Power System Reliability Analysis with Emergency Demand Response Program

Transcription

1 231 Internatonal Journal of Smart Electrcal Enneerng, Vol.2, No.4, Fall 2013 ISSN: pp.231:236 ower System Relablty Analyss wth Emergency Demand Response rogram Rahmat Aazam 1, Nosratallah Mohammadbe 1, Had Mrzae 1, Al Mansour 1, Ehsan Mohamadan 1 1 Department of Electrcal Enneerng, Scence and Research Branch, Islamc Azad Unversty, Ilam, Iran. Abstract Wth the development of restructured power systems and ncrease of prces n some hours of day and ncrease fuel prce, demand response programs were notced more by customers. demand response conssts of a seres of actvtes that governments or utltes desgn to change the amount or tme of electrc energy consumpton, to acheve better socal welfare or some tmes for mzng the benefts of utltes or consumers. In ths paper the effect of emergency demand response program on composte system relablty of a deregulated power system s evaluated usng an economc load model, AC power-flow-based load curtalment cost functon and relablty evaluaton technques. In ths paper for calculaton the relablty ndexes, the Emergency Demand Response rogram ( cost s consdered and n each contngency state, the cost wth the customer load curtalment cost s compared and the load approprate value s selected for load sheddng or partcpatng n. In the next stage, the system and nodal relablty ndexes are calculated. To nvestgate the mpact of actvty on composte relablty of restructured power systems the IEEE 6 bus Roy Bllnton Test System s utlzed. Accordng to obtaned results, usng lead to ncreasng nodal and system relablty. It can be sad that solvng problems such as congeston n transmsson lnes, power system relablty decrease at load network peak hours, s mpossble wthout customer nterferng n power market. In other hand Consumer partcpaton, makes the power markets more competton and enhance ts performance. Keywords: Emergency demand response program (, power system deregulaton, relablty IAUCTB-IJSEE Scence. All rghts reserved 1. Introducton artcpant of customers n electrcty market ncreases the compettveness of electrcty markets. When customers see prce volatlty, they modfy ther demand whch helps the magntude of prce spkes be reduced. When consumers can receve prce sgnals and can respond to them, some of them wll shft ther demand to cheaper hours when they face hgh prces. Demand Response (DR can be defned as the changes n electrc usage by end-use customers from ther normal consumpton patterns n response to changes n the prce of electrcty over tme. [1-7]. DR s dvded nto two basc groups and several subgroups [1-7]: A- Incentve-based programs: A-1- Drect Load Control (DLC A-2- Interruptble/curtal able servce (I/C A-3- Demand Bddng/Buy Back A-4- Emergency Demand Response rogram ( A-5- Capacty Market rogram (CA A-6- Ancllary Servce Markets (A/S B- Tme-based programs: B-1- Tme-of-Use (TOU program B-2- Real Tme rcng (RT program B-3- Crtcal eak rcng (CC rogram

