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Demand Bidding Optimization for an Aggregator with a Genetic Algorithm

Author

Listed:
  • Leehter Yao

    (Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Wei Hong Lim

    (Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Sew Sun Tiang

    (Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Teng Hwang Tan

    (Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Chin Hong Wong

    (Department of Engineering and Information Technology, UCSI College, Kuala Lumpur 56000, Malaysia)

  • Jia Yew Pang

    (School of Engineering and Physical Sciences, Heriot Watt University, Putrajaya 62200, Malaysia)

Abstract

Demand response (DR) is an effective solution used to maintain the reliability of power systems. Although numerous demand bidding models were designed to balance the demand and supply of electricity, these works focused on optimizing the DR supply curve of aggregator and the associated clearing prices. Limited researches were done to investigate the interaction between each aggregator and its customers to ensure the delivery of promised load curtailments. In this paper, a closed demand bidding model is envisioned to bridge the aforementioned gap by facilitating the internal DR trading between the aggregator and its large contract customers. The customers can submit their own bid as a pairs of bidding price and quantity of load curtailment in hourly basis when demand bidding is needed. A purchase optimization scheme is then designed to minimize the total bidding purchase cost. Given the presence of various load curtailment constraints, the demand bidding model considered is highly nonlinear. A modified genetic algorithm incorporated with efficient encoding scheme and adaptive bid declination strategy is therefore proposed to solve this problem effectively. Extensive simulation shows that the proposed purchase optimization scheme can minimize the total cost of demand bidding and it is computationally feasible for real applications.

Suggested Citation

  • Leehter Yao & Wei Hong Lim & Sew Sun Tiang & Teng Hwang Tan & Chin Hong Wong & Jia Yew Pang, 2018. "Demand Bidding Optimization for an Aggregator with a Genetic Algorithm," Energies, MDPI, vol. 11(10), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2498-:d:171098
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    References listed on IDEAS

    as
    1. Deng, S.J. & Oren, S.S., 2006. "Electricity derivatives and risk management," Energy, Elsevier, vol. 31(6), pages 940-953.
    2. Leehter Yao & Lei Yao & Wei Hong Lim, 2018. "A Soft Curtailment of Wide-Area Central Air Conditioning Load," Energies, MDPI, vol. 11(3), pages 1-15, February.
    3. Sezgen, Osman & Goldman, C.A. & Krishnarao, P., 2007. "Option value of electricity demand response," Energy, Elsevier, vol. 32(2), pages 108-119.
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    Cited by:

    1. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Hannes Agabus, 2023. "Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review," Energies, MDPI, vol. 16(5), pages 1-52, February.
    2. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.
    3. Zixu Liu & Xiaojun Zeng & Fanlin Meng, 2018. "An Integration Mechanism between Demand and Supply Side Management of Electricity Markets," Energies, MDPI, vol. 11(12), pages 1-23, November.

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