An Improved Scheduling Approach for Multi-Energy Microgrids Considering Scenario Insufficiency and Computational Complexity

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With the increasing energy demands and concern for environmental protection, researching and optimizing multi-energy systems have become prominent issues in the energy field. To improve the overall performance of multi-energy systems, there are two main difficulties that must be overcome: the first is the issue regarding the coupling relationships between energy sources, and the second is the uncertainties related to multiple types of energy and loads. Particularly with regard to the second difficulty, it is necessary to generate a large number of effective scenarios, as many multi-energy systems have only been built recently, and operational data exhibit uncertainties. However, at the same time, the introduction of a large number of random scenarios can lead to computational difficulties, making it impossible for a model to solve this problem. To this end, in this paper, we propose an improved scheduling approach for multi-energy microgrids, balancing scenario insufficiency and computational complexity. Latin Hypercube sampling is creatively used to generate enough uncertain scenarios, and hierarchical clustering is employed to create representative scenarios to reduce the computational complexity. Then, based on these effective clustered scenarios, a multi-energy collaborative optimization method considering the coupling relationship between energy sources is proposed. The effectiveness of this method is verified through numerical tests and sensitivity analysis. The results show that the economic cost of this method is only 0.305% higher than that of the deterministic method and that it has a certain degree of robustness and a good economic performance, but it is limited by its computational efficiency. In summary, this study provides an effective solution for collaboratively optimizing the operation of multi-energy systems, aiming to provide valuable insights for research in the energy field.