2 232 The benefts of DR nclude ncreased statc and dynamc effcency, better capacty utlzaton, prcng patterns that better reflect actual costs, reducton of prce spkes, decentralzed mtgaton of market power, and mproved rsk management. A recent study estmated the prospectve benefts of actve demand response at $7.5 bllon by 2010 (ICF Other studes, descrbed n GAO (2004, ve further detals of the benefts that have already been generated because of demand response and actve retal choce [2-7]. Emergency Demand Response program ( s the most usual demand response program when an event occurs. provdes ncentves for customers to reduce loads durng relablty events, though the curtalment s voluntary. No penalty s assessed f customers do not curtal, and the rates are prespecfed, though no capacty payments are receved [3] [7]. Emergency Demand Response rogram ( s a relablty-specfc day-of nterpretablty contract that s avalable for hours when there s a shortfall n relablty reserves. Customers can choose to allow the ISO to nterrupt ther servce, for whch the customer s pad a prce detered through a bddng process [3] [7]. s an emergency DR program that provdes mechansms where demand can be reduced on short notce when reserve shortfalls are forecasted. s a voluntary emergency program that pays customers an ncentve whch, for example, s more than 500 $/MWh n New York power market or can be the prevalng real-tme market prce for curtalments of at least four hours long when called by the ISO [3]. The New York Independent System Operator (NYISO calculated that ts demand response program provded substantal benefts to the market by helpng the power grd recover from the August 2003 Blackout. Specfcally, they estmated that on August 15, 2003, the partcpatng DR of MW provded $50.8 M (US worth of economc benefts at a cost of $5.9 M (US. Durng August 2001, hgher-than-normal temperatures forced the NYISO to nvoke emergences on August 7, 8, and 9 (18 hours n all zones and on August 10 (4.5 hours n New York Cty/Long Island and Hudson Rver, Zones F K. On August 9th, a new record peak load of 30,983 MW was establshed. Most of the capacty shortfall occurred n the New York Cty/Long Island area (Zones J K. Durng ths tme, a Internatonal Journal of Smart Electrcal Enneerng, Vol.2, No.4, Fall 2013 ISSN: varety of load management programs, ncludng the RL 1 programs (, DADR 2, and ICA, were deployed. At peak load, an estmated 1,580 MW was curtaled, of whch the RL programs contrbuted 605 MW (38 percent, wth the balance cog from other sources. At the tme the events were called, 292 partcpants had restered n the. artcpants n the provded 70 percent of all load curtalment from all RL programs. Whle 292 partcpants (712 MW restered wth the NYISO for, only 213 (617 MW actually performed when emergences were declared. Those who performed delvered only an average 418 MW per hour, or 68 percent of ther restered capablty. A plannng consderaton for future rounds of the, ven that t s a voluntary program, s that more loads have to be restered than s actually requred [3]-[4]. There s a growng concern about the relablty of power systems under a market envronment, especally after the blackouts n North Amerca and Europe n Bulk power system operators prmarly rely on adjustments n generaton output (MW movements up or down to keep the system relablty. In prncple, changes n electrcty demand could serve as well as generator movements n meetng the relablty requrements [5]. So, customer loads could be able to partcpate n these markets. The partcpaton of these resources wll ether enhance relablty or lower costs of mantanng relablty for all customers and wll save money for partcpatng customers. Ths paper nvestgates the mpacts of emergency demand response program on system and nodal relablty n a state enumeraton approach. A small relablty test system, RBTS, s studed for whch the smulaton results show that, usng emergency demand response program, the system and nodal relablty s mproved. Ths paper s organzed n fve sectons. Secton 2 defnes the load economc model whch s used to evaluate the partcpaton n emergency demand response program and explans the economc analyss formulaton. Relablty Index Calculaton s dscussed n secton 3. Secton 4 presents the numercal results whch have been tested on RBTS and fnally secton 5 s dedcated to the conclusons. 1 rce Responsve Load 2 Day-Ahead Demand Reducton rogram

3 Demand Response Economc Modellng In the bennng of the deregulaton, usually consumers dd not have effectve partcpaton n the power markets and therefore they were not able to response to the prces effectvely. However, the development of the restructured power systems has been accompaned by many problems, for example reduced system relablty. Fg. 2 shows how the demand elastcty could effect on electrcty prces [6-7]. Internatonal Journal of Smart Electrcal Enneerng, Vol.2, No.4, Fall 2013 ISSN: the relatve change n demand s smaller than the relatve change n prce, the demand s sad to be nelastc. So the elastcty coeffcents can be arranged n a by matrx E [6-7]. The detaled process of modellng and formulatng how the program effects on the electrcty demand and how the mum beneft of customers s acheved, have been dscussed n [7]. Accordngly the fnal responsve economc model s presented by (3 [7]: d0( d( d0 ( E0(, j. A( j j1 0( j E( [ ( 0( A( ] 1,2,...,. (3 0( Fg.1. Effect of demand varaton on the electrc energy prce [6] Elastcty s defned as the rato of the relatve change n demand to the relatve change n prce [7]: q dq E 0. (1 q0 dp Where: d( t : Demand changes n tme nterval t : rce changes n nterval t ( t ( t j : rce changes n tme nterval t j Accordng to equaton (2, self elastcty ( and cross elastcty ( can be wrtten as: d( t d0 ( t 0 j d( t d0 j ( t j 0 Where: d( t : Demand changes n tme nterval t ( t : rce changes n nterval t ( t j : rce changes n tme nterval t j (2 Self elastcty and cross elastcty are negatve and postve values, respectvely. If the relatve change n demand s larger than the relatve change n prce, the demand s sad to be elastc, on the other hand, f The above equaton shows how much should be the customer's demand n order to acheve mum beneft n a -hours nterval [7]. Tme perod s assumed to be one hour. Varable load curve for hours wthn one day are consdered n the smulatons [7]. 2. Modellng of The fnal response of the economc model s presented by (4. The modfed model for showng the effect of s as follows: For = Non-Event Hours 18 d ( ( ( (,. 0 d d0 E0 j A( j ( 14 0 j j For = Event Hours d( d 18 d0( E( ( E0(, j. A( A( j14 0( j ( 0 0 j 3. Relablty Index Calculaton (4 (5 Relablty assessment methodoloes of bulk power systems are clearly descrbed n [8]. A composte system contans both generaton and transmsson facltes and s sometmes desgnated as a composte generaton and transmsson system. Accordng to the method of selectng system state, there are two basc methods: state enumeraton and Monte Carlo samplng. Relablty assessment of a composte system generally nvolves the soluton of the network

