Abstract:Building an integrated energy system (IES) is an important measure to construct a clean, low-carbon, safe, and efficient modern energy system. To address the problem of traditional optimization algorithms being unable to effectively handle complex nonlinear relationships and high-dimensional control models, this paper proposes a comprehensive low-carbon control strategy for energy systems based on an improved particle swarm optimization (PSO) algorithm. Firstly, a multi-objective and multi constraint optimization model was constructed around the economic, environmental, and balanced operational goals of IES, considering three indicators: total cost, carbon emissions, and system imbalance. Then, the improved adaptive weight factors and Cauchy mutation strategy were introduced into the standard particle swarm algorithm to enhance global convergence and population diversity, resulting in the improved particle swarm algorithm having good global adaptability and faster convergence speed. Finally, taking a certain park as the research object, a numerical simulation was conducted, which effectively reduced carbon emissions without significant increase in system economic costs. The research results can provide theoretical support for the overall planning of IES.