AN ENHANCED SCREENING CURVES METHOD FOR CONSIDERING THERMAL CYCLING OPERATION COSTS IN GENERATION EXPANSION PLANNING

Transcription

1 IIT Workng Paper IIT-12-7A. Frst verson: January 211. Ths verson: November 212. Accepted for publcaton n IEEE Transactons on Power Systems AN ENHANCED SCREENING CURVES METHOD FOR CONSIDERING THERMAL CYCLING OPERATION COSTS IN GENERATION EXPANSION PLANNING C. Batlle* and P. Rodlla** Abstract Generaton capacty expanson trends have clearly evolved n the last decades. In the present context, renewable generaton technologes are expected to reach large penetraton levels. Among other effects, these technologes are changng the schedulng regme (and thus the unt-commtment costs) of the rest of the generatng facltes, ncreasng for nstance the need of cyclng conventonal thermal generaton. In ths paper we further develop the tradtonal screenng curves technque so as to ncorporate a sound representaton of the cyclng operaton of thermal unts. The so-resultng approach provdes a more comprehensve representaton of thermal operaton whle keepng the screenng curves well-known capablty to provde valuable analytc nsghts on the capacty expanson problem. 1 INTRODUCTION Non-dspatchable, not fully predctable and ntermttent energy resources (hereafter Varable Energy Resources, or smply VER) are expected to play an ncreasng role n capacty expanson plannng. Among other effects, see Pérez-Arraga and Batlle (212), one that s attractng a growng attenton n the lterature s how VER can change the short-term schedulng regme of the conventonal thermal plants, ncreasng the need of cyclng them 1, see for example Denny (27). These operaton-related ssues also mpact on the capacty expanson problem because, for nstance, flexblty wll be valued along wth low captal nvestment unts to mnmze the cost of cycled schedulng. A number of noteworthy papers have dscussed how VER can change the optmal capacty mx n the long term. For example, Lamont (28) and Ncolos and Fürsch (29) nclude the consderaton of VER n the standard screenng curves approach (hereafter SSCC) to llustrate how ncreasng VER leads n the long-run to a lower share of base-load technologes and a lower average utlzaton of the generatng capactes. Bushnell (21) and Green & Vaslakos (211) assess the long-term mpact of the ntroducton of large amounts of wnd on electrcty prces and capacty expanson (n the US and Great Brtan respectvely), extractng smlar conclusons on the bass of stylzed equlbrum models. However, there s stll a sgnfcant lack of tools dealng wth the mplcatons of detaled short-term operaton costs on the long-term capacty expanson problem, whch may no longer be neglgble when the amount of VER becomes sgnfcant. One attempt n ths respect s the one developed by Traber and Kemfert (211), where a smplfed representaton of start-up costs s ncluded n a long-term analyss focused on evaluatng the need for regulatory technology-orented ncentves. 1 The term cyclng refers to the cyclcal operatng modes of thermal plants that occur n response to dspatch requrements: on/off operaton, low-load cyclng operatons and load followng. * <Carlos.Batlle@t.upcomllas.es>. Insttute for Research n Technology, Comllas Pontfcal Unversty. Sta. Cruz de Marcenado 26, 2815 Madrd. Ph.: (+34) Also wth the MIT Energy Intatve, MIT. 77 Massachusetts Avenue, Cambrdge MA 2139, USA and wth the Florence School of Regulaton, European Unversty Insttute. ** <Pablo.Rodlla@t.upcomllas.es>. Insttute for Research n Technology, Comllas Pontfcal Unversty. Sta. Cruz de Marcenado 26, 2815 Madrd. Ph.: (+34)

