Simulation and Regulation of Market Operation in Hydro-Dominated Environment: The Yunnan Case

Transcription

1 water Artcle Smulaton and Regulaton Market Operaton n Hydro-Domnated Envronment: The Yunnan Case Fu Chen 1,2, Benx Lu 1,2, Chuntan Cheng 1,2, * and Al Mrch 3 1 Insttute Hydropower System & Hydronformatcs, Dalan Unversty Technology, Dalan 11624, Chna; chenfu@mal.dlut.edu.cn (F.C.); benxlu@dlut.edu.cn (B.L.) 2 Key Laboratory Ocean Energy Utlzaton and Energy Conservaton Mnstry Educaton, Dalan Unversty Technology, Dalan 11624, Chna 3 Department Cvl Engneerng and Center for Envronmental Resource Management, Unversty Texas at El Paso, 5 W Unversty Ave., El Paso, TX 79968, USA; amrch@utep.edu * Correspondence: ctcheng@dlut.edu.cn; Tel.: Receved: 13 July 217; Accepted: 16 August 217; Publshed: 2 August 217 Abstract: Ths paper presents an ntegrated method to obtan optmal market operaton and regulaton wth objectve reducng market prce and ncreasng electrcty consumpton n hydro-domnated electrcty markets, n whch gant cascaded hydropower facltes along dfferent rvers are man power supplers. To ths end, a comprehensve ndcator composed market prces and electrcty consumpton s proposed to evaluate stuaton hydro-domnated market operaton. Moreover, an teratve algorthm s proposed to nvestgate strategc behavors power supplers and to smulate operaton market. Furrmore, an ntegrated soluton methodology based on a mult-core parallel tabu genetc algorthm (MPTGA) s proposed to provde optmal assgnment blateral contracts, consderng market smulaton, n order to acheve optmal market regulaton. The results from case study, wth real data based on Yunnan s electrcty market, demonstrate that proposed ndcator and method are effectve and effcent to smulate and regulate market operaton, and effects MPTGA are dscussed last. Keywords: blateral contract; cascade hydropower; electrcty market; smulaton and regulaton 1. Introducton Electrcty ndustres worldwde have been experencng major reforms snce late 198s, ntroducng competton nto power supply sde through electrcty markets. After several falures n buldng electrcty markets n Chna, frst functonal market was launched n Yunnan Provnce n 215, whch s a monthly hydro-domnated market. Yunnan s located n Southwest Chna, and ts electrcty ndustry has experenced a development boom n last few years, especally n terms nstalled capacty [1]. By end 215, cumulatve nstalled capacty hydropower n Yunnan (53.4 GW) was more than half that n Unted States [2,3] (Fgure 1), accountng for more than 74% cumulatve nstalled power capacty n Yunnan. Compared wth fast development power supply sde, power demand sde wthn provnce experenced a slow ncrease durng Chna s 12th Fve-Year Plan (211 to 215) [4]. Ths phenomenon exacerbates mbalance between electrcty generaton and consumpton n Yunnan. Issues huge losses potental electrcty generaton that can be fed nto grd have become more serous n recent years. Accordng to statstcal data Chna Electrcty Councl, cumulatve nstalled generaton capacty n Yunnan (53.4 GW) accounts for 5.3% natonal total, and generaton (215 TWh) accounts for 4.% natonal total, whle total electrcty consumpton whole provnce (146 TWh) just accounts for 2.7% natonal total. However, under such crcumstances, electrcty prce n Yunnan stll remans relatvely hgh compared to that or provnces, as shown n Fgure 2. It can be seen that Water 217, 9, 623; do:1.339/w

2 Water 217, 9, Water 217, 9, Water 217, 9, crcumstances, electrcty prce n Yunnan stll remans relatvely hgh compared to that or crcumstances, electrcty prce n Yunnan stll remans relatvely hgh compared to that or provnces, as shown n Fgure 2. It can be seen that surplus generaton n Inner Mongola s as provnces, surplusas generaton shown n n Fgure Inner2. Mongola It can be s seen as twce that as much surplus as generaton n Yunnan, n but Inner electrcty Mongola prce s as twce as much as n Yunnan, but electrcty prce re s lower. The prce n Qngha s much twce re s as lower. much The as n prce Yunnan, n Qngha but s electrcty much lower prce thanre n Yunnan, s lower. and The even prce Qngha Qngha needss mport much lower than n Yunnan, and even Qngha needs mport electrcty from neghborng provnces. lower electrcty than from n Yunnan, neghborng and even provnces. Qngha needs mport electrcty from neghborng provnces. Fgure 1. Installed hydropower capacty top seven hydropower producng countres and Fgure 1. Installed hydropower capacty top seven hydropower producng countres and Yunnan Provnce. Yunnan Provnce. Surplus Generaton (TWh) Surplus Generaton Surplus Generaton Electrcty Prce Electrcty Prce Inner Schuan Yunnan Inner Gansu Nngxa Qngha Schuan Yunnan Mongola Gansu Nngxa Qngha Mongola Fgure 2. Electrcty prces and surplus generaton n some provnces Chna. Fgure 2. Electrcty prces and surplus generaton n some provnces Chna. In such partcular stuatons, vtal vtal vtal ams ams ams for for for polcy-makers polcy makers polcy makers n n n buldng buldng buldng electrcty electrcty electrcty market market market are are are to (1) to to reduce (1) reduce (1) reduce electrcty electrcty electrcty prce prce prce n n n market market market and (2) and and ncrease (2) ncrease (2) ncrease electrcty electrcty electrcty consumpton consumpton consumpton energy energy energy consumers. consumers. consumers. In a perfect In a In competton perfect competton perfect competton envronment, envronment, envronment, no powerno no suppler power power has suppler suppler ablty has has to unlaterally ablty to ablty to unlaterally unlaterally manpulate manpulate manpulate market prce. market market However, prce. prce. n ahowever, real market n n envronment, a real market real market monopolstc envronment, envronment, power monopolstc monopolstc supplers power power can nfluence supplers supplers prce can nfluence can nfluence by strategzng prce prce r by by strategc strategzng strategzng bddng r r schemes. strategc strategc Compared bddng schemes. bddng schemes. wth a rmal Compared Compared based wth wth electrcty a rmal rmal market, based based t selectrcty dffcult tomarket, read t t s bddng s dffcult dffcult and to to operaton read bddng read bddng strategyand and for cascaded operaton operaton hydropower strategy for strategy for cascaded cascaded statons, and hydropower hydropower t s more statons, statons, challengng and and tot t reduce s more s more challengng prce and ncrease to reduce to reduce electrcty prce and prce and consumpton ncrease ncrease n electrcty electrcty hydro-domnated consumpton consumpton electrcty n hydro domnated n hydro domnated markets [5]. Moreover, electrcty electrcty n order markets markets to acheve [5]. [5]. Moreover, Moreover, optmal market n order n order regulaton to acheve to acheve n hydro-domnated optmal market optmal market electrcty regulaton regulaton markets, n hydro domnated n hydro domnated t s necessary toelectrcty study markets, markets, strategc behavor t s necessary t s necessary power to study to study supplers. strategc behavor power supplers. The generaton outputs hydropower plants are lmted and strategc The generaton behavor outputs power hydropower supplers. The plants generaton are lmted outputs and nfluenced hydropower by nflows, plants are and lmted cascaded and nfluenced by nflows, and cascaded hydropower plants are capable shftng energy between nfluenced hydropowerby plants nflows, are capable and cascaded shftng hydropower energy between plants consecutve are capable perods shftng and adjacent energy locatons, between consecutve perods and adjacent locatons, a dstncton from a rmal based electrcty consecutve a dstnctonperods from a and rmal-based adjacent locatons, electrcty dstncton market. Polcy from makers rmal based should evaluate electrcty market. Polcy makers should market operaton stuaton and take actons ( present Polcy operaton makers stuaton should andevaluate take proper market actons operaton ( present stuaton study focuses and take onproper optmal actons assgnment ( present study focuses optmal assgnment blateral contracts) optmal market regulaton, study blateral focuses contracts) tooptmal obtan optmal assgnment market blateral regulaton, contracts) whch sto aobtan more complcated optmal market task. regulaton, whch s a more complcated task. whch Several s more researches complcated focustask. on bddng and operaton strateges hydropower statons n dfferent Several researches focus on bddng and operaton strateges hydropower statons n Several electrcty researches markets. focus Basls on et al. bddng [6] and and Pousnho operaton et al. strateges [7] studed hydropower strategc behavor statons va n dfferent electrcty markets. Basls et al. [6] and et al. [7] studed strategc behavor va dfferent Mxed-nteger electrcty lnear markets. programmng Basls et (MILP) al. [6] and for apousnho sngle hydropower et al. [7] studed suppler. strategc Scott et behavor al. modeled va Mxed nteger lnear programmng (MILP) for a sngle hydropower suppler. Scott et al. modeled Mxed nteger two hydropower lnear supplers programmng a decentralzed (MILP) for duopoly sngle hydropower market, but r suppler. model Scott only et al. works modeled for a two hydropower supplers n a decentralzed duopoly market, but r model only works for a two relatvely hydropower small number supplers reservors n decentralzed [8]. Ramos duopoly et al. ncorporated market, but r equlbrum model only constrants works for nto relatvely small reservors [8]. Ramos et al. ncorporated equlbrum nto relatvely r model small butnumber neglected reservors nflow[8]. uncertanty Ramos et [9]. al. ncorporated Kelman et al. ntroduced equlbrum constrants Nash-Cournot nto Electrcty Prce (RMB/kWh)

