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
Online-Zugang:	Volltext
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