Credible joint chance-constrained low-carbon energy Management for Multi-energy Microgrids

• Published in: Applied energy Vol. 377; p. 124390
• Main Authors: Cao, Zehao, Li, Zhengshuo, Yang, Chang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025
• Subjects: Credible composite ambiguity set; Distributionally robust joint chance constraint; Multi-energy microgrid; Sample-pruning; Uncertainty propagation; Uncertainty propagation; Distributionally robust joint chance constraint; Sample-pruning; Credible composite ambiguity set; Multi-energy microgrid
• ISSN: 0306-2619
• DOI: 10.1016/j.apenergy.2024.124390

Multi-energy microgrids (MEMGs) exhibit bright prospects in improving integrated energy utilization efficiency and achieving low-carbon sustainable development. However, the uncertainties of distributed energy resources and their propagation among different energy sectors require multi-energy coordinated optimization to guarantee safe and economic operation. This paper focuses on the energy management problem for grid-connected MEMGs and proposes a credible chance-constrained low-carbon energy management method. A novel credible composite ambiguity set is first established by integrating the Wasserstein metric and first-order moment information to exclude unreliable distributions that will lead to over- conservativity. Then, the linear decision rule is introduced to ensure that flexible resources among different energy sectors can be used for coherent uncertainty mitigation, and the impact of uncertainties on carbon emissions is also considered. Based on the aforementioned points, a distributionally robust joint chance constraints-based model is proposed and we show that it can be transformed into a tractable continuous linear form that is able to be solved within an acceptable computation time. Furthermore, a sample-pruning algorithm is proposed to enhance the economic performance of the optimal decision for sample sets containing extreme data. The case studies show that the proposed energy management method can strike a better balance among economic efficiency, operational reliability, and low-carbon performance than other common methods. •A distributionally-safe and low-carbon energy management for multi-energy microgrids is proposed.•The conundrum of uncertainty propagation across vectors of energy is resolved.•A novel credible composite ambiguity set is leveraged to reduce the conservativity in common DRO methods.•A new sample-pruning algorithm is put forward to enhance the economic and computational performance.