3 Water 217, 9, model to determne optmal operatons for hydropower statons n mult-stage and stochastc stuatons [1]. A varety methods have been tred for optmal hydropower operaton, wth varyng degrees success. However, most m started from vew power supplers, and only a few studes focused on market smulaton and regulaton ssues. Rangel revewed man ssues n hydro-domnated electrcty markets and argued techncal dfferences between hydro and rmal power statons [5]. The approaches to and methods for market smulaton and market regulaton were also analyzed by Rangel. Flatabo et al. ntroduced experences Nord Pool desgn and mplementaton, whch s a hydro-domnated envronment [11]. Wolak dscussed mpacts market structure and market rules on market power n dfferent markets and operaton stuatons [12]. As to mpact blateral contracts ( man research objectve n ths paper) on market operaton, studes [1,13] have shown that, n hydrormal systems, market effcency ncreases as level blateral contracts ncreases. However, levels blateral contracts are based on generaton capactes, and, n most se works, some realstc features are gnored when smulatng market such as lmtatons supplers outputs and temporal and spatal couplngs cascaded hydropower statons. Thus, re are urgent needs for polcy makers to study effectve methods for achevng optmal market regulaton n newly establshed hydro-domnated Yunnan electrcty market. Ths paper presents an ntegrated method to smulate market operaton and acheve optmal market regulaton, wth objectve reducng market prce and ncreasng electrcty consumpton n hydro-domnated envronments. A comprehensve ndcator composed market prces and electrcty consumpton s proposed to evaluate market operaton stuaton. An teratve algorthm s ntroduced to nvestgate strategc behavor power supplers and to smulate operaton electrcty market. Moreover, an ntegrated soluton methodology based on a mult-core parallel tabu genetc algorthm s proposed to solve complex, sequental, combnatoral optmzaton problem and to acheve optmal assgnment blateral contracts. The remander ths paper s organzed as follows. In Secton 2, comprehensve ndcator to evaluate market operaton stuaton s proposed, and mamatcal models for market operaton smulaton and regulaton are ntroduced n Secton 3. The teratve algorthm for market smulaton and ntegrated soluton methodology are presented n Secton 4, followed by results from real case studes n Yunnan s electrcty market n Secton 5. Fnally, conclusons are gven n Secton Indcator Market Operaton Stuaton Market smulaton s an effectve method to dentfy stuaton market operaton, and market prce and electrcty producton/consumpton are two man concerns. In hydro-domnated envronments, gant cascaded hydropower plants can easly shft energy between consecutve perods and adjacent locatons (upstream and downstream reservors) to gan extra prts. Thus, an ndcator that consders manpulaton market prce and electrcty producton/consumpton s needed to evaluate market operaton stuaton. Moreover, proper ndcator should take multple tradng perods rar than just a sngle tradng perod nto consderaton (n ths study, multple tradng perods are set to one year that contans 12 tradng perods). Snce gant cascaded hydropower plants can manpulate market prce and maxmze ts prts wthout wthdrawng any electrcty producton (y store water n reservors n one tradng perod and generate n anor perod), re mght be no drect relatonshp between market prces and electrcty producton Market Prce Indcator Market Operaton Stuaton (MPI) MPI s presented to study market operaton stuaton related to market prce. Referrng to dea well-known market power ndcators Lerner Index (LI) and Prce Cost Margn Index (PCMI) and followng practcal usage n [14], MPI can be expressed by Formula (1), obtaned from comparng prces between dfferent regulaton scenaros: ξ MPI = (p max p)/(p max p mn ) (1)

4 Water 217, 9, where ξ MPI represents market prce ndcator market operaton stuaton; p represents average market clearng prce durng multple tradng perods; and p max and p mn represent hghest and lowest average market clearng prces multple tradng perods among dfferent regulaton scenaros, respectvely. A value ξ MPI closer to 1 could ndcate better and more effcent market operaton and regulaton snce average market clearng prce p s closer to p mn. It s obvous that p max and p mn are mportant parameters when evaluatng market operaton stuaton va MPI. In ths study, a market envronment wth no regulaton mplemented s selected as hghest prce stuaton, n whch power supplers can easly exert market power. A regulated non-market envronment s selected as deal market regulaton stuaton. The electrcty prce derved from ths regulated envronment s regarded as lowest prce because system operator solves an optmzaton problem wth objectve average prce mnmzaton n ths stuaton. The average market clearng prce multple tradng perods can be calculated as follows: p = ( T t=1 p t Q t ) / ( T t=1 Q t ) (2) where t and T represent ndex and total number tradng perod, respectvely; p t represents market clearng prce n perod t; and Q t represents electrcty producton/consumpton n perod t Electrcty Consumpton Indcator Market Operaton Stuaton (ECI) The optmal method to ncrease electrcty consumpton s shftng demand curve rar than movng along curve, but ndustral producton plan can hardly adjust accordng to electrcty prce wthn a month. Thus shft demand curve s neglected n present study. ECI s presented to dentfy market operaton stuaton related to electrcty consumpton. The ndcator s obtaned through comparng electrcty consumpton between dfferent market regulaton scenaros, and ECI can be expressed by Formula (3): ( ξ ECI = Q total Q total mn ) ( ) / Q total max Q total mn where ξ ECI represents electrcty consumpton ndcator market operaton stuaton; Q total represents total electrcty producton/consumpton durng multple tradng perods; and Q total max and Q total mn represent hghest and lowest electrcty producton/consumpton durng multple tradng perods among dfferent regulaton scenaros. A value ξ ECI closer to 1 can ndcate better and more effcent market operaton and regulaton snce electrcty consumpton Q total s closer to Q total max Comprehensve Indcator Market Operaton Stuaton (CI) CI s presented to comprehensvely evaluate market operaton stuaton, takng both market prce (MPI) and electrcty consumpton (ECI) nto consderaton. It could be calculated by Formula (4): ξ CI = ηξ MPI + (1 η)ξ ECI (4) where ξ CI represents comprehensve ndcator market operaton stuaton and η [, 1] represents comprehensve coeffcent CI. Overall, a value ξ CI closer to 1 can ndcate a better electrcty market envronment, consderng defnton ξ MPI and ξ ECI. Snce CI s a lnear combnaton ξ MPI and ξ ECI, polcy makers from dfferent electrcty markets can fnd sutable CI by weghng mportance MPI and ECI and can adjust coeffcent based on dfferent practcal stuatons. (3)

5 Water 217, 9, Mamatcal Model In Yunnan electrcty market, not all power supplers wth generatng capacty can bd n market because some m are contracted n blateral contracts. At present stage, system operator assgns blateral contracts to power supplers wth purpose securng safety and stablty power system n transton perod. The electrcty prces blateral contracts are determned n market; total generaton bd n monthly market equals generaton n blateral contract plus addtonal generaton n market. To some extent, blateral contracts are bndng upon operaton power supplers. Thus, n present study, blateral contracts are regarded as proper regulatng measures. Polcy-makers evaluate market operaton stuaton by measurng CI and acheve market regulaton by assgnng optmal levels blateral contracts to dfferent power supplers n dfferent perods. However, after blateral contracts are assgned, power supplers can easly adjust r bddng and operaton strateges and game wth each or to maxmze r own prts, and market operaton stuaton and CI are changed compared wth stuaton when polcy makers ntally assgned blateral contracts. Polcy-makers should reassgn blateral contracts based on changed market operaton stuaton to acheve better regulaton. Therefore, optmal market regulaton problem s not only a game between dfferent power supplers but also between polcy makers and power supplers. To solve ths complex problem, mamatcal models dfferent power supplers and polcy makers are proposed n ths secton. When smulatng market operaton, Cournot model s selected as a game ory approach to descrbng strategc behavor power supplers. Ths wdely used model assumes that prce makers bddng behavors are based on quantty strateges. The Cournot model has some advantages compared wth or market competton models such as Bertrand model and supply functon model [15]. The Bertrand model assumes that competton s based on prce strateges, but t has lmtatons (e.g., t s unable to descrbe capacty-constraned olgopoly) for applcaton to electrcty markets [16]. The supply functon model can better capture power supplers bddng behavor than Cournot and Bertrand models. However, Cournot model s more flexble when takng or aspects electrcty market nto consderaton, ncludng blateral contracts and techncal lmts. Furrmore, calculatng equlbrum state Cournot model s more mamatcally tractable than that supply functon model. The demand n market s descrbed as elastc, but demand sde bddng s not consdered. Thus, market clearng prce at tradng perod t can be expressed as p t = β t α t Q total t (RMB/kWh), n whch α t > and β t > represent slope and ntercept nverse demand curve at tradng perod t, respectvely, and y are ftted based on real data market, and Q total t represents electrcty producton/consumpton at tradng perod t Gant Cascaded Hydropower Statons Gant cascaded hydropower statons could obtan assgnment blateral contracts from polcy makers and or supplers bddng and operaton strateges n market envronment. Then y optmze r own strateges based on prt-maxmzng objectve and bd Q,t, = 1, 2,..., I (kwh) nto market, consderng ors strateges as fxed. The operatng costs hydropower statons consst deprecaton costs, mantenance costs, salares, and or costs. The deprecaton costs and mantenance costs each staton are usually calculated based on ts fxed assets and can be regarded as fxed costs. Salares and or costs are calculated based on generaton and can be regarded as operatng costs, but y are usually too small for prt analyss (less than.1 RMB/kWh), so y can be neglected when modelng r prts for smplcty [17]. The prt-maxmzng objectve for gant cascaded hydropower statons durng multple tradng perods can be expressed as: max : π = T I t=1 =1 π,t = T I t=1 =1 Q,t p t (5)

