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A SIMULATION MODEL FOR LONG-TERM ANALYSIS OF THE ELECTRICITY MARKET

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A SIMULATION MODEL FOR LONG-TERM ANALYSIS OF THE ELECTRICITY MARKET Ilhan Or Guzay Pasaoglu Klanc Department of Industral Engneerng Department of Industral Engneerng Bogazc Unversty Istanbul Kultur Unversty
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A SIMULATION MODEL FOR LONG-TERM ANALYSIS OF THE ELECTRICITY MARKET Ilhan Or Guzay Pasaoglu Klanc Department of Industral Engneerng Department of Industral Engneerng Bogazc Unversty Istanbul Kultur Unversty 80815, Bebek/Istanbul 34156,Bakrkoy/Istanbul E-mal: E-mal: KEYWORDS Electrcty Market, System Dynamc, Smulaton. ABSTRACT After the lberalzaton of the electrcty generaton ndustry, capacty expanson decsons are made by multple selforented power companes. Unlke the regulated envronment, decson-makng of market partcpants s now guded by prce sgnal feedbacks and by an mperfect foresght of the future market condtons that they wll face. In such an envronment, decson makers need to understand long-term dynamcs of the supply and demand sde of the power market. To vsualze the compettve electrcty power market dynamcs, a smulaton model based on system dynamc phlosophy s developed n ths study. The developed model ncludes the demand module, the capacty expanson module, the power generaton sector, an accountng and fnancal module, varous compettors and a bddng mechansm (power poolng system). By means of such a decson tool, companes and regulators have a better opportunty to understand possble consequences of dfferent decsons that they may make under dfferent polces and market condtons. The man part of the paper s devoted to a detaled presentaton of the model. At the end, some fndngs of the prelmnary scenaro analyss are dscussed. INTRODUCTION Before the lberalzaton of the electrcty ndustry, nvestments n power plants used to be the result of an optmzed centralzed capacty expanson plannng at natonal or regonal level. The am of ths plannng was to determne the rght level of generatng capacty, the optmal mx of generatng technologes and the tmng of the nvestments and retrements of capacty to ensure that future demand n a certan regon would be served at mnmum cost wth an adequate level of relablty (Ku, 1995). In such an envronment, the future demand and future fuel prces are the only sgnfcant sources of uncertanty. Addtonally, producers are not prce takers,.e.,they have opportunty to dermne market prce. After lberalzaton of the electrcty ndustry, electrcty s no longer suppled by one monopolstc suppler. In the resultng compettve envronment, combned forces of supply and demand condtons, rather than the hstorc costs of the underlyng assets wll determne electrcty prces. Besdes, expected electrcty prce s a major factor whch trggers or postpones the nvestment decsons of power companes. On the other hand, although short-term demand elastcty s too low, longterm demand elastcty s between -0.5 and (Stoft, 2000). Consderng these arguments, we can say that nvestment decsons manly depend on cash flow expectatons of the nvestor and on the dynamcs of demand and supply n the power market, whle polces, constrants and avalabltes regardng, ncentves, taxes, transmsson needs, prmary energy reserves, real tme prcng ssues, seasonal effects, current plant portfolos of the producers, greatly nfluence these prmary factors. In such an envronment, the decson makers- ndvdual power companes or a publc regulatng authorty, try to answer the followng questons; How much new capacty should a generatng company buld n order to meet growng demand and to ncrease or preserve ts share n the market? At what prce should generators prce ther products n the market, n order to balance market share, solvency, short term proftablty and long term expectatons? Whether wthholdng (delberately reducng avalable electrcty wth the am of ncreasng prces) s a proftable strategy or not? Whch types of plants wll be more attractve n decentralzed, compettve envronment? How wll dfferent compettors react aganst certan behavoral trends? Under whch condtons renewable power plants wll be attractve for the power companes? How can a regulator ensure that compettve prces preval n the market, whlst suffcent ncentves for capacty expansons and new entres are mantaned? etc. To answer questons such as the ones forwarded above, decson makers need to understand long-term dynamcs of the supply and demand sde of the power market, as nvestment decson depends on t. Snce, system dynamcs focuses on learnng and understandng the system (nstead of predctng the future), to vsualze the compettve electrcty power market dynamcs, a smulaton model based on system dynamc phlosophy s developed n ths study. By means of such a tool, companes and regulators wll have the opportunty to understand possble consequences of dfferent decsons that they may make under dfferent polces and market condtons. Moreover, such a tool s a rsk-free way for the managers to gan experence n a compettve envronment whch they are not famlar