8t Mediterranean Conference on Power Generation Transmission Distribtion and Energy Conversion MEDPOWER 212 Greek Wolesale Electricity Market: Fortcog Market Canges and Bid/Cost Recovery Panagiotis E. Andrianesis George Liberopolos and Alex D. Papalexopolos Abstract-- Te Greek wolesale electricity market is based on a day-aead nit commitment market clearing and generation dispatc formlation wit co-optimization of energy and reserves witot te participation of transmission. An important featre of te market design is te cost recovery mecanism wic explicitly comsates generation nits for teir commitment costs and garantees a imm profit eqal to 1% of teir variable costs. In tis paper we evalate te crrent mecanism in comparison to an alternative bid/cost recovery mecanism by simlating te wolesale electricity market for a period of one year. As part of te stdy we take into accont te fortcog canges for te liberalization of te Greek market tat inclde te pysical and virtal sale of a portion of PPC lignite plants and we exae a likely "bsiness as sal" scenario concerning te reslting bidding strategies. Lastly we perform a sensitivity analysis wit respect to te ydro prodction and te carbon price. Index Terms--Wolesale electricity market nit commitment bid/cost recovery sensitivity analysis. I. NOMENCLATURE A. Sets-Sbsets-Indices U Generation nits indexed by U AGC Generation nits tat can operate in AGC mode (U AGC U) Hor (time period) B. Parameters Q AGC MU Q Tecnical imm/imm of nit AGC Tecnical imm/imm of nit nder AGC MD Minimm ptime/downtime of nit PR Primary reserve of nit SRR Secondary reserve range of nit TR Tertiary reserve of nit g P Price of energy (generation) of nit or pr srr P Price of primary reserve of nit or P Price of secondary reserve range of nit or SDC Stdown cost of nit ST Initial stats of nit (at or ) P. Andrianesis and G. Liberopolos are wit te Department of Mecanical Engineering University of Tessaly Volos 38334 Greece (emails: andrianesis@t.gr; glib@mie.t.gr). A. D. Papalexopolos is wit ECCO International Inc. San Francisco CA 9414 USA (e-mail: alexp@eccointl.com). X Nmber of ors nit as been "ON" at or W Nmber of ors nit as been "OFF" at or SL System load in or RES RES injections in or Imp Exp Imports/Exports in or Pmp Pmping in or PR Primary reserve irement in or SRU SRD Secondary reserve p/down irement in or TR Tertiary reserve irement in or tot G Penalty coefficient related to te energy balance constraint PR SR TR Penalty coefficients related to te reserve irements constraints C. Variables ST Stats (condition) of nit or Binary variable: 1 = ΟΝ(LINE) = OFF(LINE) G Generation (otpt) of nit or PR Primary reserve of nit or SRU SRD Secondary reserve p/down of nit or TR Tertiary reserve of nit or AGC AGC condition of nit in or Dedent binary variable: 1 = In AGC mode = Not in AGC mode V Stdown signal for nit in or Dedent binary variable: 1 = Stdown = No stdown X Nmber of ors nit as been ON at or since last startp Integer variable W Nmber of ors nit as been OFF at or since last stdown Integer variable tot G G tot sr Deficit and srpls variables related to te energy balance constraint in or PR SRU SRD TR Deficit variables related to te reserve irement constraints in or II. INTRODUCTION HIS paper considers te Greek wolesale electricity T market design wic falls into te category of markets wit non-convexities or as it is referred in te seal work of Scarf [1] markets wit "indivisibilities." Non-convexities as a featre of electricity markets are de to te commitment costs and capacity constraints of te generation nits. In teir presence te revenes of te generation nits tat participate in sc a market may not always be sfficient to cover teir total costs. In tis case it is critical to implement a recovery mecanism to comsate 1
