Coupon Incentive-based Demand Response (CIDR) in Smart Grid

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1 Coupon Inentive-based Demand Response (CIDR) in Smart Grid Haiwang Zhong, Student Member, IEEE, Le Xie, Member, IEEE, and Qing Xia, Senior Member, IEEE Abstrat--A new type of demand response (DR) program referred to as oupon inentive-based demand response (CIDR) is presented as an alternative to residential onsumer demand response programs. Enabled by pervasive mobile ommuniation apabilities and smart grid tehnologies, load serving entities (LSEs) ould offer residential onsumers oupon inentives to redue power onsumption in a given period of time, offsetting potential losses due to wholesale eletriity prie spikes. In ontrast with real-time priing or peak load priing, CIDR program maintains simple flat retail rate struture on onsumer side while providing effetive voluntary-based inentives for DR. An iterative proedure is designed to realize the real-time interation between the independent system operator, the LSEs and onsumers. CIDR an inrease the profit of the LSEs and ahieve almost the same soial welfare as under the real-time priing sheme. CIDR is ompatible with urrent flat retail rate priing sheme so the implementation is straightforward. A numerial experiment demonstrates the potential benefits of CIDR programs. Index Terms--Coupon Inentive-based Demand Response (CIDR), Real-Time Prie (), Smart Grid Communiation, Eletriity Markets. NOMENCLATURE The main mathematial symbols used throughout this paper are presented as below for quik referene. Other symbols are defined as needed throughout the paper. G Number of generators N P d Number of onsumers Total demand in wholesale market Δ P d Total demand response in wholesale market Δ P di Demand redution of onsumer i ΔP di,max Maximum demand redution of onsumer i k Response behavior parameter of onsumers C i () Cost funtion of generator i H. Zhong and L. Xie are with Department of Eletrial and Computer Engineering, Texas A&M University, College Station, TX. ( zhonghw04@tamu.edu, lxie@ee.tamu.edu) H. Zhong and Q. Xia are with State Key Lab of Power Systems, Dept. of Eletrial Engineering, Tsinghua University, Beijing 00084, China ( zhonghw04@mails.tsinghua.edu.n, qingxia@tsinghua.edu.n). D () Aggregated demand funtion S () Aggregated supply funtion ρ Real-time prie in wholesale market ρ Retail rate in retail market RR ρ ab, Δ b Coupon prie in retail market Coeffiients of supply urve Change of supply urve MC System marginal ost D I. INTRODUCTION EMAND response (DR) is one of the key features in Smart Grid. DR an be defined as a tariff or program established to motivate hanges in end-use onsumers eletri use in response to hanges in the prie of eletriity over time, or to give inentive payments designed to indue lower eletriity onsumption at times of high market pries or when grid reliability is jeopardized []. U.S. Federal Energy Regulatory Commission (FERC) estimated the ontribution from existing U.S. DR resoures at about 4,000 megawatts (MW), about 5.8% of 2008 summer peak demand [2]. Enabled by pervasive mobile ommuniation apabilities and smart grid tehnologies, real-time interation between the load serving entities (LSEs) and residential onsumers is beoming possible. For example in Texas, Onor plans to install smart meters throughout its servie area before 202 [3]. Hene, not only the industrial and ommerial onsumers but also residential onsumers an get muh more involved in DR programs [4]. However, most residential onsumers at present fae nearly flat retail rate of eletriity. By purhasing eletriity from the wholesale market at time varying pries and selling eletriity to onsumers at a flat rate, a LSE is exposed to finanial risks over short-term horizons due to the prie volatility. In order to hedge the temporal risks assoiated with prie spikes, the LSE ould ask the onsumers to redue eletriity demand during the times when spot prie spikes are likely to our, provided that onsumers an reeive finanial reward for their demand redution [5]. Consumers would normally offer to redue their demand when the revenue they derive from this redution exeeds the benefit they would ahieve by using the eletriity they hoose not to onsume [6] /2/$ IEEE

