考虑主动配电网不确定性的随机无功优化及求解

Stochastic Reactive Power Optimization and Solution Considering Uncertainties in Active Distribution Networks

为了解决分布式电源在主动配电网无功优化中的不确定输出,随机规划模型得到广泛的应用。但随机规划模型中不确定性的概率分布受到历史数据数量的限制,使得经验分布存在偏差,易导致次优的控制策略。为此,引入了数据驱动的建模方法,将模型分为两阶段求解。第一阶段在第二阶段的最恶劣概率分布下,找到了离散无功补偿设备的最优控制策略;第二阶段为变量的不确定概率分布,用于校验第一阶段的结果,并采用列与约束生成(Column-and-Constraint Generation,CCG)算法来求解模型。最后,通过算例验证所提方法的有效性。

To solve the uncertain output of distributed generators(DGs)for reactive power optimization in active distribution networks,the stochastic programming model is widely used.Therein,the probability distribution of uncertainties in the stochastic model is always pre-defined by the historical data.However,the empirical distribution can be biased due to a limited amount of historical data and thus result in a suboptimal control decision.A data-driven modeling method,which divided the model into two-stage solution was introduced.In the first stage,under the worst probability distribution of the second stage,the optimal control strategy of discrete reactive power compensation equipment was found.The second stage was the uncertainty probability distribution of the variable,which was used to verify the results of the first stage,and used the Column-and-Constraint Generation algorithm to solve the model.Finally,an example was given to verify the effectiveness of the proposed method.