tem and make tem financially wole. Apart from [1] wic demonstrates te lack of a market-clearing price in a market several approaces ave been proposed (see e.g. [2]- [5]) to deal wit tis market condition. Neverteless te isse of pricing non-convexities remains still o. In tis paper wic contines or previos work on recovery mecanisms [6]-[8] we exae teir application in te fortcog liberalized Greek electricity market. Specifically we focs on te market canges wic will reslt from te sale of a certain portion of PPC lignite nits prsed by te Eropean Union and te International Monetary Fnd. Te sale of te lignite plants can take te form of a pysical or virtal divestitre or a combination of bot. Te option of te pysical divestitre is to redce te market power of te vertically integrated doant tility and liberalize te energy sector. It involves te sale of for lignite plants of total capacity of 19 MW. Te alternative option of te virtal divestitre consists of a series of competitive economic measres wose objectives are to redce te incentive of te doant tility to exercise market power. Tese are: 1) Forward Energy Contracts and specifically Contracts for Differences (CFDs) 2) Virtal Power Plant (VPP) Actions (Pysical) 3) Virtal Power Plant (VPP) Actions (Financial) and 4) Energy Swaps or Drawing Rigts. Te implementation of te combination of tese instrments can acieve te oing of te energy market by redcing te lignite capacity available to te doant tility. Assg tat te crrent market model will contine for te next cople of years te private lignite plants wit variable cost of abot 4 /MW compared to te 7-75 /MW for te new CCGTs will materialize sbstantial profits. In tis context of market setting we analyze te existing recovery mecanism and compare it to an alternative bid/cost mecanism on a yearly basis. We also perform sensitivity analysis wit respect to te ydro prodction and te carbon price. Te remainder of te paper is organized as follows. Section III presents te Greek wolesale electricity market framework. Section IV provides te inpt data and te test cases tat are sed for te yearly simlations. Section V presents and discsses te nmerical reslts and Section VI concldes and provides directions for frter researc. III. THE GREEK ELECTRICITY MARKET FRAMEWORK Te Greek electricity pool market framework consists of a Wolesale Energy and Ancillary Services Market [9] wic deploys te following processes: 1) Day-Aead Scedling (DAS); 2) Dispatc Scedling (DS); 3) Real Time Dispatc (RTD) operation; and 4) Imbalances Settlement (IS). In te following we briefly describe te above processes (sb-section A) present te matematical formlation of te DAS model (sb-section B) and finally smmarize te crrent cost recovery mecanism (sb-section C). In tis paper we assme a single-zone design witot loss of generality since nder crrent conditions te doant transmission constraint is ardly ever binding; a more complete description of te Greek wolesale electricity market can be fond in [13]. A. Market Processes Te Hellenic Transmission System Operator (HTSO) was responsible till recently for te management and exection of te above market processes [1]. Since Febrary 1 212 te HTSO was split into two indedent entities: te Indedent Power Transmission Operator (IPTO) [11] wic performs te dties of system operation maintenance and development and te Hellenic Electricity Market Operator (HEMO) [12] wic rns te day-aead market and te Renewable Energy Sorces (RES) contracts. 1) Day-Aead Scedling (DAS) Te Day-Aead market operation is based on te DAS problem wic is solved daily simltaneosly for all 24 ors of te next day by te HEMO. Te objective is to imize te cost of balancing te energy to be absorbed wit te energy to be injected in te system wile meeting te reserve irements and te generation nits tecnical constraints. Te DAS soltion ines ow eac nit sold operate in eac or and deteres clearing prices of te energy i.e. te System Marginal Price (SMP) and of te reserves. 2) Dispatc Scedling (DS) In te time period between te DAS and RTD (te operational timescale) te IPTO exectes te DS procedre adjsting nit commitment scedling and reserves qantities to respond to canges in te nits' availability in system demand or modifications in te interconnection flows. 3) Real-Time Dispatc (RTD) Te generating nits are sbject to optimal re-dispatc in real time to meet actal system demand. Te RTD procedre is exected by te IPTO every 5 and prodces an economic dispatc for te next 5- time interval wit te nit commitment stats being inerited from te DS and te bids from te day-aead market. 