varable n the study. The ntal energy stored of BESS s generated randomly by Es. (34)-(36). Once the amount of SOC(t) s determned, the charge/dscharge power output ( bat (t)) of BESS can be calculated properly by applyng state of charge balance euatons (Es. (22) and (23)). Charge balance euaton SOC t SOC t rand t () ( 1) ( 1,1) bat B f rand( 1,1) 0 (34) Dscharge balance euaton SOC() t SOC( t 1) rand( 1,1) bat t f rand( 1,1) 0 (35) SOC f SOC( t) SOC SOC() t SOCmn f SOC( t) SOC t = 1, 2,, T (36) Step 4: Generate the ntal power outputs of desel generatng unts randomly. The ntal power outputs of desel generatng unts are mn B generated randomly by Es. (37) and (38). () t rand(0,1) t = 1, 2,, T (37) t ( ) f t ( ) t () mn mn 0 f ( t) t = 1,2,, T (38) Step 5: Create an ntal populaton randomly. Each partcle contans the energy stored n the BESS at every tme stage, and the real power generaton of desel generators. E. (39) shows a partcle. The ntal power outputs of BESS and desel generatng unts are generated randomly by Step 3-4. To satsfy the power balance euaton, the output of grd generaton grd (t) s determned by applyng E. (40). SOC(1) SOC(2) SOC( T ) 1(1) 1(2) 1( T) k X 2(1) 2(2) 2( T), = 1, 2,, Q (39) (1) (2) ( T) () t () t () t () t () t t = 1, 2,, T (40) grd D SB WT 1 Step 6: Evaluate the ftness of the partcles. For each partcle, calculate the value of the ftness functon. The ftness functon s an ndex to evaluate the ftness of the partcles. E. (1) shows the ftness functon of the generatng schedulng problem. To account for up-reserve reurement volatons (8), down-reserve reurement volatons (10), battery s charge/dscharge power lmt volatons (18), and sold/ purchased power lmt volatons (26), the total electrcty charge s augmented by nonnegatve penalty terms CS 1,t, CS 2,t, CS 3,t, and CS 4,t, respectvely, penalzng constrant volatons. Thus, the augmented cost functon TC s formed 4 A b b, t b1 t1 T ( ) TC TC r CS (41) The penalty factor (r b ) s selected as 1.0E5. The penalty terms (CS 1,t - CS 4,t ) are proportonal to the correspondng volatons and zero n case of no volaton. There are chosen hgh enough as to make constrant volatons prohbtve n the fnal soluton. Step 7: Record and update the best values. The two best values are recorded n the searchng process. Each partcle keeps track of ts coordnate n the soluton space that s assocated wth the best soluton t has reached so far. Ths value s recorded as best. Another best value to be recorded s Gbest, whch s the overall best value obtaned so far by any partcle.

7 C.-L. Chen et al.: Operatng Schedule of BESS n TOU Rate Industral Users 547 ower (kw) Wnd V Tme (hour) Fg. 2. Avalable generaton of RES for a typcal day n the summer season. Step 8: Update the velocty and poston of the partcles. Es. (31) and (32) are appled to update the veloctes of partcles. The velocty of a partcle represents a movement of the elements n a partcle. E. (33) s appled to update the poston of the partcles. The poston of a partcle s defned n E. (39). Step 9: End condtons. Check the end condton. If t s reached, the algorthm stops, otherwse, repeat steps 6-8 untl the end condtons are satsfed. In ths study, the end condtons of INSO are (1) The total operatng cost between two consecutve teratons s unchanged or the varaton of operatng cost s wthn a permtted range. (2) The varaton of Gbest s wthn a permtted range. (3) The mum number of teratons s reached. V. NUMERICAL EXAMLES To show the applcablty and effectveness of the proposed algorthm, an ndustral customer n the TC System s used as an example. Fg. 1 shows the TOU rate customer system whch ncludes wnd farm, solar V array, BESS, desel generators and utlty grd. The wnd farm ncludes two wnd turbne generators (WTGs) and the total capacty of wnd power nstalled s 40 kw. The capacty of solar-v models