6 analyss (less than.1 RMB/kWh), so y can be neglected when modelng r prts for smplcty [17]. The prt maxmzng objectve for gant cascaded hydropower statons durng multple tradng perods can be expressed as: T I T I (5) Water 217, 9, 623 max : Q p 6 18 t, t, t t 1 1 t 1 1 where and I represent ndex and total number cascaded hydropower statons; (RMB) where represents and I represent prt gant ndex cascaded and total hydropower numberstatons; cascaded hydropower statons; π (RMB) t, (RMB) represents prt represents staton at prt perod t; gant Q cascaded hydropower statons; π,t (RMB) represents prt staton t, (kwh) s obtaned through Qt, qt, t n whch q at perod t; Q,t (kwh) s obtaned through Q,t = (q,t /ω ) t n whch q,t (m 3 t, s /s) s generatng generatng dscharge hydropower staton at perod t; s relatonshp between water head dscharge hydropower staton at perod t; ω s relatonshp between water head and and water volume used per klowatt hour water volume used per klowatt-hour (m 3 (m 3 /kwh), whch s a characterstc curve turbne at hydropower staton, as shown /kwh), n Fgure whch 3; and s a t characterstc (s) s duraton curve one sngle turbne at hydropower tradng perod. staton, as shown n Fgure 3; and t (s) s duraton one sngle tradng perod. Water volume used per klowatt-hour (m 3 /kwh) Water head Fgure Fgure 3. The 3. The characterstc curve relatonshp between between water water head head and water andvolume water volume used per used per klowatt-hour. klowatt hour. The constrants faced by gant cascaded hydropower statons are: The constrants faced by gant cascaded hydropower statons are: Mass conservaton equaton: Mass conservaton equaton: Vt, 1 Vt, ut, qt, dt, t (6) where V t, (m 3 ) represents ntal V,t+1 storage = V,t + (uhydropower,t q,t d,t staton ) t at perod t; u t, (6) represents nflow hydropower staton; and d t, represents spll dscharge where hydropower V,t (m staton 3 ) represents at perod t. ntal storage hydropower staton at perod t; u,t represents nflow hydropower staton; and d,t represents spll dscharge Fnal reservor storage constrant: hydropower staton at perod t. Fnal reservor storage constrant: V T, 1 VT (7) V where VT, 1 (m 3 ) represents reservor storage,t+1 = VT at end (7) multple perods hydropower where staton V,,T+1 and t (ms 3 fxed ) represents at VT (m 3 ), reservor whch s storage determned at by end dspatchng multplecenter perods hydropower system staton operator,, and consderng t s fxed at non market VT (m 3 ), whch functons s determned hydropower by staton dspatchng such as center flood control, system operator, rrgaton, consderng and navgaton. Therefore, non-market future functons value hydropower reservor storage staton can be such gnored. as flood control, rrgaton, Maxmum and and navgaton. mnmum Therefore, turbne dscharge future constrant: value reservor storage can be gnored. Maxmum and mnmum turbne dscharge constrant: mn max q q q (8), t q mn q,t q max (8) where q mn and q max represent maxmum and mnmum turbne dscharge hydropower staton I, respectvely. The relatonshps between q mn, q max, and net head are neglected n ths study. Mnmum and maxmum power output constrant: q.t N mn N.t N max (9) where N.t = 3.6ω represents power output by hydropower staton at tme perod t and N mn and N max represent mnmum and maxmum power outputs by hydropower staton, respectvely.

7 Water 217, 9, Lower and upper reservor storage constrant: V mn V.t V max (1) where V mn (m 3 ) and V max (m 3 ) represent lower and upper bounds ntal reservor storage hydropower staton, respectvely. Mnmum and maxmum release constrant: R mn R.t R max (11) where R.t represents water release hydropower staton at perod t; R.t conssts generatng dscharge q,t and spllage dscharge d,t ; and R mn and R max represent mnmum and maxmum releases hydropower staton, respectvely. Mnmum blateral contract constrant: B.t Q.t (12) where B.t (kwh) represents blateral contract assgned to hydropower staton at perod t. The generaton hydropower staton at perod t must be no less than B.t, whch s determned by polcy makers. Note that water delays between upstream and downstream statons are usually 2 to 3 h, so y are not consdered n monthly operaton cascaded hydropower statons. Addtonally, snce day-ahead generaton schedules and real-tme operaton hydropower statons are stll regulated by system operator n monthly market, frequency control or or servces [18,19] are not consdered n ths study. Moreover, envronmental constrants are also excluded [2] Thermal Power Statons Thermal power statons could also obtan blateral contracts from polcy makers and or supplers strateges n market. They optmze r own strateges based on prt-maxmzng objectve and bd Q g,t, g = 1, 2,..., G (kwh) nto market. The prt-maxmzng objectve for rmal power statons durng multple tradng perods can be expressed as: max : π = T G t=1 g=1 [ Qg,t p t C g ( Qg,t )],g G (13) where g and G represent ndex and total number rmal power statons, respectvely, and C g (q) represents generatng cost rmal power staton g. Md-term (monthly) schedulng rmal power requres determnaton number onlne unts. Thus constrants faced by rmal power statons are [21]: Number onlne unts: u mn g,t u g,t u max g,t (14) where u g,t represents number onlne unts rmal power staton g at perod t and u mn g,t and u max g,t represent mnmum and maxmum onlne unts rmal power staton g, respectvely. Onlne unt peak and valley mnmum duraton tme: T peak g,t T peak,mn g, T valley g,t T valley,mn g (15)

8 Water 217, 9, where T peak g,e (h) and T valley g,e rmal power staton g, respectvely, and T peak,mn g (h) represent tme duraton at eth peak and valley perod (h) and Tg valley,mn (h) represent mnmum tme duraton onlne unts n peak and valley perods, respectvely. Mnmum blateral contract constrant: B g,t Q g,t (16) where B g,t (kwh) represents blateral contract assgned to rmal power staton g at perod t. The generaton rmal power staton g at perod t must be no less than B g,t, whch s determned by polcy makers Power Exchange Polcy Makers Polcy makers receve bd nformaton from power supplers, clear market, and make bddng and clearng nformaton avalable to all power supplers. Meanwhle, polcy makers could evaluate market operaton stuaton by measurng CI value n market equlbrum state and n regardng B,t and B g,t as decson varables to acheve optmal market regulaton, wth objectve CI maxmzaton: max : ξ CI (17) The constrants faced by polcy makers are mnmum and maxmum blateral contracts that are assgned to dfferent power supplers at dfferent tme perods: B mn,t B mn g,t B,t B max,t (18) B g,t B max g,t (19) where B,t mn and B,t max respectvely represent mnmum and maxmum levels blateral contracts assgned to hydropower staton at perod t and B mn g,t and B max g,t respectvely represent mnmum and maxmum levels blateral contracts assgned to rmal power staton g at perod t. Note that no blateral contract s assgned to rmal power statons n order to ncrease consumpton clean energy and reduce consumpton fossl-based energy n hydro-domnated electrcty markets. Thus B mn g,t = B g,t = B max g,t =. 4. Soluton Methodology 4.1. Market Smulaton and Market Equlbrum State An teratve algorthm s ntroduced to smulate operaton electrcty market [13,22,23]. Power supplers that own gant cascaded hydropower or rmal power statons and polcy makers are consdered to be optmzng r own objectves separately n algorthm. The technology mx s neglected n present study based on practcal stuaton Yunnan for smplcty. Each ndvdual power suppler s optmal bddng and operaton strategy s best response to market when or supplers strateges are fxed. Usng ths nformaton for whole electrcty supply system, a market smulaton can be performed to learn more about plausble behavors and stuatons n market. When smulatng operaton market, power supplers compete to maxmze r own prts, and each power suppler adjusts ts own strategy accordng to ors strategc behavors. A market equlbrum state wll be acheved when no one can ncrease r own prt by unlaterally adjustng r strategy. The proposed teratve algorthm helps to smulate operaton market and to obtan market equlbrum state. The specfc steps teratve algorthm are as follows: Step One: Assume that certan levels blateral contracts are assgned to dfferent power statons at dfferent perods. Intalze bddng and operaton strategy each power suppler n market randomly and make ths nformaton avalable to or power supplers.

