AUTHOR=Gu Taiyu , Zhu Yidong , Tian Ye , Chen Xiangli , Cheng Yanhong , Gao Wei 

TITLE=Multi-objective day-ahead resilience improvement method for distribution network with high renewable energy penetration considering uncertainty of load and source sides

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2025.1416309

DOI=10.3389/fenrg.2025.1416309

ISSN=2296-598X

ABSTRACT=With the increasing integration of a high proportion of renewable energy, the fluctuation characteristics of distributed power generation such as wind and photovoltaic energy affect the safe and stable operation of the power system. Improving the operational resilience of the distribution network is of great significance for ensuring reliable power supply and improving user satisfaction with electricity consumption. In this paper, a multi objective day-ahead resilience improvement method for distribution network is proposed. Firstly, a detailed mathematical model of distribution network and its internal components was established; then, taking into account the economic costs of network loss and wasted renewable power punishment, as well as voltage safety margin indicators, a multi-objective optimization model is given, and the multi-objective optimization problem is transformed into a single objective optimization problem through the weight method. Meanwhile, considering the uncertainty of both source and load sides, a clear equivalence class method is adopted to transform the uncertain optimization problem into a deterministic optimization problem. Due to the existence of nonlinear and non-convex terms in the model, in order to reduce computational complexity, particle swarm optimization (PSO) algorithm is used to achieve the optimal solution. Finally, the effectiveness and feasibility of the proposed method are demonstrated with the modified IEEE-33 node testing system.