2 2 In the literature, there are primarily two types of DR, priebased DR and inentive-based DR programs []. Various types of prie-based DR have been proposed, suh as time-of-use priing (TOU), ritial peak priing (CPP), peak load priing (PLP), and real-time priing () [7], [8]. Under these priing shemes, the flutuation of wholesale eletriity prie is passed to residential ustomers so that the ustomers pay what the eletriity is worth at different time of the day. A program to expose residential onsumers to hourly was piloted in Chiago in 2003 and expanded in 2007 [9], [0]. However, most onsumers are risk-averse and not used to making eletriity deisions on a daily or hourly basis. Also equity issues may arise from time-varying retail rate, for example the night shift workers. Consequently, time-varying retail rate has some diffiulty in large-sale deployment. Various types of inentive-based DR programs have also been proposed. In perentage terms, about 93% of the peak load redution from existing DR resoures in the U.S. is provided by inentive-based DR programs []. Among all inentive-based DR programs, interruptible load ontrat (ILC) is one of the most popular approahes to induing demand redution, whih has been adopted by the utilities or regulators sine 980s [2]-[4]. Although the onsumers benefit from savings in their eletriity bills and from inentives provided by the ontrat [5], they are usually psyhologially relutant to redue their demand when they are requested to do so. Peak time rebate (PTR) is another type of inentive-based DR program. An experiment involving 23 residential onsumers of the City of Anaheim Publi Utilities (APU) was arried out in 2005 [6]. However, the rebate paid to onsumers is pre-determined to be very high, whih does not reflet the atual supply-demand ondition at different operating onditions. Inspired by the overbooking strategy in airline industry, a new type of DR program referred to as oupon inentivebased demand response (CIDR) is proposed in this paper. LSEs ould offer residential onsumers oupon inentives to redue power onsumption, offsetting LSEs potential losses due to wholesale eletriity prie spikes. The proposed CIDR has several signifiant advantages over the previously proposed methods. First, from LSEs perspetive, when the eletriity prie spikes our in wholesale market, the LSE ould offer oupons to indue demand redution instead of paying the high wholesale prie. Even if no onsumer responds to the oupon inentives, it won t be worse than the status quo with flat rate sheme. Seond, from the onsumer s perspetive, it is a voluntarybased program whih onsumers have the right but not the obligation to partiipate in. In ontrast to prie-based DR whih imposes wholesale eletriity prie risks upon onsumers, CIDR offers rebates to onsumers. So it will be muh more easily aepted by onsumers. Third, from the soiety s perspetive, based on the assumption of rationality in miro-eonomis and fully partiipation of onsumers, CIDR is shown to indue demand redution and ahieve almost the same soial welfare as under the sheme. Last but not the least, CIDR is ompatible with prevailing flat retail rate priing sheme. It an be deployed in largesale in short period of time. The remaining of this paper is organized as follows. In setion II, the CIDR model is formulated. The optimal oupon priing strategy from LSE s perspetive is derived by using Karush Kuhn Tuker (KKT) ondition. In Setion III, some implementation issues in terms of information exhange mehanism, hoie of onsumer baseline, adjustment of oupon prie, timeline of CIDR are investigated. In Setion IV, a numerial experiment is arried out to demonstrate the potential benefits of CIDR programs. In Setion V, major findings are summarized and future work is suggested. II. FORMULATION OF COUPON INCENTIVE-BASED DEMAND RESPONSE SCHEME The proposed CIDR is formulated as follows. We present the models of onsumers, LSEs and ISO/RTO, respetively. A. Consumer Although residential onsumers fae flat retail rate under CIDR sheme, they are inherently elasti [7].Consumers will respond to oupon inentives offered by the LSE based on the rationality assumption. A typial demand urve is shown in Fig.. Eah point on the demand urve denotes the onsumer s willingness-to-pay. Assume that the retail rate is ρ. Then a rational onsumer will onsume the amount of d eletriity. If the LSE offers the onsumer a oupon with value ( ρ2 ρ). Then the onsumer will redue the onsumption level from d to d 2 beause the eletriity bills savings and the oupon rebates offered by the LSE outweigh the lost benefit. Consumer s response behavior funtion is proposed in () (2), whih represent the onsumer s response to oupon inentives. ρ = kδ P, i =,2, L, N () Prie di 0 ΔP P, i =,2, L, N (2) ρ 2 ρ di di,max d 2 B d A Quantity Fig. A typial demand urve of onsumers To simplify the analysis, all the onsumers are assumed to have the same response behavior under given oupon prie. A typial response behavior funtion is shown in Fig. 2.