s 37.8 kw. The avalable renewable power generaton for all tme perods, whch can be calculated from the Es. (16) and (17), s gven n Fg. 2. A smulated BESS wth a capacty of 180 kwh/30 kw s ntegrated nto ths system. The mnmum SOC s lmted to 20% and the chargng/dschargng effcency s 0.9. The ntal/end state of charge s set to be 120 kwh. There are three dentcal desel generators. The generaton cost coeffcents of a desel generator are a = 0.264, b = , and C f = 26.0 NT$/lter. The mum and mnmum generaton lmts of a desel generator are 100 kw and 40 kw, respectvely. As llustrated n Fg. 1, the solar-v modules and BESS are connected to a step-up transformer va an nverter. The effcency of the nverter s The mnmum and mum loads for the study perod of 24 h are 125 kw and 250 kw, respectvely. Ignorng the renewable power generaton, the system emergency up/down reserve s assumed to be 20% of the forecasted load n the comng hours (r 1 % = 20%). In ths study, the ncreased up/down spnnng reserve reurement s calculated as a smple fracton (r 2 % = 20%) of the predcted renewable power generaton to compensate for possble fluctuatons n power of the renewable sources. The mum up/down spnnng reserves of the desel generator (or utlty grd) could not contrbute more than 20 percent of ts rated capacty. Lke other stochastc methods, the proposed INSO has a number of parameters that must be selected. The solutons obtaned from the INSO largely depend on the number of partcles Q and control parameters (e.g.,, c1, c2, c3, c3 mn ). Unfortunately, the approprate selecton of these parameters justfes the prelmnary efforts reured for ther expermental determnaton. From our experence, the value of populaton sze Q s an mportant parameter of INSO and must be determned expermentally dependng sgnfcantly on the problem beng solved. The recommended value of weghtng factor s: = 0.2~0.4, c1 = 0.8~2.5, c2 = 0.8~ 2.5, c3 = 0.2~0.4, c3 mn = 0.01~0.05. In the studed cases, the parameters of INSO are selected as follows: Q = 1000, = 0.3, c1 = 1.2, c2 = 0.8, c3 = 0.4, c3 mn = 0.05, ter = All the computaton s performed on a C Intel Core(TM) 2 Quad Q GHz CU computer wth 3.49 GRAM sze, and computer programs were developed n FORTRAN. Dfferent scenaros are consdered and the studed cases are stated n detal as follows: 1. Case 1: Evaluaton of operatng polcy of the TOU rate customer system The frst study case s to llustrate the optmal operatng polcy of the next tme stage for the TOU rate customer system n real-tme applcaton. The mum purchased power of the utlty grd s assumed to be 350 kw and the prcng structures for hgh-voltage three-secton TOU rate customers n the TC system s shown n Table 1. To llustrate the effects of ncorporatng the RES and BESS nto the TOU system on the exstng generaton schedulng problem, Fg. 3 shows the electrcal energy changes n the BESS, whose power outputs are shown n Fg. 4. The results show that the BESS was fully charged durng the lght load hours (hour 1-4) when the prce of electrcty s cheaper (0.82 NT$/kWh). The BESS then dscharged durng peak load hours (hour and hour 13-17) when electrcty charges are hgh (3.45 NT$/kWh). Optmal amount of energy purchased from utlty grd durng perod can be determned by usng the proposed software. The fnal power outputs of the utlty grd durng a typcal 1-day load are also shown n Fg. 4. In ths study case, t s found that these three desel generators are shut down for all tme perods due to ther hgh prces. As a result, the functon of the ntellgent optmzer developed can shave the peak of the load curve by the output of the BESS to save on energy costs and reduce the rsk of the BESS runnng out of energy n a peakdemand reducton applcaton. The numercal results of the developed software can also be summarzed to develop expert knowledge for BESS controller desgn.