wth. Besdes, the model s sutable to test over 50,000 dfferent scenaros whch may be needed for decson makers to be consdered n ther strategc plannng. Durng the modellng process, varous submodels have been developed and tested before they were ntegrated to form a complete system model. At each phase of the modellng, besdes lterature, expert opnons have been consdered n determnng the model structure, relatonshp between enttes, parameters and equatons used n the model. Currently, the study s at the scenaro analyss phase, 64 scenaros have already been confgured, run and and analysed. After scenaro analyss s completed, valdaton of the model wll be accomplshed. Then, by addng a userfrendly nterface to the model, the decson tool wll be completed. The man part of the paper wll be devoted to a detaled presentaton of the model. At the end, some fndngs of the prelmnary scenaro analyss are ntroduced. LITERATURE REVIEW System dynamc methodology has been used for analyss of the prvatzaton and deregulaton processes of electrcty markets all over the world (Bunn and Larsen, 1992; Bunn, Larsen and Vlakos, 1993; Bunn et al, 1997; Vlakos, 1998) used system dynamc methodology n ther studes whch focus on deregulated electrcty market. Research on nvestment cycles-boom and busts- observed n the Unted States electrcty market s presented n (Ford 1999; Ford 2000). Columban energy market has also been analyzed n (Dyner and Larsen1997) FEATURES OF THE DEVELOPED MODEL General characterstc In ths study, the post deregulaton Turksh Electrcty Market s modeled based on system dynamc approach, through STELLA 7.01 R. The model smulates the development of the lberalzed power market for a 20 years perod, on a monthly bass. Besdes, every 5 years t s possble to pause the current smulaton run (so called a game ) so that the player can update varous parameters f he or she so chooses. The stuaton that players are faced wth, at the start of the smulaton, s a market that has recently been opened to competton. Constrants on transmsson capacty between the varous regons are not consdered n the model. The most mportant factors, whch are consdered n the model, are brefly presented n the remanng of the paper. Players n the Model There are fve players n the model whch are of three types. These are, ) the ncumbent (holdng a major share of the current market), )three Independent Power Producers (IPP) who are generally smaller n sze but are more focused and agle (regardng generator nventores, decson makng and cash flows) and tend to stand and succeed n the market by competng wth the ncumbent, and ) one New Entrant (NE) whch may enter the market accordng to the market condtons. The model-user selects hs/her role at the begnnng of the game: 1. Ether he assumes one of the above stated player types (actve player), and the rest of the players (passve players) are drven by the computer, or 2. He nputs all the parameters descrbng the market behavor; then, all the players are drven by the computer (passve players). In the model, default rules/settngs are defned for each player. Characterstc of the Power Companes The Incumbent exsts n the market at the begnnng of the model. As prevously stated, t holds a major share n the market. The Incumbent has the hghest capacty and hghest captal among all players. The Incumbent s man busness objectve s to mantan hs current market share and proftablty. In the model there are 3 Independent Power Producer groups. These power generators also exst n the market at the begnnng of the model. In comparson to the market share of the Incumbent, these players have lower market shares. Ther man busness objectve s to mantan ther current market share and proftablty. They have the chance to takeover dvested plants of the ncumbent when (because of ant-monopolstc consderatons) the regulator requres the Incumbent to sell some of ts power plants. New Entrant (NE) does not exst n the market at the begnnng of the model. He has lower cash than the other players. He has low (debt fnancng) credblty because of hs low book value (fxed assets). He can enter the market n 2 condtons; after hs/her new power plants under constructon are completed or by takng over the dvested plants (by the regulator) of the ncumbent. There are some advantages of takng over dvested plants. Snce dvested plants are already bult and delverng electrcty, there s no need to wat for ther constructon. Addtonally, as the dvested plant s an already exstng one, ts book value s lower than that of new plant nvestment; so, t s cheaper to take over capacty rather than constructng new capacty. The Regulator The Regulator (a passve player n the model), oversees the market and ams to facltate the creaton of compettve condtons and to ensure far prces for the consumers. Gven that the Incumbent has a large market share ntally, the Regulator s partcularly aware of the potental abuse of ths power (and possble monopolstc tendences). The regulator orders dvestment f he deems t necessary. Ths means that the regulator requres the Incumbent to sell some of ts plants, f the market share of the Incumbent s greater than or equal to a