3 3 ρ ritial ρ Δ P di ΔP di,max Fig. 2 A typial response behavior urve of onsumers B. LSE The LSE s objetive is maximizing its own profit. Objetive funtion: max Obj= ρ RR ( P d ΔP d ) ρ ( P d ΔP d ) ρ Δ P d (3) Subjet to: ) Consumer s behavior onstraints ρ = kδ Pdi, i =,2, L, N (4) 2) Maximum demand redution onstraints 0 ΔPdi Pdi,max, i =,2, L, N (5) 3) Total demand response onstraints N Δ Pd = ΔP (6) di i= The first term in the objetive funtion is retail revenue, the seond term is eletriity purhasing ost from wholesale realtime market and the last term is oupon payment to the onsumers. C. ISO/RTO The ISO/RTO lears the market and determines the wholesale. The objetive of the market learing proess is soial welfare maximization. Given the fat that residential onsumers still enjoy a flat rate struture, the LSEs submit expeted (inelasti) demand to the ISO/RTO in real-time market. Therefore, the formulation beomes as follows. Objetive funtion: G min Ci( Pgi) (7) i= Subjet to: G N Pgi = ( Pdj ΔPdj) (8) i= j= min P P P max, i=,2, L, G (9) gi gi gi Δ P dj is a fixed value submitted by LSEs. D. Soial Welfare Soial welfare is defined as below [9]. Soial welfare = Consumer Benefit Produer Cost E. Optimal Coupon Priing Strategy for the LSE Optimal oupon priing strategy from the LSE s perspetive is derived in this setion. Coupon prie is assumed to be uniform to all the onsumers in $/MWh. From (3), the first order KKT ondition an be derived as below. Obj = ρ P + ρ + ΔP ρ ΔP RR d d d = ρ ρ + ρ P ΔP kδp ( ) RR d d d = ρ Δ + Δ ( ) RR 2k Pd ρ Pd Pd = 0 (0) Assume that the aggregated ost funtion is: 2 C = a ( Pd Δ Pd) + b( Pd Δ Pd) + onst () Then the orresponding supply funtion is: b ( Pd Δ Pd) = ρ (2) Suppose a signifiant wind ramp is expeted one hour ahead in the real-time operation, the new supply funtion is: ( b+δb) ( Pd Δ Pd) = ρ (3) Here, Δ b denotes the sudden wind ramp. So, the new ost funtion is: 2 C = a( Pd Δ Pd) + ( b+δb)( Pd Δ Pd) + onst (4) Then the the LSE faes an be represented as: ρ = ( Pd Δ Pd ) + ( b+δ b) (5) So, = (6) By substituting (5) and (6) into (0), the following equation is obtained. ρrr 2kΔ Pd + ( Pd Δ Pd ) + ( b+δ b) + ( Pd Δ Pd ) = 0 (7) The optimal demand response under CIDR sheme is: ρrr + 4aPd + ( b +Δb) Δ Pd = (8) 2k+ 4a Therefore, the orresponding optimal oupon prie is: k ρrr + 4aPd + ( b+δb) ρ = (9) 2k+ 4a With the knowledge of onsumer s response behavior funtion, the information about the system supply urve, and the expeted wind ramp alert, the LSE an deide the optimal oupon prie by using (9). In the next setion, the implementation of CIDR in today s eletriity system is proposed. III. IMPLEMENTATION ISSUES OF CIDR With the theoretial basis in previous setion, the proposed CIDR sheme an be implemented in the eletriity market where advaned metering and two-way ommuniation infrastruture are readily available. The information exhange sheme, hoie of onsumer baseline, adjustment of oupon

4 4 prie and the timeline of CIDR are suggested in this setion. A. Information Exhange Sheme A three-layer information exhange sheme among the ISO/RTO, the LSEs and onsumers are designed as shown in Fig. 3. N ( Pdi ΔPdi ) i= ρ m = ρ t,0 ( ) ρt,,0 = λ ρt,0 ρrr,0< λ < ΔP d ρ Δ P d 2 ρ ΔPdN ρ Fig. 3 Proposed three-layer information exhange struture for CIDR implementation In the highest layer, the ISO/RTO ollets demand bids from LSEs side and supply offers from generation side. Then ISO/RTO lears the market. is determined and broadasted to LSE. In the middle layer, the LSEs ollet demand redution offers from residential onsumers, adjust the oupon prie aording to