Zero-carbon industrial park source-grid-load-storage coordinated optimisation incorporating green electricity consumption

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.