8 548 Journal of Marne Scence and Technology, Vol. 23, No. 4 (2015) SOC (kwh) ower (kw) Tme (hour) Fg. 3. Electrcal energy changes n the BESS (Case 1). 280 Grd BESS Tme (hour) Fg. 4. Outputs of BESS and Grd durng a typcal daly load (Case 1). ower (kw) Grd BESS Tme (hour) Fg. 5. Outputs of BESS and Grd durng a typcal daly load (Case 3). 2. Case 2: redcton of electrcty cost savngs from the expected new customer system To evaluate the mpact and economc benefts of the nstallaton of RES/BESS, the developed INSO software, descrbed n secton 5, s a useful tool for the TOU rate ndustral users to predct the energy cost and the cost savngs from the expected new customer systems n off-lne applcaton. Table 2 gves a good ndcator to understand the effects of the RES/BESS on the total electrcty cost savngs from the expected new customer system n the second study case. For the prevous TOU system, f RES and BESS were excluded n the system, the total electrcty cost s NT$ n case 2.1. Because of the RES/BESS ntegraton, t s necessary to update the energy flow control strateges from each component to fully explore the TOU rate customer system benefts. From Table 2. Comparson of electrcty cost savng under varous smulaton cases. Case RES BESS TC (NT$) Savng (%) 2.1 Wthout Wthout Wth Wthout % 2.3 Wth Wth % the results n Table 2, the sgnfcant benefts of electrcty cost savngs from the new TOU rate customer system are expected. As shown n case 2.2, a 7.75% electrcty cost savng s acheved when the TOU system ncludes the RES. Obvously, the nstallaton of the BESS creates a further electrcty cost savng of 10.70% n case 2.3. Numercal results gve a good ndcator to provde valuable nformaton for nstallaton of the RES and the capacty of BESS as electrcty cost savers n the TOU system. Thus, dfferent amounts of the RES/BESS can be added to the orgnal system to evaluate the sgnfcant benefts of annual electrcty cost savngs. In ths way, the economc penetraton lmt of the RES and optmal capacty of the BESS nto a gven TOU system can be determned. 3. Case 3: Further study wth hgher three-secton TOU rate structures scenaro Further study wth hgher three-secton TOU rate structures scenaro s consdered n the thrd study case to understand how varatons n the prcng structures on the operaton of the TOU customer system. In the Table 1, the energy costs of peak load, medum load and lght load perods are assumed to be 10.45, 7.00 and 2.82 NT$/kWh, respectvely. Fg. 5 shows the fnal power outputs of the utlty grd and BESS durng a typcal 1-day load. Wth the low purchasng prce (2.82 NT$/kWh) of the energy, most of load demand s provded by the utlty grd durng the off-peak load perods (hour 1-6). However, the power outputs of the utlty grd decreases very rapdly durng peak-load perods when the prces of purchased power are very hgh (10.45 NT$/kWh). Note that the average full load cost (AFLC) of a desel generator s about 8.58 NT$/kWh that s lower than the energy costs (10.45 NT$/kWh) of peak load perods. It s found to be more cost-effectve to start up two desel generators durng peak load hours (hour and hour 13-15). Swtchng on three desel generators n parallel results n operatng the unts at lower effcences compared to the two unts snce they generate a hgher percentage of power based on ther ratngs. As a result, the developed INSO software s a useful tool for the TOU rate ndustral user to mze the contrbuton of desel generators, RES and BESS for reducng the electrcty cost of grd dspatch. 4. Case 4: Evaluaton of operatng polcy consderng sold/purchased power from utlty grd In the last study case, the smulaton ncludes test runs for the prevous TOU system to descrbe the sold/purchased power from utlty grd n the future market-based mcro grd system.