4 234 Internatonal Journal of Smart Electrcal Enneerng, Vol.2, No.4, Fall 2013 ISSN: confguraton under random outage stuatons 3 Cost CLC, Q CLC : s the customer nterrupton cost (contngences. Varous technques, dependng upon buses under contngency S. the adequacy crtera used and the ntent behnd these studes, are used n analysng the adequacy of a power Cost, Q : s the cost for ISO. system. The three basc technques used n network G and D are generaton output and load power buses solutons are as follows: vector. and 1 A network flow method Q s the mum output of power of 2 DC load flow method generators; and Q s the mum output of 3 AC load flow method power of generators vector. If the qualty of power supply ncludng acceptable voltage levels and approprate generatng unt MVAr lmts s an mportant requrement, more accurate AC load flow methods such as Newton- Raphson, Gauss-Sedel technques must be utlzed to calculate the relablty ndces. The technques of dentfyng and analysng problems n a system state are the same. These nclude power flow and contngency analyss for problem recognton and optmal power flow for remedal actons. In our followng smulaton, the enumeraton smulaton method s adopted to select system states. The formulaton of load curtalment cost deteraton under contngency S usng AC load flow and customer nterrupton load cost and cost and generaton cost can be depcted by the optmzaton of Equaton (6. Mn CostG, QG + Cost, Q + Cost, Q CLC CLC Q Q - d Q Q d Q Q Q 0 0 Q CLC CLC Q Q for S 1,2,3,..., N (6 c CLC, V 1,..., n (7 Q Q, V 1,..., Load Q Load CLC 1,..., n n 1,..., n (8 (9 (10 V V V (11 T T (12 S: set of all falure system state. N : Number of system falures. c Cost G, Q G : s generators cost n power system. and Q are power values vector. and CLC Q are curtalment load values vector. CLC V s voltage buses vector. T s power flow on a branch. T s mum capacty lmt of a lne or transformer. Ths optmal model of load curtalment cost s a problem that s solved by mat power software. Indces of system relablty are: EDLC: Expected duraton of load curtalment of the overall system: NC D j, EDLC ( hours / day (13 Where N = Number of system nterruptons n hour. C D = Duraton of the jth system nterrupton, n hour. LOL: Loss of load probablty of the overall system: EDLC LOL = (occurrence / day (14 EDNS: Expected demand not supply of the overall system: NC DNS Sys EDNS = ( MW / day (15 Sys DNS j, = System demand not suppled n MW for the jth nterrupton, n hour. EENS: Expected energy not suppled, n [MWh/day], s the total amount of energy whch s expected not to be delvered to the loads. NC ENS Sys EENS = ( MWh / day (16 Sys ENS j, = System energy not suppled n MWh for the jth nterrupton, n hour. 3 CLC: Customer Load Curtalment