the and demand redution in previous round. If oupon prie is updated, the LSEs will broadast the latest oupon information to eah onsumer. In the lowest layer, onsumers make their own deision under given oupon prie and then submit their demand redution offers to the LSEs. The proposed information exhange sheme links the wholesale market and retail market through oupon prie signal. B. Choie of Consumer Baseline Inentive-based DR programs in eletriity markets depend ritially on the hoie of onsumer baseline [8]. In the implementation of proposed CIDR, the baseline is hosen based on past power onsumption data of the onsumers during the past day, week, month or even year. This is due to the fat that the eletriity onsumption exhibits strong yli patterns over the time [20]. C. Adjustment of Coupon Prie Sine the LSE annot perfetly estimate the onsumer s response behavior funtion, oupon prie ould be inreased gradually by a small prie step in the implementation. A multi-round oupon prie adjustment proedure is presented in Fig. 4. Step : When a potential wholesale prie spike is expeted, the ISO/RTO determines the ρ t,0 (whih is usually greater than retail rate) assuming that there s no DR. Step 2: The LSE sets the initial oupon prie at ρt,,0 = λ( ρt,0 ρrr),0< λ <. Set the iteration indiator m =. Step 3: The oupon information is broadasted to all the onsumers. Step 4: The onsumers respond to the oupon inentives and submit the demand redution offers to the LSEs. ρ tm, Δd itm,, m= m+ Fig. 4 Flowhart of oupon prie adjustment for CIDR implementation Step 5: The LSEs bid to ISO/RTO with these DR. Step 6: The ISO/RTO lears the market and determines the ρ with DR. tm, Step 7: If the LSE s profit is inreased with regard to the previous round, inrease the oupon prie by a small prie step and set m = m + and then go to Step 3; otherwise, stop the adjustment proess. The LSEs and onsumers maximize their own benefit in the multi-round interations. Even though no onsumer responds to oupon inentives, it won t be worse than the status quo with flat retail rate sheme. D. Timeline of CIDR The timeline of CIDR is designed to make it pratiable, whih is shown in Fig. 5. Fig. 5 Proposed timeline for CIDR implementation When a potential wholesale prie spike is expeted one hour ahead in the real-time operation, the LSE an broadast

5 5 oupon information to onsumers immediately. In the approahing 45 minutes, the LSE and onsumers an interat iteratively (oupon prie and orresponding demand redution) until the LSE s profit no longer inreases. Empirially, one hour is enough for the onsumers to adjust their energy usage shedule. Closure gate will be 5 minutes ahead of the operation interval. The settlement proedure will be started 5 minutes after the operation interval. All the demand redution of responsive onsumers will be reorded in the smart meter database. At the end of eah month, the oupon payment will show up as an independent item in eletriity bills. IV. CASE STUDY An example in [8] is taken and modified to evaluate the performane of the CIDR presented in this paper. The inherent demand elastiity on the retail level is assumed to be The aggregated demand urve is: D( p) = p The aggregated supply urve is: S( p) = 97p+ 300 The system marginal ost an be expressed as: P d 300 MC = 97 Suppose that a sudden wind ramp of GW is expeted one hour ahead in real-time operation, the new supply urve beomes as follows. S ' ( p ) = 97 p 700 The updated system marginal ost an be expressed as: ' P d MC = 97 The aggregated supply urve and demand urve are illustrated in Fig. 6. Three ases are onsidered as