A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems....A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.展开更多
在双碳背景下,促进清洁能源消纳与碳减排已成为电网发展的重要目标。文章提出了一种优化方法,考虑可再生能源不确定性与系统功率储备进行系统调度。在预测误差分析的基础上,提出了一种基于负荷损失概率的评估方法,以确定系统运行储备功...在双碳背景下,促进清洁能源消纳与碳减排已成为电网发展的重要目标。文章提出了一种优化方法,考虑可再生能源不确定性与系统功率储备进行系统调度。在预测误差分析的基础上,提出了一种基于负荷损失概率的评估方法,以确定系统运行储备功率。将系统运营成本和CO_(2)排放量建模为优化目标。优化过程包括两个阶段,在第一阶段优化中利用混合整数线性规划(mixed integer linear programming,MILP)方法进行确定性优化,生成具有规定功率储备要求的发电机组组合。在第二阶段优化中考虑预测不确定性,对运行计划进行调整,使用可控发电机来处理预测偏差。将不同方法下可用运行储备功率、成本和CO_(2)排放量在仿真中进行了对比。与传统确定性调度方法相比,在保证目标安全水平的同时,所提出的方法节省了大约15%的经济成本和环境成本。展开更多
基金Projects(50275150,61173052) supported by the National Natural Science Foundation of ChinaProject(14FJ3112) supported by the Planned Science and Technology of Hunan Province,ChinaProject(14B033) supported by Scientific Research Fund Education Department of Hunan Province,China
文摘A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.
文摘在双碳背景下,促进清洁能源消纳与碳减排已成为电网发展的重要目标。文章提出了一种优化方法,考虑可再生能源不确定性与系统功率储备进行系统调度。在预测误差分析的基础上,提出了一种基于负荷损失概率的评估方法,以确定系统运行储备功率。将系统运营成本和CO_(2)排放量建模为优化目标。优化过程包括两个阶段,在第一阶段优化中利用混合整数线性规划(mixed integer linear programming,MILP)方法进行确定性优化,生成具有规定功率储备要求的发电机组组合。在第二阶段优化中考虑预测不确定性,对运行计划进行调整,使用可控发电机来处理预测偏差。将不同方法下可用运行储备功率、成本和CO_(2)排放量在仿真中进行了对比。与传统确定性调度方法相比,在保证目标安全水平的同时,所提出的方法节省了大约15%的经济成本和环境成本。