9 C.-L. Chen et al.: Operatng Schedule of BESS n TOU Rate Industral Users 549 Table 3. erformance of dfferent algorthms after ten runs. Algorthms SO-IW CNSO INSO arameter settng Q = 1000; Iter = 2000; = 0.8; mn = 0.3; c1 = 1.2; c2 = 0.8 Q = 1000; Iter = 2000; = 0.3; c1 = 1.2; c2 = 0.8 c3 = 0.4; c3 mn = 0.05 Q = 1000; Iter = 2000; = 0.3; c1 = 1.2; c2 = 0.8 c3 = 0.4; c3 mn = 0.05 Average cost (NT$) Worst cost (NT$) Best cost (NT$) Average computng tme (sec) urchasng prce (NT$/kWh) Tme (hour) Fg. 6. The prces of purchased power from grd utlty at each tme perod n the Case 4. ower (kw) 250 Grd BESS Tme (hour) Fg. 7. Outputs of BESS and Grd durng a typcal daly load (Case 4). Fg. 6 shows the prces of purchased power from grd utlty at each tme perod n the case 4. The prces of sold power at each tme perod are dentcal to those of purchased power. As shown n Fg. 7, the optmal operatng schedule of customer system s very senstve to TOU rate prcng structures. In the study case, the battery capacty was not large enough to supply the load for the perod of tme. Most of load demand s provded by the utlty grd (or desel generator). Snce the low purchasng prce of the energy provded by the utlty grd, the BESS can store electrcal power durng the off-peak load perods (hour 1-4). The BESS system then dscharge rapdly durng peak-load perods (hour and hour 15-17) when the prces of sold/purchased power are very hgh. It s found that all of the three desel generators produce more power to cover the load durng peak load hours (hour 9-19) and the excess power s sold back to the utlty grd. Ths mechansm Table 4. Comparson of TC under varous Q n the case 3 by usng INSO algorthm. artcle numbers Best cost (NT$) Average cost (NT$) can sgnfcantly reduce the electrcty charges, ncrease the economc benefts of energy generated by the desel generators and provde the necessary flexblty for smoothng of renewable power generaton. Numercal results gve a good ndcator to provde valuable nformaton for TOU rate ndustral users n the future market-based mcro grd system to reach the mnmum electrcty charge. 5. Comparatve Study of SO-IW, CNSO and INSO To evaluate the performance of the proposed algorthm, several computer programs, ncludng SO-IW (artcle swarm optmzaton wth nerta weght), CNSO (SO-IW usng common another partcle behavor) and INSO (CNSO wth an ntellgent judgment mechansm), were developed to solve the case 3. Owng to the randomness of the heurstc algorthms, ther performance cannot be judged by a sngle run result. Many trals wth dfferent ntal condtons should be made to acure a useful concluson about the performance. Table 3 shows the worst cost, average cost, and best cost acheved for 10 tral runs. From the results, the superorty of the INSO and CNSO algorthms over basc SO-IW can be notced. Although multple local mnmum solutons exst n ths studed case, the proposed INSO can stll fnd a better soluton than SO-IW, by 4.37 percent euvalent to (refer to Table 3: Best cost). Furthermore, the soluton reached by the proposed INSO s also better than CNSO, by 0.39 percent euvalent to The above results demonstrated the merts of the proposed algorthm. Table 4 shows the so-

10 550 Journal of Marne Scence and Technology, Vol. 23, No. 4 (2015) luton of INSO after ten runs under dfferent partcle numbers. From ths result, the average cost of ten runs decreased when the partcle number ncreased. It s also observed that the total operaton cost s not senstve to the partcle number. In fact, several dfferent cases were studed and the results show that the fnal results of INSO are better than those of SO-IW and CNSO. The success of the proposed INSO algorthm to jump out of the local optmal soluton s, thus, confrmed. VI. CONCLUSIONS Wth ncreasng of the development of smart grd and restructurng of the power ndustry, more and more renewable energy sources wll penetrate nto the grd, whch brngs challenges to operatng schedule problem for a TOU rate ndustral user. Ths