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Optimal Demand Response Aggregation in Wholesale Electricity Markets Masood Parvania, Student Member, IEEE, Mahmud Fotuhi-Firuzabad, Senior Member, IEEE, and Mohammad Shahidehpour, Fellow, IEEE
Abstract—Advancements in smart grid technologies have made it possible to apply various options and strategies for the optimization of demand response (DR) in electricity markets. DR aggregation would accumulate potential DR schedules and constraints offered by small- and medium-sized customers for the participation in wholesale electricity markets. Despite various advantages offered by the hourly DR in electricity markets, practical market tools that can optimize the economic options available to DR aggregators and market participants are not readily attainable. In this context, this paper presents an optimization framework for the DR aggregation in wholesale electricity markets. The proposed study focuses on the modeling strategies for energy markets. In the proposed model, DR aggregators offer customers various contracts for load curtailment, load shifting, utilization of onsite generation, and energy storage systems as possible strategies for hourly load reductions. The aggregation of DR contracts is considered in the proposed price-based self-scheduling optimization model to determine optimal DR schedules for participants in day-ahead energy markets. The proposed model is examined on a sample DR aggregator and the numerical results are discussed in the paper.
C. Constants
Index Terms—DR aggregator, energy market, hourly demand response, load reduction, price-based unit commitment.
I. NOMENCLATURE A. Indices and Abbreviations
Index of load reduction contracts. Abbreviation of load curtailment strategy.
LS
Abbreviation of load shifting strategy.
OG
Abbreviation of onsite generation strategy.
ES
Abbreviation of energy storage strategy.
Offered load reduction initiation cost of th contract of strategy . Price and quantity of th contract of strategy at time . Minimum load reduction duration of the th contract of strategy . Maximum load reduction duration of the th contract of strategy . Maximum number of daily load curtailment. Bidding startup cost of th OG contract. Bidding startup fuel of th OG contract. Lower limit on power generation of th OG contract. Upper limit on power generation of th OG contract. Ramp-up limit of th OG contract. Ramp-down limit of th OG contract. Minimum on time of th OG contract. Minimum off time of th OG contract. Fuel coefficient of th OG contract.
Index of hour. LC
Energy market price at hour .
Symbol for any of the load reduction strategy. B. Sets Set of load reduction contracts of strategy . Set of scheduling hours. Manuscript received October 03, 2012; revised February 20, 2013; accepted April 01, 2013. Date date of current version November 25, 2013. M. Parvania and M. Fotuhi-Firuzabad are with the Center of Excellence in Power System Control and Management, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran (e-mail:
[email protected],
[email protected]). M. Shahidehpour is with the Galvin Center for Electricity Innovation at Illinois Institute of Technology, Chicago, IL 60616 USA (e-mail:
[email protected]). Digital Object Identifier 10.1109/TSG.2013.2257894 1949-3053 © 2013 IEEE
Upper fuel consumption limit in th OG contract. Energy capacity of ES facility of th ES contract. Power rating of ES facility of th ES contract. Discharge efficiency of ES facility of th ES contract. Charging ramp of ES facility of th ES contract. Discharging ramp of ES facility of th ES contract. Energy retention time of th ES contract. Maximum number of charge-discharge cycles of th ES contract.
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D. Variables Load reduction status indicator of th load reduction contract of strategy at time ; 1 if the contract is scheduled, 0 otherwise. Starting indicator of th load reduction contract of strategy at time . Stopping indicator of th load reduction contract of strategy at time . Total scheduled load reduction of strategy time . Total cost of load reduction strategy
at
at time .
