Impacts of Generation-Cycling Costs on Future Electricity Generation Portfolio Investment

Transcription

1 1 mpacts of Generaton-Cyclng Costs on Future Electrcty Generaton Portfolo nvestment P. Vthayasrchareon, Member, EEE, and. F. MacGll, Member, EEE Abstract Ths paper assesses the mpacts of ncorporatng short-term generaton dspatch nto long-term generaton portfolo plannng frameworks. A case study of a power system wth coal, combned cycle gas turbne (CCGT), open cycle gas turbne (OCGT) and wnd generaton optons hghlghts that ncorporatng operatonal crtera nto the long-term generaton nvestment and plannng analyss can have operatng, economc and emssons mplcatons for the dfferent generaton portfolos. The extent of the mpacts depends on a number of factors ncludng dspatch strateges, carbon prce and the mx of technologes wthn the portfolo. As varable generaton wthn power systems ncreases and carbon prcng begns to change the mert order, such short-term operatonal consderatons wll become more sgnfcant for generaton nvestment and plannng. ndex Terms Monte Carlo smulaton, generaton plannng, portfolo analyss, generaton dspatch, operatonal constrants. NTRODUCTON ECSON makng n generaton nvestment and plannng Drequres a long-term perspectve amdst consderable uncertantes n expected future demand, fuel prces, plant constructon costs and wder energy and clmate polces such as carbon prcng. Gven the long plannng horzon, generaton nvestment and plannng frameworks often gnore the actual detals of short-term electrcty ndustry operaton [1]. For example, many generaton plannng models are based on the use of a Load Duraton Curve (LDC), where the chronology s removed, to determne a future optmal generaton technology portfolo. n realty, however, generatng plants have sgnfcant nter-temporal operatng constrants such as mnmum operatng levels, ramp rates, and startup/shutdown tmes. There are also operatng expenses assocated wth ntertemporal generaton dspatch such as plant startup costs. Generaton portfolo analyss frameworks 1 have been ncreasngly employed for generaton nvestment and plannng to determne optmal generaton portfolos wth dfferent costrsk profles [2, 3]. Optmal generaton portfolos fall along the so called effcent fronter, representng cost-rsk tradeoffs among possble generaton portfolos. However, smlar to most long-term generaton plannng models, operatonal ssues ncludng unt constrants and nter-temporal generaton dspatch are not generally consdered [4]. Ths work has been supported n part by Australan Renewable Energy Agency (ARENA). The authors are wth the Centre for Energy and Envronmental Markets and School of Electrcal Engneerng and Telecommuncatons, Unversty of New South Wales, Sydney, Australa (emal: peerapat@unsw.edu.au). Recent growth of varable renewable generaton such as wnd and solar has ncreased the complexty of electrcty ndustry operaton as well as posng operatonal challenges for conventonal plants through ncreased cyclng [5, 6]. There are also addtonal costs due to cyclng operaton ncludng startup/shut down of generatng unts [7, 8]. Generaton portfolos that appear attractve under standard long-term generaton portfolo plannng frameworks mght have challengng operatonal requrements gven expected demand patterns and the varablty of hgh renewable penetratons. Furthermore, the addtonal costs assocated wth cyclng operaton could potentally alter the mert of dfferent canddate generaton portfolos. n prevous work, we have presented a probablstc generaton portfolo modelng tool for assessng future generaton portfolos under hgh level of uncertantes [9, 10]. Despte the capablty of the tool n addressng uncertantes and rsks assocated wth long-term generaton plannng, there are stll nherent lmtatons n the method t apples wth regard to ssues assocated wth short-term electrcty ndustry operaton. The work presented n ths paper ams to address these lmtatons by mplementng a post-processng extenson to the tool whch ncorporates generatng unt constrants and nter-temporal generaton dspatch. Ths paper ntends to provde a hgh-level assessment of the potental mpacts of short-term operatonal aspects on the techncal vablty, economcs and emssons of generaton portfolos that appear favorable from the ntal nvestment and plannng framework. The post-processng assessment ncludes ndces of possble volatons of operatng constrants such as number of starts/stops, ramp rates, the economc and emssons mplcatons of dfferent dspatch strateges around mnmum plant operatng levels.. PROBABLSTC GENERATON PORTFOLO MODELNG TOOL The generaton nvestment and plannng tool mplemented n our prevous work assesses the costs of possble future electrcty generaton portfolos gven uncertan future fuel prces, carbon prces, plant captal costs, and electrcty demand. The tool extends conventonal LDC methods by ncorporatng potentally correlated uncertantes for key cost assumptons and future demand usng Monte Carlo Smulaton (MCS). The expected costs, cost uncertantes and CO 2 emssons of a range of potental new-buld generaton portfolos n a gven future year are drectly obtaned from several thousand repeated scenaros wth probablstc nput parameters. The cost spread for a generaton portfolo can, for

