A novel conditional diffusion model for joint source-load scenario generation considering both diversity and controllability

Wei Zhao, Zhen Shao, Shanlin Yang and Xinhui Lu

Applied Energy, 2025, vol. 377, issue PC, No S030626192401938X

Abstract: The intermittency of renewable energy and the volatility of multi-energy loads result in multiple joint source-load uncertainties and source-load spatio-temporal mismatch for the scenario generation of deep decarbonized power systems (DDPSs). To address these challenges, a novel model named conditional diffusion-based scenario generation (CDSG) is proposed for controllable joint source-load scenario generation (controllable-JSLSG). The CDSG model incorporates a specialized scenario noise prediction network that includes a conditional spatio-temporal fusion module (CSTFM) and a conditional scenario noise estimation module (CSNEM). Specifically, the CSTFM is devised to excavate the complex spatio-temporal correlations among source-load scenarios, and the CSNEM is utilized to adaptively learn the weights between condition information and source-load scenarios. Then the pretrained CDSG effectively models the nonlinear and irregular spatio-temporal dynamics of source-load, contributing to the controllability and diversity of the generated source-load scenarios. Results verified on real-world datasets demonstrate that CDSG can generate source-load scenarios satisfying dynamic fluctuation properties and frequency-domain characteristics, and ensuring the complex spatio-temporal correlations can be captured. Moreover, compared with other advanced benchmarks, CDSG achieves optimal diversity while maintaining high-quality, indicating its potential for the generation of realistic and diverse joint source-load scenarios. Finally, an optimal dispatch of DDPS is also simulated to evaluate the practical feasibility of the source-load scenarios generated by CDSG.

Keywords: Controllable joint source-load scenario generation; Conditional diffusion models; Joint source-load uncertainty; Spatio-temporal correlations (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124555

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