Robust Optimal Operation Method for Active Distribution Networks with Multiple Types of Regulation Resources Considering Source and Load Uncertainties

The increasing prevalence of renewable energy sources and the heightened uncertainty in load demands within active distribution networks (ADNs) have led to more fluctuations in power flow and voltage levels during operational periods.In light of these challenges, this paper proposes a robust optimization framework specifically designed for ADNs, which carefully balances system security, economic efficiency, and operational flexibility with multiple types of regulation resources.Firstly, a comprehensive regulation methodology is employed to integrate a variety of dispatchable resources.Secondly, the proposed model accounts for the inherent uncertainties related to load demand and the output of renewable energy generation by using the robust optimization (RO) technique.The proposed robust operational model for ADNs aims to minimizing power losses within the network and reducing voltage deviations, thereby improving overall network performance and reliability.Thirdly, the proposed model is linearized and reformulated as a convex optimization problem utilizing second-order cone relaxation techniques, and a relaxed cooperative co-evolution algorithm is implemented to solve it efficiently.Numerical results across various scenarios indicate that, compared to the conventional model without regulation resources, the proposed robust optimization model with multiple types of regulation resources can reduce voltage fluctuations by 89.6% and network losses by 12.9%.The proposed algorithm demonstrates better computational performance compared to conventional methods.