4) Imbalances Settlement (IS) Te IPTO deteres an ex post System Imbalance Marginal Price (SIMP) on an orly basis by execting te Ex Post Imbalance Pricing (EXPIP) procedre after te Dispatc Day. Tis procedre is similar to te DAS procedre except tat it ses actal system demand and actal nit commitment stats and availability. Te generators' deviations are divided into instrcted and ninstrcted. Positive instrcted deviations are paid te relevant SIMP wereas positive ninstrcted deviations are not paid. Negative instrcted deviations are carged as bid wereas negative ninstrcted deviations are carged te relevant SIMP. Load deviations are settled at te SIMP. Te reserves qantities tat are provided in real time are paid at te relevant DAS prices. B. Te DAS Model In te erein presented DAS model we consider all available generation resorces namely termal and ydro plants imports RES injections exports and pmping stations. For te prposes of or analysis we assme imports exports and pmping as parameters of te optimization problem. Reserves inclde primary secondary 2
p and down and tertiary reserve. Te prodcers sbmit energy s for eac or of te following day as a stepwise fnction of price-qantity pairs wit sccessive prices being strictly non-decreasing. For simplicity we assme a single price bid for energy. Te prodcers also sbmit reserve bids as price-qantity pairs as well as teir commitment costs. Energy and reserves bid caps are establised in order to prevent excessive price spikes in case te available capacity is insfficient to meet te demand. Te price cap for te energy s is crrently set at 15/MW and for te reserve s (primary and secondary) at 1/MW. Te tecnical caracteristics of te generation nits tat constitte te constraints of te DAS problem inclde te tecnical imm/imm otpt te AGC imm/imm te imm reserve availability and te imm p/down times. Ramp p/down limits are not considered in or analysis as tey are rarely binding and teir impact on te annal reslts is negligible. Te DAS market process can be formlated as a Mixed Integer Programg (MIP) problem. In te following and nless oterwise mentioned refers to te general set U and to te 24- orizon of te DAS problem. Objective Fnction: Generation Cost + Reserves Cost (1) + Commitment Cost + Penalty Cost Cost Components: g Generation Cost = P G (2) pr srr Reserves Cost = { P PR + P ( SRU + SRD )} (3) Commitment Cost = V SDC (4) tot tot tot sr G ( G + G ) + PR PR Penalty Cost = + SR ( SRU + SRD ) + TR TR (5) Constraints: Energy Balance: tot tot sr G + Imp+ RES+ G G = SL + Exp + Pmp (6) Reserve Reqirements: PR + PR PR SRU + SRU SRU SRD + SRD SRD TR + TR TR Capacity and Reserve Limits: G + PR + SRU + TR Q ( ST AGC ) + AGC AGC (7) (8) (9) (1) (11) G SRD Q ( ST AGC ) + AGC AGC + PR ST PR SRU SRD AGC SRR TR ST TR (12) (13) (14) (15) Availability and Stats-Related Constraints: avail ST ST (16) AGC ST (17) AGC = U (18) Minimm Up/Down Times: ( X 1 MU)( ST 1 ST ) (19) ( W 1 MD)( ST ST 1) (2) Constraints for Dedent Variables: V = ST 1 (1 ST ) (21) X = ( X 1 + 1) ST (22) W = ( W + 1)(1 ST ) (23) 1 Initial Conditions: ST ST = = = AGC (24) X X (25) W W (26) wit G PR SRU SRD TR. Note tat te commitment cost in (4) incldes only te stdown cost. According to te Grid and Power Excange Code [9] te stdown cost is considered to be eqal to te warm startp cost. Te objective is to deter te DAS program wic concerns a rater sort orizon (24) relative to te time and effort it takes to start p some nits from reacing a soltion in wic it easily sts down tese nits. A discssion on a longer DAS orizon is fond in [14]. Te alty cost in (5) is an additional term imposed in te objective fnction to deal wit problem infeasibilities. Also note tat constraints (19)-(23) are not linear. Tese constraints are replaced wit eqivalent ineqalities as is sown in [13] Apdix A. Te reslting formlation after tese replacements is a Mixed Integer Linear Programg (MILP) problem tat can be modeled and solved wit any MILP solver. Once te MILP problem is solved a Linear Programg (LP) problem is created by fixing te integer variables at teir optimal vales and dropping te constraints tat involve only integer variables. Te LP formlation allows for te calclation of clearing prices sing marginal pricing teory [15]. Te energy clearing price is ten detered as te sadow price of te energy balance constraint (6). C. Cost Recovery Mecanism Te recovery mecanism crrently in se in te Greek market provides a) explicit comsation for te commitment costs in case tese costs are incrred as a reslt of te market otcome (generation scedling) and b) additional payments so tat te generation nit ends p wit a profit eqal to 1% of its variable cost in case tis profit is not reaced trog te market revenes for energy 3
