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A Two-Stage Cooperative Dispatch Model for Power Systems Considering Security and Source-Load Interaction

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  • Haiteng Han

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Chen Wu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Zhinong Wei

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Haixiang Zang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Guoqiang Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Kang Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Tiantian Wei

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

Abstract

In modern power systems with more renewable energy sources connected, the consideration of both security and economy becomes the key to research on power system optimal dispatch, especially when more participants from the source and load sides join in the interaction response activities. In this paper, we propose a two-stage dispatch model that contains a day-ahead multi-objective optimization scheduling sub-model that combines a hyper-box and hyper-ellipse space theory-based system security index in the first stage, and an intraday adjustment scheduling sub-model that considers active demand response (DR) behavior in the second stage. This model is able to quantitatively analyze the relationship between the security and economy of the system dispatch process, as well as the impacts of the interaction response behavior on the wind power consumption and the system’s daily operating cost. The model can be applied to the evaluation of the response mechanism design for interactive resources in regional power systems.

Suggested Citation

  • Haiteng Han & Chen Wu & Zhinong Wei & Haixiang Zang & Guoqiang Sun & Kang Sun & Tiantian Wei, 2021. "A Two-Stage Cooperative Dispatch Model for Power Systems Considering Security and Source-Load Interaction," Sustainability, MDPI, vol. 13(23), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13350-:d:693455
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    References listed on IDEAS

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    1. 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.
    2. Cui, Hantao & Li, Fangxing & Hu, Qinran & Bai, Linquan & Fang, Xin, 2016. "Day-ahead coordinated operation of utility-scale electricity and natural gas networks considering demand response based virtual power plants," Applied Energy, Elsevier, vol. 176(C), pages 183-195.
    3. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    4. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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    Cited by:

    1. Jianfeng Dai & Cangbi Ding & Xia Zhou & Yi Tang, 2022. "Adaptive Frequency Control Strategy for PMSG-Based Wind Power Plant Considering Releasable Reserve Power," Sustainability, MDPI, vol. 14(3), pages 1-17, January.

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