2 IIT Workng Paper IIT-12-7A Wth the objectve of fllng ths gap, we propose an augmented SSCC methodology bult on a heurstc short-term optmzaton model. The short-term model provdes a detaled representaton of the economc schedulng and the resultng producton costs, ncludng those manly drven by cyclng operaton. In ths respect, we develop a partcularly detaled representaton of O&M costs, whch as we show, are called to play an ncreasng role under heavy cyclng regmes. The so-resultng model named as LEEMA model (Low-Emssons Electrcty Market Analyss), adds further detal to the SSCC, see e.g. Baldck et al. (211), whle keepng the well-known capablty to provde valuable analytc nsghts on the capacty expanson problem at a very low computatonal cost 2. The paper s structured as follows: secton 2 descrbes the conventonal SSCC method, so as to ntroduce some relevant deas and termnology. Then, secton 3 descrbes the LEEMA model, whch s used n secton 4 to solve a real-sze case example wth VER. Fnally, secton 5 gathers the conclusons. 2 THE CONVENTIONAL SCREENING CURVES The SSCC method, orgnally proposed n Phls et al. (1969), serves to compute the optmal mx of generatng technologes. It ams at mnmzng the total supply costs, relyng on a smplfed representaton of both the generaton cost structure (just nvestment fxed costs and varable energy costs) and dspatch schedulng crtera. In order to pave the way for the later descrpton of the approach proposed, we next revew ths classc methodology as a sequence of two modelng steps: The computaton of the generatng unts producton profles. The dentfcaton of the technologes capable of producng each of the profles prevously computed at a mnmum cost. 2.1 GENERATING UNITS SCHEDULING (MERIT-ORDER-BASED DISPATCH) When just varable energy costs and no operatng constrants are consdered, the optmal unt commtment of an already nstalled generaton mx smply entals loadng the thermal unts accordng to the mert order establshed by ther varable energy costs. In the fgure below, we have represented the so-resultng producton profle for a 1MW capacty thermal plant both over the chronologcal net load curve 3 (CNLC), and over the net load duraton curve (NLDC). The CNLC s computed by subtractng on a chronologcal and hourly bass the value of wnd generaton from the load (and NLDC by sortng these CNLC values). GW CNLC NLDC 1MW load slce = 19.8GW Fg. 1. 1MW producton profles. t = 18 hrs The algorthm developed copes wth real-sze problems (9 dfferent technologes and 876 hours) n a few seconds (6.3s wth an Intel Core 7-26 Processor, 8M Cache), evaluatng the optmal capacty expanson and the expected chronologcal hourly economc dspatch. 3 Snce costs expressons are extremely smplfed and no nter-temporal constrants are consdered, no chronology of demand and producton has to be taken nto account, snce t adds no relevant nformaton. However, we wll also explctly represent the underlyng dspatch over the CNLC, because we wll later use ths chronologc representaton n the approach proposed. 2

3 An enhanced screenng curves method for consderng thermal cyclng operaton costs n generaton expanson plannng In ths scheme, there are two equvalent varables that can be used to dentfy each of the 1MW 4 producton profles. Frst, the loadng pont ( ),.e. the demand level at whch the plant s loaded. The plant that s loaded at wll produce n the hourly perods n whch the correspondng demand values go above the value. The other alternatve s to dentfy each partcular 1MW producton profle by the assocated number of hours of producton (t ). There s a one-to-one relatonshp between these two varables, snce = NLDCt (). As shown n Fgure 1, by makng reference to the producton profle assocated to the =19.8 GW or mplyng 18 hours of producton we are pontng exactly at the same producton profle (the one represented n sold cyan). 2.2 COSTS LINKED TO PRODUCTION PROFILES: TRADITIONAL APPROACH The total annual cost of supplyng a certan 1MW profle, TC, wth a 1MW generaton unt of technology s computed as the sum of the annualzed captal costs, producton costs, CC, the annual energy fuel EFC, and the annual operaton and mantenance (O&M) costs, OMC. TC= CC+ EFC+ OMC (1) The annual energy fuel producton costs per nstalled MW are computed as the energy fuel cost, efc (n $/MWh), tmes the number of hours t produces n a year, t. O&M s broken down n two components: annual fxed O&M costs, FOMC (n $/MW) and annual varable (energy-related) O&M cost, eomc (n $/MWh). If we rearrange these terms dependng on whether they correspond to fxed costs, to varable energy-related costs, ec, we get: FC, or FC = CC + FOMC TC() t = FC+ ec t; ec = efc + eomc (2) 2.3 COMPUTING THE OPTIMAL GENERATION MIX Computng the optmal generaton mx entals calculatng whch technology can provde at the lowest cost each of the 1MW slces makng up the load curve. Ths s easly acheved by representng the cost functons of all the technologes as a functon of t, see equaton (2), and then selectng for each t (recall that each value makes reference to a 1MW load slce) the technology supplyng the correspondng profle at the lowest cost. We llustrate ths n the case example next. 2.4 CASE EXAMPLE OF THE CONVENTIONAL SSCC APPROACH The conventonal SSCC s next appled for a real-sze case example wth VER. The objectve s to further llustrate ts use and also to create a reference benchmark later used to compare the results of the refned model developed n ths paper. The hourly demand and hourly wnd producton consdered were the hstorcal values n the Spansh system n 21 (the nstalled capacty of wnd amounted to 2 GW). The conventonal thermal generaton operaton costs data can be found below n Table I n secton 4. 4 For the sake of clarty n the model descrpton we use a 1MW producton profle. However, both the conventonal and the proposed methodology allows n a qute straght forward manner consderng more real profles (ndeed the real-sze case example s computed consderng a unt s sze of 4 MW). 3

