Two-Layer Optimal Scheduling and Economic Analysis of Composite Energy Storage with Thermal Power Deep Regulation Considering Uncertainty of Source and Load

Chao Xing, Jiajie Xiao, Xinze Xi, Jingtao Li, Peiqiang Li () and Shipeng Zhang
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Chao Xing: Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650217, China
Jiajie Xiao: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Xinze Xi: Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650217, China
Jingtao Li: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Peiqiang Li: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Shipeng Zhang: College of Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China

Energies, 2024, vol. 17, issue 19, 1-20

Abstract: A two-layer scheduling method of energy storage that considers the uncertainty of both source and load is proposed to coordinate thermal power with composite energy storage to participate in the peak regulation of power systems. Firstly, considering the characteristics of thermal power deep peak regulation, a cost model of thermal power deep peak regulation is constructed and fuzzy parameters are used to manage the uncertainty of wind, photovoltaics, and load. Secondly, based on the peaking characteristics and operating costs of composite energy storage, a two-layer optimal scheduling model of energy storage is constructed. The upper layer takes pumped storage as the optimization goal to improve net load fluctuation and the optimal peak load benefit; the lower layer takes the system’s total peak load cost as the optimization goal and obtains a day-before scheduling plan for the energy storage system, using an improved gray wolf algorithm to process it. Finally, we verify the effectiveness of the proposed strategy based on an IEEE 39-node system.

Keywords: composite energy storage; two-layer optimal scheduling; uncertainty of source and load; improved gray wolf algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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