基于信息间隙决策理论鲁棒优化的源荷协同恢复策略

殷天然; 陈红方; 陈祺炜

doi:10.3969/j.issn.1008-0198.2025.05.010

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基于信息间隙决策理论鲁棒优化的源荷协同恢复策略

电网运行与控制 | 更新时间：2026-01-10

- 基于信息间隙决策理论鲁棒优化的源荷协同恢复策略
- Source-Load Coordinated Restoration Strategy Based on IGDT Robust Optimization
- 湖南电力 2025年45卷第5期页码：71-79
- 作者机构：
  
  国网江苏省电力有限公司南京供电分公司,江苏,南京,210019
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.05.010
  中图分类号： TM711
- 网络出版：2025-11-11，
  
  纸质出版：2025-11-11
- 稿件说明：
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殷天然, 陈红方, 陈祺炜. 基于信息间隙决策理论鲁棒优化的源荷协同恢复策略[J]. 湖南电力, 2025, 45(5): 71-79.

殷天然, 陈红方, 陈祺炜. Source-Load Coordinated Restoration Strategy Based on IGDT Robust Optimization[J]. 2025, 45(5): 71-79.
殷天然, 陈红方, 陈祺炜. 基于信息间隙决策理论鲁棒优化的源荷协同恢复策略[J]. 湖南电力, 2025, 45(5): 71-79. DOI： 10.3969/j.issn.1008-0198.2025.05.010.

殷天然, 陈红方, 陈祺炜. Source-Load Coordinated Restoration Strategy Based on IGDT Robust Optimization[J]. 2025, 45(5): 71-79. DOI： 10.3969/j.issn.1008-0198.2025.05.010.

摘要

电网灾后恢复面临机组启动时间与负荷恢复量双重不确定性的挑战

对恢复进程的安全性构成显著威胁。因此

提出一种融合信息间隙决策理论（information gap decision theory

IGDT）鲁棒优化的源荷协同恢复策略。首先对不确定参数的可能波动区间进行数学表征

进而构建以最大化机组恢复出力和负荷加权恢复量为目标的多目标不确定性优化模型。依托IGDT理论将不确定性模型转化为满足最低恢复性能要求的确定性优化问题

采用非支配排序遗传算法高效求解。该方法实现源荷在多个时步之间的协调恢复

为调度人员制定兼顾安全与效益的恢复方案提供了有效支撑。基于新英格兰10机39节点系统的算例验证了该策略的鲁棒性与有效性。

Abstract

Post-disaster power system restoration is challenged by the dual uncertainties of unit start-up times and load recovery amounts

which poses substantial threats to restoration security. Therefore

a source-load coordinated restoration strategy based on Information Gap Decision Theory(IGDT) robust optimization is proposed. Firstly

the possible fluctuation ranges of uncertain parameters are mathematically characterized. Subsequently

a multi-objective uncertain optimization model is established with the goals of maximizing both units restoration output and load-weighted restoration amount. Relying on the IGDT theory

this uncertainty model is transformed into a deterministic optimization problem that meets minimum restoration performance requirements

which is then efficiently solved using the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ). The proposed approach achieves coordinated cross-time-step restoration of sources and loads

providing effective support for operators to develop restoration plans that balance security and efficiency. Case studies on the New England 10-machine 39-bus system validate the robustness and effectiveness of the strategy.

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