Many-objective Optimization Method Based on Dimension Reduction for Operation of Large-scale Cooling Energy Systems
作者机构：School of Electric Power EngineeringSouth China University of TechnologyGuangzhou 510640China
出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)
年 卷 期：2023年第9卷第3期
页 面：884-895页
核心收录：
学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学]
基 金：supported by the Key-Area Research and Development Program of Guangdong Province(2020B010166004) Natural Science Foundation of China(52007066)
主 题：Dimension reduction group search optimization large-scale cooling energy system many-objective optimization
摘 要：Large-scale cooling energy system has developed well in the past ***,its optimization is still a problem to be tackled due to the nonlinearity and large scale of existing *** the scale of problems without oversimplifying the actual system model is a big challenge *** paper proposes a dimension reduction-based many-objective optimization(DRMO)method to solve an accurate nonlinear model of a practical large-scale cooling energy *** the first stage,many-objective and many-variable of the large system are pre-processed to reduce the overall scale of the optimization *** relationships between many objectives are analyzed to find a few representative *** control variables are extracted to reduce the dimension of variables and the number of equality *** the second stage,the manyobjective group search optimization(GSO)method is used to solve the low-dimensional nonlinear model,and a Pareto-front is *** the final stage,candidate solutions along the Paretofront are graded on many-objective levels of system *** candidate solution with the highest average utility value is selected as the best running *** are carried out on a 619-node-614-branch cooling system,and results show the ability of the proposed method in solving large-scale system operation problems.
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