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Licensed Unlicensed Requires Authentication Published by De Gruyter June 28, 2019

Demand Response of an Industrial Buyer considering Congestion and LMP in Day-Ahead Electricity Market

  • Arvind Kumar Jain EMAIL logo


This paper proposes a methodology for developing Price Responsive Demand Shifting (PRDS) based bidding strategy of an industrial buyer, who can reschedule its production plan, considering power system network constraints. Locational Marginal Price (LMP) methodology, which is being used in PJM, California, New York, and New England electricity markets, has been utilized to manage the congestion. In this work, a stochastic linear optimization formulation comprising of two sub-problems has been proposed to obtain the optimal bidding strategy of an industrial buyer considering PRDS bidding. The first sub-problem is formulated as to maximize the social welfare of market participants subject to operational constraints and security constraints to facilitate market clearing process, while the second sub-problem represents the industrial buyer’s purchase cost saving maximization. The PRDS based bidding strategy, which is able to shift the demand, from high price periods to low price periods, has been obtained by solving two subproblems. The effectiveness of the proposed method has been tested on a 5-bus system and modified IEEE 30-bus system considering the hourly day-ahead market. Results obtained with the PRDS based bidding strategy have been compared with those obtained with a Conventional Price-Quantity (CPQ) bid. In simulation studies, it is observed that the PRDS approach can control the LMPs and congestion at the system buses. It is also found that PRDS can mitigate the market power by flattening the demand, which led to more saving and satisfying demand.


[1] Hunt S. Making competition work in electricity. New York: Wiley, 2002:71–85.Search in Google Scholar

[2] Quantifying Demand Response Benefits in PJM. A report for PJM Interconnection and mid atlantic distributed resource initiative (MADRI), 2007. Available at: in Google Scholar

[3] Borenstein S. The long-run efficiency of real-time electricity pricing. Energy J. 2005;26:93–116.10.5547/ISSN0195-6574-EJ-VOL26-NO3-5.BERTSEKASSearch in Google Scholar

[4] Federal Energy Regulatory Commission. Assessment of Demand Response & Advance Metering Staff Report, 2008.Search in Google Scholar

[5] American public power association. A Brief Description of the Six Regional Transmission Organizations (RTOs) Available at: in Google Scholar

[6] Li F. Continuous locational marginal pricing (CLMP). IEEE Trans Power Syst. 2007;22:1638–46.10.1109/TPWRS.2007.907521Search in Google Scholar

[7] Singh K, Padhy NP, Sharma JD. Influence of price responsive demand shifting bidding on congestion and LMP in pool-based day-ahead electricity markets. IEEE Trans Power Syst. 2011;26:886–96.10.1109/TPWRS.2010.2070813Search in Google Scholar

[8] Bompard E, Carpaneto E, Chicco G, Gross G. The role of load demand elasticity in congestion management and pricing. Proceedings of the 2000 IEEE PES Summer Meeting, Seattle. 2000;4:2229–34.10.1109/PESS.2000.867338Search in Google Scholar

[9] Bruno S, Benedictis MD, Scala ML, Wangensteen I. Demand elasticity increase for reducing social welfare losses due to transfer capacity restriction: a test case on Italian cross-border imports. Electr Power Syst Res. 2006;76:557–66.10.1016/j.epsr.2005.10.004Search in Google Scholar

[10] Valero S, Ortiz M, Senabre C, Alvarez C, Franco FJG, Gabaldon A. Methods for customer and demand response policies selection in new electricity markets. IET Proc Gener Transm Distrib. 2007;1:104–10.10.1049/iet-gtd:20060183Search in Google Scholar

[11] Shayesteh E, Moghaddam MP, Taherynejhad S, El Eslami MKS. Congestion management using demand response programs in power market. Proceedings of the 2008 IEEE PES General Meeting, 2008;1–8.10.1109/PES.2008.4596877Search in Google Scholar

[12] Albadi MH, El-Saadany EF. A summary of demand response in electricity markets. Electr Power Syst Res. 2008;78:1989–96.10.1016/j.epsr.2008.04.002Search in Google Scholar

[13] Aalami HA, Moghaddam MP, Yousefi GR. Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appied Energy. 2010;87:243–50.10.1016/j.apenergy.2009.05.041Search in Google Scholar

[14] Saele H, Grande OS. Demand response from house hold customers: experience from a pilot study in Norway. IEEE Trans Smart Grid. 2011;2:102–9.10.1109/TSG.2010.2104165Search in Google Scholar