4 IIT Workng Paper IIT-12-7A Schedulng regmes calculaton Once the CNLC s obtaned, we can get the schedulng regme for each load level (loadng pont) as descrbed prevously. Fg. 2 shows the basc smplfed schedulng regme correspondng to one of these loadng ponts (3 GW) Feb. 15th - 21st, GW 25 Wnd Fg. 2. Schedulng regme detal correspondng to the tradtonal SSCC Optmal generaton mx calculaton Fg. 3 shows the total producton cost curve per nstalled MW for each technology as a functon of the number of producton hours (frng hours). The ntersectons of these curves determne the number of hours of producton that separate the annual regmes where the dfferent technologes are optmal. The least-cost technologes are thus determned by the lower envelope curve (the sold lne). Installed capactes are determned by smple nspecton n the NLDC. K$ NSE NGCC Coal Nuclear 4 GW NLDC LDC 876 NSE 2.5 NGCC 15.4 Coal Nuclear 12. Fg. 3. Optmal generaton mx resultng from the conventonal SSCC 2.5 EQUIVALENT FORMULATION OF THE CONVENTIONAL SSCC We next propose an alternatve formulaton that expresses the total producton costs as a functon of what has been prevously defned as the loadng pont (MW), nstead of t. In the conventonal model just revewed, changng ths varable translates nto expressng the hours of producton as a functon of the loadng pont, ( ) t,.e. the nverse of the load duraton curve functon. Thus, the cost functon s:

5 An enhanced screenng curves method for consderng thermal cyclng operaton costs n generaton expanson plannng TC ( ) = FC + ec t ( ) (3) The cost functons from the prevous example usng ths new formulaton lead to the curves represented n Fg. 4. We have chosen to nvert the x-axs (now representng the loadng ponts, expressed n GW) to mantan the resemblance wth the conventonal SSCC representaton of the problem, where the x- values closer to the orgn correspond to peak demand. 6 Total Cost [k$] Loadng Ponts 25 2 NSE NGCC 15.4 Coal 1.6 Nuclear 12. Fg. 4. Conventonal SSCC method n the equvalent formulaton. As shown n the fgure, for loadng ponts close to the maxmum load the curves are relatvely flat, snce at a suffcently hgh value of loadng pont, the operatng patterns and hence the total costs are very smlar (plants run very few hours per year). At lower values of loadng pont, the slope ncreases n magntude (reflectng how the operaton condtons and the resultng costs are hghly dependent on the loadng poston), but then falls agan to zero as all loadng ponts below a certan threshold load level (whch s always lower or equal to the mnmum load) mply the same pattern of contnuous base-load operaton. Modelng total costs as a functon of allows drectly obtanng the amount of optmal capacty to be nstalled for each technology, snce the ntervals n the x-axs defnng the lower envelope are drectly expressed n terms of capacty. As shown next, ths formulaton proves to be better suted when complex dspatches are to be consdered (and t s not possble to just characterze them by the number of frng hours). 3 THE LEEMA MODEL: A SOPHISTICATED SSCC APPROACH The LEEMA model s structured n two modules: Frst, the schedulng module performs a heurstc optmzaton that calculates the detaled chronologcal hourly producton profles. Second, the economc operaton and plannng module derves the producton cost functons that would result f each of the prevous producton profles were to be suppled by each one of the conventonal thermal technologes beng consdered 5. Then, we derve the technology whch s most cost-effcent at producng each profle, from both the captal and operatng cost perspectve If the producton profle turns out to be unfeasble for a certan technology, (for example, for nvolvng exceedng the maxmum number of annual starts) then the assocated cost of supplyng that producton profle wth that technology s set to nfnte. 5

