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Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach

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  • Guoqiang Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Weihang Qian

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Wenjin Huang

    (Yancheng Power Supply Company of State Grid Jiangsu Electric Power Company, Yancheng 224002, Jiangsu Province, China)

  • Zheng Xu

    (Yancheng Power Supply Company of State Grid Jiangsu Electric Power Company, Yancheng 224002, Jiangsu Province, China)

  • Zhongxing Fu

    (Yancheng Power Supply Company of State Grid Jiangsu Electric Power Company, Yancheng 224002, Jiangsu Province, China)

  • Zhinong Wei

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Sheng Chen

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

The present study establishes a stochastic adaptive robust dispatch model for virtual power plants (VPPs) to address the risks associated with uncertainties in electricity market prices and photovoltaic (PV) power outputs. The model consists of distributed components, such as the central air-conditioning system (CACS) and PV power plant, aggregated by the VPP. The uncertainty in the electricity market price is addressed using a stochastic programming approach, and the uncertainty in PV output is addressed using an adaptive robust approach. The model is decomposed into a master problem and a sub-problem using the binding scenario identification approach. The binding scenario subset is identified in the sub-problem, which greatly reduces the number of iterations required for solving the model, and thereby increases the computational efficiency. Finally, the validity of the VPP model and the solution algorithm is verified using a simulated case study. The simulation results demonstrate that the operating profit of a VPP with a CACS and other aggregated units can be increased effectively by participating in multiple market transactions. In addition, the results demonstrate that the binding scenario identification algorithm is accurate, and its computation time increases slowly with increasing scenario set size, so the approach is adaptable to large-scale scenarios.

Suggested Citation

  • Guoqiang Sun & Weihang Qian & Wenjin Huang & Zheng Xu & Zhongxing Fu & Zhinong Wei & Sheng Chen, 2019. "Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach," Energies, MDPI, vol. 12(10), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1918-:d:232683
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    References listed on IDEAS

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    1. Jianying Li & Minsheng Yang & Yuexing Zhang & Jianqi Li & Jianquan Lu, 2023. "Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-15, April.

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