| 引用本文: | 丁泽宇,何川,刘绚,南璐,刘天琪.考虑决策依赖不确定性的配电网分布鲁棒多等级加固策略[J].电力自动化设备,2025,45(7):87-96 |
| DING Zeyu,HE Chuan,LIU Xuan,NAN Lu,LIU Tianqi.Distributionally robust multi-level reinforcement strategy for distribution network considering decision-dependent uncertainty[J].Electric Power Automation Equipment,2025,45(7):87-96 |
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| 摘要: |
| 为提高配电网韧性、增强电网抵御极端灾害风险的能力，提出一种计及线路加固、应急资源配置、需求响应、电动汽车、线路重构等措施协调的考虑决策依赖不确定性的配电网分布鲁棒韧性提升策略。考虑到线路存在加固后仍被破坏的可能性，提出一种考虑决策依赖不确定性的多等级加固模型，同时建立线路重构、需求响应、电动汽车、移动式紧急电源协同的优化模型。考虑极端风速波动性，进一步提出考虑决策依赖不确定性的配电网三层分布鲁棒多等级加固模型。采用列与约束生成方法对所提模型进行求解，并在保留重要故障状态的基础上削减故障状态，以提升模型计算效率。通过算例分析验证了所提模型的有效性。 |
| 关键词: 极端天气灾害 决策依赖不确定性 分布鲁棒优化 多等级加固 需求响应 线路重构 |
| DOI：10.16081/j.epae.202504007 |
| 分类号:TM727 |
| 基金项目:国家自然科学基金资助项目(52277114) |
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| Distributionally robust multi-level reinforcement strategy for distribution network considering decision-dependent uncertainty |
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DING Zeyu1, HE Chuan1, LIU Xuan2, NAN Lu1, LIU Tianqi1
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1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China;2.College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
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| Abstract: |
| To improve the resilience of distribution networks and enhance their ability to withstand extreme disaster risks, a distributionally robust resilience enhancement strategy considering decision-dependent uncertainty is proposed, which coordinates multiple measures, including line hardening, emergency resource allocation, demand response, electric vehicles, and network reconfiguration. Considering the possibility that hardened lines may still be damaged, a multi-level hardening model incorporating decision-dependent uncertainty is introduced. Additionally, a coordinated optimization model integrating network reconfiguration, demand response, electric vehicles, and mobile emergency generator is established. To further address the impact of extreme wind speed fluctuations, a three-layer distributionally robust multi-level hardening model considering decision-dependent uncertainty is proposed. The column-and-constraint generation method is employed to solve the proposed model, while fault state reduction techniques are applied to retain critical failure scenarios and improve computational efficiency. Case studies are conducted to verify the effectiveness of the proposed model. |
| Key words: extreme disasters decision-dependent uncertainty distributionally robust optimization multi-level hardening demand response line reconfiguration |
