The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Smart Grids
Volume 13 - 2025 |
doi: 10.3389/fenrg.2025.1416309
This article is part of the Research Topic Advances in Renewable Energy System Monitoring, Situational Awareness, and Control View all 19 articles
Multi-objective day-ahead resilience improvement method for distribution network with high renewable energy penetration considering uncertainty of load and source sides
Provisionally accepted- 1 State Grid Liaoning Electric Power Co., Ltd, Shenyang, Liaoning Province, China
- 2 Electric Power Technology Collaboration, Beijing, China
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.
Keywords: Resilience Improvement, Distribution network, Multi objective optimization, Clear equivalence class method, particle swarm optimization (PSO) algorithm
Received: 12 Apr 2024; Accepted: 30 Jan 2025.
Copyright: © 2025 Gu, Zhu, Chen, Gao and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Taiyu Gu, State Grid Liaoning Electric Power Co., Ltd, Shenyang, 110004, Liaoning Province, China
Yidong Zhu, State Grid Liaoning Electric Power Co., Ltd, Shenyang, 110004, Liaoning Province, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.