基于最优场景生成算法的主动配电网无功优化

针对间歇性分布式电源输出功率的不确定性和随机性，提出采用Wasserstein距离指标和K-means聚类场景削减技术生成最优场景，将随机优化问题转换为确定性优化问题。建立了风—光—荷多场景树模型，并以有功网损最小、电压偏差最小作为目标函数，考虑储能荷电状态约束影响，建立含间歇性分布式电源的主动配电网无功优化数学模型，并采用人工蜂群算法对模型进行求解。仿真分析得出基于Wasserstein距离指标和K-means聚类场景削减技术生成的最优场景能较精确地体现分布式电源有功出力的随机特性。最后，以IEEE-33节点配电系统为例进行仿真分析，验证了所提方法的有效性和可行性。

Reactive power optimization of active distribution network based on optimal scenario generation algorithm

Considering the uncertainty and stochasticity of intermittent distributed generations (DGs), a scenario method using Wasserstein distance metric and K-means cluster scenes reduction technique to generate optimal scene is proposed in this paper. So the stochastic optimization problem is transformed into a deterministic optimization problem. The multi-scenario tree models of wind-photovoltaic-load are established. A multi-objective reactive power optimization mathematical model of active distribution network containing intermittent DGS is built, which includes objectives that are the total active power losses and the voltage deviations of the bus, and considering energy storage states of charge characteristic constraints. Also, the artificial bee colony algorithm is used to solve the optimization problems. Simulation results show that the optimal scenes based on the Wasserstein distance indicators and K-means cluster technology reflect the random feature of distributed generation active power output more accurately. Finally, the simulation analysis of IEEE-33 bus distribution test system is carried out to verify the effectiveness and feasibility of the proposed method. This work is supported by National Natural Science Foundation of China (No. 51467009).