Load reduction initiation cost of th contract of strategy at time . Startup cost of th OG contract at time . Startup fuel of th OG contract at time . Power generation of th OG contract at time . Discharging power of th ES contract at time . II. INTRODUCTION
D
EMAND-SIDE participation is acknowledged as the inevitable solution to enhance the economic efficiency of electricity markets, reduce peak demand and price volatility, and improve the reliability of electric power systems [1]–[5]. In recent years, various demand response (DR) programs have been promoted by power system operators to encourage an active involvement of demand-side and capture potential benefits in electricity markets. The implementation of advanced metering infrastructure and other smart grid technologies promises to further increase the use of DR in electricity market operations. Regulatory agencies are changing market rules and removing barriers for the DR integration in electricity markets. For example, the FERC order 719 required ISOs in U.S. to accept DR bids comparable to other resources in wholesale markets [6]. DR is provided to commercial and industrial (C&I) customers and to residential customers at LV distribution systems. While in practice, DR can provide system services at wholesale electricity markets. However, DR requirements in wholesale markets, such as the minimum curtailment level, could curtail eligible customers or leave off potential small customers from participating in DR programs. The DR aggregation is acknowledged as an efficient solution to increasing the exposure of large volumes of consumers to wholesale electricity markets. In this regard, DR aggregators participate in electricity markets as a medium between the ISO and retail customers. DR aggregators work with retail customers to identify and offer appropriate DR programs that would allow customers to participate in the ISO’s market clearing program. The aggregators work with load serving entities (LSEs) to provide customers with advanced metering data for the monitoring and control of real-time electricity consumption. The day-ahead energy market clearing when considering the DR participation is studied in the literature [7]–[9]. A market
clearing model is presented in [8] to incorporate DR into security-constrained unit commitment (SCUC) for economic and security purposes. In [8], DR with its intertemporal characteristics is provided by aggregators. A hierarchical DR management framework is introduced in [9] in which DR aggregators submit bids for hourly load reductions in day-ahead energy markets. The DR load reduction bids are treated in [9] as a distinct product in markets which is comparable to energy offers provided by generating units. A model is proposed in [10] for analyzing the economic impact of DR aggregation in electricity markets. It is concluded in [10] that DR aggregation will lower the average market price and provide savings to customers in electricity markets. However, the proposed models in [8]–[10] have studied the ISO’s market clearing problem by incorporating DR rather than offering a model for the aggregators’ business process. The modeling of traditional participants in electricity markets such as GENCOs was studied and matured over the many years of operation experience. However, DR aggregators are new to electricity markets and there is a growing interest in developing operation models for such market participants. Our study in this paper will develop a model for decision-making by DR aggregators in wholesale electricity markets. The aggregators’ solution to the price-based self-scheduling problem will specify the hourly schedule for the aggregators’ market participation. Reference [11] proposed a multi-layered adaptive load management framework which categorizes the electricity market into three layers (end-user customers, DR aggregators, system operator.) DR aggregators are modeled in [11] as individual customers without considering the aggregators’ self-scheduling problem. Furthermore, thermal loads in [11] have limited the generality of the model. References [12]–[15] consider demandside agents which submit bids to electricity markets. However, these references have not considered the DR aggregation. In essence, the self-scheduling problem of DR aggregators for participation in wholesale energy markets was not discussed thoroughly in the technical literature. In this paper, we propose a framework for optimizing the participation of DR aggregators in day-ahead wholesale energy markets. In the proposed framework, DR aggregators optimize their bids by considering specific DR contracts for local customers to elicit their load reduction. The DR aggregators utilize the proposed price-based self-scheduling model to determine its optimal participation schedule in day-ahead energy markets. The rest of this paper is organized as follows. Section III presents the proposed business model for a generic DR aggregator in wholesale electricity markets. The proposed price-based self-scheduling model for DR aggregators is presented in Section IV. Section V presents the results of studies conducted on a sample aggregator. Finally, conclusions are drawn in Section VI. III. DR AGGREGATOR BUSINESS MODEL A. Model Overview The proposed model for the optimal DR aggregation in wholesale energy markets is shown in Fig. 1. In Fig. 1, the ISO initializes the DR program in the day-ahead market by sending
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Fig. 2. Classification of DR contracts in day-ahead energy markets. Fig. 1. Structure for DR aggregator correspondence with ISO and customers.