2 2 some dstrbutons, be represented by standard devaton (SD) and s referred to here as cost uncertanty, whch carres a smlar meanng to rsk n the economc and fnance contexts. The tool apples fnancal portfolo analyss technques to determne an effcent fronter contanng optmal generaton portfolos gven tradeoffs between expected (average) cost and ts assocated cost uncertanty. Results from a prevous case study of an electrcty ndustry wth coal, CCGT, OCGT, and wnd generaton optons are used to demonstrate use of the tool [10]. Smulated half-hourly wnd generaton estmates are subtracted from electrcty demand to obtan a resdual demand and then rearranged to get a resdual LDC (RLDC) [2]. Ths RLDC s then served by thermal technologes n the portfolos. The expected yearly generaton cost and cost uncertanty of dfferent thermal generaton portfolos obtaned from the prevous case study are shown n Fg. 1. An effcent fronter (EF) 2 contanng the optmal generaton portfolos (labeled A - E) s represented by a sold lne. Ths result s also the bass of the case study presented later n Secton V. Fg. 1. Results from the tool showng the expected cost, assocated cost uncertanty and CO 2 emssons of generaton portfolos [10]. Despte ts capablty n addressng rsk and uncertanty n generaton plannng, the operatonal aspect was not consdered. Such lmtaton s addressed n ths paper through a post-processng extenson descrbed n the next secton.. POST-PROCESSNG EXTENSON TO THE MONTE CARLO BASED DECSON SUPPORT TOOL n ths extenson approach, canddate generaton portfolos are taken from the ntal MCS analyss and then run each through a year of sequental half-hourly constraned economc dspatch to meet resdual demand (demand net of renewable generaton) 3. A range of operatng constrants for the dfferent generaton technologes s ncorporated n ths dspatch to assess ther potental operatng, economc and emssons mplcatons for dfferent generaton portfolos. These operatonal constrants nclude mnmum generaton levels and 2 Along the fronter, the expected cost cannot be reduced wthout ncreasng cost uncertanty and vce versa. 3 All avalable renewable generaton s assumed to be dspatched. potentally other crtera assocated wth the startup/shutdown of generatng unts durng dspatch ntervals. Some key operatng mplcatons of constraned dspatch for dfferent generaton portfolos are assessed by countng the number of startups and ramp-rate volatons of each generaton technology wthn a portfolo over the year of smulated operaton. The potental economc mplcatons of addtonal startup costs and ncreased runnng costs are also assessed ncludng ther mpact on overall ndustry costs and hence, potentally, the EF of optmal generaton portfolos. Emssons mplcatons are also assessed based on changes n the annual CO 2 emssons of the dfferent canddate portfolos. The post-processng analyss n ths paper s not ntended to solve detaled economc dspatch, unt commtment and producton schedulng. Mnmum startup/shutdown tmes and ramp rate constrants are not mposed. However, ther mplcatons can stll be assessed, n part, based on how often these constrants are volated by the smulated dspatch. A. Central Economc Dspatch Objectve and Constrants The dspatch objectve functon s to mnmze total operatng costs n each dspatch perod takng nto account the chronology of generaton dspatch subject to generator and demand balancng constrants as shown n (1) - (4). Mnmze VC.(P 1 ). {0,1} (2) where VC s the varable operatng cost of generatng unt ($/MWh), P s the output of generatng unt at perod t (MW), and s on-off decson varable ndcatng whether unt s onlne or offlne n perod t. 1 t (1) P D (3) mn P max where D t s the demand n perod t (MW), P P (4) P and max P are mn the mnmum and maxmum output of generatng unt. Analyss s undertaken for two dspatch models wth dfferent startup/shutdown crtera for generaton: 1) Mn Start/Stop - keepng all large thermal plant on-lne f possble by sharng loadng reductons; and 2) Max Low-Cost Gen - dspatchng the lowest operatng cost plants at hghest possble outputs whlst shuttng down the hgher cost unts where not requred. Both dspatch models assume that every ndvdual unt of the same technology has the same operatng and cost characterstcs. Therefore, generatng unts of the same technology are dspatched equally as well as sharng the startups/shutdowns. Dspatch crtera are shown n Table. TABLE THE TWO GENERATON DSPATCH MODELS CONSDERED N THE SMULATON Mn Start/Stop Dspatch Mnmze the start/shutdown of generatng unts. Dspatch low cost unts at part-load to allow other unts to reman onlne although they are less economcal to run. Startups/shutdowns only occur when the onlne unts cannot ncrease or reduce ther outputs any further. Max Low-Cost Gen Dspatch Maxmze outputs of lowest cost unts n each dspatch perod Dspatch lowest cost technology close to ts maxmum capacty. Shutdown occurs f outputs of the lowest cost unts would have to be reduced.