6 IIT Workng Paper IIT-12-7A 3.1 THERMAL SCHEDULING OPTIMIZATION MODULE The frst module carres out the heurstc optmzaton of the schedulng of the thermal plants. Ths schedulng s computed on the bass of a constant mert-order-based dspatch 6 where the schedulng of a thermal plant just depends on ts poston n the mert order (the loadng pont at whch t s scheduled). As t s well-known, one of the results provded by the unt commtment problem s that, gven the cost nvolved n stoppng and re-startng plants, one schedulng alternatve s to keep the plant runnng at the mnmum stable output so as to avod ncurrng n the subsequent start-up costs. To take ths fact nto account, we defne a heurstc optmzaton algorthm whch consders three relevant parameters that determne the optmal schedulng n ths respect: frst, the rato ( µ ) between the maxmum capacty and the mnmum stable load of the thermal unts, second, the maxmum number of consecutve hours a unt would be wllng to be kept runnng at the mnmum stable load (ml )to avod a subsequent start (denoted as t ml ), and thrd, the amount of nflexble generaton capacty (manly nuclear) producng ( I g ). Thus, we go beyond the tradtonal formulaton of the SSCC and dvde the generaton technology types nto flexble and nflexble. For the flexble ones, snce the dspatch s technology ndependent, we have to assume some general values for the parameters that serve to approxmately represent all potental generatng technologes: µ s consdered to be 4% (.e. the mnmum load lmt for a 4 MW plant would be 16 MW). Regardng the other two parameters, n the case example we consder that t ml equals 1 hours (the value usually ranges from 8 to 12 hours). Regardng the amount of nflexble capacty nstalled we have consdered two scenaros: 8 and 12 GW respectvely. The consderaton of these two scenaros wll allow us llustratng the relevant mpact that ncreasng the amount of nflexble capacty may have n a context wth large penetraton of wnd Heurstc optmzaton of start-up decsons At each loadng pont, the heurstc optmzaton of the producton profle s carred out n three consecutve steps: Step 1: we compute a prelmnary schedulng of the unt at. Ths dspatch (see the upper chart n Fg. 5) corresponds to the one mplctly consdered by the conventonal SSCC methodology. In the perod shown, four starts and four off-lne perods precedng each start can be dentfed. toff( n, ) s the vector storng n each poston the number of hours the unt s off-lne pror to each of the starts, denoted wth ndex n. Step 2: we evaluate f the duraton of each off-lne perod precedng a start exceeds the t ml threshold. Those exceedng the threshold are dscarded and not further analyzed. Step 3: we check n each case f there s enough flexblty avalable so as to allow avodng each start by producng at the mnmum load regme durng valley hours. Ths flexblty s the producton output that plants scheduled at lower loadng ponts could reduce f needed, to allow the plant analyzed avodng the start. Let us recall that snce the mnmum output of a 1 MW capacty plant s µ, ts 6 Thermal unts produce followng a fxed mert order whch does not change wth operatng condtons (generators are assumed to never be out of order for mantenance). A unt can only produce f all unts whch are earler n the mert order are also producng (at least at ther mnmum stable load) 6

7 An enhanced screenng curves method for consderng thermal cyclng operaton costs n generaton expanson plannng capablty to reduce load s (1 µ ). The nstalled capacty of flexble generaton below loadng pont amounts for ( I g ). Then, the avalable flexblty at a certan loadng pont, denoted as φ ( ), can be drectly estmated as: φ ( ) = (1 µ )( I g ) (4) φ( ) corresponds to the mnmum level of producton that unts scheduled below loadng pont can provde. Therefore, only f ths value s lower than the demand n the perod analyzed, there wll be enough system flexblty. Let us check these two condtons n the example n Fg. 5: Step 2: consderng that t ml = 12 hours, starts 1, 2 and 4 do fulfll the economc crteron, whle on the contrary t would be not worth avodng start 3 (snce t would mply producng 34 hours at the mnmum load regme). Step 3: let us assume that the flexblty of the groups producng below s φ ( ). Then, the producton of these groups cannot be reduced below φ( ) (see the red lne n the upper chart of Fg. 5). Ths reducton s not enough to avod starts 1 and 3, snce the load values n ths valley hours are below ths threshold. On the contrary ths flexblty allows avodng starts 2 and 4. Thus, only starts 2 and 4 are fnally avoded, leadng to the dspatch represented n the lower chart n Fg Results provded by the thermal schedulng module The thermal schedulng module provdes the producton profle of generaton for each loadng pont. Fg. 6 llustrates these profles for a certan week of the smulaton scope consdered. As hghlghted n the red box, we can see how there are a large number of unts producng durng valley hours as a consequence of the heurstc optmzaton dspatch. Contrary to the conventonal SSCC, the new 1 MW load slces assocated to the loadng ponts are no longer rectangular, but rather detaled profles (lower chart n Fg. 6). 7

8 IIT Workng Paper IIT-12-7A φ ( ) Step 1 Step 2 Step 3 toff(,1) = 11 toff(,3) = 34 t off (,4) = Economc dspatch 3 4 h Fg. 5. Economc dspatch calculaton consderng system flexblty. h GW Fg. 6. Producton profles per loadng pont. As shown n Fg. 7, these producton profles mplctly nclude relevant peces of nformaton regardng the dspatch that are later needed n the technology optmzaton module. Apart from the already ntroduced t off, we defne:

