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Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response

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  • Shuaijia He

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China
    College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

  • Junyong Liu

    (College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

Abstract

Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model.

Suggested Citation

  • Shuaijia He & Junyong Liu, 2024. "Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response," Energies, MDPI, vol. 17(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:795-:d:1334925
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    References listed on IDEAS

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    1. Yin, Mingjia & Li, Kang & Yu, James, 2022. "A data-driven approach for microgrid distributed generation planning under uncertainties," Applied Energy, Elsevier, vol. 309(C).
    2. Xu, Weiwei & Zhou, Dan & Huang, Xiaoming & Lou, Boliang & Liu, Dong, 2020. "Optimal allocation of power supply systems in industrial parks considering multi-energy complementarity and demand response," Applied Energy, Elsevier, vol. 275(C).
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