3 3 The man tradeoff between these two dspatch models s between startup costs and runnng costs. Mn Start/Stop dspatch saves on startup costs by mnmzng shutdowns but ncurs hgher runnng costs, whle Max Low-Cost Gen ncurs hgher startup costs but saves on runnng costs snce the lowest cost unts are dspatched near ther maxmum capacty. 4 The two dspatch models provde a bass for comparng the extremes of these two general dspatch approaches. Actual dspatch and schedulng are, of course, far more complex n practce as there are numerous addtonal factors that need to be consdered such as network constrants and plant mantenance schedules. B. Operatng Costs and CO 2 Emssons Calculatons Total annual operatng costs of each generaton portfolo consst of runnng costs and start-up costs as expressed n (5). TOC($) TRC TSC (5) where TRC and TSC are the total annual runnng cost ($) and total annual startup cost ($) of the generatng portfolo. The total runnng costs of each generaton portfolo s determned based on (6). TRC VC. (6) T t 1 1 P where VC s the varable operatng cost of generatng unt ($/MWh), P s the output of generatng unt n the portfolo at perod t (MW), s the number of generatng unts n the portfolo and T s the number of dspatch perod n a year. The varable costs consst of varable O&M, fuel, and any carbon costs. Total annual startup costs of each generaton portfolo consst of the startup fuel cost and startup carbon cost of generatng unts n the portfolo, as expressed n (7). where T,t ) t 1 1 fuel carbon others TSC ( S S S (7) fuel carbon S, S, others S are the start-up fuel cost, startup carbon cost and other assocated costs durng startup of generatng unt n the portfolo at perod t respectvely. These other potental costs nclude ncreased O&M, ncreased forced outages, unt lfe shortenng, ncreased unt heat rate, and startup manpower [11]. CO 2 emssons of each portfolo s determned from (8). where T ) t 1 1 runnng start total CO (CO CO (8) runnng 2 CO and start CO are the emssons (tco 2 ) 2 durng the operaton and startup of unt n perod t respectvely. V. DESCRPTON OF THE CASE STUDY The case study for ths work consders coal, CCGT, OCGT and wnd generaton optons based on [10] and as shown n Secton. The data for ths study are based prmarly on actual demand and wnd generaton from South Eastern Australa, and a number of Australan specfc consultances 4 For both dspatch models, peakng OCGTs are only dspatched when coal and CCGT are already runnng at ther capacty (assumng OCGT has the hghest runnng costs, whch s vald for the assumed fuel and carbon prces). studes on plant captal and fuel costs. A case study wth 5% wnd penetraton and an expected carbon prce of $30/tCO 2 was chosen to demonstrate the post-processng extenson. The shares of coal, CCGT and OCGT are vared from 0% to 100% n 10% ntervals resultng n 66 possble thermal generaton portfolos. The EF for ths case study s, as noted above, shown n Fg. 1 A. Demand profle and the nstalled generaton capacty The actual 30-mnute combned demand and wnd generaton for the states of South Australa (SA), Vctora (VC), and Tasmana (TAS) n Australa was used for the smulaton, and are shown n Fg. 2. The resdual demand was obtaned by subtractng wnd generaton from total demand. Fg mnute demand and wnd generaton n South Eastern Australa. B. Operatng Characterstcs of Generatng Unts Operatng and cost characterstcs of each technology are shown n Table [12, 13]. The amount of fuel used durng a startup are estmated based on hot start condtons (offlne 0-6 hours) [14]. Other potental costs assocated wth startng up generatng unts ncludng ncreased O&M, forced outages, unt heat rate, and manpower [11]. These costs are estmated to be between 2-5 tmes the startup fuel costs [14, 15]. TABLE OPERATNG CHARACTERSTCS OF EACH TECHNOLOGY Characterstcs Coal CCGT OCGT Unt sze (MW) Mnmum generaton (MW) Ramp rate (MW/hour) Fuel used durng startup (GJ) 2,500 1, Startup fuel cost ($/start) 50,000 7,850 1,040 Other startup costs ($/start) 250,000 23,550 2,080 CO 2 emssons durng startup (tco 2) Prces and emsson ntenstes for each fuel type are estmated based on [12, 16, 17] and are shown n Table. TABLE PRCE AND EMSSON NTENSTY OF EACH FUEL TYPE Coal Natural gas Ol Prce ($/GJ) Emsson ntensty (tco 2/GJ) V. SMULATON RESULTS AND ANALYSS Generator unt outputs at 30-mnute ntervals for each generaton portfolo consdered are smulated for both dspatch models over the year. The operatng, economc and emssons mplcatons of ncorporatng operatonal aspects nto the dspatch are assessed for the canddate generaton portfolos, whch are those on or near the Effcent Fronter (EF).