considering bot te day-aead market and te imbalances settlement. Frter information on recovery mecanisms in markets wit non-convexities can be fond in [6]- [8][13][16]. IV. INPUT DATA AND TEST CASES Te objective of tis paper is to evalate te existing recovery mecanism wit respect to an alternative bid/cost recovery mecanism. To obtain meaningfl reslts we evalate tis impact on an annal basis by iteratively solving te daily market model. For eac day of te year we solve te market model twice: te first time by execting te DAS and te second time by execting te EXPIP as previosly described. Te DS and RTD processes are not considered in or analysis. In tis paper te sole difference between te DAS and te EXPIP lies in te system load and te RES forecast errors; ence te dispatcing of te conventional generation nits is also different. Te remaining parameters are kept constant to facilitate te comparisons. In wat follows we list te inpt data (sb-section A) and describe te test cases tat are sed for te simlations (sb-section B). A. Inpt Data Te inpt data refer to an instance representing te Greek electricity market for te year 211. 1) System Load and Reserve Reqirements For te system orly load (DAS system load declarations and ex post/actal system load) and te reserve irements we sed te data of te year 211 wic are pblicly available in [1]. Te alty coefficients for te violation of te constraints (6)-(1) were set at 25 ( /MW) for te energy balance 2 ( /MW) for te primary reserve 15 ( /MW) for te secondary reserve (bot p and down) and 1 ( /MW) for te tertiary reserve. 2) Conventional Generation Te conventional generation nits in operation are sown in Table I. Unit Type TABLE I CONVENTIONAL GENERATION UNITS (AS OF DECEMBER 211) Nmber of Units Installed Capacity (MW) Cost-Based Energy Offers ( /MW) Lignite 18 4456 29-45 CCGT 1 3976 71-16 OCGT 3 147 18.8-19 Gas 2 339 18-118 Oil 4 698 112-116 Hydro 15 316 12 Total Capacity: 12632 Te energy s inclde also te emissions cost calclated wit a vale of 7 /tco 2 e and as a reference reflect cost-based bidding. For te reserve bids we assmed tat te generation nits bid at te average prices tat were observed in 211 i.e. 1 /MW for te primary reserve 4.3 /MW for te secondary reserve p and 6.8 /MW for te secondary reserve down. Te maintenance scedle and te otage rate for te conventional generation nits (termal and ydro) were assmed to be te same as in te year 211. For te needs of or analysis we generated Bernolli-distribted random otages for eac day based on te Eqivalent Demand Forced Otage Rate (EFOR D ) vales wic provide a measre of te probability tat a generation nit will not be available de to a forced otage and assmed a 2-day otage repair time. For simplicity we did not consider eac ydro nit separately; instead we considered an aggregate nit wit a total available capacity of 257MW taking into accont te average EFOR D of te ydro nits. For te mandatory ydro prodction we sed te data for te ydro prodction in 211. 3) Imports Exports Pmping RES For imports exports and te pmping profile we sed te DAS data for te year 211. For te prposes of or analysis we assmed tat te DAS vales remain constant in real time and terefore are te same wit te ex post ones. For te RES injections we sed te forecasts in te DAS and te actal (ex post) vales in te IS. B. Test Cases Te test cases tat are considered in tis paper are listed below. Case 1: All nits bid at teir cost. Tis case serves as a reference. Case 2: Bidding strategy for private nits wit crrent carbon price. In tis case 4 lignite nits and 5 CCGT nits (privatelyowned) are simlated to deploy a bidding strategy different tan te cost-bidding strategy. Te lignite nits bid 15% iger tan teir variable cost dring off-peak ors wereas tey bid at cost dring