predetermned level. The generatng capacty up for sale s offered to each player on an equal opportunty bass. Producers; (the NE and the IPPs) may buy the offered part of the Incumbent s plants as much as ther fnancal power, (cash and credt) let them. Addtonally, n case of dvestment, dstrbuton companes offer debt loans wth comparatvely lower nterest rates to Independent Power Producers and the New Entrant. Ths s manly due to ant-monopolstc consderatons. Buyers Consumers and dstrbuton companes are not explctly modelled, but ths part s reflected to the model wthn the framework of a detaled demand profle. Demand Transmsson losses and reserve margn requrements are assumed to be ncluded n the demand profle. Consequently, there s a sngle electrcty prce for the overall regon, so that prce dfferences wthn the regon due to transmsson congeston are not taken nto account. At the begnnng of the smulaton, peak demand, longterm demand elastcty and annual growth rate of demand are entered to the model. Addtonally, f the player wants, he/she can change these values every fve year. Default annual growth rate of electrcty demand s 7%. As prevously mentoned, the smulaton model uses monthly-based perods. In the model, each day of a month s assumed smlar n terms of demand characterstc, wth each day beng dvded nto 9 tme ntervals and each nterval s demand beng a predetermned percentage of peak demand (these percentages are determned based on hourly load curves data obtaned from TEIAS the Turksh Electrcty Transmsson Company). Durng a smulaton run, the monthly peak demand values are updated wth respect to annual peak demand growth rates. Then, based on the hourly load curves, and peak demand projectons, demand projectons for each month and each tme nterval are ndvdually determned. In the model, long-term demand elastcty s gven consderaton. When runnng the model, at the begnnng of each year, last 4 year s average electrcty prce s calculated as a reference market prce and then compared wth the average prce of the last year. If the last year s market prce s hgher than the calculated reference market prce, demand s further adjusted due to demand elastcty. (demand elastcty s defned as the percentage change n demand that occurs n response to a percentage change n prce). In addton to the above attrbutes and characterstcs consdered n the demand module, dfferent load curve patterns are also offered to the model user to use n hs scenaro analyss. Power Plants There are several plant types n Turkey categorzed accordng to ther prmary energy resources. These resources play key roles on cost and avalablty features. In the model, each player has the opportunty to buld/own generatng plants out of 8 dfferent plant types as long as he/she has the necessary fundng (ether as captal and/or fnancng) These 8 plants are; geothermal, wnd, mported hard coal, desel ol, lgnte, natural gas, small hydropower plants, large hydropower plants. However, due to some restrctons of Stella, the overall avalable capacty (n MW) of each plant type (rather than the capacty of each ndvdual plant) s consdered for each player (for buldng, ownng, operatng, deprecatng and dsposng). Decdng on the plant type to be bult s a strategc decson for ndvdual players. Decson makers have to evaluate these power plants based on ther margnal cost, capacty and avalablty factor, CO 2 emsson (f CO 2 tax s applcable), varable cost, fxed cost, economc plant lfe and capacty cost characterstcs. The model s equpped wth monthly avalablty data for wnd and hydropower plants. Besdes, based on hstorcal data of the last 30 years, the precptaton profle of Turkey s determned as 65% moderately rany, 20% dry and 15% very rany. To reflect ths flexblty to the model, durng a smulaton run, at the begnnng of each year, the amount of annual precptaton s randomly generated consderng the seasonalty effect and then the avalablty for hydropower plants s determned based on the generated precptaton fgures. Besdes, t s assumed that as power plants get older, ther avalablty also decreases. Power Pool Snce, the model s developed over monthly-based perods, the bddng process s also based on monthly perods and an electrcty poolng system s mplemented. As mentoned, each day of a month s assumed smlar n terms of demand characterstc. However, the electrcty demand of each month s dfferent from one other. In the model, for each succeedng month, the generators declare ther producton capactes and submt bds to the regulator. Each bd specfes a certan 30 mnute nterval for a certan day and offers a certan amount of power (n MWh) under a certan unt prce. To satsfy the next month s demand, the Regulator determnes the amount of the electrcty to be bought from each generator and the system margnal prce for each tme nterval of the next month. Durng ths phase, the players are contracted by the regulator (.e. ther bds accepted) n ascendng order of prce. The most expensve unt bought establshes the system margnal prce (SMP) whch all contracts receve durng that month for that tme nterval. In the model, f the generators offer the same prce, frst prorty s gven to the NE then