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A hierarchical framework for renewable energy sources consumption promotion among microgrids through two-layer electricity prices

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  • Wu, Y.J.
  • Liang, X.Y.
  • Huang, T.
  • Lin, Z.W.
  • Li, Z.X.
  • Hossain, Mohammad Farhad

Abstract

With the increasing penetration of distributed renewable resources (RES) and flexible loads, distribution networks are gradually developing into an active distribution network (ADN), which includes many microgrids (MGs). By considering the network structure, operational targets, and transactional relationship of the ADN, this paper proposes a “physical-transactional” scheme to promote RES consumption through specifically designed price mechanisms. The proposed price forming mechanisms consider the traditional transactions and incorporate the supply-demand ratio into the dynamic price forming process to better suit the highly penetrated renewable energy and small-scale local markets. Through this framework, each player can maximize his/her interest, such as system security, social welfare, the satisfaction of electricity consumption, etc., while renewable energy consumption and sharing can be maximumly promoted. The exponential penalty function-based decomposition and Lyapunov theory-based solution for the multilayer optimization model are employed to solve the inter-layer coordination and individual optimization simultaneously. Finally, a modified IEEE-69 system is taken as an example to verify that the proposed method. Our simulation shows that the proposed framework can promote renewable energy consumption and sharing and improve the social welfare of the ADN.

Suggested Citation

  • Wu, Y.J. & Liang, X.Y. & Huang, T. & Lin, Z.W. & Li, Z.X. & Hossain, Mohammad Farhad, 2021. "A hierarchical framework for renewable energy sources consumption promotion among microgrids through two-layer electricity prices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
  • Handle: RePEc:eee:rensus:v:145:y:2021:i:c:s1364032121004287
    DOI: 10.1016/j.rser.2021.111140
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    References listed on IDEAS

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    Cited by:

    1. Zhuochao Wu & Weixing Qian & Zhenya Ji, 2022. "A Demand Response Transaction Method for Integrated Energy Systems with a Trigonometric Membership Function-Based Uncertainty Model of Costumers’ Responsive Behaviors," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    2. Yin, Linfei & Qiu, Yao, 2022. "Neural network dynamic differential control for long-term price guidance mechanism of flexible energy service providers," Energy, Elsevier, vol. 255(C).
    3. Yang Tang & Yifeng Liu & Weiqiang Huo & Meng Chen & Shilong Ye & Lei Cheng, 2023. "Optimal Allocation Scheme of Renewable Energy Consumption Responsibility Weight under Renewable Portfolio Standards: An Integrated Evolutionary Game and Stochastic Optimization Approach," Energies, MDPI, vol. 16(7), pages 1-22, March.
    4. Kılıç Depren, Serpil & Kartal, Mustafa Tevfik & Ertuğrul, Hasan Murat & Depren, Özer, 2022. "The role of data frequency and method selection in electricity price estimation: Comparative evidence from Turkey in pre-pandemic and pandemic periods," Renewable Energy, Elsevier, vol. 186(C), pages 217-225.

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