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The Role of Demand Response Aggregators and the Effect of GenCos Strategic Bidding on the Flexibility of Demand

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  • Nur Mohammad

    (Department of Electrical Engineering and Electronic Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh)

  • Yateendra Mishra

    (School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane 4000, Australia)

Abstract

This paper presents an interactive trading decision between an electricity market operator, generation companies (GenCos), and the aggregators having demand response (DR) capable loads. Decisions are made hierarchically. At the upper-level, an electricity market operator (EMO) aims to minimise generation supply cost considering a DR transaction cost, which is essentially the cost of load curtailment. A DR exchange operator aims to minimise this transaction cost upon receiving the DR offer from the multiple aggregators at the lower level. The solution at this level determines the optimal DR amount and the load curtailment price. The DR considers the end-user’s willingness to reduce demand. Lagrangian duality theory is used to solve the bi-level optimisation. The usefulness of the proposed market model is demonstrated on interconnection of the Pennsylvania-New Jersey-Maryland (PJM) 5-Bus benchmark power system model under several plausible cases. It is found that the peak electricity price and grid-wise operation expenses under this DR trading scheme are reduced.

Suggested Citation

  • Nur Mohammad & Yateendra Mishra, 2018. "The Role of Demand Response Aggregators and the Effect of GenCos Strategic Bidding on the Flexibility of Demand," Energies, MDPI, vol. 11(12), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3296-:d:185547
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    1. Ayón, X. & Gruber, J.K. & Hayes, B.P. & Usaola, J. & Prodanović, M., 2017. "An optimal day-ahead load scheduling approach based on the flexibility of aggregate demands," Applied Energy, Elsevier, vol. 198(C), pages 1-11.
    2. Ampimah, Benjamin Chris & Sun, Mei & Han, Dun & Wang, Xueyin, 2018. "Optimizing sheddable and shiftable residential electricity consumption by incentivized peak and off-peak credit function approach," Applied Energy, Elsevier, vol. 210(C), pages 1299-1309.
    3. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    4. Huiru Zhao & Yuwei Wang & Sen Guo & Mingrui Zhao & Chao Zhang, 2016. "Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling," Energies, MDPI, vol. 9(9), pages 1-20, September.
    5. Wang, Qi & Zhang, Chunyu & Ding, Yi & Xydis, George & Wang, Jianhui & Østergaard, Jacob, 2015. "Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response," Applied Energy, Elsevier, vol. 138(C), pages 695-706.
    6. Faruqui, Ahmad & George, Stephen, 2005. "Quantifying Customer Response to Dynamic Pricing," The Electricity Journal, Elsevier, vol. 18(4), pages 53-63, May.
    7. Beibei Wang & Xin Fang & Xiayang Zhao & Houhe Chen, 2015. "Bi-Level Optimization for Available Transfer Capability Evaluation in Deregulated Electricity Market," Energies, MDPI, vol. 8(12), pages 1-17, November.
    8. Shariatzadeh, Farshid & Mandal, Paras & Srivastava, Anurag K., 2015. "Demand response for sustainable energy systems: A review, application and implementation strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 343-350.
    9. Cappers, Peter & Goldman, Charles & Kathan, David, 2010. "Demand response in U.S. electricity markets: Empirical evidence," Energy, Elsevier, vol. 35(4), pages 1526-1535.
    10. Zugno, Marco & Morales, Juan Miguel & Pinson, Pierre & Madsen, Henrik, 2013. "A bilevel model for electricity retailers' participation in a demand response market environment," Energy Economics, Elsevier, vol. 36(C), pages 182-197.
    11. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    12. Ghasemi, Ahmad & Mortazavi, Seyed Saeidollah & Mashhour, Elaheh, 2016. "Hourly demand response and battery energy storage for imbalance reduction of smart distribution company embedded with electric vehicles and wind farms," Renewable Energy, Elsevier, vol. 85(C), pages 124-136.
    13. Khan, Aftab Ahmed & Razzaq, Sohail & Khan, Asadullah & Khursheed, Fatima & Owais,, 2015. "HEMSs and enabled demand response in electricity market: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 773-785.
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    1. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
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    3. Esmaeili Aliabadi, Danial & Chan, Katrina, 2022. "The emerging threat of artificial intelligence on competition in liberalized electricity markets: A deep Q-network approach," Applied Energy, Elsevier, vol. 325(C).

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