A Novel Renewable Energy Scenario Generation Method Based on Multi-Resolution Denoising Diffusion Probabilistic Models

Donglin Li (), Xiaoxin Zhao (), Weimao Xu, Chao Ge and Chunzheng Li
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Donglin Li: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Xiaoxin Zhao: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Weimao Xu: Economic Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110015, China
Chao Ge: Economic Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110015, China
Chunzheng Li: Economic Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110015, China

Energies, 2025, vol. 18, issue 14, 1-22

Abstract: As the global energy system accelerates its transition toward a low-carbon economy, renewable energy sources (RESs), such as wind and photovoltaic power, are rapidly replacing traditional fossil fuels. These RESs are becoming a critical element of deeply decarbonized power systems (DDPSs). However, the inherent non-stationarity, multi-scale volatility, and uncontrollability of RES output significantly increase the risk of source–load imbalance, posing serious challenges to the reliability and economic efficiency of power systems. Scenario generation technology has emerged as a critical tool to quantify uncertainty and support dispatch optimization. Nevertheless, conventional scenario generation methods often fail to produce highly credible wind and solar output scenarios. To address this gap, this paper proposes a novel renewable energy scenario generation method based on a multi-resolution diffusion model. To accurately capture fluctuation characteristics across multiple time scales, we introduce a diffusion model in conjunction with a multi-scale time series decomposition approach, forming a multi-stage diffusion modeling framework capable of representing both long-term trends and short-term fluctuations in RES output. A cascaded conditional diffusion modeling framework is designed, leveraging historical trend information as a conditioning input to enhance the physical consistency of generated scenarios. Furthermore, a forecast-guided fusion strategy is proposed to jointly model long-term and short-term dynamics, thereby improving the generalization capability of long-term scenario generation. Simulation results demonstrate that MDDPM achieves a Wasserstein Distance (WD) of 0.0156 in the wind power scenario, outperforming DDPM (WD = 0.0185) and MC (WD = 0.0305). Additionally, MDDPM improves the Global Coverage Rate (GCR) by 15% compared to MC and other baselines.

Keywords: renewable energy sources; scenario generation; diffusion model; multi-scale forecasting; time series decomposition (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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