Voltage Security-Constrained Energy Storage Planning Model Considering Multi-Agent Collaborative Optimization in High-Renewable Power Systems
Résumé fourni par la source
Enhancing system strength to ensure voltage security has become a critical challenge for power systems with high penetration of renewable energy (RE). As China accelerates its clean-energy transition, the conventional grid dominated by synchronous generators is evolving into a dual-high system characterized by both high shares of wind–solar generation and extensive power-electronic interfaces. This shift fundamentally alters the mechanisms of voltage support, rendering traditional short circuit ratio (SCR) index inadequate for describing grid strength. To address this gap, this study proposes a multi-renewable-station short circuit ratio (MRSCR) index that quantitatively evaluates the voltage support strength of RE-dominated systems, and further analyzes the mechanism by which multiple agents on the generation and grid sides affect MRSCR, enhancing the generality and applicability of the proposed index. The MRSCR is further formulated as a voltage security constraint and integrated into an energy storage planning model considering multi-agent collaborative optimization. The proposed model jointly optimizes the siting and capacity configuration of grid-forming energy storage under voltage security constraints. Case studies on the IEEE 14-bus system and a real provincial grid show that incorporating the MRSCR indicator effectively enhances the system’s voltage support performance and operational resilience, achieving these improvements with only a 5.45% increase in daily operating cost compared with baseline planning results. The framework provides a practical offline tool for energy storage planning, enabling both enhanced renewable integration and improved voltage security.
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Contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Voltage Security-Constrained Energy Storage Planning Model Considering Multi-Agent Collaborative Optimization in High-Renewable Power Systems
- Date Crossref
- 17/12/2025
- Éditeur
- MDPI AG
- Type
- journal-article
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