1. 华北电力大学 能源动力与机械工程学院,北京,102206
2. 华北电力大学 河北省低碳高效发电技术重点实验室,河北,保定,071003
网络首发:2025-07-14,
纸质出版:2025
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高建强,蔡杜钟,刘春涛,危日光. 基于多目标沙猫群算法的含碳捕集虚拟电厂优化调度动力工程学报, 2025, 45(7): 1126-1133 https://doi.
org/10.19805/j.cnki.jcspe.2025.240301
高建强,蔡杜钟,刘春涛,危日光. 基于多目标沙猫群算法的含碳捕集虚拟电厂优化调度动力工程学报, 2025, 45(7): 1126-1133 https://doi. DOI: 10.19805/j.cnki.jcspe.2025.240301.
org/10.19805/j.cnki.jcspe.2025.240301 DOI:
针对现有智能算法在求解多目标虚拟电厂优化调度问题存在的收敛速度慢、易陷入局部最优解等问题
建立了以收益最大和碳排放量最小为目标函数的含碳捕集虚拟电厂优化调度模型
并采用多目标沙猫群算法对所提模型进行优化求解
将优化结果与多目标灰狼算法和多目标遗传算法进行比较
并采用熵权-逼近理想解排序法对多目标沙猫群算法优化得到的各方案进行筛选
得到兼顾经济性和环保性的综合最优方案。结果表明:多目标沙猫群算法得到的方案优于其他2种算法;综合最优方案的系统总收益为60.26万元
碳排放量为249.15 t
相比只考虑系统收益最大化所得的方案
该方案系统收益虽下降了8.90%
但碳排放量下降了41.95%。
Aiming at the problems such as slow convergence speed and easy falling into local optimal solution of existing intelligent algorithms in solving the problem of multi-objective optimal scheduling of virtual power plant
an optimal scheduling model of virtual power plant containing carbon capture was established with maximum income and minimum carbon emission as the objective functions
and the multi-objective sand cat swarm optimization(MOSCSO) was used to optimize the proposed model. The optimization results were compared with the multi-objective grey wolf optimization and multi-objective genetic optimization. The entropy weight-TOPSIS multi-objective decision method was used to screen the schemes optimized by MOSCSO
and the comprehensive optimal scheme with both economic and environmental protection was obtained. Results show that the scheme obtained by the MOSCSO is superior to the other two optimizations. The total system income of the comprehensive optimal scheme is 602 600 yuan
and the carbon emission is 249.15 t. Compared with the scheme that only considers the maximum system income
although the system income of this scheme is reduced by 8.90%
the carbon emission is reduced by 41.95%.
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