| 引用本文: | 杨白洁,刘洪,葛少云,李俊锴,张世达.基于分布鲁棒机会约束的高韧性配电网多类型储能配置[J].电力自动化设备,2024,44(7):45-53 |
| YANG Baijie,LIU Hong,GE Shaoyun,LI Junkai,ZHANG Shida.Configuration of multi-type energy storage system in high resilience distribution network based on distributionally robust chance constraints[J].Electric Power Automation Equipment,2024,44(7):45-53 |
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| 摘要: |
| 针对当前多类型储能系统(ESS)配置尚未充分计及正常工况与极端灾害等多元场景下源、网、荷多重不确定性复杂耦合影响的问题，提出了一种兼顾配电网韧性和经济性的多类型ESS配置方法。提出了面向多元场景差异化需求的多类型ESS优化配置框架，建立了以配电网韧性和经济性最优为目标的配置模型。考虑元件故障、源荷出力等多重不确定性，将原模型构建为两阶段分布鲁棒机会约束模型。提出了基于Big-M和条件风险价值理论的系统化线性方法，基于列和约束生成算法对线性化模型求解。在改进的IEEE 33节点配电网与43节点交通网算例上验证，结果表明，所提方法可显著降低负荷损失及投资，提升配电网韧性和经济性。 |
| 关键词: 配电网 韧性 多类型储能系统 条件风险价值 分布鲁棒机会约束 |
| DOI：10.16081/j.epae.202405025 |
| 分类号:TM715 |
| 基金项目:天津市自然科学基金资助项目(22JCZDJC00820)；国家自然科学基金资助项目(72361033) |
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| Configuration of multi-type energy storage system in high resilience distribution network based on distributionally robust chance constraints |
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YANG Baijie, LIU Hong, GE Shaoyun, LI Junkai, ZHANG Shida
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Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
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| Abstract: |
| Addressing the complex coupling effects of multi-dimensional uncertainties arising from various scenarios such as normal operating conditions and extreme disasters of source-network-load interactions, a configuration method for multi-type energy storage system(ESS) is proposed, which considers both the resilience and economic efficiency of the distribution network. A multi-type ESS optimization configuration framework tailored to diverse demands across multiple scenarios is introduced, a model is established aiming at optimizing the resilience and economic efficiency of the distribution network. Considering multiple uncertainties such as component failure and source-load output, the original model is constructed as a two-stage distributionally robust chance-constrained model. A systematic linear approach based on Big-M and conditional value-at-risk theory is proposed, and the linearized model is solved using column-and-constraint generation algorithm. The case study of the improved IEEE 33-bus distribution network and 43-node transportation network demonstrates that the proposed method significantly reduces load losses and investment while enhancing the resilience and economy of the distribution network. |
| Key words: distribution network resilience multi-type energy storage system conditional value at risk distributionally robust chance constraints |
