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Game-Theory Modeling for Social Welfare Maximization in Smart Grids

Author

Listed:
  • Yu Min Hwang

    (Department of Wireless Communications Engineering, Kwangwoon University, Seoul 01897, Korea)

  • Issac Sim

    (Department of Wireless Communications Engineering, Kwangwoon University, Seoul 01897, Korea)

  • Young Ghyu Sun

    (Department of Wireless Communications Engineering, Kwangwoon University, Seoul 01897, Korea)

  • Heung-Jae Lee

    (Department of Electrical Engineering, Kwangwoon University, Seoul 01897, Korea)

  • Jin Young Kim

    (Department of Wireless Communications Engineering, Kwangwoon University, Seoul 01897, Korea)

Abstract

In this paper, we study the Stackelberg game-based evolutionary game with two players, generators and energy users (EUs), for monetary profit maximization in real-time price (RTP) demand response (DR) systems. We propose two energy strategies, generator’s best-pricing and power-generation strategy and demand’s best electricity-usage strategy, which maximize the profit of generators and EUs, respectively, rather than maximizing the conventional unified profit of the generator and EUs. As a win–win strategy to reach the social-welfare maximization, the generators acquire the optimal power consumption calculated by the EUs, and the EUs obtain the optimal electricity price calculated by the generators to update their own energy parameters to achieve profit maximization over time, whenever the generators and the EUs execute their energy strategy in the proposed Stackelberg game structure. In the problem formulation, we newly formulate a generator profit function containing the additional parameter of the electricity usage of EUs to reflect the influence by the parameter. The simulation results show that the proposed energy strategies can effectively improve the profit of the generators to 45% compared to the beseline scheme, and reduce the electricity charge of the EUs by 15.6% on average. Furthermore, we confirmed the proposed algorithm can contribute to stabilization of power generation and peak-to-average ratio (PAR) reduction, which is one of the goals of DR.

Suggested Citation

  • Yu Min Hwang & Issac Sim & Young Ghyu Sun & Heung-Jae Lee & Jin Young Kim, 2018. "Game-Theory Modeling for Social Welfare Maximization in Smart Grids," Energies, MDPI, vol. 11(9), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2315-:d:167367
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    References listed on IDEAS

    as
    1. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    2. Marzband, Mousa & Azarinejadian, Fatemeh & Savaghebi, Mehdi & Pouresmaeil, Edris & Guerrero, Josep M. & Lightbody, Gordon, 2018. "Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations," Renewable Energy, Elsevier, vol. 126(C), pages 95-106.
    3. Werner Dinkelbach, 1967. "On Nonlinear Fractional Programming," Management Science, INFORMS, vol. 13(7), pages 492-498, March.
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    Cited by:

    1. Faezeh Akhavizadegan & Lizhi Wang & James McCalley, 2020. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning," Energies, MDPI, vol. 13(5), pages 1-18, March.
    2. Tengfei Ma & Junyong Wu & Liangliang Hao & Huaguang Yan & Dezhi Li, 2018. "A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach," Energies, MDPI, vol. 11(10), pages 1-19, October.
    3. Bogdan-Constantin Neagu & Ovidiu Ivanov & Gheorghe Grigoras & Mihai Gavrilas & Dumitru-Marcel Istrate, 2020. "New Market Model with Social and Commercial Tiers for Improved Prosumer Trading in Microgrids," Sustainability, MDPI, vol. 12(18), pages 1-43, September.
    4. Sara Haghifam & Kazem Zare & Mehdi Abapour & Gregorio Muñoz-Delgado & Javier Contreras, 2020. "A Stackelberg Game-Based Approach for Transactive Energy Management in Smart Distribution Networks," Energies, MDPI, vol. 13(14), pages 1-34, July.

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