9 Water 217, 9, Step Two: For each power suppler, solve ts own prt maxmzaton model consderng or power supplers strateges as fxed n order to determne best ndvdual responses to market. Step Three: Proceed wth teraton process untl no power supplers strateges are updated compared wth prevous teraton round. The algorthm converges when no ndvdual power suppler can unlaterally mprove ts prt, ndcatng a state market equlbrum. Inflow uncertanty s an mportant factor when we study operaton hydropower statons n hydro-domnated envronments. Snce man rver basns n Yunnan have large volumes and stable nflows, a frequency analyss method s used to descrbe nflows to hydropower plants n each rver basn. Fve nflow scenaros dfferent frequences (5%, 25%, 5%, 75%, and 95% nflow relablty) are derved through a frequency analyss method, and nflow s obtaned by combnaton se fve scenaros when analyzng bddng and operaton strateges. A progressve optmalty algorthm, dscrete dfferental dynamc programmng, and a successve approxmaton method are combned to solve optmal problem faced by gant cascaded hydropower statons, as ntroduced n [21]. A hybrd search algorthm consstng a heurstcs search, a progressve optmalty algorthm, and a local optmum avodng strategy s used to solve optmal problem faced by rmal power statons, as ntroduced n [24]. The soluton methodology for polcy makers to acheve optmal market regulaton n hydro-domnated electrcty markets s ntroduced n Secton Mult-Core Parallel Tabu Genetc Algorthm (MPTGA) The aforementoned algorthm enables polcy makers to evaluate effects a certan level blateral contracts on market operaton stuaton by measurng CI. The optmal levels blateral contracts have to be found among many dfferent plausble levels blateral contracts, whch form a huge soluton space for polcy makers [25]. Ths s a complex, sequental, combnatoral optmzaton problem that can take a long tme to solve because optmal market regulaton s based on market smulaton, whch s, n turn, based on ndvdual prt maxmzatons. The Genetc Algorthm (GA), a random ntellgent optmzaton method, whch can mprove computatonal effcency. Snce GA s robust and adaptable, t has been wdely used n dfferent applcatons [26,27]. However, possblty gettng trapped n local optma remans a general ptfall conventonal GA due to dffculty confrmng global optmalty solutons. Ths ssue can mpose a lmt on applcaton GA to large-scale, complex optmzaton problems wth very large soluton spaces. Based on problem mentoned before, present study frst solves GA problem, startng from mult-subpopulatons, to mantan dversty populaton. Secondly, a mgraton strategy s adopted to exchange nformaton between sub-populatons n order to break enclosed envronment n case local optma are acheved. A cone topology model s used to mgrate chromosomes between sub-populatons, whch takes both separatons between sub-populatons and dversty global populaton nto consderaton. Thrdly, a tabu search strategy s ntroduced to ncrease computaton effcency by avodng calculatng same chromosome repeatedly through a tabu hash table. Fnally, takng advantage natural parallelsm GA by dvdng ntal populaton nto several sub-populatons, mult-core parallel computng s used to furr reduce computaton tme. Fgure 4 shows schematc ths mult-core parallel computng strategy. It performs each sub-populaton s genetc manpulaton as a task, usng a thread pool for task schedulng and reasonable dstrbuton computng resources, (.e., effcent use CPU resources) [27]. Above all, MPTGA s acheved to solve proposed problem.

10 computng s used to furr reduce computaton tme. Fgure 4 shows schematc ths mult core parallel computng strategy. It performs each sub populaton s genetc manpulaton as a task, usng a thread pool for task schedulng and reasonable dstrbuton computng resources, (.e., effcent use CPU resources) [27]. Above all, MPTGA s acheved to solve proposed Water 217, 9, problem. Core Core CPU #1 #2 Thread Pool Core #n-1 Core #n Thread 1 Sub populaton GA evoluton Thread 2 Sub populaton GA evoluton Thread n-1 Sub populaton GA evoluton Thread n Sub populaton GA evoluton Soluton 1 Soluton 2 Soluton n-1 Soluton n Compare to get optmal soluton Fgure Mult-core Mult core parallel computng strategy. The The specfc specfc process process applyng MPTGA for for solvng CI-maxmzaton CI maxmzaton problem s descrbed n n followng followng steps: steps: Step Step One: One: determne determne encodng. encodng. The The present present study study adopts adopts real coded real-coded genetc genetc algorthms algorthms (GA) because t s more effcent and precse than bnary coded GA. Accordng to precson (GA) because t s more effcent and precse than bnary-coded GA. [ Accordng mn max requrements, dvde level blateral contract B t, nto l parts between ] to precson Bt,, B t, requrements, dvde level blateral contract B, n,t nto l parts between B,t mn, B,t max, n represent each represent gene neach chromosome gene n chromosome by an nteger by an d, (d nteger =, 1, d.,.. d, l).,1,..., The l gene. The can gene be converted can be converted to levels to blateral levels contracts blateral by contracts followng by followng formula: formula: B B mn max mn t, Bt, t, Bt, (2),t = B,t mn + d ( ) l B,t max B,t mn (2) l Step Step Two: Two: ntalze ntalze thread thread pool pool wth wth r threads. Step Step Three: Three: determne determne length length chromosome chromosome L accordng accordng to to number number power power statons statons and number tradng perods. and number tradng perods. Step Four: ntalze each sub populaton randomly, and assgn m to dfferent threads for GA Step Four: ntalze each sub-populaton randomly, and assgn m to dfferent threads for executon. GA executon. Step Fve: wthn each sub populaton, check wher global tabu hash table contans Step Fve: wthn each sub-populaton, check wher global tabu hash table contans chromosome; f global tabu hash table contans chromosome, return ts correspondng chromosome; f global tabu hash table contans chromosome, return ts correspondng ftness. ftness. Orwse contnue to Step Sx. Orwse Step contnue Sx: complete to Step Sx. GA executon on each thread (.e., crossover, mutaton, and selecton at each Step generaton) Sx: complete and each GA sub populaton. executon on When eachcomputng thread (.e., crossover, chromosome mutaton, ftness, and decode selecton at chromosome, each generaton) smulate and each market sub-populaton. operaton va When proposed computngteratve chromosome algorthm ntroduced ftness, decode n Secton chromosome, 4.1, and smulate compute market objectve operaton functon va value based proposed on teratve penaltes algorthm for volatng ntroduced n constrants. Secton 4.1, Save and compute chromosome objectve and ts ftness functon value value nto based tabu onhash table. penaltes for volatng constrants. Step Seven: Save when chromosome evoluton andreaches ts ftness mgraton value ntopont tabu (every hash g generatons), table. select top m fttest Step Seven: chromosomes when n evoluton one sub populaton, reaches put mgraton m nto pont next (every sub populaton s g generatons), buffer select zone, and top m fttest get m chromosomes nfrom one sub-populaton, buffer zone nto put m sub populaton. nto next sub-populaton s buffer zone, and get m chromosomes Step Eght: from stop and buffer compute zone ntoresult sub-populaton. usng fttest chromosome when termnaton condton Step Eght: s met. stop Orwse, and compute return to Step result Sx. usng fttest chromosome when termnaton condton s met. Orwse, return to Step Sx. Thus, optmal assgnment blateral contracts for achevng optmal market regulaton n hydro-domnated electrcty markets can be obtaned. The ntegrated flowchart soluton method s llustrated n Fgure 5.

