Multi-Energy Microgrid Data-Driven Distributionally Robust Optimization Dispatch Considering Uncertainty Correlation

LI Jiawei; SUN Qinghe; WANG Qiong; YE Yujian; HU Heng; ZHANG Xi

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Electric Power Construction ›› 2025

Multi-Energy Microgrid Data-Driven Distributionally Robust Optimization Dispatch Considering Uncertainty Correlation

LI Jiawei¹, SUN Qinghe², WANG Qiong¹, YE Yujian³, HU Heng², ZHANG Xi²

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Abstract

[Objective] Multi-energy microgrids (MEMGs) can integrate multiple energy carriers to improve energy efficiency, thereby contributing to the achievement of the "dual carbon" goals. [Methods] The paper proposes a data-driven distributionally robust optimization scheduling method for MEMGs that accounts for uncertainty correlations and outlier data. First, an ambiguity set incorporating uncertainty correlations is introduced based on the Copula function. A distributionally robust optimization scheduling model with opportunity constraints is then formulated, integrating the ambiguity set and opportunity constraints to address uncertainty correlations. Second, since the distributionally robust optimization model cannot be solved directly, a worst-case transformation method is derived for the proposed ambiguity set using dual theory, McCormick relaxation, and Conditional Value-at-Risk approximation. This transforms the distributionally robust model into a linear deterministic model, enabling efficient solution via optimization solvers. Subsequently, a sample pruning algorithm is proposed, which iteratively generates sub-samples from the original dataset by removing outliers and extreme data points. This approach mitigates the adverse effects of such data on the distributionally robust opportunity constraint scheduling results. [Results] Finally, case simulations demonstrate that the proposed distributionally robust model effectively eliminates unrealistic distributions in the ambiguity set, resulting in an 8.16% reduction in out-of-sample costs. The proposed sample pruning algorithm further reduces out-of-sample costs by 3.33%. [Conclusions] The proposed method enhances scheduling efficiency and ensuring reliability, which collectively demonstrate its clear superiority.

Key words

multi-energy microgrid / distributionally robust optimization / uncertainty correlation / chance constraints / data-driven

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LI Jiawei, SUN Qinghe, WANG Qiong, YE Yujian, HU Heng, ZHANG Xi. Multi-Energy Microgrid Data-Driven Distributionally Robust Optimization Dispatch Considering Uncertainty Correlation[J]. Electric Power Construction. 2025

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