the information to aggregators to submit their DR bids. In this model, DR aggregators actively correspond with ISO and customers for maximizing DR. In this way, all customers have an opportunity to participate in DR programs via the hierarchical DR management framework of Fig. 1. Depending on the market structure, DR aggregators could be an existing market participant (e.g., distribution system operator, load-serving entity) which also acts as an aggregator [16]. In this paper, we assume DR aggregators are financial entities which are independent of system operators, and responsible for offering customer DR to wholesale electricity markets. In the long-run, DR aggregators design multiple techno-economic contracts for customers, and help them qualify for participation in DR programs. In this step, also called enrollment and qualification [17], [18], aggregators assess customers’ DR capability and assign contracts for their actual DR quantities. The aggregators utilize various performance evaluation methodologies in order to determine proper load reduction quantities corresponding to the customers’ physical load reduction strategies. The DR price is also assigned based on the agreement between the aggregator and the customers. In the day-ahead operation, aggregators compile customers’ DR contracts and utilize the proposed self-scheduling model to determine the optimal DR offer in the day-ahead market. Accordingly, the DR data passed onto the ISO will be reduced significantly. In addition, aggregators provide a valuable customer service as it would be difficult for individual customers to evaluate the profitability of various DR provisions in electricity markets. The aggregators receive the awarded hourly DR schedule from the ISO once the market is cleared, and require the customers with accepted offers to reduce loads during the contracted DR hours. In real time, DR aggregators would monitor the customers’ actual DR participation for settlement purposes [17]–[19]. In this paper, we focus on the day-ahead operation of DR aggregators and present a self-scheduling model for the optimal participation of aggregators in the day-ahead energy market. In the following, we present the structure of the DR contracts and the proposed self-scheduling model for the aggregators. B. Structure of DR Contracts DR aggregation can be applied to various programs (e.g., energy, capacity, ancillary services) and types (e.g., customer class, load reduction strategy, customer location.) In this paper, load reductions provided by DR participants act as negative
load. The types of DR contracts in the day-ahead energy markets are shown in Fig. 2. In Fig. 2, DR contracts are categorized based on the customer class in which different DR strategies reflect customer physical constraints for load reduction. In this paper, load curtailment (LC) and load shifting (LS), and the utilization of onsite generation (OG) and energy storage (ES) are recognized as physical load reduction strategies [1], [17]. In the proposed DR aggregation framework, aggregators would accumulate multiple customers with similar DR strategies and prices, into a single DR contract for satisfying minimum load curtailment requirements. This setting would significantly reduce the number of DR contracts in the aggregators’ self-scheduling problem. In practice, an aggregator may emphasize specific class of customers or load reduction strategies rather than encompassing all DR options, which would further reduce the number of DR correspondence with customers. We consider an all-encompassing DR aggregator in this paper with all possible DR options for participating in the day-ahead market. The four load reduction strategies are described below. 1) Load Curtailment: In the LC strategy, customers reduce their total electricity usage in the DR scheduled program without shifting the designated load to any other time period. For example, residential customers might turn off lights or turn up the thermostat, commercial customers might turn off non-essential office equipment, and industrial customers might curtail less productive operation hours in order to participate in a more lucrative DR program. 2) Load Shifting: In the LS strategy, customers reschedule and shift their electricity usage to other hours. For example, a residential customer might delay operating its appliances until later hours in a day, or an industrial facility might reschedule a batch production process to other hours in a day or alternate days in a week. 3) Utilizing Onsite Generation: In the OG strategy, customers reduce their load by turning on an onsite or backup generator to supply some or all of their own electricity loads [1], [17], [18]. Although the participants may have a minor interruption of their electrical usage, their net load would be reduced in the DR program. Hence, utilizing a behindthe-meter onsite generation is equivalent to the customer load curtailment from the power system point of view, although the onsite generation could have its own technical constraints for the customer participation in DR.