9 An enhanced screenng curves method for consderng thermal cyclng operaton costs n generaton expanson plannng µ 24 h starts t toff(,1) = 1 off(,3) = 11 toff(,2) = 12 toff(,4) = 2 p ( )={...,, 1,..., 1,.643,.4,.4,.4, 1,..., 1,,...} Fg. 7. Varables defnng a producton profle. Apart from the already ntroduced t off, we defne: p ( ), s the annual producton profle vector that corresponds to the unt scheduled at,.e. a yearly vector storng the producton n each hourly perod,.e. p ( ) = {( ph, 1),..., ph (, )}, where ph (, ) stands for the producton that corresponds to the loadng pont n hour h (hours are consdered n chronologc order). S ( ), s the total number of annual starts. F ( ), s the total number of annual frng hours Once the thermal schedulng module has estmated the producton profles of the unts as a sole functon of the loadng pont, the next step s to determne the technologes that would supply such profles at the lowest cost. 3.2 TECHNOLOGY OPTIMIZATION MODULE The followng sources of costs are consdered: captal costs, energy fuel producton costs, fuel start-up cost and operaton and mantenance costs (both fxed and varable). For 1 MW nstalled of one partcular technology operatng at loadng pont, TC( ) takes now the form: TC( ) = CC + EFC( ) + SFC( ) + OMC( ) (5) Where we have now ntroduced wth respect to (1) SFC( ), as the total annual fuel start-up cost Energy Fuel Cost The total annual varable operatng fuel cost s: H EFC( p ( )) = efc( ph (, )) ph (, ) h= 1 (6) Where the average energy producton fuel cost level, see Fg. 8. Btu/MWh Mnmum load efc n each hour (n $/MWh) s a functon of the output Heat rate NGCC 1% Fg. 8. Unt heat rate as a functon of the output level (4MW NGCC), (Wood and Wollenberg, 1996). 85% MW Effcency 9

10 IIT Workng Paper IIT-12-7A Start-up fuel cost The fuel cost of each start s a functon of the number of hours the unt has been off before startng. Usually three dfferent types of starts are consdered: hot start, when the unt has been less than 1 hours out of operaton, warm start, for more than ten but less than 5 hours and cold start, for more than 5 hours. Typcal values for the start fuel costs for the technologes consdered can be found n secton O&M costs O&M costs are usually dvded nto fxed and varable cost components. Fxed O&M costs nclude mnor perodc wages, mantenances, property taxes, faclty fees, nsurances and overheads, whle varable O&M usually nclude nspectons that are trggered after certan accumulated operaton condtons are met (e.g. number of operatng hours wth a baselne fuel type and frng temperature, number of starts or trps, etc.). We next focus on ths varable O&M component. Most of these prevous nspecton procedures are reflected n the so-called Long-Term Servce Agreements (LTSA), see Sundhem (21). For example, n the case of gas turbnes, the most relevant mlestone embedded n the LTSA s the hot-gas-path nspecton (aka major overhaul) for t represents the major drver behnd the LTSA cost. The methodology to determne the mantenance ntervals of ths major overhaul s based on the defnton of a Mantenance Interval Functon (MIF) relatng the maxmum number of starts and frng hours before a mantenance s trggered. The shape of the functon vares between manufacturers, see Power Plannng Assocates (22) and Balevc et al. (21). In Fg. 9, we represent three of the most well-known type MIFs for gas turbnes. Number of starts The annual varable O&M cost Opton B Opton C Opton A Falure regon Frng hours Fg. 9. Baselne functons for mantenance nterval. The annual varable O&M cost of the unt producng at a certan, s drectly computed from the MIF and also from the number of annual frng hours (F ) and starts (S ) the schedulng module has determned for such producton profle. Let the cyclng rato ρ ( ) be the quotent between the number of frng hours and starts. Ths rato drectly affects the wear and tear of the plant; the lower ths rato, the larger the effect of O&M costs n average producton costs. We can compute the threshold condtons trggerng the major mantenance, fndng the pont of the MIF that exactly fulflls ths same ρ( ) 7. * S and * F, by smply 7 Ths entals assumng that ths rato remans constant throughout the whole perod precedng the major overhaul. 1