4 4 A. mplcatons of ncorporatng operatonal constrants Fg. 3 llustrates an example of 30-mnute constraned dspatch of a generaton portfolo (40% coal, 20% CCGT, 40% OCGT) durng a typcal month. For the moderate carbon prce of $30/tCO 2 assumed, coal plants stll have the lowest operatng costs, and therefore are dspatched as base-load generaton whle CCGTs are consdered to be the ntermedate load followng plants. OCGTs are only dspatched durng the hgh demand perods. Generally, outputs of the base-load unts n Mn Start/Stop dspatch are vared more frequently than Max Low-Cost Gen dspatch, n order to enable as many unts as possble to reman onlne. These dfferent generaton patterns nfluence the cyclng of generatng unts, operatng costs, and emssons of the generaton portfolos. operate the base-load coal unts near ther maxmum capacty by shuttng down CCGT unts where possble. Portfolo F (50% coal, 20% CCGT, 30% OCGT) has the hghest number of average startups per unt for CCGT - around 270 starts/year. Ths number s largely n the typcal range of desgned starts for recently nstalled CCGT unts of around 250 starts per year. Ths desgn crteron s wdely expected to ncrease to over 350 starts n the future gven technology advances [18]. All generaton portfolos were able to meet the maxmum 30-mnute ramps snce there are suffcent fast response gas plants. There appear to be no major concerns n the operatonal vablty of any generaton portfolos for ether dspatch model. The results also hghlght that, other than the dspatch model, the frequency of unt startups depends on the mx of technologes n the portfolos. 2) Economc mpacts Fg. 4 compares the expected costs and cost uncertanty (SD of costs) of the canddate portfolos for the cases wth and wthout operatng constrants, for both dspatch models. 5 The orgnal EF wthout the operatng constrants s compared wth the modfed EFs for each dspatch model. Fg. 3. Generaton patterns of each technology for a typcal month of a generaton portfolo for both dspatch models. 1) Operatonal mpacts The average numbers of unt startups for the canddate portfolos for both dspatch models are shown n Table V. TABLE V AVERAGE NO. OF STARTUPS/UNT/YEAR FOR BOTH DSPATCH MODELS Portfolo Mn Start/Stop Max Low-Cost Gen Coal CCGT OCGT Coal CCGT OCGT A) 40% coal, 20% CC, 40% OC B) 40% coal, 30% CC, 30% OC C) 30% coal, 40% CC, 30% OC D) 30% coal, 50% CC, 20% OC E) 30% coal, 60% CC, 10% OC F) 50% coal, 20% CC, 30% OC Snce CCGT unts are the hgher operatng cost large thermal plant under assumed fuel and carbon prces, they ncur more frequent startup/shutdown than the base-load coal unts, partcularly for Max Low-Cost Gen dspatch. Among the canddate portfolos, coal plants do not ncur any startup/shutdowns n ether dspatch models snce all the coal unts can mantan operaton above ther mnmum operatng level, even durng low-demand perods. OCGT unts are not often requred to startup snce they are rarely dspatched. For Mn Start/Stop dspatch, CCGT unts n each portfolo are rarely shutdown (and hence startup) snce all coal and CCGT unts can operate above ther mnmum levels for all perods. However, for portfolos wth hgh shares of coal, the base-load coal unts may have to ramp up/down more often. For Max Low-Cost Gen dspatch, CCGT unts ncur far more frequent startups snce ths dspatch model attempts to Fg. 4. Effcent fronters (EFs) of optmal generaton portfolos after ncorporatng the operatng constrants. Generally, ncorporatng short-term operatonal constrants ncreases the overall ndustry generaton costs of portfolos for both dspatch models due to ncreased runnng costs and addtonal startup costs. However, the extent of these cyclng cost mpacts vares accordng to the mx of technologes n generaton portfolos, whch subsequently affects the relatve cost-rsk profles of generaton portfolos, and hence the EF. As shown n Fg. 4, portfolo B (40% Coal, 30% CCGT, 30% OCGT) s replaced by portfolo H (30% Coal, 30% CCGT, 40% OCGT) on the EF when operatng constrants are ncorporated for both dspatch strateges. For ths case study, the addtonal costs due to the operatng constrants are generally small, representng less than 1% of total generaton costs. Whlst the economc mpacts of consderng operatng constrants are relatvely lmted for ths 5 The cost uncertanty of each portfolo s unchanged snce ths study does not consder uncertantes assocated wth short-term operaton.

