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Open Access

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

Yang Liu1Xianbang Chen2 ( )Lei Wu2Yanli Ye1
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
ECE Department, Stevens Institute of Technology, Hoboken, NJ 07030 USA
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Abstract

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.

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CSEE Journal of Power and Energy Systems
Pages 1182-1192
Cite this article:
Liu Y, Chen X, Wu L, et al. Distributionally Robust Economic Dispatch Using IDM for Integrated Electricity-heat-gas Microgrid Considering Wind Power. CSEE Journal of Power and Energy Systems, 2023, 9(3): 1182-1192. https://doi.org/10.17775/CSEEJPES.2021.03940

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Received: 22 May 2021
Revised: 07 September 2021
Accepted: 01 November 2021
Published: 06 May 2022
© 2021 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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