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4) Utilizing ES System: In the ES system strategy, customers utilize their ES system to supply some or all of their electricity needs during the DR program. ES can be a large modular storage device or an aggregated capacity of small distributed storage systems. By utilizing ES, the net load of DR participants will be reduced as ES is discharged and will increase as ES is charged in energy markets. The proposed load reduction strategies would help aggregators and the ISO coordinate DR activities more comprehensively. In addition, measurement and verification processes of DR, using the customers’ physical load reduction data, would be implemented more easily in electricity markets. IV. PROPOSED SELF-SCHEDULING MODEL AGGREGATOR
FOR
the load reduction initiation cost. The proposed formulation for LC contracts is given as follows:
(2)
(3) (4) (5)
DR (6)
An aggregator’s objective in the proposed self-scheduling model is to maximize its payoff (i.e., total revenue minus the cost) in the day-ahead energy market. The aggregator makes profit by selling the DR product to the energy market and paying off the DR participants for the hourly load reduction. Therefore, the incentive for DR aggregation is the difference between DR prices in customer contracts and hourly market prices of electricity charged to customers. The objective function in an aggregator’s self-scheduling model is maximized as follows:
(1) The first line in the objective function (1) is the revenue for selling off the aggregated amounts of four load reduction strategies, and the second line is the cost of paying off the contracted customers for the load reductions. In (1), load reductions are compensated at forecasted energy market prices and aggregator locations [20]. The hourly forecast for the market price of energy is assumed to be determined by aggregators and known by applying numerical techniques such as time series and artificial neural network [21]. Aggregators utilize historical market price data along with their DR operation experience in order to forecast hourly market prices. Accordingly, the proposed model is based on the assumption that the impacts of multiple DR aggregators and other market specifications on market prices are already included in the price forecasting of self-scheduling problem. The objective function (1) is subjected to the specific constraints for the load reduction strategies presented below. A. Load Curtailment The LC contracts include the load curtailment price which is determined based on agreements between aggregators and participating customers, as well as the associated LC quantity , which is the aggregated load curtailment of registered customers in the th contract. The LC quantity is determined based on the aggregator’s assessment of customers’ DR capability [17], [18]. The LC contracts also include minimum duration for load reduction, maximum duration for load reduction, maximum number of daily load curtailments, and
(7) (8) (9) The aggregated load reduction provided through LC contracts at time , and the associated cost function, , are formulated in (2)–(3). A binary variable is associated with the th LC contract in (2)–(3) which becomes 1 if the offer is scheduled by the aggregator. The LC cost function includes the load reduction initiation cost which is defined in (4). The binary variable indicates whether the load reduction in the th contract would be started at hour . The constraints on minimum duration of load reduction, maximum duration of load reduction, and maximum number of daily load curtailments are given in (5)–(7), respectively. Constraint (8) governs the contracts start/stop indicators, while constraint (9) assures that the binary variables will not become 1 simultaneously. B. Load Shifting The proposed model for the LS contracts is presented by (10)–(17). The LS model includes the aggregated load reduction, the associated cost function and load reduction initiation cost of LS contracts in (10)–(12), as well as the constraints on minimum and maximum duration of load reduction in (13)–(14). The LS contracts include three sets of indicator binary variables to indicate status, starting and stopping of the th LS contract at hour . The relations between the binary variables are presented in (15)–(16). The LS contracts also encompass three data points, designated by , which indicates that under the th LS contract customers would shift in the day-ahead horizon the percent of their loads from period to period. The aggregator assures in (17) that the LS contracts would be offered in the period. The shifting information of the scheduled LS contract would be sent by the aggregator to the ISO along with the information on other contracts. (10)
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(11) (12) (13)
(14) (15) (16) (17)
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through ES contracts and the associated cost function at time , defined by (28)–(29), are the sum of the scheduled load reduction and its cost stated by ES contracts. Constraint (30) implies that the total load reduction should be smaller than the ES power rating, while constraints (31), (32) limits the rate of ES discharging between any two successive hours. The aggregator makes sure in (33) that the total offered ES capacity in a contract over the scheduling hours, considering the discharging efficiency, is not larger than the energy capacity of the registered ES fleet. Constraint (34) limits the minimum number of daily ES charge/discharge cycles. The energy retention constraint in the th ES contract is shown in (35). According to (35), the energy in an ES fleet will not be kept longer than its energy retention time [22]. The binary variable constraints are shown in (36)–(37). (28)
