含高比例分布式光伏的配电网多目标概率规划方法

针对分布式光伏出力不确定性造成的配电网规划成本增加、运行稳定性降低问题，文章提出了一种含高比例分布式光伏的配电网多目标概率规划方法。通过K-means聚类对光伏出力数据进行场景削减，得到典型场景集及其概率模型，基于蒙特卡洛概率潮流生成不确定性场景，模拟分布式光伏实际运行情况。基于所得不确定性场景，建立双层概率规划模型：上层以投资建设成本最小和光伏渗透率最大为目标，对分布式光伏及储能进行选址定容，下层考虑分布式光伏出力的不确定性，以概率潮流下的运维成本、网损成本、购电成本和电压偏差指数最小为目标，对分布式光伏出力以及储能各时段充放电功率进行优化。采用改进的粒子群(particle swarm optimization, PSO)算法对概率规划模型进行求解。采用安徽某地光伏出力作为典型数据，以IEEE 33节点系统为算例开展多场景算例分析，结果表明：与传统规划方法对比，所提方法能够提升光伏渗透率和配电网运行稳定性，并降低综合成本。

Multi-objective probabilistic planning method for distribution network with high proportion of distributed photovoltaics

In response to the cost and stability issues in distribution networks caused by uncertainty of distributed PV output, this paper proposes a multi-objective probabilistic planning method for distribution networks with high proportion of distributed PV. Firstly, PV output data is reduced through K-means clustering to obtain typical scenarios and their probability models. Uncertainty scenarios are generated using Monte Carlo probabilistic power flow. Secondly, a bi-level probabilistic planning model is established. The upper level minimizes costs and maximizes PV penetration, determining the location and capacity of distributed PV and energy storage. The lower level minimizes operational costs, network loss costs, power purchase costs, and voltage deviation index under probabilistic power flow, optimizing distributed PV and energy storage operation. An improved PSO algorithm is used to solve the model. Case study is conducted using IEEE 33-node system and actual PV output data from a certain country in Anhui. Results show that the proposed method improves PV penetration and operational stability while reducing costs compared to traditional planning methods.