11 Water 217, 9, Thus, optmal assgnment blateral contracts for achevng optmal market regulaton n hydro domnated electrcty markets can be obtaned. The ntegrated flowchart soluton Water 217, 9, method s llustrated n Fgure 5. Start Determne encodng. Represent gene by an nteger, and convert levels blateral contracts nto genes Parallel computng Sub populaton 1 execute GA Sub populaton 2 execute GA Sub populaton r-1 execute GA Sub populaton r execute GA Intalze thread pool wth r threads Intalze each sub populaton and dstrbute sub populatons to dfferent threads Mgraton strategy Start evoluton Chromosome ftness calculaton Mgraton? Put m fttest chromosome nto next sub`s buffer Get m fttest chromosome put nto ths sub Convergence? Compare to get optmal soluton N Convergence? Y End Tabu search strategy Tubu hash table contan chromosome? Y Save chromosome and ftness n tubu hash table Return ftness Execute crossover and mutaton N Ftness functon Power suppler 1 Max Intalze each power suppler s nformaton and make nmatonfor avalable to ors Intalze each power suppler s bddng strategy Informaton avalable Power suppler 2 Max Power suppler Max Compare bddng strateges n ths round wth strateges n prevous round N Market Smulaton Informaton avalable Convergence? Y Fttest chromosome Informaton avalable Power suppler n Max Informaton avalable Fgure 5. Integrated soluton soluton methodology methodology based on based a mult core on a mult-core parallel tabu parallel genetc tabu algorthm genetc algorthm (MPTGA). (MPTGA) Case Case Study Study Case Study Background In In Yunnan Provnce, gant cascaded hydropowerstatons are are manly manly located located along along two two rver rver basns: basns: Lancang Lancang Rver (LR, upstream Mekong Mekong Rver) Rver) and and Jnsha Jnsha Rver Rver (JR, (JR, upstream upstream Yangtze Rver). There are sx hydropower statons along LR and seven hydropower statons along Yangtze Rver). There are sx hydropower statons along LR and seven hydropower statons along JR. JR. Snce cascaded hydropower statons have close hydraulc and electrcal connectons, n ths study Snce cascaded hydropower statons have close hydraulc and electrcal connectons, n ths study we we consder LR and JR as two strategc players n market; although, n actualty, not all consder LR and JR as two strategc players n market; although, n actualty, not all hydropower hydropower plants along same rver belong to same stakeholder. Xluodu s excluded from plants along same rver belong to same stakeholder. Xluodu s excluded from study study because t transfers part ts electrcty drectly to Zhejang Provnce through ±8 kv because t transfers part ts electrcty drectly to Zhejang Provnce through ±8 kv X-Zhe ultra X Zhe ultra hgh voltage drect current transmsson lnes and rest to Guangdong Provnce hgh through voltage ±5 drect kv Nu Cong current transmsson hgh voltage lnes drect and current rest transmsson to Guangdong lnes. Provnce The or through olgopolstc ±5 kv Nu-Cong power supplers hgh voltage that are drect consdered current n ths transmsson case are lnes. rmal Thepower orstatons olgopolstc from Chna power Guodan supplers that Corporaton are consdered (CGC, see n ths Fgure case 6 and are Table rmal 1 for detals). powerthe statons multple fromtradng Chnaperods Guodan consst Corporaton 12 (CGC, months see(from Fgure January 6 and Table to December). 1 for detals). The reservors The multple ntal tradng and perods fnal water consst levels are 12 months fxed at (from a January normal to hgh December). water level. The The reservors parameters ntal and demand fnal curve watern levels each are month fxed are atftted a normal based hgh on real water level. data The from parameters Yunnan s electrcty demand market curve n 215; n each non market month are demand ftted based s removed on realfrom data fromdemand Yunnan s electrcty curve; and market t s nftted 215; as non-market 641 and demand t s ftted sas removed.4 from at each tradng demandperod. curve; The and αproposed t s ftted as 641 algorthm and β t s s ftted mplemented as.4n atjava eachlanguage tradng perod. n Java The 2 proposed platform algorthm Standard s Edton mplemented (J2SE) on na Java language Lenovo T54p. n Java The operatng 2 platformsystem Standard s Wndows Edton 7, (J2SE) and on acpu Lenovo conssts T54p. one TheInter(R) operatng Core(TM) system s Wndows 7 471MQ@2.5GHz 7, and CPU wth conssts 12. GB RAM. one Inter(R) Core(TM) 7-471MQ@2.5GHz wth 12. GB RAM.

12 Water 217, 9, Water 217, 9, Lancang Rver Jnanqao Longkakou Jnsha Rver Lyuan Aha Xluodu Yangzongha Gongguoqao Xaowan Dachaoshan Manwan Xuanwe Nuozhadu Jnghong Guanynyan Ludla Xaolongtan Gant cascaded hydropower staton Thermal power staton Fgure 6. Locatons gant cascaded hydropower statonsand and rmal power power statons n n Yunnan. Table 1. The man attrbutes statons as power supplers. Normal Dead Regulatng Hydropower Power Regulaton Normal Hgh Dead Water Regulatng Installed Capacty Capacty Hgh Water Water Storage Plant Suppler Ablty Level Storage (1 8 m 3 ) Gongguoqao LR Daly 225 MW Level 133 Level.49 (1 8 m 3 ) Gongguoqao Xaowan LR Annual Daly 7225 MW MW Xaowan Manwan LR Annual Seasonal 3 MW MW MW MW Dachaoshan LR Annual 225 MW Manwan Nuozhadu LR LR Plurennal Seasonal 3 MW MW MW MW Dachaoshan Jnghong LR Annual Seasonal MW MW Nuozhadu Lyuan LR JR Plurennal Weekly 665 MW MW Aha JR Daly 4 MW Jnghong LR Seasonal 35 MW Jnanqao JR Daly 6 MW Longkakou Lyuan JR JR Weekly Daly 366 MW MW Aha Ludla JR Weekly Daly 364 MW MW Guanynyan JR Weekly 6 MW Jnanqao JR Daly 6 MW Xuanwe CGC - 3 MW Longkakou Yangzongha CGC JR Daly - 2 MW MW Xaolongtan Ludla CGC JR Weekly MW MW Guanynyan JR Weekly 6 MW Xuanwe CGC 3 MW CI Coeffcent Analyss and Selecton Yangzongha CGC 2 MW MW 2 Xaolongtan In ths case, all CGC gant cascaded hydropower 3 statons MW 2 along LR and JR (except Xluodu) and rmal power statons n CGC are taken nto consderaton. The hydraulc constrants faced by LR and 5.2. CI JR Coeffcent can be obtaned Analyss through and Selecton mult-year schedule and hstorcal operaton cascaded hydropower In ths case, statons, all gant whch cascaded are nothydropower dscussed nstatons detal nalong ths study. LR The and mnmum JR (except and Xluodu) maxmum and levels rmal blateral power contracts statons n assgned CGC are totaken eachnto hydropower consderaton. staton The athydraulc each perod constrants are determned faced by proportonal LR and JR can to r be obtaned averagethrough generaton capactes mult year over schedule same and perod hstorcal n hstory. operaton For smplcty, n cascaded ths casehydropower study, mnmum statons, and whch maxmum are not levels dscussed are set n atdetal 3% and n ths 7% study. The average mnmum generaton and capacty maxmum over levels same blateral perod ncontracts hstory, respectvely. assgned to The each parameters hydropower MPTGA staton n at ths each study perod are are set as determned follows: total proportonal populatonto sze: r 5; average maxmum generaton number capactes teratons: over 1; same eltsm perod rate: n.2; hstory. crossover For rate: smplcty,.8; mutaton n ths rate: case.2; study, mgraton mnmum number: and 5; and maxmum mgraton levels nterval are generaton: set 3% 5. and 7% average Frstgeneraton all, mpacts capacty dfferent over CIsame coeffcents perod onn market hstory, regulaton respectvely. are analyzed, The parameters and a proper value MPTGA n CI ths coeffcent study are sset selected as follows: for furr total study populaton about sze: smulaton 5; maxmum and regulaton number teratons: market operaton. 1; eltsm Fgure rate: 7.2; llustrates crossover mpacts rate:.8; varyng mutaton CI coeffcents rate:.2; mgraton average number: market 5; and clearng mgraton prce and nterval total generaton: electrcty 5. consumpton under correspondng optmal market regulaton scenaros. If CI coeffcent Frst all, turns tompacts, n ths case, dfferent η n Formula CI coeffcents (4) equals on market, and n regulaton ξ CI = ηξ are MPI analyzed, + (1 η)ξ and ECI a turns proper nto value ξ CI = ξ ECI CI. coeffcent Under thss crcumstance, selected for furr objectve study about optmal smulaton market regulaton and regulaton could be descrbed market asoperaton. electrcty consumpton Fgure 7 llustrates maxmzaton. mpacts As varyng can be seen CI coeffcents from Fgureon 7, when average CI coeffcent s, electrcty consumpton under optmal regulaton s hghest compared to that market clearng prce and total electrcty consumpton under correspondng optmal market regulaton scenaros. If CI coeffcent turns to, n ths case, n Formula (4) equals, and

