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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. 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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