11 An enhanced screenng curves method for consderng thermal cyclng operaton costs n generaton expanson plannng 9 N * ( ) Starts 45 N ( ) ρ( ) Frng Hours F ( ) 8 F * ( ) Fg. 1. Unt threshold condtons trggerng the major mantenance. The fracton of the major overhaul cost to be mputed n one year s the quotent between F and The annual varable O&M cost s thus the product of ths quotent and the cost of a major overhaul nspecton MOC : * F. VOMC ( ) = ( F ( )/ F ( )) MOC * (7) 4 REAL CASE EXAMPLE WITH LARGE VER PENETRATION Next, we analyze wth the LEEMA model the real-sze case example ntroduced back n secton II.D. The objectve s to llustrate the mpact that representng short-term cyclng operaton costs may have on long-term expanson analyses. 4.1 CASE EXAMPLE DATA ASSUMPTIONS We consdered four generatng technologes: nuclear, coal (sngle advanced unt PC), natural gas combned cycle (CCGT) and onshore wnd. A ffth technology s used so as to represent the nonserved energy (NSE) value. Ths vrtual technology has zero nvestment captal costs and a varable cost of 1 $/MWh. Table I contans the data used. TABLE I THERMAL GENERATING TECHNOLOGIES COST STRUCTURES Nuclear Coal CCGT Wnd Captal* [k$/mw-yr] FOM* [k$/mw-yr] VOM* [$/MWh] Varable [$/MWh] Cold Start** [$/MW] Hot Start** [$/MW] HRE Loss [%] * Data taken from the Energy Informaton Admnstraton (21). ** These data have been calculated as a reasonable average of the dfferent estmatons provded by a number of representatves of utltes and manufacturers consulted and they are n lne wth the ones that can be found n the lterature, as for nstance n Troy et al. (21). FOM stands for Fxed O&M, VOM for Varable O&M, HREL for the relatve Heat Rate Effcency Loss that takes place when the unt produces at the mnmum stable load. The MOC [$/MW] s the VOM [$/MWh] tmes the mantenance nterval perod mplctly assumed n Energy Informaton Admnstraton (21) (24 hours). 11

12 IIT Workng Paper IIT-12-7A To calculate the annualzed captal costs, dfferent economc lfe values and requred rates of return were consdered: 4 years for the three thermal technologes and 2 years for wnd. The rates of return were assumed to be 7% for coal and CCGT, and 5% for wnd and nuclear 8. CCGTs are subject to a mantenance contract havng the characterstcs of those we have denoted as Opton A. Accordng to the references consulted, the cost of a major mantenance ranges from 2 mllon to 6 mllon dollars, see for nstance Power Plannng Assocates (22) or Wembrdge et al. (29). The major mantenance cost for a 54 MWs CCGT s consdered to be 4 mllon US$. The maxmum amount of starts and frng hours defned n the baselne functon depend on the type of turbne and manufacturer. They can range from 8, to 24, hours and 4 to 9 starts for hot-gaspath nspectons (Balevc et al., 21). We assumed 6 starts and 24 frng hours. Coal plants category nvolves an mmense varety of dfferent plant desgns. Ths makes much more complcated to opt for a comprehensve representaton of O&M contracts. After dscussng ths ssue wth dfferent representatves of the ndustry, we opted for assumng an Opton A contract (24 hours and 75 starts). We also assumed that coal plants cannot start more than 75 tmes a year; nether exceeds the threshold of one daly start. Nuclear s assumed to produce at base-load, so t does not start. The consequence s that the producton profles exceedng these threshold condtons wll be assumed to be not feasble regmes for these technologes. We consder two scenaros of nuclear nflexble 9 nstalled capacty (8 and 12). We frst analyze n further detal the frst case (8 MW), and then show the results obtaned when ncreasng nstalled nuclear capacty up to 12 MW. 4.2 RESULTING PRODUCTION PROFILES (8 MW NUCLEAR) The model computes the detaled producton profle assocated to the unts beng loaded at each of the loadng ponts. In Fg. 11 we represent the two major varables summarzng and characterzng each of these producton profles: the number of starts and the number of frng hours (we have also represented hours at full-load and hours at mnmum-load producton regme) 1. Usually, the hgher the loadng pont, the lower the cyclng rato assocated to the correspondng producton profle. Snce the lower ths rato the larger the effect of O&M costs (n average producton costs), these unts at the peak are consequently the ones subject to larger O&M costs. Let us emphasze that larger number of starts does not necessarly mply larger O&M costs (agan, speakng n terms of average producton costs). For nstance, as shown n Fg. 11, for the unt loaded at loadng pont 37 GW (peak unt) the number of starts s 25, and the number of frng hours s 18, thus resultng n a cyclng rato of The unt loaded at 3 GW (md-range unt), whch starts almost 16 tmes (ndeed t s the unt startng the largest number of tmes), presents a much hgher cyclng rato (18), so t has lower O&M costs. 8 We consdered a lower value for the case of wnd, snce untl now, t s an ncome-regulated generaton technology, sgnfcantly less exposed to market prce rsk. In the case of nuclear, snce consderng a rate of return of 7% would turn t nto a not compettve technology, we have opted for consderng a lower rate of return. We assume that the regulator wll decde to mplement a way to fnancally subsdze nvestments n nuclear generaton technology, as for nstance offerng a long-term hedge only for ths technology, n a smlar way RES-E technologes are supported, see e.g. Department of Energy & Clmate Change (211). 9 Accordng to EURELECTRIC (21), properly desgned or refurbshed nuclear plants may perform n a rather flexble mode, but n most power systems (wth e.g. the exceptons of France and Germany) nuclear plants are operated n a pure base-load mode, manly based on securty rather than economc reasons. 1 Let us recall that the x-axs (loadng ponts) has been reversed so as to keep resemblance wth the tradtonal screenng curves methodology when later representng producton costs. Thus, t has to be born n mnd that the x-values whch are close to the orgn correspond to peak (net) demand values. 12