to IPPs fnally to the Incumbent, to encourage new entry. In the lberalzed electrcty market, the Regulator offers, an addtonal prce mark-up, whch s desgned to provde an ncentve for future nvestment of addtonal generaton capacty. Ths mark-up s based on VOLL (value of loss load) and LOLP (Loss of Load Probablty). (Bunn et. al 1997). The regulator assesses and sets the value for loss of load (VOLL). In our model, mentoned prce mark-up s also offered to the generators. LOLP s a functon of excess capacty. Excess Capacty n turn s calculated by the Equaton 1. TotalInsta lledcapac ty PeakDemand ExcessCapa cty = (1) PeakDemand In the smulaton, the default LOLP functon s made avalable to the players and the game setter has the opportunty to change ths functon at the begnnng of the game. Incentves If the market share of the ncumbent s more than a predetermned level, low cost loans (n other words, dscounted loans) are made avalable for the IPPs and the NE as an addtonal ncentve. Capacty payments based on VOLL and LOLP can be consdered as another ndrect ncentve. In the EU countres t s amed that by 2010, 22 % of the energy generated wll be produced by renewable resources. In lne wth ths EU am, varous renewable energy power plants (REPP) ncentves are mplemented n the smulaton model: These are; government subsdy loans and extra tax exemptons. Besdes the nvestment ncentve offered for the REPP, there s also an addtonal CO 2 tax for thermal power plants (natural gas, hard coal, desel ol and lgnte type of power plants). The Incentve for REPP and the exercse of the CO 2 tax are left optonal n the model (.e. to be actvated by the game setter). Proft Margn Expected Proft margns of all player types are nput to the model by the players at the begnnng of each game and then can be updated every 5 years. Due to the mechancs of system dynamcs and Stella Software, proft margns are not calculated wthn the system; n other words, prce optmzaton s out of the scope of ths model. Taxaton Tax s another ssue gven consderaton n the model. If not set otherwse at the begnnng of the game, the tax rate s fxed to 30 % by default. Tax s calculated for earnngs after fxed costs, varable costs, nterest costs and deprecaton costs. (The method deployed for deprecaton s lnear for a perod of 20 years.) Accordng to the present relevant legslaton n Turkey, 40 % of fxed asset nvestments may be deducted from the pre-tax proft (whch means that no tax s ncurred untl cumulatve pre-tax proft gets more than 40% of the new nvestment). The mplementaton of the mentoned tax exempton s left optonal n the model (.e. to be actvated by the game setter). Behavor of Power Companes n the Market In the model, t s assumed that power companes may want to overtake rsk at 3 dfferent levels of aggresson. These levels are descrbed as; Aggressve, Neutral and Conservatve. We assumed that as companes gets more aggressve, ther expected nternal rate of return for a new nvestment and ther proft margns decreases, accordngly, ther wllngness to buld new power plants (at less expected proft and therefore at ncreased rsk) ncreases. Long-term Contracts Long-term contracts can optonally be ncluded n the model. If no long-term contracts are ncluded, ths means pure poolng mechansm prevals the model (No long-term contracts are consdered n the default model). The game-setter may change ths opton on the model nterface and nclude long-term contracts n the system. In ths case, the actve producer s asked to defne the porton of hs/hers capacty that he/she wshes to be allocated to long-term contracts (%15, %10, %5, %0). A reference value for the long term contract prce s determned based on the average prce of the last 5 years. Long-term contract prce for the frst 5 years, on the other hand, may be nput by the player at the begnnng of the game (the default value of whch s assumed to be 80 $/MWh). The porton of passve players capactes that are to be allocated to long-term contracts vary dependng on ther aggresson level. Smlarly, contract prces of the passve players also depend on ther aggresson level. Aggressve players are assumed to take more rsks and as such be wllng to have less long-term contracts, compared to more conservatve players. As the number of long-term contracts n the market ncreases, the contract prce of the next contract s assumed to be more compettve (.e. lower). If long term contract opton s actvated, long term contract prce changes for passve players, are llustrated n Table 1. Table 1: Relatonshp between long-term contract prce, aggressveness of the player and supply amount of the passve players that allocated to the long term contract. Behavor % of player s capacty allocated wth long term contracts Contract Prce ($/MWh) Aggressve 5 (1-0.05) x last 5 years market prce Neutral 10 (1-0.10) x last 5 years market prce Conservatve 15 (1-0.15) x last 5 years market prce Reserve & Capacty Addton Lmt Employment of electrcty generaton alternatves are lmted wth resource avalablty and reserves. Reserves can be nput by the player at the begnnng of the game and/or upda
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