This research demonstrates the impacts of an operation optimization model on renewable energy use and carbon emissions reduction in source-grid-load-storage integration.
As the energy structure changes and the power system gets more intelligent, the comprehensive source-grid-load-storage initiative has shown good benefits in improving energy utilization efficiency and promoting renewable energy adoption. This study aims at the power demand response and operation optimization problems in the integrated source-grid-load-storage project. It constructs a mathematical model that comprehensively considers the balance of supply and demand, economic benefits and environmental impacts. By introducing advanced forecasting algorithms and optimization technologies, accurate forecasting of power demand and optimal allocation of resources are achieved. The experiment results show that the proposed model can significantly reduce the system's operating costs, improve the use of renewable energy, and reduce carbon emissions to a certain extent. Specifically, compared with the traditional operating model, this model can cut the system's operating costs by 15%-20%, raise the utilization rate of renewable energy to over 80%, and reduce carbon emissions by about 10%. In addition, this study also explores the adaptability and robustness of the model in different scenarios, providing theoretical support and a decision-making basis for the practical application of source-grid-load-storage integration projects.
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Shang et al. (2025) studied this question.
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