paper proposes a novel approach to solvng the operatng schedule of a renewable energy based TOU rate ndustral user usng INSO. Based on the load condton of the TOU user, the avalable renewable power generaton, and the energy left n the BESS, the proposed INSO can be used to determne the optmal operatng schedule of the TOU rate customer system, and that the results are reasonable. The results show that the algorthm can be used to determne the optmal operatng polcy of the next tme stage n real-tme applcaton. Ths functon can save on energy costs and reduce the rsk of the BESS runnng out of energy n a peak-demand reducton applcaton. In off-lne applcaton, the results from the smulaton exercse wll also be a useful tool to ad decsons regardng the captal nvestment for usng RES/BESS for the customer system. The INSO can be used to test the users system n many load condtons under dfferent seasons, summarzng test results to develop expert knowledge for BESS controller desgn. The computer program developed n ths paper can therefore be a powerful tool for TOU rate ndustral users n the future market-based mcro grd system. ACKNOWLEDGMENTS Ths work was supported by the Natonal Scence Councl, Tawan, ROC, under Grant NSC E REFERENCES Bakrtzs, A. G. and V. etrds (1996). A genetc algorthm soluton to the unt commtment problem. IEEE Trans. WRS-11, Chang, T. J., Y. T. Wu, H. Y. Hsu, C. R. Chu and C. M. Lao (2003). Assessment of wnd characterstcs and wnd turbne characterstcs n Tawan. Renewable Energy 28, Chen, C. L. (2007). Smulated annealng based optmal wnd-thermal coordnaton schedulng. IET roc.-gener. Transm. Dstrb 1, Chen, F., S. M. Lu, C. C. Wang and Y. L. Chang (2008). romoton strateges for renewable energy n Tawan. Renewable and Sustanable Energy Revews 12, Chen, F., S. M. Lu, E. Wang and K. T. Tseng (2010). Renewable energy n Tawan. Renewable and Sustanable Energy Revews 14, Gang, Z. L. (2013). artcle swarm optmzaton to solvng the economc dspatch consderng the generator constrants. IEEE Trans. on ower Systems 182, Hopkns, M. D., A. ahwa and T. Easton (2012). Intellgent dspatch for dstrbuted renewable resources. IEEE Trans. on Smart Grd. 3, Huang, C. C., M. J. Chen, Y. T. Lao and C. N. Lu (2012). DC mcrogrd operaton plannng. Internatonal Conference on Renewable Energy Research and Applcatons (ICRERA), 1-7. Jabr, R. A. and B. C. al (2009). Intermttent wnd generaton n optmal power flow dspatchng. IET Gener. Transm. Dstrb. 3, Juste, K. A., H. Kat, E. Tanaka and J. Hasegawa (1999). An evolutonary programmng soluton to the unt commtment problem. IEEE Trans. WRS- 14, Kennedy, J. and R. Eberhart (1995). artcle swarm optmzaton. roc. IEEE Internatonal Conference Neural Networks, Lee, T. Y. (2007). Operatng schedule of battery energy storage system n a tme-of-use rate ndustral user wth wnd turbne generators: a mult-pass teraton partcle swarm optmzaton approach. IEEE Trans. Energy Converson 22, Lee, T. Y. and N. Chen (1994). Effect of the battery energy storage system on the tme-of-use rates ndustral customers. IEE roc.-gener. Transm. Dstrb. 141, Lee, T. Y. and C. L. Chen (2009). Wnd-V capacty coordnaton for a tmeof-use rate ndustral user. IET Renewable ower Generaton 3, Mozafar, B., M. Bashrvand, M. Nkzad and S. Solayman (2012). A SCUCbased approach to determne tme-of-use tarffs. The 11th Internatonal Conference on Envronment and Electrcal Engneerng (EEEIC), avlos, S. G. (2006). Technue challenges assocated wth the ntegraton of wnd power nto power systems. Renewable & Sustanable Energy Revews 10, Sh, Y. and R. Eberhart (1998). A modfed partcle swarm optmzer. roc. IEEE Internatonal Conference Evolutonary Computaton, Anchorage, Alaska, Wood, A. J. and B. F Wollenberg (1996). ower Generaton Operaton and Control. 2nd ed., Wley, New York. Wu, Y. K., C. Y. Lee and G. H. Shu (2011). Tawan s frst large-scale offshore wnd farm connecton a real project case study wth a comparson of wnd turbne. IEEE Transactons on Industry Applcatons 47,