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Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

Author

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  • Zhanle Wang

    (Faculty of Engineering and Applied Science, University of Regina, Regina, S4S 0A2, Canada
    Department of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China)

  • Raman Paranjape

    (Faculty of Engineering and Applied Science, University of Regina, Regina, S4S 0A2, Canada)

  • Zhikun Chen

    (Department of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China)

  • Kai Zeng

    (Department of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China)

Abstract

Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.

Suggested Citation

  • Zhanle Wang & Raman Paranjape & Zhikun Chen & Kai Zeng, 2019. "Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing," Energies, MDPI, vol. 12(15), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2867-:d:251634
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    References listed on IDEAS

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    1. Poria Astero & Bong Jun Choi & Hao Liang & Lennart Söder, 2017. "Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs," Energies, MDPI, vol. 10(10), pages 1-18, October.
    2. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
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    Cited by:

    1. Astriani, Yuli & Shafiullah, GM & Shahnia, Farhad, 2021. "Incentive determination of a demand response program for microgrids," Applied Energy, Elsevier, vol. 292(C).
    2. Sayfutdinov, Timur & Patsios, Charalampos & Greenwood, David & Peker, Meltem & Sarantakos, Ilias, 2022. "Optimization-based modelling and game-theoretic framework for techno-economic analysis of demand-side flexibility: A real case study," Applied Energy, Elsevier, vol. 321(C).
    3. Tope Roseline Olorunfemi & Nnamdi I. Nwulu, 2021. "Multi-Agent Based Optimal Operation of Hybrid Energy Sources Coupled with Demand Response Programs," Sustainability, MDPI, vol. 13(14), pages 1-20, July.

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