peak ors. Te CCGT nits bid 15% iger tan teir variable cost dring peak ors wereas tey bid at cost dring off-peak ors. We ave distingised between peak and off peak ors so tat tey bot represent 5% of te total ors for te year 211 load levels. Te median was 599MW; vales above 599MW are considered peak wereas vales below 599MW are considered off-peak. Te jstification for tis strategy is tat lignite nits are more likely to be price-makers dring offpeak ors wereas te CCGT nits are more likely to be price-makers dring peak ors. Case 2 refers to a normal ydro prodction (neiter dry nor wet year) and a relatively low carbon price eqal to 7 /tco 2 e. Tis case is considered te "bsiness as sal" case on wic we perform te following sensitivity analysis. 1) Sensitivity analysis wit respect to te ydro prodction Case 2D: Case 2 for a Dry Year Case 2W: Case 2 for a Wet Year 2) Sensitivity analysis wit respect to te carbon price Case 2M: Case 2 for a Medim Carbon Price Case 2H: Case 2 for a Hig Carbon Price Cases 2M and 2H refer to carbon prices eqal to 15 /tco 2 e and 3 /tco 2 e respectively. For eac of te above six cases we exae te performance of te following two recovery mecanisms: Mecanism A: Te crrent recovery mecanism wic is described in Section III and 4
Mecanism B: A bid/cost recovery mecanism wic comsates te generation nits so tat tey recover teir as-bid costs. V. NUMERICAL RESULTS We modeled te market model and te simlation procedre wit GAMS 23.7.3 [17] and solved it wit CPLEX 12.3 solver on an Intel Core i5 at 2.67GHz wit 6GB RAM. In Table II we present te annal aggregate reslts of te simlations for eac Case and Mecanism. General Remarks on Recovery Mecanisms A and B Te recovery payments are significantly iger nder Mecanism A (crrent) tan nder Mecanism B (proposed). Tey range from 124.1 to 155.5M nder Mecanism A and from 27.8 to 38.8M nder Mecanism B (see rows 2a and 2b). Recall tat te crrent recovery mecanism explicitly comsates for te commitment costs wic amont to abot 65M on average annally (not sown in te reslts). In addition te generation nits are garanteed a profit of 1% of teir variable cost wic also leads to ig recovery payments particlarly for te extra-marginal gas nits. Te reslting plifts (de to te recovery payments) tat are passed onto te consmption range from 2.42 to 3.4 /MW for Mecanism A wereas tey range from.54 to.76 /MW for Mecanism B (see rows 4a and 4b). Also note tat te profits of te termal generation nits are iger nder Mecanism A tan nder Mecanism B in all cases (see rows 5a and 5b). Remarks on te "Bsiness as Usal" Case (Case 2) Te "bsiness as sal" case (Case 2) leads to iger energy prices compared to Case 1 wic is qite expected as a reslt of te bidding strategy of te 4 lignite and 5 gas nits. Te weigted average SMP (WASMP) is increased by abot 4.5 /MW (see rows 1 and 3 Cases 1 and 2). Case 2 also leads to redced energy generation from te private gas nits compared to Case 1 by abot 27% (see row 14 Cases 1 and 2) as a reslt of teir iger bids. Teir profits are redced by 22% nder Mecanism A wereas tey increase nder Mecanism B (see rows 9a and 9b). Tis is an indication tat te bidding strategy performs well nder Mecanism B. Hence te oter gas nits are likely to respond to tis strategy and tis wold be an interesting area for frter researc. Note tat te bidding strategy tat is employed by te 4 lignite nits does not significantly affect teir annal prodction; lignite still serves te base load. Remarks on te Hydro Prodction Sensitivity Analysis In te dry year case (Case 2D) te ydro prodction is sbstitted by gas (see rows 13 and 16 Cases 2 and 2D) and vice versa in te wet year case (Case 2W). Te reslts sow a small increase in te WASMP in Case 2D and a small decrease in Case 2W (see row 3 Cases 2 2D 2W). Te recovery payments do not exibit significant canges (see rows 2a and 2b Cases 2 2D 2W). Remarks on te Carbon Price Sensitivity Analysis Te increased carbon price (Case 2H) redces te profits of te lignite nits de to te ig emissions cost (see rows TABLE II ANNUAL AGGREGATE RESULTS (Crrent) ("Bsiness As Hydro Prodction Carbon Price Recovery Row Usal") Sensitivity Analysis Sensitivity Analysis Mecanism Case 1 Case 2 Case 2D Case 2W Case 2M Case 2H PAYMENTS (M ) Energy Payments 1 3279.8 3483.7 355.2 3476.3 365 3961.8 Recovery Payments 2a A 155.5 125.8 124.1 125. 