C. Utilizing Onsite Generation The proposed model for equivalent load reduction provided by OG contracts is presented by (18)–(27). The total equivalent load reduction provided through OG contracts is formulated in (18) which is the sum of power production by customers’ onsite generation fleet. The associated cost function and the startup cost of the generation fleet of the th OG contract are presented in (19)–(20). The minimum and maximum limits on power generation of the generation fleet of the OG contract is shown by (21), while (22)–(25) show the ramp up/down and minimum on/off time constraints of the generation fleet, respectively. The upper limit on the total fuel consumption of the generation fleet of the th OG contract is applied in (26). The start-up fuel requirement is shown in (27). (18) (19) (20) (21) (22) (23) (24)
(25) (26) (27) D. Utilizing ES System An ES contract specifies characteristics of the ES fleet of customers. The proposed model for ES contracts is presented in (28)–(37). The total load reduction provided by the aggregator
(29) (30) (31) (32) (33) (34)
(35) (36) (37) In the proposed algorithm, (1)–(37) present the self-scheduling model of the DR aggregator in the day-ahead energy market. The proposed model is formulated as a mixed-integer linear programming (MILP) problem which can be solved using any available MILP solvers. The results of the model determine the optimal participation offer of a DR aggregator in the day-ahead energy market for a given market price profile. The optimal DR offer encompasses the hourly price and the quantity for load reduction contracts. The load reduction provided by LS and ES contracts would also include periods for shifting loads and charging the ES fleet. The rational DR aggregators would charge the ES systems and recover the reduced load in LS contracts during the hours when the forecasted price of energy is low. This may result in an unanticipated load growth in some hours which would complicate the ISO’s operation and may cause security problems in the system. In our proposed DR aggregation framework, the ISO would treat ES systems as aggregated negative load contracts, and co-optimizes the charging periods of ES systems and the shifting periods of LS contracts during day-ahead scheduling horizon, while scheduling load reductions and clearing the day-ahead market. Accordingly, local DR will participate in the implementation of system efficiency.
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TABLE I DATA OF THE LC CONTRACTS
TABLE II ADDITIONAL DATA FOR THE LS CONTRACTS Fig. 3. Optimal LC Scheduling—Study 1.
TABLE III DATA OF THE OG CONTRACTS
TABLE IV DATA OF THE ES CONTRACTS
Fig. 4. Optimal LS Scheduling—Study 1.
V. STUDY RESULTS A DR aggregator with the contracts data given in Tables I–IV is considered for demonstrating the features of the proposed model. The data for the three LC contracts are given in Table I. The data for the LS contracts are the same as those of the LC contracts, except the additional shifting data are given in Table II. The data for the OG and the ES contracts are also given in Tables III and IV. The proposed optimization problem for the DR aggregator self-scheduling introduced in Section IV, is solved using the MILP solver CPLEX 12.0 [23]. The computation time in all the studies was trivial, while the upper bound on the duality gap was set to be zero. A. Study 1: Base Results The aim of this study is to show how a DR aggregator can participate in the day-ahead energy market given energy market prices and considering physical DR constraints reflected as DR contracts. The energy market prices in this study are based on PJM marginal prices given on July 9th, 2012 [24]. The optimal schedules for the four DR strategies are shown in Figs. 3–6, along with the hourly market prices. The daily optimal aggregator payoff in this case is $1508.8. In all four figures, the hourly schedules are zero at hours 0–12 and 19–24, when the market price is smaller than the lowest contract price ($40/MWh). In Fig. 3, the 1st and 2nd LC contracts are scheduled respectively at hours 13–18 and 15–18, when the LC price is larger than
Fig. 5. Optimal OG Scheduling—Study 1.
market price and the payoff is positive. Although the market price at hour 19 is also larger than the price of 1st LC contract ($42.76/MWh versus $40/MWh), the contract is not scheduled at hour 19 due to the stated 6-hours duration for the maximum load reduction in Table I. In Fig. 4, the 1st and 2nd LS contracts are scheduled respectively at hour 13–16 and 15–18. The 1st LS contract is not scheduled at hours 16–19, when the market price is larger than $40/MWh. This is because the load reduction period for the 1st LS contract is stated as hours 10–16 in Table II. It has to be noted that the 3rd LC and LS contracts are not scheduled at hour 17 when the market price is larger than $50/MWh in Figs. 3 and 4. This is because of the 3-hour duration constraint for the minimum load reduction in the contracts. In Fig. 5, the 1st and 2nd OG contracts are scheduled in all the hours when the contract price is larger than the market price (hours 13–19 for 1st contract and hours 15–18 for 2nd contract).
