Optimization of renewable energy-based integrated energy systems: A three-stage stochastic robust model

Yuan Gao, Mustafa Tahir, Pierluigi Siano, Yue Bi, Sile Hu and Jiaqiang Yang

Applied Energy, 2025, vol. 377, issue PD, No S030626192402018X

Abstract: The integration of renewable energy into an integrated energy system (REIES) represents a promising approach to achieving clean and low-carbon energy consumption. However, the inherent uncertainty and variability of renewable energy sources and load demands present significant challenges to the operational efficiency and stability of REIES. To address these challenges, we propose a three-stage stochastic robust optimization (TRSRO) model. This model accounts for market electricity prices, source-load scenario probabilities, and output uncertainties to optimize system configurations in REIES. It also includes the optimization of power exchanges with the regional grid and strategic operational scheduling. Initial scenarios of uncertainty variables are generated using the spectral normalized conditional Wasserstein generative adversarial network (SNCWGAN), forming polyhedral uncertainty sets. Comprehensive norm constraints are then applied to capture probability distribution uncertainties within these sets. To improve the efficiency of solving the model, we introduce a two-layer column constraint generation algorithm, considering variable alternate iterations (TLCCG-VAI). The results demonstrate that the proposed method can achieve a 12.16 % reduction in total cost compared to the baseline method. Furthermore, the TLCCG-VAI algorithm reduces runtime by 82.97 % while maintaining the same level of solution accuracy.

Keywords: Three-stage stochastic robust optimization; Integrated energy systems; Uncertainty; Generative adversarial network; Column constraint generation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124635

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