13 Water 217, 9, n 1 CI MPI ECI CI ECI turns nto. Under ths crcumstance, objectve Water 217, 9, optmal market regulaton could be descrbed as electrcty consumpton maxmzaton. As can be seen from Fgure 7, when CI coeffcent s, electrcty consumpton under optmal regulaton s hghest compared to that dfferent CI coeffcents. However, average dfferent CI coeffcents. However, average market clearng prce s also hghest snce market clearng prce s also hghest snce MPI s not consdered by polcy makers when. MPI s not consdered by polcy makers when η =. As CI coeffcent ncreases, MPI plays more As CI coeffcent ncreases, MPI plays more mportant role when regulatng market mportant role when regulatng market operaton. Thus average market clearng prce and operaton. Thus average market clearng prce and electrcty consumpton n market electrcty consumpton n market both decrease as CI coeffcent ncreases. If CI coeffcent both decrease as CI coeffcent ncreases. If CI coeffcent turns to 1, n ths case, η n Formula (4) equals 1, and n ξ CI turns = ηξ MPI to 1, n ths case, + (1 η)ξ ECI n CI MPI ECI turns nto ξ CI = ξ MPI CI MPI Formula (4) equals 1, and n 1 turns nto. Under ths. Under ths crcumstance, objectve optmal market regulaton could be descrbed as crcumstance, objectve optmal market regulaton could be descrbed as mnmzaton mnmzaton average market clearng prce. average market clearng prce. Therefore, polcy makers could select proper CI coeffcent when achevng optmal market Therefore, polcy makers could select a proper CI coeffcent when achevng optmal market regulaton, regulaton, accordng accordng to to actual actual stuaton stuaton specfc specfc electrcty electrcty market. market. In ths In case, ths we case, can tell we can tell from Fgure 7 that that.7.7or or.8.8 would would be sutable be sutable CI coeffcents, CI coeffcents, snce, snce, under se under crcumstances, se crcumstances, electrcty consumpton remans at at a relatvely a relatvely hgh hgh level, level, whle whle average average market market clearng clearng prce s prce s relatvely low. Therefore,.7 s selected as CI CIcoeffcent for for furr furr study study on market on market smulaton smulaton and and regulaton n n followng subsectons. AMCP (RMB/MWh) Hghest Electrcty Consumpton but Hghest AMCP AMCP EP Relatvely hgher Electrcty Consumpton and lower AMCP Lowest AMCP but Lowest Electrcty Consumpton Electrcty Consumpton (TWh) Comprehensve Coeffcent CI Fgure Fgure 7. Impacts 7. Impacts Comprehensve Indcator Market Operaton Stuaton (CI) coeffcent on on average average market market clearng clearng prce prce and electrcty and electrcty consumpton. consumpton Market Smulaton and Regulaton 5.3. Market Smulaton and Regulaton As dscussed n prevous secton,.7 s selected as CI coeffcent when polcy makers acheve As dscussed optmal nregulaton prevous secton, market.7 operaton s selected n ths ascase. CI The coeffcent market operaton when polcy stuatons makers acheve under optmal dfferent regulaton levels blateral market contracts operaton should nbe ths compared case. The to market show operaton effectveness stuatons under dfferent proposed levels model blateral n achevng contracts optmal should market be regulaton. compared Kelman to showet al. effectveness uses blateral contracts proposed to model reduce n achevng market power optmal n market hydrormal regulaton. systems Kelman [1]. The et al. results uses blateral show that contracts prces decrease to reduce and market power generaton n hydrormal ncreases as systems amount [1]. The contracts resultssgned showby that prces generators decrease ncreases. and generaton The results from ncreases as [13] amount ndcate that contracts exercse sgned by market generators power drops ncreases. sgnfcantly The when results re from s an [13] ncrease ndcate that generaton contractng. Followng smlar practces n se studes, when blateral contracts are exercse market power drops sgnfcantly when re s an ncrease n generaton contractng. assgned to power supplers n proporton to r capactes, dfferent levels blateral contracts for Followng smlar practces n se studes, when blateral contracts are assgned to power supplers comparson are set n Table 2. Table 3 summarzes electrcty producton (EP) and prts n proporton power supplers, to ras capactes, well as dfferent average levels market blateral clearng prce contracts (AMCP) for comparson and CI value are set under n Table 2. Tabledfferent 3 summarzes levels blateral electrcty contracts. producton It can be seen (EP) that, andas prts CI value power ncreases, supplers, total EP as well n as average market market ncreases clearng and prce AMCP (AMCP) decreases and n general. CI valuewhen under optmal dfferent levels levels blateral blateral contracts contracts. It canderved be seen from that, proposed as CImodel valueare ncreases, adopted, CI total value EPs n hghest, market ncreases total EP s andhghest, AMCP decreases and namcp general. s When lowest optmal across levels dfferent blateral scenaros. contracts These derved results prove fromthat CI proposed s an effectve model are adopted, CI value s hghest, total EP s hghest, and AMCP s lowest across dfferent scenaros. These results prove that CI s an effectve ndcator to evaluate market s operaton, and solvng proposed model could obtan optmal levels blateral contracts between hydropower statons durng multple tradng perods for market operaton regulaton.

14 Water 217, 9, Water 217, 9, ndcator to evaluate market s operaton, and solvng proposed model could obtan optmal levels blateral contracts between hydropower statons durng multple tradng perods for market operaton regulaton. Table 2. Dfferent regulaton scenaros for comparson. Scenaro Table 2. Dfferent regulaton Descrpton scenaros for comparson. Scenaro One 3% each staton s average generaton Descrpton capacty over same perod n hstory Two 4% each staton s average generaton capacty over same perod n hstory One 3% each staton s average generaton capacty over same perod n hstory Three 5% each staton s average generaton capacty over same perod n hstory Two 4% each staton s average generaton capacty over same perod n hstory Four 6% each staton s average generaton capacty over same perod n hstory Three 5% each staton s average generaton capacty over same perod n hstory Fve 7% each staton s average generaton capacty over same perod n hstory Four 6% each staton s average generaton capacty over same perod n hstory Optmal Fve 7% Optmal each level staton s blateral average generaton contracts derved capacty from over proposed same perod method n hstory Optmal Optmal level blateral contracts derved from proposed method Table 3. Man market operaton results n dfferent regulaton scenaros. Table 3. Man market operaton results n dfferent regulaton scenaros. EP LR EP JR EP CGC Prt LR Prt JR Prt CGC AMCP Scenaro EP (TWh) LR EP (TWh) JR EP (TWh) CGC (1 Prt 8 RMB) LR (1 Prt 8 RMB) JR Prt (1 8 CI RMB) CGC (RMB/MWh) AMCP Scenaro CI (TWh) (TWh) (TWh) (1 One RMB) ( RMB) ( RMB) (RMB/MWh) One Two Two Three Three Four Four Fve Optmal Fve Optmal Dachaoshan 8 35 Nuozhadu 1 Jnghong Gongguoqao Xaowan Manwan Longkakou 2 25 Ludla Lyuan 2 Guanynyan Aha Jnanqao Power output bd n market Power output assgned n blateral contract Water level Inflow Generatng dscharge Fgure Fgure Optmal levels blateral contracts and correspondng operaton schedules gant gant cascaded hydropower statons. More specfcally, optmal levels blateral contracts and correspondng optmal More specfcally, optmal levels blateral contracts and correspondng optmal operaton operaton gant cascaded hydropower statons are llustrated n Fgure 8. Snce Xaowan and gant cascaded hydropower statons are llustrated n Fgure 8. Snce Xaowan and Nuozhadu Nuozhadu have good regulatng abltes (annual or plurennal) and huge storage capactes have good regulatng 8 3 abltes 8 3(annual or plurennal) and huge storage capactes ( ( m, m ), y can reallocate nflow and adjust r operaton schedules 8 m 3, m 3 ), y can reallocate nflow and adjust r operaton schedules between dfferent between dfferent months. Therefore, monthly operaton schedules Xaowan and Nuozhadu months. Therefore, monthly operaton schedules Xaowan and Nuozhadu have great nfluences have great nfluences on overall generaton capacty LR and entre system as well. on overall generaton capacty LR and entre system as well. Manwan, Dachaoshan, Manwan, Dachaoshan, and Jnghong also have good regulatng abltes (seasonal or annual), but and r Jnghong storage also capactes have good are relatvely regulatng small, abltes as shown (seasonal n Table or 1. annual), Thus y but can r adjust storage r monthly capactes are relatvely small, as shown n Table 1. Thus y can adjust r monthly operaton schedules to optmze r prt to some extent, but y have lttle nfluence on generaton capacty system throughout year. Gongguoqao and all hydropower statons along JR have poor

