Optimal Configuration of Transformer–Energy Storage Deeply Integrated System Based on Enhanced Q-Learning with Hybrid Guidance

This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource...

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Veröffentlicht in:Processes Jg. 13; H. 10; S. 3267
Hauptverfasser: Li, Zhe, You, Li, Kang, Yiqun, Tan, Daojun, Cai, Xuan, Xiong, Haozhe, Liu, Yonghui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 13.10.2025
Schlagworte:
Algorithms
Alternative energy sources
Analysis
Arbitrage
Artificial intelligence
Convergence
Economic impact
Electric power systems
Electric transformers
Electrical equipment
Electricity
Energy industry
Energy management systems
Energy storage
Learning
Machine learning
Multiple objective analysis
Optimization
Optimization models
Renewable resources
Resource allocation
Technological change
China
ISSN:2227-9717, 2227-9717
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Zusammenfassung:This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource allocation and the superior capability of artificial intelligence algorithms in addressing multi-dimensional, multi-constrained optimization challenges. A multi-objective optimization model is first formulated with dual objectives: minimizing voltage deviation levels and comprehensive economic costs. To overcome the limitations of conventional methods in complex power systems—particularly regarding solution quality and convergence speed—an enhanced Q-learning with hybrid guidance algorithm is proposed. The improved algorithm demonstrates strengthened local search capability and accelerated late-stage convergence performance. Validation using a real-world urban power grid in China confirms the method’s effectiveness. Compared to traditional approaches, the proposed solution achieves optimal TES-DIS planning through autonomous learning, demonstrating (1) 70.73% cost reduction and (2) 89.85% faster computational efficiency. These results verify the method’s capability for intelligent, simplified power system planning with superior optimization performance.
Bibliographie:ObjectType-Article-1
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr13103267