PARVANIA et al.: OPTIMAL DEMAND RESPONSE AGGREGATION IN WHOLESALE ELECTRICITY MARKETS
Fig. 6. Optimal ES Scheduling—Study 1.
Fig. 7. Optimal LC Scheduling—Study 2.
In these contracts, the other constraints of OG contracts are not restrictive. The 3rd OG contract is not scheduled at hour 17 when the market price is larger than the contract price. This is due to the inclusion of $100 startup cost in the OG contract which makes the payoff negative at this hour. In Fig. 6, the 1st ES contract is scheduled at hours 13–18. The restrictive constraint in this contract is the total energy capacity of the ES fleet which would limit the total scheduled load reduction to 54 MW over the scheduling horizon. The 2nd ES contract is scheduled at hours 15–18, when the payoff is positive. The 3rd ES contract is scheduled at hour 17 when the market price is larger than the contract price. The scheduled ES in this contract is limited to rated 10 MW of the ES fleet. B. Study 2: Impact of Market Price The impact of market price on the optimal aggregator scheduling is investigated in this study. A PJM energy market price profile on January 13th, 2012 [24] is selected for this study. This price profile exhibits two peak prices over the 24-hours scheduling horizon. The DR contracts are the same as those of Study 1. The optimal schedules of the four DR strategies along with the hourly market prices are shown in Figs. 7–10. The optimal aggregator payoff in this case is $2764.8. In Fig. 7, the 1st and 2nd LC contracts are scheduled at all hours when the payoff is positive. These LC contracts are not restricted by minimum or maximum duration constraint for load curtailment. The 1st LC contract is also profitable at hours 8–12. However, it is not scheduled because of the constraint on the maximum number of daily load curtailments. The 3rd LC contract is activated at hour 18 and remained active through
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Fig. 8. Optimal LS Scheduling—Study 2.
Fig. 9. Optimal OG Scheduling—Study 2.
Fig. 10. Optimal ES Scheduling—Study 2.
hour 20 when the market price drops below the contract price ($46.66/MWh versus $50/MWh). This is due to the 3-hour minimum duration for load reduction. In Fig. 8, the 1st LS contract is not scheduled which is due to the negative payoff for the LS scheduling at hours 10–16. However, the 2nd and the 3rd LS contracts are scheduled for the 3-hour minimum duration for load reduction at hours 18–20. Similar to the 3rd LC contract in Fig. 7, the market price at hour 20 is lower than that of the 3rd LS contract in Fig. 8. However, the contract is scheduled at these hours because the total payoff at the 3-hour period is positive. In Fig. 9, the generation fleet of the 1st OG contract is started up to provide load reduction at hours 8–21 and it is not turned off at hours 13–17 when the market price is lower than the contract price. Instead, it is reduced to the 1 MW minimum production level. This is due to the startup cost of the generation fleet included in the contract. The total scheduled load reduction for the
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1st OG contract in the day-ahead is capped by the available fuel. The 2nd and the 3rd OG contracts are scheduled respectively at hours 18–21 and 18–19 when the scheduling payoff is positive. In Fig. 10, the maximum available capacity of 1st ES contract (i.e., 54 MWh) is scheduled by the DR aggregator. The 2nd ES contract is scheduled at hours 8, 11, 18, 19 when the market price is larger than the contract price. The 3rd ES contract is only scheduled at hours 18, 19. The scheduled ES contracts are limited to 12 hours in Fig. 10, which is due to the constrained energy retention time. VI. CONCLUSION This paper has presented a framework for DR aggregation in wholesale electricity markets. In the proposed framework, the DR aggregator offers multiple contracts and provides services to electricity customers in electricity markets. The characteristics of DR contracts are designed in terms of physical constraints of four load reduction strategies (i.e., load curtailment, load