15 Water 217, 9, Water 217, 9, operaton schedules to optmze r prt to some extent, but y have lttle nfluence on generaton capacty system throughout year. Gongguoqao and all hydropower statons regulatng along JR abltes have poor on regulatng monthlyabltes scale (daly on or weekly), monthly whch scale (daly means or that weekly), y can whch onlymeans reallocate that y nflow can only wthn reallocate a week ornflow a day. wthn Thus, a nweek order or to a day. maxmze Thus, n r order prts, to maxmze y usually r reman prts, operatng y usually at normal reman hgh operatng water at levels normal throughout hgh water year levels and throughout generate accordng year and to generate nflow. Moreover, accordng to from Fgure nflow. 8, Moreover, we can tell from thatfgure sometmes 8, we can nflow tell that exceeds sometmes generatng nflow dscharge exceeds n flood generatng season, dscharge but n water flood level season, remans but unaltered. water level That remans s manly unaltered. becausethat suchs statons manly because already generate such statons maxmum already generate power output, maxmum so power surplusoutput, nflowso results surplus n water nflow spllage results duen towater r poor spllage regulatng due to r abltes. poor Inregulatng terms rmal abltes. power In terms statons, rmal r operatng power statons, costs are r much operatng hgher than costs those are much hydropower hgher than staton, those so y hydropower are not compettve staton, nso hydro-domnated y are not compettve envronments. n However, hydro domnated transton envronments. perod from However, a regulated n power transton system perod to afrom deregulated a regulated one, rmal power system power s to mportant a deregulated for ensurng one, rmal securty power s mportant powerfor system, ensurng snce rmal securty power statons power are system, close to snce rmal load center. power Thus, statons yare keepclose generatng to load at mnmum center. Thus, power y output keep because generatng at mnmum settng constrant power output condtons. because settng constrant condtons. Fgure 9 llustrates llustrates generaton generaton schedules schedules Xaowan Xaowan and Nuozhadu and Nuozhadu under dfferent under dfferent market operaton market operaton regulaton regulaton scenaros. scenaros. By comparng By comparng generaton generaton schedule schedule Xaowan Xaowan n optmal n scenaro optmal scenaro wth those wth n those or n fveor regulaton fve regulaton scenaros, scenaros, we can tell we from can tell Fgure from 9aFgure that Xaowan 9a that generates Xaowan less generates electrcty less nelectrcty dry season n (from dry December season (from to Aprl) December n optmal to Aprl) regulaton n scenaro. optmal Meanwhle, regulaton scenaro. XaowanMeanwhle, keeps operatng Xaowan at a relatvely keeps operatng hgh water a level relatvely (lower hgh thanwater flood level control (lower level than 1236flood m) before control and level durng 1236 m) before flood season and durng (from June flood to October), season (from as njune Fgure to 9b. October), Durngas n flood Fgure season, 9b. Durng Xaowan rases flood ts season, generatng Xaowan output rases n order ts generatng to ncrease output total n electrcty order to ncrease producton total LR. electrcty The generatng producton schedule LR. The downstream generatng staton schedule Xluodu downstream s smlar tostaton Xaowan, Xluodu as cans be smlar seen from to Xaowan, Fgure 9c,d. as can Itsbe power seen outputs from Fgure are lower 9c,d. nits power dry season outputs andare hgher lower n n flood dry season season nand optmal hgher n regulaton flood scenaro season than n n or optmal scenaros. regulaton Sncescenaro upstream than n staton or Xaowan scenaros. wll Snce ncrease ts upstream generatng staton dscharge Xaowan n wll flood ncrease seasonts to rase generatng ts power dscharge output, n nflow flood downstream season to rase staton ts Nuozhadu power output, wll ncrease nflow correspondngly. downstream Thus, staton evennuozhadu f t operates wll at ancrease relatvely correspondngly. low water level before Thus, and even durng f t operates flood at season, a relatvely Nuozhadu low water couldlevel alsobefore generate and suffcent durng electrcty flood season, producton Nuozhadu n flood could season also generate and store suffcent enoughelectrcty water to brng producton water n level flood backseason to a normal and store hgh water enough level water to end brng water floodlevel season. back to a normal hgh water level at end flood season. Power output Power output One Two Three Four Fve optmal (a) power output at each tradng perod Xaowan One Two Three Four Fve optmal (c) power output at each tradng perod Nuozhadu Water level Water level One Two Three Four Fve optmal (b) water level at each tradng perod Xaowan One Two Three Four Fve optmal (d) water level at each tradng perod Nuozhadu Fgure 9. Generatng schedules Xaowan and Nuozhadu under dfferent regulaton scenaros: (a) power output at each tradng perod Xaowan; (b) water level at each tradng perod Xaowan; (c) power output at each tradng perod Nuozhadu; (d) water level at each tradng perod Nuozhadu. Fgure 1 shows market clearng prce and electrcty consumpton each month n Fgure 1 shows market clearng prce and electrcty consumpton each month n dfferent regulaton scenaros. Compared wth outcomes n optmal regulaton scenaro, dfferent regulaton scenaros. Compared wth outcomes n optmal regulaton scenaro, entre system s wllng to transfer electrcty producton/consumpton from flood season to entre system s wllng to transfer electrcty producton/consumpton from flood season dry season ( same trend as n operaton schedules Xaowan and Nuozhadu) n order to to dry season ( same trend as n operaton schedules Xaowan and Nuozhadu) n order

16 Water 217, 9, Water 217, 9, rase market clearng prces n flood season. Overall, cascaded hydropower statons could abuse to r rase regulatng market abltes clearngto prces gan extra n prt floodn season. market Overall, envronment. cascadedit hydropower can be seen from statons Fgure could 1 abuse that r electrcty regulatng producton abltesncreases to gan extra n prt dry n season market and decreases envronment. n It flood can season be seenn from Fgure or fve 1 that regulaton electrcty scenaros producton compared ncreases wth n optmal dry season regulaton and decreases scenaro. n Ths flood operaton season n schedule or results fve regulaton n about an scenaros average compared.96% producton wth reducton optmal regulaton durng multple scenaro. tradng Ths operaton perods, schedule but an average results 3.98% n about extra an average prt. When.96% polcy producton makers reducton regulate durng market multple wth tradng levels perods, but blateral an average contracts 3.98% derved extra from prt. When proposed polcymodel, makers regulate wllngness market to abuse wthmarket levels power blateral drops, contracts resultng derved n lowest from average proposed market model, clearng wllngness prce and to hghest abuse market electrcty power producton drops, resultng durng n multple lowest tradng average perods. market In clearng summary, prce and proposed hghest ndcator electrcty and producton model durng are effectve multple n tradng achevng perods. optmal In summary, regulaton proposed market operaton ndcator and n order model to buld area effectve better electrcty n achevng market optmal envronment. regulaton market operaton n order to buld a better electrcty market envronment. Prce (RMB/MWh) One Two Three Four Fve optmal (a) electrcty prce each tradng perod Consumpton (TWh) One Two Three Four Fve optmal (b) electrcty consumpton each tradng perod Fgure 1. The monthly electrcty consumpton and market clearng prce under dfferent regulaton scenaros: (a) electrcty prce each tradngperod; (b) (b) electrcty consumpton each each tradng tradng perod. perod Effects MPTGA To verfy effect proposed MPTGA, same parameters for seral and mult-core mult core stuatons a mult-core mult core parallel genetc algorthm (MPGA) and MPTGA are calculated and compared. Speedup, S p, S and p, and effcency, effcency, E P, aree two P, sgnfcant are two sgnfcant ndces for evaluatng ndces for evaluatng performances performances models [28,29]. models They are [28,29]. calculated They are as follows: calculated as follows: Sp T1 TP, EP Sp P (21) S p = T 1 /T P, E P = S p /P (21) where T 1 represents computaton tme for completng task n seral wth a sngle CPU core where T 1 represents computaton tme for completng task n seral wth a sngle CPU core and and T T P represents computaton tme for task usng parallel processng wth P CPU cores. P represents computaton tme for task usng parallel processng wth P CPU cores. As lsted lsted n n Table Table 4, MPGA 4, MPGA and MPTGA and MPTGA both have both postve have effects postve on reducng effects on computatonal reducng tme. computatonal Compared tme. wth Compared MPGA, MPTGA wth MPGA, costsmptga less tmecosts to reach less tme convergence to reach snce convergence t avods snce repeated t avods calculaton repeated calculaton same chromosomes same chromosomes n late partn late algorthm part because algorthm adopton because tabu adopton search strategy. tabu The search speedup strategy. ncreases The as speedup number ncreases CPU as cores number ncreases because CPU cores degree ncreases parallelsm because rses degree wth more parallelsm cores. However, rses wth more cores. cause However, more costs more ncores communcatng cause more and costs schedulng communcatng between dfferent and schedulng cores, whch, between n turn, dfferent decreases cores, effcency, whch, n resultng turn, decreases n devatons between effcency, resultng observed n speedup devatons and between deal speedup. observed speedup and deal speedup. Table 4. Seral and parallel results a mult-core mult core parallel genetc algorthm (MPGA) and MPTGA. 6. Conclusons Method Indce Seral 2 Core 4 Core Method Indce Seral 2-Core 4-Core Computaton tme (s) 313, ,947 81,331 Computaton tme (s) 313, ,947 81,331 MPGA MPGA Speedup Effcency Computaton tme (s) 16,285 8,952 4,994 4,994 MPTGA Speedup MPTGA Speedup Effcency Effcency In ths paper, an ntegrated method s proposed for achevng optmal market regulaton n hydro-domnated envronments. In proposed method, a comprehensve ndcator s ntroduced for