125.4 147.7 2b B 29.8 28.1 27.9 28.5 27.8 38.8 PRICES ( /MW) WASMP 3 7.67 75.11 75.58 74.92 78.72 85.53 Uplift (de to recovery) 4a A 3.4 2.46 2.42 2.44 2.45 2.88 4b B.58.55.55.56.54.76 PROFITS (M ) 5a A 986. 1119.5 1142.3 116.5 912.3 557. Termal Generation 5b B 86.3 121.8 146.1 19.9 814.7 448. Lignite 6a A 885.5 113. 127.6 15.9 83.5 423.3 6b B 832.6 958.4 974.3 949.7 75.1 37.4 4 Lignite (*) 7a A 213. 244.4 249.7 242.6 193.6 13.4 7b B 21.2 232.1 238.7 23.7 182. 94.4 Gas 8a A 99.7 15.6 114. 99.7 18. 132.9 8b B 27.7 63.4 71.8 6.3 64.6 77.7 5 Gas (*) 9a A 8.1 62.4 69.5 58. 65.4 85.9 9b B 21.1 46.7 52.8 44.1 49.1 62.2 ENERGY (TW) Termal Generation 1 42.55 42.51 43.34 41.69 42.53 42.56 Lignite 11 29.29 29.26 29.3 29.17 29.27 27.18 4 Lignite (*) 12 6.91 6.87 6.9 6.83 6.85 5.32 Gas 13 13.19 13.19 13.98 12.46 13.18 15.28 5 Gas (*) 14 1.8 7.82 8.54 7.27 8.1 9.93 Oil 15.7.7.7.7.7.6 Hydro Generation 16 3.66 3.69 2.86 4.51 3.69 3.69 Note: Te nits wit te bidding strategy i.e. te 4 lignite nits and 5 gas (CCGT) nits are marked wit an asterisk (*) 5
8t Mediterranean Conference on Power Generation Transmission Distribtion and Energy Conversion MEDPOWER 212 6a and 6b Cases 2 and 2H). Te WASMP and terefore te energy revenes increase wit te carbon price (see row 3 Cases 2 2M 2H). Higer energy revenes bt also iger variable costs create an ambigos otcome as tey are opposing forces in te need for recovery payments. Under te medim carbon price scenario (Case 2M) tese two forces prodce more or less te same otcome as nder te low carbon price scenario (Case 2) wereas nder te ig carbon price scenario (Case 2H) te otcome is iger recovery payments (see rows 2a and 2b Cases 2 2M 2H). VI. CONCLUDING REMARKS In te context of te fortcog canges in te Greek electricity sector and particlarly te sale of lignite plants we considered te existing cost recovery mecanism and compared it to an alternative bid/cost mecanism on an annal basis. We also performed sensitivity analysis wit respect to te ydro prodction and te carbon price. In tis paper we did not aim at evalating te incentive compatibility of te recovery mecanisms wic as been investigated in or previos work [6]-[8]. Instead we adopted several assmptions on te bidding beavior of te generating nits and focsed on te annal magnitde of te recovery payments nder tese assmptions. We also discssed te implications on te energy prices nits' profits and energy generation mix. Te reslts sowed tat te recovery payments are significantly lower nder te alternative bid/cost recovery mecanism. However we sold note tat a reglated cap may be needed to deter te nits from sbmitting particlarly ig bids (see [7] for a preliary discssion). Some interesting directions for frter researc wold be to consider te privatization of ydro plants and more complicated bidding strategies. More scenarios cold also be considered wit respect to oter parameters sc as te fel prices te nit otages and RES forecast errors. VII. REFERENCES [1] H.E. Scarf Te allocation of resorces in te presence of indivisibilities J. Econ. Perspectives vol. 8 no. 4 pp. 111-128 1994. [2] R.P. O Neill P.M. Sotkiewicz B.F. Hobbs M.H. Rotkopf and W.R.Jr. Stewart Efficient market-clearing prices in markets wit nonconvexities Erop. J. Oper. Res. vol. 164 pp. 269-285 25. [3] W.W. Hogan and B.J. Ring. (23 Mar.). On imm-plift pricing for electricity markets Working Paper Jon F. Kennedy Scool of Government Harvard University. [Online]. Available: ttp://ksgome. arvard.ed/~wogan/plift_3193.pdf [4] M. Bjørndal and K. Jörnsten Eqilibrim prices spported by dal price fnctions in markets wit non-convexities Erop. J. Oper. Res. vol. 19 pp. 768-789 28. [5] C. Riz A.J. Conejo and S.A. Gabriel "Pricing non-convexities in an electricity pool" IEEE Trans. Power Syst. 212. [6] P. Andrianesis G. Liberopolos and G. Kozanidis Energy-reserve markets wit non-convexities: An empirical analysis in Proc. IEEE/PES Power Tec 29 Conf. Bcarest Romania Jn. 28-Jl. 2 29. [7] P. Andrianesis G. Liberopolos G. Kozanidis and A. Papalexopolos "Recovery mecanisms in a joint energy/reserve day-aead electricity market