follows. A. Case : Referene Case All onsumers are under a flat retail rate. The inherent demand flexibility is not utilized due to flat retail rate of eletriity. This ase represents the status quo in whih onsumers do not respond to the prie signal in the market. This serves as a benhmark against whih the performane of other ases is evaluated. Prie($/MWh) Demand urve Original supply urve Supply Curve with sudden wind ramp Quantity(MW) Fig. 6 The aggregated supply urve and demand urve x 0 4 In the referene ase, the retail rate of eletriity is $00/MWh. The eletriity demand equals 20,000MW. For simpliity, the onsumer baseline levels for all the ases are set at the atual demand levels for the referene ase. B. Case 2: All onsumers are exposed to the wholesale. Assume that all onsumers will respond to the aording to their atual elastiity. For simpliity, all onsumers are assumed to have the same elastiity. C. Case 3: CIDR All onsumers fae flat retail rate whereas the demand redutions are paid by the LSE at oupon pries. Assume all onsumers will respond to the oupon inentives aording to their atual response behavior funtions. Optimal oupon prie in Case 3 is obtained by using (9). And orresponding demand response is obtained by using (8). Table I summarizes the results for all ases. Two important indexes in Table I are defined as below. Purhasing Cost + Coupon Payment Average Cost = Peak Demand Consumer benefit is defined as the area beneath the demand urve between 0MW and peak demand. TABLE I COMPARISON OF DIFFERENT PRICING SCHEMES Case : Referene ase Case 2: Case 3: CIDR Peak Demand(MW) Demand Response(MW) Purhasing Cost(million $) Retail Revenue(million $) Coupon Payment($) LSE s Cost(million $) Average Cost($/MWh) LSE s Profit($) Real-time Prie($/MWh) Prie Consumers Fae($/MWh) Consumer s Rebate($) Consumer Benefit(million $) Soial welfare(million $) In Case (the referene ase), all demand is insensitive to the real-time wholesale market prie. The average ost is $05.08/MWh. The uniform flat retail rate would stay at $00/MWh. So the LSE loses 0,523 dollars. In Case 2, the peak demand is redued by 0.46% to 9,908MW for a net inrease of soial welfare of 200 dollars. The average ost is $04.6/MWh. Sine the onsumers are exposed to, they suffered from the prie volatility. The LSE won t lose money in this ase. In Case 3, the peak demand is redued by 4.64% to 9,032MW. The average ost of servie, whih inludes the oupon payment is $02.63/MWh. Although the average ost is higher than that in Case, the LSE loses less, i.e. 49,943 dollars. The relationship between the LSE s profit and oupon prie is illustrated in Fig. 7. The optimal oupon prie is

6 6 $48.39/MWh. LSE's Profit($) -4 x Coupon Prie($/MWh) Fig. 7 The relationship between LSE s profit and oupon prie In terms of peak load redution, the demand response in Case 3 is greater than that in Case 2. Hene, it will relieve the stress of grid operation and improve the system reliability. In terms of soial welfare, the soial welfare under CIDR sheme is very lose with sheme. V. CONCLUSIONS This paper presents the design and evaluation of a new type of DR program referred to as oupon inentive-based demand response (CIDR). The LSEs broadast oupon information to the residential onsumers when the market equilibrium ondition hanges signifiantly due to unexpeted hange of operating ondition (e.g. a sudden wind ramp). Under CIDR sheme, the onsumers inherent flexibility is exploited while the base onsumption is not exposed to wholesale real-time prie volatility. CIDR is ompatible with today s eletriity tariff struture so it an be easily implemented