5 235 Indces of load pont relablty are: LEDLC: Load pont expected duraton of load curtalment NC,k D k LEDLC ( hours / day (17 = Number of nterruptons occurrng n hour, at N C,k Bus k. k D, = Duraton of the jth nterrupton n hour at Bus k. j LEDNS: Load pont expected demand not supply: N C, k DNS Busk LEDNS = ( MW / day (18 Busk DNS j, = Demand not suppled n MW for the jth nterrupton, n hour at Bus k. LEENS: Load pont expected energy not suppled (MWh/day N c, k Busk ENS LEENS = ( MWh / day (19 ENS, Busk j = Energy not suppled n MWh for the jth nterrupton, n hour at Bus k. 4. Numercal Results In order to show the effect of emergency demand response program on system relablty of a deregulated power system, a case study based on the IEEE 6-bus system s presented n ths secton. Roy Bllnton Test System (RBTS has 11 generatng unts, wth the total nstalled capacty of 0 MW and a total system peak demand of 185MW spreadng out among 5 system buses. The sngle lne dagram of RBTS s shown n Fg. 2. Internatonal Journal of Smart Electrcal Enneerng, Vol.2, No.4, Fall 2013 ISSN: The amount of ncentve n program formulaton s assumed to be equal to 500 $/MWh (exstng ncentve n New York market. The elastcty of the load s shown n Table1. Table.1. Self and cross elastctes eak Off-eak Low eak Off-eak Low Load curve of Md-Atlantc reon New York network was selected for testng and analysng the effect of program, Fg. 3 [4]. The load curve s dvded nto three ntervals: low load perod (12.00 p.m. to 9:00 a.m., off-peak perod (10:00 a.m. to 13:00 p.m. and 19:00 p.m. to 12:00 p.m. and peak perod (14:00 p.m. to 18:00 p.m.. Demand (MW 5.8 x hour Fg3. Load curve of Md-Atlantc reon New York network The load curves before and after mplementaton of emergency demand response program s represented n Fg. 4. As t can be seen, by mplementaton of emergency demand response program, based on the dfference between elastctes n dfferent perods, loads are transferred from peak perods to valley perods. Wthout emergency demand response program, the IEEE 6-bus system peak load s 315 MW; consderng demand response programs, however, the IEEE 6-bus system peak load s MW. 5 x 10 4 Demand Wth Demand Wthout 4.8 Load (MW Fg.2. Sngle lne dagram of the RBTS hour Fg4. Effect of n Md-Atlantc load curve

6 236 In order to show the effect of emergency demand response program on the load curve and system and nodal relablty of a deregulated power system, the same test system, RBTS, has been smulated usng the relablty evaluaton technques. Above program mze the proft of customers moreover nfluencng the system and load pont relablty. Two scenaros wll be observed n ths paper: 1- Test of system wthout consderng emergency demand response program, 2- Test of system wth consderng emergency demand response program. The smulaton results for the relablty of total system have been shown n Table II. Table.2. System Relablty Indces of the RBTS Wthout Consderng Consderng LOL EDLC (h EDNS (MW EENS (MWh For system nodes the results can be seen n Table III, IV, and V. The results show that emergency demand response program mproves the relablty of the system. Comparng the nodal relablty ndces wth and wthout consderng emergency demand response program, t can be seen that the nodal relablty s also mproved when emergency demand response program s consdered. Table.3. Load ont Expected duraton of load curtalment [Hour] Wthout Consderng Internatonal Journal of Smart Electrcal Enneerng, Vol.2, No.4, Fall 2013 ISSN: Concluson Consderng Load Load Load Load Table.4. Load ont Expected demand Not Suppled (MWh/day Wthout Consderng Consderng Load Load Load Load Table.5 Load ont Expected Energy Not Suppled (MWh/day Wthout Consderng Consderng Load Load Load Load Ths paper evaluated the effects of demand response programs especally emergency demand response program on system and load pont relablty of a deregulated power system usng an economc load model, AC power-flow-based load curtalment cost functon and relablty evaluaton technques. In ths paper for calculaton the relablty ndexes, the Emergency Demand Response rogram ( cost s consdered and n each contngency state, the cost wth the customer load curtalment cost s compared and the load approprate value s selected for load sheddng or partcpatng n. In the next stage, the system and nodal relablty ndexes are calculated. From the smulaton results t can be seen that emergency demand response program mproves the system relablty and nodal relablty of a deregulated power system. Accordng to obtaned results, usng lead to ncreasng nodal and system relablty. It can be sad that solvng problems such as congeston n transmsson lnes, power system relablty decrease at load network peak hours, s mpossble wthout customer nterferng n power market. In other hand Consumer partcpaton, makes the power markets more competton and enhance ts performance. References [1] C. Rver, rmer on demand sde management, Report for the World Bank, February [2] U. S. Department of Energy, Energy polcy Act of 2005, secton 1252, February [3] FERC report, Regulatory commsson survey on demand response and tme based rate programs/ tarffs, August Avalable at [4] [5] J. Wellnghoff, Collaboratve dalog on demand response, FERC report, 12 Nov. 2006, avalable at [6] D. S. Krschen, G. Strbac,. Cumperayot, D. Mendes, Factorng the elastcty of demand n electrcty prces, IEEE Trans. on power sys., Vol.15, No.2, pp , May [7] H. Aalam, G. R. Yousef and M. arsa Moghadam, Demand Response Model Consderng and TOU rograms, IEEE/ES Transmsson and Dstrbuton Conference & Exhbton, 2008, Chcago, USA. [8] R. Bllnton and R. N. Allan, Relablty Evaluaton of ower Systems, 2nd Edton, lenum ress, New York, 1996.