17 Water 217, 9, evaluatng market operaton stuaton, an teratve algorthm s ntroduced for market smulaton, and a soluton methodology based on MPTGA s presented to reduce market prce and ncrease electrcty consumpton through optmal levels blateral contracts. The followng major conclusons could be made from numercal results and analyss Yunnan s electrcty market: (1) The proposed comprehensve ndcator composed market prce and electrcty consumpton s an effectve method to evaluate market operaton stuaton. Dfferent coeffcents comprehensve ndcator used by polcy makers could result n dfferent stuatons market operaton. (2) The optmal level blateral contracts derved from proposed model proves t to be an effectve measure to acheve optmal market regulaton n hydro-domnated electrcty markets snce proposed method takes full consderaton energy shftng abltes and temporal and spatal couplngs gant cascaded hydropower statons. (3) The proposed MPTGA based soluton methodology could substantally promote an effcent soluton and reduce computatonal tme n comparson to MPGA. (4) The proposed ntegrated method could be used n or smlar hydro-domnated envronments n r early stages wth regulated power supplers, wth some mnor modfcatons accordng to dfferent practcal stuatons. (5) Ths study focuses on monthly operaton cascaded hydropower statons nstead weekly or daly operaton because Yunnan electrcty market s a monthly transacton market at present stage. Future studes wll focus on weekly or daly smulaton and regulaton market operaton n hydro-domnated envronments as transacton tme scale changes. Acknowledgments: Ths study was supported by Natonal Natural Scence Foundaton Chna (No ), Major Internatonal Jont Research Project from Natonal Nature Scence Foundaton Chna (512114), and Chna Postdoctoral Scence Foundaton (216M59225). Author Contrbutons: Fu Chen developed and solved proposed model and carred out analyss. Fu Chen and Benx Lu proposed soluton methodology. Chuntan Cheng and Al Mrch helped wth Englsh revson manuscrpt. Conflcts Interest: The authors declare no conflct nterest. References 1. Henng, T.; Wang, W.; Magee, D.; He, D. Yunnan s Fast-paced large hydropower development: A powershed-based approach to crtcally assessng generaton and consumpton paradgms. Water. 216, 8, 476. [CrossRef] 2. Ura-Martnez, R.; O Connor, P.; Johnson, M.M. 214 Hydropower Market Report; Oak Rdge Natonal Laboratory: Oak Rdge, TN, USA, Cheng, C.; Yan, L.; Mrch, A.; Madan, K. Chna s boomng hydropower: systems modelng challenges and opportuntes. J. Water Resour. Plan. Manag. 216, 143, [CrossRef] 4. Hong, L.; Zhou, N.; Frdley, D.; Raczkowsk, C. Assessment Chna s renewable energy contrbuton durng 12th Fve Year Plan. Energy Polcy 213, 62, [CrossRef] 5. Rangel, LF. Competton polcy and regulaton n hydro-domnated electrcty markets. Energy Polcy 28, 36, [CrossRef] 6. Scott, T.; Read, E. Modellng hydro reservor operaton n a deregulated electrcty market. Int. Trans. Oper. Res. 1996, 3, [CrossRef] 7. Ramos, A.; Ventosa, M.; Rver, M. Modelng competton n electrc energy markets by equlbrum constrants. Utl. Polcy 1999, 7, [CrossRef] 8. Kelman, R.; Barroso, L.; Perera, M. Market power assessment and mtgaton n hydrormal systems. IEEE Trans Power Syst. 21, 16, [CrossRef] 9. Basls, C.; Bakrtzs, A. Md-term stochastc schedulng a prcemaker hydro producer wth pumped storage. IEEE Trans. Power Syst. 211, 26, [CrossRef]

18 Water 217, 9, Pousnho, H.M.I.; Contreras, J.; Catalao, J. Operatons plannng a hydro producer actng as a prce-maker n an electrcty market. In Proceedngs IEEE Power and Energy Socety General Meetng, San Dego, CA, USA, July 212; pp Flatabo, N.; Doorman, G.; Grande, O.S.; Randen, H.; Wangensteen, I. Experence wth Nord Pool desgn and mplementaton. IEEE Trans. Power Syst. 23, 18, [CrossRef] 12. Wolak, F.A. Market desgn and prce behavor n restructured electrcty markets: An nternatonal comparson. Top. Regul. Econ. Polcy 2, 36, Vllar, J.; Rudnck, H. Hydrormal market smulator usng game ory: Assessment market power. IEEE Trans. Power Syst. 23, 18, [CrossRef] 14. Sweetser, A. Measurng a domnant frm s market power n a restructured electrcty market, a case study Colorado. Utl. Polcy 1999, 7, [CrossRef] 15. L, G.; Sh, J.; Qu, X. Modelng methods for GenCo bddng strategy optmzaton n lberalzed electrcty spot market A state---art revew. Energy 211, 36, [CrossRef] 16. Borensten, S.; Bushnell, J.; Knttel, C.R. Market power n electrcty markets: Beyond concentraton measures. Energy J. 1999, 2, [CrossRef] 17. Gaudard, L.; Gabb, J.; Bauder, A.; Romero, F. Long-term uncertanty hydropower revenue due to clmate change and electrcty prces. Water Resour. Manag. 216, 3, [CrossRef] 18. Ortner, A.; Graf, C. Mult-market unt-commtment and capacty reserve prces n systems wth a large share hydro power: A case study. In Proceedngs 213 1th Internatonal Conference on European Energy Market (EEM), Stockholm, Sweden, May 213; pp Chazarra, M.; Pérez-Díaz, J.I.; García-González, J. Optmal energy and reserve schedulng pumped-storage power plants consderng hydraulc short-crcut operaton. IEEE Trans. Power Syst. 217, 32, [CrossRef] 2. Carpenter, D.; Haas, J.; Olvares, M.; de la Fuente, A. Modelng mult-seasonal lnk between hydrodynamcs a reservor and ts hydropower plant operaton. Water 217, 9, 367. [CrossRef] 21. Wu, X.; Cheng, C.; Wang, J.; Tang, H.; L, C. Long term hydropower optmal operaton model wth electrc power transmsson restrctons for absorbed energy Maxmzaton. Proc. CSEE 211, 31, Cheng, C.; Chen, F.; L, G.; Tu, Q. Market equlbrum and mpact market mechansm parameters on electrcty prce n yunnan s electrcty market. Energes 216, 9, 463. [CrossRef] 23. Weber, J.D.; Overbye, T.J. A two-level optmzaton problem for analyss market bddng strateges. Power Eng. Soc. Summer Meet. 1999, 2, Wang, J.; Lao, S.; Cheng, C.; Ca, H. Optmzaton medum-term rmal power boot based on hybrd search algorthm. Proc. CSEE 211, 31, Shafe-khah, M.; Moghaddam, M.P.; Shekh-El-Eslam, M.K. Ex-ante evaluaton and optmal mtgaton market power n electrcty markets ncludng renewable energy resources. IET Gener. Transm. Dstrb. 216, 1, [CrossRef] 26. Lu, B.; Lao, S.; Cheng, C.; Wu, X. A mult-core parallel genetc algorthm for long-term optmal operaton large-scale hydropower systems. In Proceedngs World Envronmental & Water Resources Congress 216, West Palm Beach, FL, USA, May Cheng, C.; Wang, W.; Xu, D.; Chau, K.W. Optmzng hydropower reservor operaton usng hybrd genetc algorthm and chaos. Water Resour. Manag. 28, 22, [CrossRef] 28. Cheng, C.; Wang, S.; Chau, K.; Wu, X. Parallel dscrete dfferental dynamc programmng for multreservor operaton. Envron. Model. Stw. 214, 57, [CrossRef] 29. Zhang, X.; Beeson, P.; Lnk, R.; Manowtz, D.; Izaurralde, R.C.; Sadegh, A.; Thomson, A.M.; Sahajpal, R.; Srnvasan, R.; Arnold, J.G. Effcent mult-objectve calbraton a computatonally ntensve hydrologc model wth parallel computng stware n Python. Envron. Model. Stw. 213, 46, [CrossRef] 217 by authors. Lcensee MDPI, Basel, Swtzerland. Ths artcle s an open access artcle dstrbuted under terms and condtons Creatve Commons Attrbuton (CC BY) lcense (