in the near future. It is demonstrated that the LSEs, the onsumers and the soiety an all benefit from the proposed DR sheme. The optimal oupon priing strategy from LSE s perspetive is derived by using KKT ondition. Implementation issues in terms of information exhange mehanism, hoie of onsumer baseline, adjustment of oupon prie, timeline of CIDR are also presented. Future work should investigate the performane of the proposed CIDR when unertainty of onsumer s response behavior is inluded. It is also important to investigate differentiation of oupons between different types of onsumers. The empirial test of this proposal is also under study using Texas A&M ampus eletriity grid. REFERENCES [] U.S Department of Energy, Benefit of demand response in eletriity market and reommendations for ahieving them, Feb [2] National Ation Plan for Energy Effiieny (200). Coordination of energy effiieny and demand response. 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Kathan, Demand response in U.S. eletriity markets: Empirial evidene, Energy, vol. 35, no. 4, pp , Apr 200 [2] C. S. Chen and J. T. Leu, Interruptible load ontrol for Taiwan power ompany, IEEE Trans. Power Syst., vol. 5, pp , May 990. [3] C. W. Tan and P. Varaiya. Interruptible eletri power servie ontrats, Journal of Eonomi Dynamis and Control, pp , May 993. [4] T. W. Gedra and P. Varaiya. Markets and Priing for Interruptible Eletri Power, IEEE Trans. Power Syst., vol. 8, no., pp , Feb 993. [5] L. A. Tuan and K. Bhattaharya. Competitive framework for prourement of interruptible load servies, IEEE Trans. Power Syst., vol. 8, no. 2, pp , May [6] Wolak Frank A. Residential onsumer response to real-time priing: the Anaheim ritial peak priing experiment, [7] C. S. King and S. Chatterjee, Prediting alifornia demand response, Publi Util. Fortnightly, vol. 4, no. 3, pp , [8] Hung-po Chao. Demand response in wholesale eletriity markets: the hoie of onsumer baseline, Journal of Regulation Eonomi, vol. 39, pp , 20. [9] F.C. Sheppe, M. Caramanis, R. Tabors and R. Bohn, Spot Priing of Eletriity, Boston: Kluwer Aademi Publishers, 988. [20] G. Gross and F. D. Galiana, Short-term load foreasting, Proeedings of IEEE, vol. 75, no. 2, pp , 987 Haiwang Zhong (S 0) reeived his B.S. degree from Eletrial Engineering Department of Tsinghua University, China, in 2008, where he is urrently working towards the Ph.D. degree. He is a visiting Ph.D. student in the Department of Eletrial and Computer Engineering, Texas A&M University in aademi year. His researh interest inludes eletriity market, eonomi dispath. Le Xie (S 05-M 0) reeived the B.E. degree in eletrial engineering from Tsinghua University, Beijing, China, in 2004, the M.S. degree in engineering sienes from Harvard University, Cambridge, MA, in June 2005, and the Ph.D. degree from the Department of Eletrial and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA in Currently, he is an Assistant Professor at the Department of Eletrial and Computer Engineering, Texas A&M University, College Station. His industry experiene inluded an internship at ISO-New England and an internship at Edison Mission Energy Marketing and Trading. His researh interest inludes modeling and ontrol of large-sale omplex systems, smart grids appliation with renewable energy resoures, and eletriity markets. Qing Xia (M 0-SM 08) reeived his Ph.D. degree from Eletrial Engineering Department of Tsinghua University, China, in 989. He is now a Professor at the same University. His researh interests inlude eletriity market, generation sheduling optimization, power system planning and load foreasting.