Optimization Method for Regulating Resource Capacity Allocation in Power Grids with High Penetration of Renewable Energy Based on Seq2Seq Transformer

Chunyuan Nie, Hualiang Fang (), Xuening Xiang, Wei Xu, Qingsheng Lei, Yan Li, Yawen Wang and Wei Yang
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Chunyuan Nie: State Grid Corporation of China, Central China Branch, Wuhan 430077, China
Hualiang Fang: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Xuening Xiang: State Grid Corporation of China, Central China Branch, Wuhan 430077, China
Wei Xu: State Grid Corporation of China, Central China Branch, Wuhan 430077, China
Qingsheng Lei: Economic and Technological Research Institute, State Grid Hubei Electric Power Company, Wuhan 430077, China
Yan Li: State Grid Corporation of China, Central China Branch, Wuhan 430077, China
Yawen Wang: Economic and Technological Research Institute, State Grid Hubei Electric Power Company, Wuhan 430077, China
Wei Yang: School of Law, Wuhan University, Wuhan 430072, China

Energies, 2025, vol. 18, issue 19, 1-20

Abstract: With the high penetration of renewable energy integrated into the power grid, the system exhibits strong randomness and volatility. To balance these uncertainties, a large amount of flexible regulating resources is required. This paper proposes an optimization method based on a Seq2Seq Transformer model, which takes stochastic renewable energy and load data as inputs and outputs the allocation ratios of various regulating resources. The method considers renewable energy stochasticity, power flow constraints, and adjustment characteristics of different regulating resources, while constructing a multi-objective loss function that integrates ramping response matching and cost minimization for comprehensive optimization. Furthermore, a multi-feature perception attention mechanism for stochastic renewable energy is introduced, enabling better coordination among resources and improved ramping speed adaptation during both model training and result generation. A multi-solution optimization framework with Pareto-optimal filtering is designed, where the Decoder outputs multiple sets of diverse and balanced allocation ratio combinations. Simulation studies based on a regional power grid demonstrate that the proposed method effectively addresses the problem of regulating resource capacity optimization in new-type power systems.

Keywords: Seq2Seq Transformer; high penetration of renewable energy; regulating resources; attention mechanism; Pareto-optimal filtering (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: 2025
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