ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Smart Grids
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1535211
This article is part of the Research TopicAdvanced Data-Driven Uncertainty Optimization for Planning, Operation, and Analysis of Renewable Power SystemsView all 18 articles
Data-driven Industrial Park Microgrids Robust Optimization Method
Provisionally accepted
- 1State Grid Taizhou Power Supply Company, Taizhou, China
- 2State Grid Zhejiang Electric Power Co., Ltd., Hangzhou, Jiangsu Province, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
In order to accurately describe the impact of the volatility and randomness of renewable energy output power on the operation of industrial park microgrids, a data-driven robust optimization method for industrial park microgrids is proposed. Firstly, based on the traditional interval set, the uncertain parameters of renewable energy output are modeled using a polyhedral set. Then, an ellipsoidal uncertainty set is established using historical data of renewable energy output. By connecting high-dimensional ellipsoidal vertices, a data-driven convex hull polyhedron set is established. Then, the uncertain parameters are better enveloped by scaling the convex hull set. A data-driven robust optimization model for industrial park microgrid was further established, and the column and constraint (C&CG) generation algorithm was used to solve the model. Finally, simulation comparisons were conducted through examples, and the results showed that the datadriven industrial park microgrids robust optimization method can reduce conservatism and improve the robustness of optimization results, demonstrating the effectiveness of the proposed method.
Keywords: Industrial park microgrids 1, data-driven 2, robust optimization 3, convex hull set 4, column and constraint generation algorithm 5
Received: 27 Nov 2024; Accepted: 06 Aug 2025.
Copyright: © 2025 Ru, Li, Lu and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chuanhong Ru, State Grid Taizhou Power Supply Company, Taizhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.