wit non-convexities" in Proc. EEM1 Conf. Madrid Spain 23-25 Jne 21. [8] P. Andrianesis G. Liberopolos G. Kozanidis and A.D. Papalexopolos "A recovery mecanism wit loss-related profits in a day-aead electricity market wit non-convexities" in Proc. IEEE PowerTec 211 Conf. Trondeim Norway 19-23 Jne 211. [9] (212) Reglatory Atority for Energy (RAE) website. [Online]. Available: www.rae.gr/codes/main.tm [1] (212) Te Hellenic Transmission System Operator website. [Online]. Available: ttp://www.desmie.gr [11] (212) Hellenic Electricity Market Operator website. [Online]. Available: www.lagie.gr [12] (212) Indedent Power Transmission Operator website. [Online]. Available: www.admie.gr [13] P. Andrianesis P. Biskas and G. Liberopolos "An overview of Greece s wolesale electricity market wit empasis on ancillary services" Electr. Power Syst. Res. vol. 81 no. 8 pp. 1631-1642 Ag. 211. [14] P. Andrianesis G. Liberopolos P. Biskas and A. Bakirtzis "Medim-Term Unit Commitment in a Pool Market" in Proc. EEM11 Conf. Zagreb Croatia 25-27 May 211. [15] F.C. Scweppe M.C. Caramanis R.D. Tabors R.E. Bon Spot Pricing of Electricity Klwer Academic Pblisers Boston MA 1988. [16] C. Baslis P. Biskas and A. Bakirtzis "A profit imization model for a power prodcer in a pool-based energy market wit cost recovery mecanism" in Proc. EEM11 Conf. Zagreb Croatia 25-27 May 211. [17] A. Brooke D. Kendrick and A. Meeras "GAMS User s Gide" Redwood City CA: Te Scientific Press 199. [Online]. Available: ttp://www.gams.com/docs/docment.tm VIII. BIOGRAPHIES Panagiotis Andrianesis gradated from te Hellenic Military Academy (21) received is B.A. (24) degree in Economics from te National and Kapodistrian University of Atens and is Diploma (21) in Electrical Engineering from te National Tecnical University of Atens. He also received is M.Sc. (211) degree in Prodction Management from te University of Tessaly Volos Greece were e is crrently a doctoral stdent. George Liberopolos (B.S. 85 M.Eng. 86 Mec. Engr. Cornell University P.D. 93 Manf. Engr. Boston University) is Professor of Operations Management and Head of te Prodction Management Laboratory in te Dept of Mec. Engr. University of Tessaly (UTH) Volos Greece. Prior to joining UTH e was Lectrer in te Dept of Manf. Engr. Boston University and Visiting Researcer in te Laboratoire d Informatiqe Université Paris 6 France. He is/was editorial board member of Flexible Services and Manfactring Jornal IIE Transactions and OR Spectrm. He as co-edited eigt special isses/volmes of jornals/books wit temes in te area of qantitative analysis of manfactring systems. He as pblised nmeros scientific papers in IEEE INFORMS and oter jornals mostly in operations researc/management and atomatic control. His researc interests inclde applied probability operations researc and atomatic control models and metodologies applied to prodction and operations control. Dr. Liberopolos is a member of INFORMS te Hellenic Operations Researc Society and te Tecnical Camber of Greece. Alex D. Papalexopolos (M 8 SM 85 F 1) received te Electrical and Engineering Diploma from te National Tecnical University of Atens Greece in 198 and te M.S. and P.D. degrees in Electrical Engineering from te Georgia Institte of Tecnology Atlanta Georgia in 1982 and 1985 respectively. Crrently e is president and fonder of ECCO International a specialized Energy Conslting Company wic provides conslting and software services on electricity market design and system operations witin and otside te U.S. to a wide range of clients sc as Reglators Governments Utilities and ISOs. He as designed some of te most complex energy markets in te world. Prior to forg ECCO International e was a Director of te Electric Indstry Restrctring Grop at te Pacific Gas and Electric Company in San Francisco California. He as made sbstantial contribtions in te areas of network grid optimization and pricing energy market design and competitive bidding and implementation of EMS applications and real time control fnctions. He as pblised nmeros scientific papers in IEEE and oter Jornals. Dr. Papalexopolos is a Fellow of IEEE te 1992 recipient of PG&E s Wall of Fame Award and te 1996 recipient of IEEE s PES Prize Paper Award. 6