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<p>The increasing integration of high-proportion renewable energy into the power system has brought significant challenges to inter-regional dispatching due to the uncertainty of wind and photovoltaic power generation. This paper proposes an inter-regional dispatching framework based on confidence gap decision theory (CGDT) to enhance system flexibility and economic efficiency under renewable energy uncertainty. In the proposed approach, CGDT integrates probabilistic confidence intervals and kernel density estimation to characterize the stochastic distribution of renewable energy prediction errors, achieving a balance between operational costs and risk tolerance through adjustable confidence levels. The proposed model aims to minimize the total cost, including generation, transmission losses, demand response incentives, and curtailment penalties—while transforming uncertain constraints into deterministic equivalents. Case studies on the HRP-38 system demonstrate that CGDT effectively reduces operational costs and enhances robustness across different confidence levels. Sensitivity analyses reveal that increasing flexible load participation and expanding transmission line capacities further improve renewable energy integration and cost-effectiveness.</p>

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Research on inter-regional dispatching for high-proportion renewable energy power system based on confidence gap decision theory

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Yanhe Li,
  • Shenkai Pan,
  • Bingtuan Gao,
  • Xiaofeng Liu

摘要

The increasing integration of high-proportion renewable energy into the power system has brought significant challenges to inter-regional dispatching due to the uncertainty of wind and photovoltaic power generation. This paper proposes an inter-regional dispatching framework based on confidence gap decision theory (CGDT) to enhance system flexibility and economic efficiency under renewable energy uncertainty. In the proposed approach, CGDT integrates probabilistic confidence intervals and kernel density estimation to characterize the stochastic distribution of renewable energy prediction errors, achieving a balance between operational costs and risk tolerance through adjustable confidence levels. The proposed model aims to minimize the total cost, including generation, transmission losses, demand response incentives, and curtailment penalties—while transforming uncertain constraints into deterministic equivalents. Case studies on the HRP-38 system demonstrate that CGDT effectively reduces operational costs and enhances robustness across different confidence levels. Sensitivity analyses reveal that increasing flexible load participation and expanding transmission line capacities further improve renewable energy integration and cost-effectiveness.