shifting, utilizing onsite generation and utilizing energy storage systems). A price-based self-scheduling model is proposed for a DR aggregator which seeks to maximize the aggregator’s payoff for participation in day-ahead energy markets. The proposed model is based on MILP formulation and takes into account the characteristics of different DR contracts. The flexibility of the proposed self-scheduling model for DR aggregators is examined by considering complicated DR constraints and applying various market prices data. The following observations are based on the proposed results: 1) DR aggregation would reduce the number of DR data passed onto the ISO. 2) Physical constraints can affect the optimal DR offer and the payoff of DR aggregators in day-ahead energy markets. These issues would highlight the importance of a precise aggregation model for customer load reductions. 3) The hourly DR aggregation offers the optimal scheduling of supply, which could maximize aggregator payoffs. 4) LC contracts are mostly constrained by minimum and maximum durations of load curtailments. The maximum number of daily load curtailments also becomes an important factor when hourly market prices exhibit multiple spikes during a day. 5) The LS contract is more applicable from a customer’s point of view than LC as load is not curtailed in LS and the total energy consumed remains unchanged. 6) The scheduled OG contracts are mainly limited by the availability of fuel. The startup cost of generation could also play an important role in certain cases. 7) The load reduction provided through ES contracts is technically more flexible comparing to other strategies. The limiting parameters are the rated power and energy capacity as well as energy retention time of ES fleets. REFERENCES [1] “Benefits of demand response in electricity markets and recommendations for achieving them,” U.S. Department of Energy,, 2006.
[2] R. N. Boisvert, P. A. Cappers, and B. Neenan, “The benefits of customer participation in wholesale electricity markets,” Electricity J., vol. 15, no. 3, pp. 41–51, Apr. 2002. [3] H. J. Wellinghoff and D. L. Morenoff, “Recognizing the importance of demand response: The second half of the wholesale electric market equation,” Energy Law J., vol. 28, no. 2, pp. 389–419, 2007. [4] Federal Energy Regulatory Commission, “Assessment of demand response and advanced metering,” [Online]. Available: http://www.ferc. gov/ [5] M. Parvania, M. Fotuhi-Firuzabad, and M. Shahidehpour, “Assessing impact of demand response in emission-constrained environments,” in Proc. 2011 IEEE PES General Meeting, Detroit, MI, USA, Jul. 2011. [6] Federal Energy Regulatory Commission, “Wholesale competition in regions with organized electric markets,” FERC Order No. 719, [Online]. Available: [Online]. Available: http://www.ferc.gov/ [7] M. Parvania, M. Fotuhi-Firuzabad, and M. Shahidehpour, “Demand response participation in wholesale energy markets,” in Proc. IEEE Power Energy Society General Meeting, San Diego, CA, USA, Jul. 22–26, 2012. [8] A. Khodaei, M. Shahidehpour, and S. Bahramirad, “SCUC with hourly demand response considering intertemporal load characteristics,” IEEE Trans. Smart Grid, vol. 2, no. 3, pp. 564–571, Sep. 2011. [9] M. Parvania and M. Fotuhi-Firuzabad, “Integrating load reduction into wholesale energy market with application to wind power integration,” IEEE Systems J., vol. 6, no. 1, pp. 35–45, Mar. 2012. [10] C. Chen, S. Kishore, W. Zhifang, M. Alizadeh, and A. Scaglione, “How will demand response aggregators affect electricity markets?—A Cournot game analysis,” in Proc. 5th ISCCSP, May 2012, pp. 1–6. [11] J.