Many-objective Optimization Method Based on Dimension Reduction for Operation of Large-scale Cooling Energy Systems

卷:9

Article

关键词: INTEGRATED ELECTRICITY;    COMBINED HEAT;    DEMAND;    POWER;    BUILDINGS;    STRATEGY;    DISPATCH;   

DOI  :  10.17775/CSEEJPES.2021.08160

来源: SCIE

【 摘 要 】

Large-scale cooling energy system has developed well in the past decade. However, its optimization is still a problem to be tackled due to the nonlinearity and large scale of existing systems. Reducing the scale of problems without over-simplifying the actual system model is a big challenge nowadays. This paper proposes a dimension reduction-based many-objective optimization (DRMO) method to solve an accurate nonlinear model of a practical large-scale cooling energy system. In the first stage, many-objective and many-variable of the large system are pre-processed to reduce the overall scale of the optimization problem. The relationships between many objectives are analyzed to find a few representative objectives. Key control variables are extracted to reduce the dimension of variables and the number of equality constraints. In the second stage, the many-objective group search optimization (GSO) method is used to solve the low-dimensional nonlinear model, and a Pareto-front is obtained. In the final stage, candidate solutions along the Paretofront are graded on many-objective levels of system operators. The candidate solution with the highest average utility value is selected as the best running mode. Simulations 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.