13 An enhanced screenng curves method for consderng thermal cyclng operaton costs n generaton expanson plannng 16 Number of starts starts 8 4 Frng hours (basc SSCC) Total frng hours Frng hours at Pmax Frng hours at Pmn hours Loadng Ponts [GW] Fg. 11. Number of starts and frng hours per loadng pont. The prevous chart shows how the basc SSCC method underestmates the total number of frng hours thermal unts produce, whle also overestmates the producton that unts carry out at the plant s maxmum output (because all frng hours are assumed to be at full load n the basc SSCC). Addtonally, the basc SSCC assumes a full base load regme for all unts beng loaded below the mnmum net demand value (975 MW n the case example). Conversely, the proposed approach consders that below that level some unts, although keepng a contnuous producton regme, produce sometmes at mnmum load so as to leave room for the mnmum stable load producton of unts stuated at hgher loadng ponts. 4.3 OPERATING COSTS COMPONENTS FOR A CCGT UNIT To llustrate the dfferences between the costs computed wth the conventonal and the proposed approach, we break down and represent the cost components for one partcular technology (CCGT). Later, we analyze and show the new resultng SSCC ncludng all technologes. Fuel start costs: Fg. 12 shows n sold red the fuel start cost versus loadng pont. Ths cost has tradtonally been consdered as neglgble n most long-term studes 11. Varable O&M costs: Fg. 12 also ncludes the resultng annualzed cost for the varable O&M component. When compared wth the start fuel costs, t s evdent that these O&M costs have a consderably hgher relevance. The O&M costs estmated wth LEEMA can also be compared wth those mplctly stemmng from the applcaton of the conventonal methodology (the per-mwh O&M cost multpled by the load factor). The dfferences at certan loadng ponts can be qute large (e.g. at loadng pont 25 GW, the tradtonal approach estmates 13 k$/mw, nstead of 31 k$/mw). These dfferences are the consequence of takng nto account how the number of starts mpacts nto the annual varable O&M costs. Under the lght of ths result, we can conclude that gnorng the effect of starts may not be an accurate approach n a context wth large penetraton of VER Ths was justfed by two arguments, frst, the cost of a start s low when compared wth other long-term costs, and second, the number of starts has tradtonally been relatvely small (and partcularly n systems wth a certan amount of hydro resources). 13

14 IIT Workng Paper IIT-12-7A 3 Annual O&M costs Total Cost [k$] Fuel Start cost 35 3 Annual O&M costs (basc SSCC) Loadng Ponts [GW] Fg. 12. Fuel start costs and Varable O&M costs per nstalled MW. Varable energy producton costs: The fact that some unts produce at mnmum load output to avod some starts, when compared wth the conventonal methodology, leads to hgher producton and thus hgher producton cost at loadng ponts close to peak demand. Ths dfference may be sgnfcant: e.g. at loadng pont 25 GW, t s around 5 k$/mw (around a 2% ncrease). The opposte occurs at lower loadng ponts, where the producton s lower n the proposed methodology. We also represent the prevous O&M costs n the fgure above, as well as the dfferences wth the conventonal methodology, so as to show ts relatve weght n terms of total costs Varable energy producton costs (basc SSCC) Varable energy producton costs Total Cost [k$] Annual O&M costs (basc SSCC) Annual O&M costs Loadng Ponts [GW] Fg. 13. Energy fuel cost curves per nstalled MW. 4.4 OPTIMAL GENERATION MIX The new SSCC are obtaned by just addng the dfferent costs components. Fg. 14 reflects the addton of these curves for all the technologes consdered. It also shows the results obtaned wth the conventonal methodology. In the resultng mx obtaned wth the conventonal methodology, nuclear has been fxed to 8MW to allow for the comparson. 14