-Y. Joo and M. D. Ilic, “A multi-layered adaptive load management (ALM) system: Information exchange between market participants for efficient and reliable energy use,” in Proc. 2010 IEEE PES Transm. Distrib. Conf. Expo., Apr. 2010, pp. 637–642. [12] L. Ya’an and G. Xiaohong, “Purchase allocation and demand bidding in electric power markets,” IEEE Trans. Power Syst., vol. 18, no. 1, pp. 106–112, Feb. 2003. [13] E. Gómez-Villalva and A. Ramos, “Optimal energy management of an industrial consumer in liberalized markets,” IEEE Trans. Power Syst., vol. 18, pp. 716–723, 2003. [14] A. B. Philpott and E. Pettersen, “Optimizing demand-side bids in day-ahead electricity markets,” IEEE Trans. Power Syst., vol. 21, pp. 488–498, 2006. [15] R. Herranz, A. M. S. Roque, J. Villar, and F. A. Campos, “Optimal demand-side bidding strategies in electricity spot markets,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1204–1213, Aug. 2012. [16] F. Rahimi and A. Ipakchi, “Demand response as a market resource under the smart grid paradigm,” IEEE Trans. Smart Grid, vol. 1, no. 1, pp. 82–88, Jun. 2010. [17] North America Energy Standard Bureau (NAESB), Requirements Specifications for Wholesale Standard DR Signals Jan. 12, 2010, [Online]. Available: [Online]. Available: http://www.naesb.org/ [18] North America Energy Standard Bureau (NAESB), “Business practices for measurement and verification of wholesale electricity demand response,” Feb. 16, 2009 [Online]. Available: http://www.naesb.org/ [19] S. Coe, A. Ott, and D. Pratt, “Demanding standards,” IEEE Power Energy Mag., vol. 8, no. 3, pp. 55–59, May–Jun. 2010. [20] Demand Response Compensation in Organized Wholesale Energy Markets Federal Energy Regulatory Commission (FERC), Order No. 745, Mar. 15, 2011 [Online]. Available: http://www.ferc.gov/ [21] M. Shahidehpour, H. Yamin, and Z. Li, Market Operations in Electric Power Systems. New York, NY, USA: Wiley, 2002. [22] J. Eyer and G. Corey, Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide Sandia National Laboratories, Albuquerque, NM, USA, SAND2010-0815, Feb. 2010. [23] CPLEX 12.2 Manual. Armonk, NY, USA: IBM Corporation, 2011. [24] [Online]. Available: http://www.pjm.com/markets-and-operations/energy/real-time/monthlylmp.aspx Masood Parvania (S’09) received the B.S. degree in electrical engineering from Iran University of Science and Technology (IUST), Tehran, Iran, in 2007, and the M.S. degree in electrical engineering from Sharif University of Technology, Tehran, Iran, in 2009, where he is currently pursuing the Ph.D. degree. Since 2012, he has been a Research Associate in the Robert W. Galvin Center for Electricity Innovation at Illinois Institute of Technology, Chicago, IL, USA. His research interests include power system reliability and security assessment, as well as operation and optimization of smart electricity grids.
PARVANIA et al.: OPTIMAL DEMAND RESPONSE AGGREGATION IN WHOLESALE ELECTRICITY MARKETS
Mahmud Fotuhi-Firuzabad (SM’99) received the B.Sc. degree in electrical engineering from Sharif University of Technology, Tehran, Iran, in 1986 and the M.Sc. degree in electrical engineering from Tehran University, Tehran, Iran in 1989, and the M.Sc. and Ph.D. degrees in electrical engineering from the University of Saskatchewan, Saskatoon, SK, Canada, in 1993 and 1997, respectively. Currently, he is a Professor and Head of the Department of Electrical Engineering, Sharif University of Technology. He is also an Honorary Professor in the Universiti Teknologi Mara (UiTM), Shah Alam, Malaysia. He is a member of the Center of Excellence in Power System Management and Control. Dr. Fotuhi-Firuzabad is the Chair of IEEE Iran Section and serves as an Editor of the IEEE TRANSACTIONS ON SMART GRID.
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Mohammad Shahidehpour (F’01) is the Bodine Chair Professor and Director of Robert W. Galvin Center for Electricity Innovation at Illinois Institute of Technology. He is a Research Professor at King Abdulaziz University in Jeddah, Saudi Arabia, and Honorary Professor in North China Electric Power University in Beijing and Sharif University in Tehran. Dr. Shahidehpour was the recipient of an Honorary Doctorate from the Polytechnic University of Bucharest in Romania. He is an IEEE Distinguished Lecturer and the Editor-in-Chief of the IEEE Transactions on Smart Grid. He was the Chair of the 2012 IEEE Innovative Smart Grid Technologies Conference and the Chair of the 2012 Great Lakes Symposium on Smart Grid and the New Energy Economy. Dr. Shahidehpour received the 2012 IEEE PES Outstanding Power Engineering Educator Award.
