ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1598553
This article is part of the Research TopicAdvanced Technologies for High-quality Development of Distribution Systems with Distributed Energy ResourcesView all 5 articles
A Dual-layer Planning Method Based on Improved MOPSO for Distribution Networks Considering Source-load Temporal Uncertainty
Provisionally accepted
- Economics and Technology Research Institute, State Grid Fujian Electric Power Company, Fuzhou, China
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The integration of distributed generations (DGs) and time-varying loads introduces significant uncertainties in distribution network planning. Existing methods often rely on simplified scenarios (e.g., typical days), which fail to capture the full temporal volatility of wind, solar, and load profiles. To address this challenge, this paper proposes a dual-layer planning framework integrating scenario reduction and multi-objective optimization. First, the AP-DTW-K-medoids method is developed to reduce 500 wind-solar-load scenarios to 6 representative clusters, improving the Davies-Bouldin Index (DBI) by 25.5% compared to traditional clustering. Second, a dual-layer model decouples investment decisions (upper layer) and operational dynamics (lower layer), enabling cost-effective DG and energy storage (ES) allocation. Third, an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with adaptive inertia weights accelerates convergence by 25%. Case studies on the IEEE-33 bus system demonstrate a 1.41% reduction in total costs and 7.87% lower voltage deviations compared to conventional methods. The proposed framework provides a scalable solution for uncertainty-aware distribution network planning.
Keywords: Distribution networks, source-load temporal characteristics, dual-layer planning, improved MOPSO, Scenario reduction
Received: 23 Mar 2025; Accepted: 30 May 2025.
Copyright: © 2025 Wu, Lin and Liao. 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: Jinlin Liao, Economics and Technology Research Institute, State Grid Fujian Electric Power Company, Fuzhou, China
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