Kai Liu1, Jin Yang1, Bin Zhang1, Hongwei Zhang1, Huakang Du2,3This email address is being protected from spambots. You need JavaScript enabled to view it., Hongmin Chen2, and Xianguang Jia4
1Dali Power Supply Bureau, Yunnan Power Grid Co., Ltd., Dali, Yunnan, 671000, China
2Longshine Technology Group Co., Ltd., Wuxi, Jiangsu, 214000, China
3Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China
4Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China
Received: October 23, 2025 Accepted: November 30, 2025 Publication Date: December 27, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Under the "dual carbon" goals, balancing the economic benefits of multiple stakeholders while promoting local consumption of distributed renewable energy in industrial parks presents a critical challenge. To address this, this paper proposes a coordinated optimization scheduling model for industrial parks that integrates generation, grid, load, and storage. First, a multistakeholder optimization model is constructed to maximize the weighted total economic benefits of the Distribution System Operator (DSO), Load Aggregator (LA), and end-users, thereby identifying an economic equilibrium point. To solve this high-dimensional, constrained model, an improved Rime optimization algorithm (I-RIME) is proposed, which integrates a differential mutation strategy to enhance global search capability and a constraint repair operator with an adaptive penalty mechanism. Finally, simulations were conducted in MATLAB using actual data from an industrial park in Yunnan, with day-ahead load and generation forecasts provided by a KOA-WNN model. The results show that the proposed IRIME algorithm effectively yields high-quality feasible solutions. Compared with foundational meta-heuristic algorithms like RIME, PSO, and BWO, the proposed method improves the objective function by 110%, 6.72%, and 5.25%, respectively, demonstrating its superiority and its ability to significantly enhance the overall economic benefits for all stakeholders.
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