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An Economic Dispatch Method of Microgrid Based on Fully Distributed ADMM Considering Demand Response

Author

Listed:
  • Dan Zhou

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Xiaodie Niu

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yuzhe Xie

    (State Grid Ningbo Power Supply Company, Ningbo 315000, China)

  • Peng Li

    (State Grid Ningbo Power Supply Company, Ningbo 315000, China)

  • Jiandi Fang

    (State Grid Ningbo Power Supply Company, Ningbo 315000, China)

  • Fanghong Guo

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

Aiming at the problem that the existing alternating direction method of multipliers (ADMM) cannot realize totally distributed computation, a totally distributed improved ADMM algorithm that combines logarithmic barrier function and virtual agent is proposed. We also investigate economic dispatch for microgrids considering demand response based on day-ahead real-time pricing (RTP), which forms a source-load-storage collaborative optimization scheme. First, three general distributed energy sources (DERs), renewable energy resources (RESs), conventional DERs and energy storage systems (ESSs), are considered in the method. Second, the goal of economic dispatch is to minimize the sum of three energy generation costs and implement the optimal power allocation of dispatchable DERs. Specifically, the approach not only inherits the fast computational speed of ADMM but also uses barrier function and virtual agent to handle inequality and equality, respectively. Moreover, the approach requires no coordination center and only the communication between current agent and adjacent agent to achieve totally distributed solution for every iteration, which can preserve information privacy well. Finally, a 30-node microgrid system is used for case analysis, and the simulation results demonstrate the feasibility and effectiveness of the proposed approach. It can be found that, the proposed approach converges to the optima when p = 0.01, v = 100, t 0 = 0.01 and μ = 2.

Suggested Citation

  • Dan Zhou & Xiaodie Niu & Yuzhe Xie & Peng Li & Jiandi Fang & Fanghong Guo, 2022. "An Economic Dispatch Method of Microgrid Based on Fully Distributed ADMM Considering Demand Response," Sustainability, MDPI, vol. 14(7), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3751-:d:777069
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    References listed on IDEAS

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    1. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    2. Nwulu, Nnamdi I. & Xia, Xiaohua, 2017. "Optimal dispatch for a microgrid incorporating renewables and demand response," Renewable Energy, Elsevier, vol. 101(C), pages 16-28.
    3. 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.
    4. Mohamed, Mohamed A. & Jin, Tao & Su, Wencong, 2020. "Multi-agent energy management of smart islands using primal-dual method of multipliers," Energy, Elsevier, vol. 208(C).
    5. Thamer Alquthami & Ahmad H. Milyani & Muhammad Awais & Muhammad B. Rasheed, 2021. "An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
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    1. Xiaoqing Bai & Chun Wei & Peijie Li & Dongliang Xiao, 2023. "Editorial for the Special Issue on Sustainable Power Systems and Optimization," Sustainability, MDPI, vol. 15(6), pages 1-3, March.

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