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Optimal scheduling of virtual power plant with mobile energy storage for power grid resilience improvement

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  • Zhou, Yucheng
  • Lu, Xinhui

Abstract

This study proposes a two-stage optimal scheduling model for a virtual power plant (VPP) with mobile energy storage (MES), aiming to reduce the operational costs of the VPP and enhance the resilience of the power grid during disasters. The first stage is day-ahead scheduling, which aims to minimize the VPP operating costs by optimizing day-ahead electricity market transactions and microturbine (MT) output. To develop a cost-effective and reliable VPP operation plan, joint day-ahead scenario generation for wind turbine (WT) output, photovoltaic (PV) output, and load demand is performed using the R-vine Copula model during the day-ahead scheduling stage. The second stage is intraday scheduling, which aims to minimize operational costs and penalties from load loss and market transaction deviations by refining energy storage dispatch and market trading strategies. To improve the effectiveness of intraday scheduling, a model predictive control (MPC) method based on rolling time-domain optimization is introduced to mitigate WT and PV generation and load forecasting errors. Tests on the IEEE 33-bus distribution system show that the optimal scheduling model for a VPP with MES can reduce operating costs by 19.19% and load shedding by 70.26%, demonstrating its effectiveness in cost reduction and grid resilience enhancement.

Suggested Citation

  • Zhou, Yucheng & Lu, Xinhui, 2026. "Optimal scheduling of virtual power plant with mobile energy storage for power grid resilience improvement," Renewable Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:renene:v:267:y:2026:i:c:s0960148126005859
    DOI: 10.1016/j.renene.2026.125760
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