[15] Jain AK, Srivastava SC, Singh SN, Srivastava L. Demand responsive bidding strategy of a buyer in a uniform electricity market. Proceeding of the 2011 International conference on Innovative Smart Grid Technologies Kollam, Kerla, India. 2011;268–73.10.1109/ISET-India.2011.6145394Search in Google Scholar

[16] Contreas J, Candiles O, Fuente JIDL, Gomez T. Auction design in day-ahead electricity markets. IEEE Trans Power Syst. 2001;16:409–17.10.1109/59.932276Search in Google Scholar

[17] Zhong H, Xia Q, Xia Y, Kang C, Xie L, He W, et al. Integrated dispatch of generation and load: a pathway towards smart grids. Electric Power Syst Res. 2015;120:206–13.10.1016/j.epsr.2014.04.005Search in Google Scholar

[18] Zhong H, Xia Q, Kang C, Ding M, Yao J, Yang S. An efficient decomposition method for integrated dispatch of generation and load. IEEE Trans Power Syst. 2015;30:2923–33.10.1109/TPWRS.2014.2381672Search in Google Scholar

[19] Fang X, Hu Q, Li F, Wang B, Li Y. Coupon-based demand response considering wind power uncertainty: a strategic bidding model for load serving entities. IEEE Trans Power Syst. 2016;31:1024–37.10.1109/TPWRS.2015.2431271Search in Google Scholar

[20] Fang X, Li F, Hu Q. Strategic CBDR bidding considering FTR and wind power. IET Gener Transm Distrib. 2016;10:2464–74.10.1049/iet-gtd.2015.1305Search in Google Scholar

[21] Surender Reddy S, Bijwe PR, Abhyankar AR. Faster evolutionary algorithm based optimal power flow using incremental variables. Int J Electr Power Energy Syst. 2014;54:198–210.10.1016/j.ijepes.2013.07.019Search in Google Scholar

[22] Surender Reddy S, Bijwe PR, Abhyankar AR. Real-time economic dispatch considering renewable power generation variability and uncertainty over scheduling period. IEEE Syst J. 2015;9:1440–51.10.1109/JSYST.2014.2325967Search in Google Scholar

[23] Surender Reddy S, Momoh JA. Realistic and transparent optimum scheduling strategy for hybrid power system. IEEE Trans Smart Grid. 2015;6:3114–25.10.1109/TSG.2015.2406879Search in Google Scholar

[24] Surender Reddy S, Bijwe PR, Abhyankar AR. Optimal posturing in day-ahead market clearing for uncertainties considering anticipated real-time adjustment costs. IEEE Syst J. 2015;9:177–90.10.1109/JSYST.2013.2265664Search in Google Scholar

[25] Surender Reddy S, Bijwe PR, Abhyankar AR. Co-optimization of energy and demand-side reserves in day-ahead electricity markets. Intl J Emerging Electr Power Syst. 2015;16:195–206.10.1515/ijeeps-2014-0020Search in Google Scholar

[26] Surender Reddy S. Multi-objective based congestion management using generation rescheduling and load shedding. IEEE Trans Power Syst. 2017;32:852–63.Search in Google Scholar

[27] Liu H, Tesfatsion I, Chowdhury AA. Derivation of locational marginal price for restructured whole sale power markets. J Energy Markets. 2009;2:3–27.10.21314/JEM.2009.029Search in Google Scholar

[28] de la Torre S, Conejo AJ, Contreras J. Simulating oligopolistic pool based electricity markets: A multi period approach. IEEE Trans Power Syst. 2003;18:1547–55.10.1109/TPWRS.2003.818746Search in Google Scholar

[29] Jain AK, Srivastava SC. Optimal bidding strategy under transmission congestion using genetic algorithm. Proceeding of the 2007 International conference on Genetic and Evolutionary Methods Las Vegas, USA, 2007:88–94.Search in Google Scholar

[30] Ferrero RW, Shahidehpour SM, Ramesh VC. Transaction analysis in deregulated power systems using game theory. IEEE Trans Power Syst. 1997;12:1340–7.10.1109/59.630479Search in Google Scholar

Received: 2017-12-05
Revised: 2019-05-31
Accepted: 2019-06-05
Published Online: 2019-06-28

© 2019 Walter de Gruyter GmbH, Berlin/Boston

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