多源动态最优潮流的分布鲁棒优化方法

Distributionally Robust Optimization of Multi-energy Dynamic Optimal Power Flow

针对大规模清洁能源接入电网引起的系统鲁棒性和经济性协调问题,提出含风–光–水–火多种能源的分布鲁棒动态最优潮流模型。采用分布鲁棒优化方法将风光不确定性描述为包含概率分布信息的模糊不确定集。将模糊不确定集构造为一个以风光预测误差经验分布为中心,以Wasserstein距离为半径的Wasserstein球。在满足风光预测误差服从模糊不确定集中极端概率分布情况下最小化运行费用。由于梯级水电厂模型为混合整数模型,为了提高计算效率,将交流潮流近似为解耦线性潮流。最后,某703节点实际电力系统的仿真结果表明,所提方法可以通过控制样本大小和Wasserstein半径置信度的方法有效平衡系统的鲁棒性与经济性。

Aiming at the system robustness and economic coordination caused by large-scale renewable energy access to the grid,this paper proposed a distributionally robust dynamic optimal power flow model considering wind-solar-hydrothermal energy sources.The uncertainty of wind and solar power was described by distributionally robust optimization method through the ambiguity set that containing all possible probability distributions.The ambiguity set was constructed as a Wasserstein ball centered at the empirical distribution of the wind-solar forecasting error with the radius of Wasserstein metric.The optimal solution was obtained by minimizing the operational cost while satisfying the worst-case probability distribution of the wind-solar forecasting error.Because the cascade hydropower model is a mixed integer model,the AC power flow was approximated as a decoupled linear power flow to improve computational efficiency.The simulation results of a 703-node system show that the proposed method can effectively balance the robustness and economy of the system by controlling sample size and Wasserstein radius confidence.