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.
Liu et al. (Sun,) studied this question.