Energies 2025, 18(19), 5218; https://doi.org/10.3390/en18195218 (registering DOI) - 1 Oct 2025
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
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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.
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(This article belongs to the Special Issue Advancements in Power Electronics for Power System Applications)
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