A Novel Renewable Energy Scenario Generation Method Based on Multi-Resolution Denoising Diffusion Probabilistic Models
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,...
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2025-07-01
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| author | Donglin Li Xiaoxin Zhao Weimao Xu Chao Ge Chunzheng Li |
| author_facet | Donglin Li Xiaoxin Zhao Weimao Xu Chao Ge Chunzheng Li |
| author_sort | Donglin Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-acb3eaa6a8a441359e1a0e94f67a3a99 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj-art-acb3eaa6a8a441359e1a0e94f67a3a992025-08-20T03:58:30ZengMDPI AGEnergies1996-10732025-07-011814378110.3390/en18143781A Novel Renewable Energy Scenario Generation Method Based on Multi-Resolution Denoising Diffusion Probabilistic ModelsDonglin Li0Xiaoxin Zhao1Weimao Xu2Chao Ge3Chunzheng Li4School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaEconomic Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110015, ChinaEconomic Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110015, ChinaEconomic Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110015, ChinaAs 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.https://www.mdpi.com/1996-1073/18/14/3781renewable energy sourcesscenario generationdiffusion modelmulti-scale forecastingtime series decomposition |
| spellingShingle | Donglin Li Xiaoxin Zhao Weimao Xu Chao Ge Chunzheng Li A Novel Renewable Energy Scenario Generation Method Based on Multi-Resolution Denoising Diffusion Probabilistic Models Energies renewable energy sources scenario generation diffusion model multi-scale forecasting time series decomposition |
| title | A Novel Renewable Energy Scenario Generation Method Based on Multi-Resolution Denoising Diffusion Probabilistic Models |
| title_full | A Novel Renewable Energy Scenario Generation Method Based on Multi-Resolution Denoising Diffusion Probabilistic Models |
| title_fullStr | A Novel Renewable Energy Scenario Generation Method Based on Multi-Resolution Denoising Diffusion Probabilistic Models |
| title_full_unstemmed | A Novel Renewable Energy Scenario Generation Method Based on Multi-Resolution Denoising Diffusion Probabilistic Models |
| title_short | A Novel Renewable Energy Scenario Generation Method Based on Multi-Resolution Denoising Diffusion Probabilistic Models |
| title_sort | novel renewable energy scenario generation method based on multi resolution denoising diffusion probabilistic models |
| topic | renewable energy sources scenario generation diffusion model multi-scale forecasting time series decomposition |
| url | https://www.mdpi.com/1996-1073/18/14/3781 |
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