5 5 case study, they do have an mpact on whch portfolos le on the EF. Neglectng these constrants n the long-term portfolo nvestment and plannng framework, therefore, may mpact selecton of the most approprate portfolo n some cases. Furthermore, these costs wll become more sgnfcant f demand varablty and renewable penetratons ncrease. 3) Emsson mpacts ncorporatng operatonal constrants resulted n emssons reductons for Mn Start/Stop dspatch, partcularly for portfolos wth large shares of coal (.e. 50%). Ths s because hgh emttng coal plants are dspatched at lower load factors under ths dspatch n order to permt low-emsson CCGT unts to reman on-lne. For Max Low-Cost Gen dspatch, t appears that the CO 2 emssons of the portfolos are about the same wth the unconstraned dspatch case gven that the lowoperatng cost coal plants are dspatched near ther maxmum capacty, whch s also the case n the unconstraned dspatch. The results suggest, therefore, that Mn Start/Stop dspatch represents a more approprate opton for reducng overall emssons n ths partcular case study. B. mpacts of dfferent carbon prces and wnd penetratons n ths case study, the mert order of generaton technology dd not change untl the carbon prce reaches $60/tCO 2 at whch pont CCGT replaces coal unts as the lowest cost generaton. As a result coal unts ncur frequent output changes ncludng starts/stops. Coal unts have hgh startup costs and are relatvely nflexble due to ther typcally low ramp rates and hgh mnmum operatng levels. At a hgh carbon prce, therefore, the operatonal and economc mplcatons assocated wth the ncluson of the short-term operatng constrants may be qute sgnfcant. n such a scenaro, Mn Start/Stop dspatch stll does not present major operatonal mplcatons snce coal unts are kept onlne most of the tme by reducng the output of baseload CCGT unts. For Max Low-Cost Gen dspatch, however, the average startups for coal unts are between starts/unt/year. Ths s sgnfcantly hgher than the typcal desgn number of starts of 20 per year wthout the need to replace major parts due to fatgue effects [19]. The ncluson of operatonal constrants also results n frequent ramp-rate volatons of the coal unts. Such operatng patterns under Max Low-Cost Gen dspatch can lead to major economc mpacts. Hgher wnd penetratons can also be expected to have sgnfcant operatng, economc and emssons mpacts on generaton portfolos. Beyond ts hgh captal but low operatng costs, the ncreased varablty of wnd generaton at hgh penetratons poses addtonal challenges for conventonal generators due to ncreased cyclng operaton. V. CONCLUSONS Ths paper apples a post-processng extenson to assess the operatonal, economc and emsson mpacts of ncorporatng short-term operatonal constrants nto the results of a longterm generaton portfolo nvestment and plannng tool. The case study results provde some nsghts nto how dfferent future generaton portfolos mght be mpacted by dfferent possble dspatch strateges. The results for ths partcular case study may seem to suggest that these operatonal constrants have moderate mpacts on the most approprate generaton portfolos, and the overall ndustry costs obtaned from the ntal generaton nvestment plannng analyss. However, n future low-carbon electrcty ndustres wth hgh levels of varable renewable generaton and hgh carbon prces, these mpacts are lkely to be more sgnfcant due to ncreased cyclng of thermal generatng unts. There are some lmtatons n the post-processng extenson to the tool. The constraned dspatch dd not consder shutdown and mnmum synchronzaton tme of generatng unts. Dspatch was only undertaken at 30-mnute ntervals. Network and securty constrants were not consdered. These lmtatons and the mplcatons of hgher wnd penetratons and carbon prces wll be further explored n future work. V. REFERENCES [1] J. 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