Multiscale optimization scheduling method for comprehensive energy based on dual carbon targets and GSA algorithm

In the scenario of uncertainty in comprehensive energy output, the large scale of generated data and the introduction of uncertain factors in data mining can easily lead to scheduling analysis falling into local optima. To address the challenges of uncertainty factors and multi-scale characteristics in energy system optimization scheduling, this paper proposes a comprehensive energy multi-scale scheduling model based on dual carbon goals. The model aims to minimize carbon emissions, minimize energy costs, and achieve energy supply-demand balance. Corresponding objective functions are set, and multiple constraints such as energy supply-demand balance constraints, energy conversion and storage constraints, and renewable energy generation constraints are considered. A comprehensive energy multi-scale optimization scheduling model is constructed. This paper introduces the GSA algorithm to solve the model, which performs independent optimization at different time scales and coordinates through feedback mechanisms to output the globally optimal scheduling scheme. The experimental results show that this method exhibits significant superiority in energy utilization efficiency and carbon emission reduction of integrated energy systems.