为降低光伏出力不确定性对多区域综合能源系统（IES）经济性与安全性的影响以及提升多区域IES系统在多种极端场景下的稳定运行能力,提出一种计及光伏出力不确定性的多区域综合能源系统多场景优化调度策略。针对光伏发电的不确定性,采用拉丁超立方抽样和改进型人工蜂群K-均值聚类算法形成典型光伏场景集。根据供热管道热特性和热能传输动态特性建立热网络模型。融合光伏场景信息,分别将日前阶段的运行成本以及实时阶段最恶劣光伏场景下系统的调整成本作为优化目标,构建两阶段分布鲁棒优化调度模型。采用列与约束生成（C&CG）算法对两阶段模型进行求解。最后,通过算例验证了所提策略的正确性和可行性。
Abstract
In order to reduce the influence of photovoltaic output uncertainty on the economy and safety of a multi-region integrated energy system（IES） and improve the stable operation capability of a multi-area IES system in various extreme scenarios, a multi-scene optimization scheduling strategy for multi-area integrated energy system considering the uncertainty of photovoltaic output is proposed. Aiming at the uncertainty of photovoltaic power generation, Latin hypercube sampling and improved artificial bee colony K-means clustering algorithm are used to form a typical photovoltaic scene sets. A thermal network model is established according to the thermal characteristics of heating pipes and the dynamic characteristics of thermal energy transmission. Based on photovoltaic scene information, the operating cost in the day-ahead stage and the adjustment cost of the system under the worst photovoltaic scenario in the real-time stage are taken as the optimization objectives, and a two-stage distribution robust optimization scheduling model is constructed. The two-stage model is solved using a column-and-constraint generation （C&CG） algorithm. Finally, the correctness and feasibility of the proposed strategy are verified by an example.
关键词
可再生能源 /
不确定性分析 /
调度算法 /
多区域综合能源系统 /
多场景 /
分布鲁棒优化
Key words
renewable energy /
uncertainty analysis /
scheduling algorithms /
multi-area integrated energy system /
multi-scenario /
distribution robust optimization
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基金
安徽省高校与合肥综合性国家科学中心协同创新项目（GXXT-2022-023)
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