Distributionally Robust Economic Dispatch Using IDM for Integrated Electricity-heat-gas Microgrid Considering Wind Power

Multi-energy microgrids, such as integrated electricity-heat-gas microgrids (IEHS-MG), have been widely recognized as one of the most convenient ways to connect wind power (WP). However, the inherent intermittency and uncertainty of WP still render serious power curtailment in the operation. To this end, this paper presents an IEHS-MG model equipped with power-to-gas technology, thermal storage, electricity storage, and an electrical boiler for improving WP utilization efficiency. Moreover, a two-stage distributionally robust economic dispatch model is constructed for the IEHS-MG, with the objective of minimizing total operational costs. The first stage determines the day-ahead decisions including on/off state and set-point decisions. The second stage adjusts the day-ahead decision according to real-time WP realization. Furthermore, WP uncertainty is characterized through an Imprecise Dirichlet model (IDM) based ambiguity set. Finally, Column-and-Constraints Generation method is utilized to solve the model, which provides a day-ahead economic dispatch strategy that immunizes against the worst-case WP distributions. Case studies demonstrate the presented IEHS-MG model outperforms traditional IEHS-MG model in terms of WP utilization and dispatch economics, and that distributionally robust optimization can handle uncertainty effectively.

Automated summary

This paper proposes an integrated electricity-heat-gas microgrid (IEHS-MG) model equipped with power-to-gas technology, thermal storage, electricity storage, and an electrical boiler to enhance wind power utilization efficiency. A two-stage distributionally robust economic dispatch model is constructed to minimize total operational costs. The case studies demonstrate the superiority of the presented IEHS-MG model in terms of wind power utilization and dispatch economics, as well as the effectiveness of distributionally robust optimization in handling uncertainty.