Low Carbon Optimization Scheduling of Micro Grid Based on Improved Particle Swarm Optimization Algorithm

作者：Yingjun Sang, Zhang Wen-zhi, Jing Ma, Chen Quanyu, Tao Jinglei, Yuanyuan Fan · 发表于：IEEE Access · 年份：2024 · DOI：10.1109/access.2024.3406036 · 被引用次数：14 · 研究领域：Integrated Energy Systems Optimization、Smart Grid Energy Management、Energy Load and Power Forecasting

This article proposes a low-carbon operation analysis method for micro grids based on improved particle swarm optimization algorithm. Corresponding improvements have been made to the inertia weight, learning factor, and individual extreme of the algorithm, depicting the comprehensive low-carbon operation information of micro grids under the influence of carbon emission quotas and carbon trading mechanisms from the perspective of data visualization. The low-carbon scheduling of micro grids is carried out from three perspectives: environmental protection, economy, and comprehensiveness, which compensates for the limitations of focusing on traditional low-carbon operation and provides a powerful tool for analyzing low-carbon operation of micro grids. Firstly, establish the energy consumption cost and carbon emission cost functions of the micro grid system, add the two cost functions together and take the minimum sum to form the objective function of this article. Then, based on the characteristics of each unit, a low-carbon model is constructed to constrain the carbon emissions of each unit. Finally, simulation analysis was conducted on the micro grid system based on the improved particle swarm optimization algorithm, verifying the effectiveness and practicality of the proposed algorithm. The simulation results show that the improved particle swarm optimization algorithm can quickly and effectively reduce energy consumption and carbon emission costs, and improve the comprehensiv...