考虑多重不确定参数的配电网概率无功优化

针对同一区域内多个分布式电源及波动负荷的随机性和相关性对配电网模型带来的多重不确定性，构建了考虑多重不确定参数的概率无功优化模型，并将其转化为多场景确定性优化问题求解。模型中，三阶多项式正态变换(TPNT)改进的Nataf变换解耦了分布式发电机组和波动负荷的相关性，Gaussian-Hermite积分法处理了目标函数和约束项涉及的概率潮流计算。所建模型使用多策略融合粒子群优化算法进行优化求解，得到确定的控制变量最优解和状态变量统计矩。将该模型应用于接入多个分布式光伏发电机组和波动负荷的IEEE33节点配电系统进行仿真测试，其结果验证了所提概率无功优化模型和解法的适应性和有效性。

Probabilistic reactive power optimization for distribution network considering multiple uncertainties

Randomness and correlation of multiple distributed generators and fluctuating loads in close proximity brings multiple uncertainties to the model of distribution network. In allusion to the problem, a probabilistic reactive power optimization model considering multiple uncertainties is built and solved as a multi-scene deterministic optimization problem. In the model, third-order polynomial normal transformation (TPNT) simplified Nataf transformation is used to decouple correlation of distributed generators and fluctuating loads, and Gaussian-Hermite integration method is used for probabilistic power flow calculation in objective functions and constraints. The model is optimized by MSI-PSO, and a certain optimal scheme of control variables and statistical moments of state variables are achieved. The simulation is carried out on IEEE 33-bus system with multiple photovoltaic generators and fluctuating loads, and the result verifies the adaptability and availability of the proposed model and strategy for probabilistic reactive power optimization.