15 An enhanced screenng curves method for consderng thermal cyclng operaton costs n generaton expanson plannng Total Cost [k$] 6 NSE 4 2 Coal Nuclear 75 starts starts CCGT Total cost (basc SSCC) Total cost (LEEMA) NSE CCGT 15.4 GW Coal 14.6 Nucl. 8. NSE CCGT 17. GW Coal 13.2 Nucl. 8. Fg. 14. Total cost by technology and loadng pont computed (8 MW). The coal cost curve has been depcted wth a dotted lne for certan loadng ponts. The reason s that the operaton regme exceeds the prevously descrbed lmts. Due to the dfferent amount of frng hours consdered and the mpact of consderng starts, LEEMA provdes hgher costs than the basc SSCC at the heaver cyclng regmes assocated to hgh loadng ponts (whle the opposte occurs at low loadng ponts). These effects favor the nstallaton of the more flexble technologes, n ths case CCGT, to the detrment of those less flexble, n ths case coal. The nstalled capactes resultng both from the tradtonal SSCC and LEEMA are shown n the fgure. 4.5 THE IMPACT OF THE LACK OF FLEXIBILITY IN A CONTEXT WITH VER Wth the objectve of llustratng how less flexble unts are less economcal when nuclear (assumed to be fully nflexble) s ncreased n a context wth larger VER penetraton, we have run the model for the case of nstallng 12 MW of nuclear (n the conventonal methodology, nuclear has also been fxed to the same value, whch s ndeed the value we obtaned back n secton 2.4.2). Both CCGT and coal costs ncrease, wth a larger ncrement for the latter than the former. Ths leads to a further reducton of the coal nstalled capacty wth respect to CCGT. Total Cost [k$] 6 NSE 4 2 Coal Nuclear 75 starts starts CCGT NSE CCGT 15.4 GW Coal 1.6 Nucl. 12. NSE CCGT 19. GW Coal7.2 Nucl Total cost (basc SSCC) Total cost (LEEMA) Fg. 15. Total cost by technology and loadng pont computed (12 MW). 15

16 IIT Workng Paper IIT-12-7A 4.6 PRODUCTION PROFILES OF THE RESULTING MIXES To llustrate the schedulng results of the model once the mx s determned, Fg. 16 shows the dspatches for both scenaros (8 MW and 12 MW of nuclear), for the same two partcular days contaned wthn the smulaton. The blue box hghlghts the stuaton n the valley hours n both scenaros: as a consequence of the dfferent system s avalable flexblty, a larger number of unts are able to avod the next day start when nstalled nuclear capacty s 8 MW GW 25 Feb. 8th 15 Feb. 8th Nuclear 8 GW 5 CONCLUSION Fg. 16. Hourly schedulng of the unts. 5 Nuclear 12 GW We augment the screenng curves method to nclude further detal on both the economc schedulng regme and the assocated producton costs. We represent four major sources of costs: nvestment captal costs, energy fuel cost, start fuel cost and a detaled representaton of O&M cost. We analyze the not-so-well-known mpact of the number of starts on these O&M costs, argung that these cyclng costs should no longer be gnored when facng long-term expanson analyss nvolvng a large penetraton of VER. The man dfferences when compared wth the conventonal screenng curves analyss have been shown by means of a real-sze case example. We show how the computed operaton costs and the resultng mx can be sgnfcantly dfferent. ACKNOWLEDGMENT We are ndebted to Andrea Vega for her valuable contrbuton n the development of the model. We also thank Davd Soler, Prof. Ignaco J. Pérez-Arraga, Prof. Julán Barquín, Prof. Javer García- González, Prof. Mchel Rver (IIT), Luz A. Barroso (PSR), Juan José Alba (Endesa), Antono Canoyra (Gas Natural Fenosa), and two anonymous revewers. REFERENCES I. J. Pérez-Arraga and C. Batlle, 212. Impacts of ntermttent renewables on electrcty generaton system operaton. Economcs of Energy and Envronmental Polcy, vol. 1, num. 2, 212. E. Denny, 27. A Cost Beneft Analyss of Wnd Power. Ph.D. Thess, School of Electrcal, Electronc and Mechancal Engneerng, Natonal Unversty of Ireland, Unversty College Dubln, Ireland,

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