冷冻水集中处理厂占建筑总能耗和成本的很大一部分。冷冻水集中处理厂的设计将对能源成本产生重大影响。本文提出了一种多目标优化设计方法，用于冷冻水集中装置的优化设计。该方法将整个系统模型与多目标遗传算法优化求解器相结合，以最小化年度能源成本、初始成本、生命周期成本或这些成本的任意组合。考虑的设计变量包括冷冻水和冷凝器水管直径、冷冻水供应温度以及冷凝器和冷冻水温差。该方法将冷负荷分析、水头和能量计算与整个冷水厂模型相结合。泵头计算，包括管道、所有配件、阀门和装置，通过开发的冷却水和冷凝器水流模型实现。 能量计算是通过使用通用的冷却器、风扇和泵模型来完成的。该方法在一栋现有的三层八万八千平方英尺的建筑上进行了测试。选择年能源成本与初始成本、初始成本与生命周期成本作为两个目标函数，采用双目标遗传算法进行优化求解，以获得一组更好的设计决策解。利用建筑能耗全仿真模型生成每小时的冷负荷，然后找到优化设计变量，使这两个目标函数最小化。测试结果表明，与经验法则或传统设计方法相比，该方法将获得更好的结果。根据项目规格和地点，生命周期成本节约可能高达8%。引用:2019年年度会议，密苏里州堪萨斯城，扩展摘要

Chilled water central plant accounts a large portion of total energy use and cost in building. The chilled water central plant design will have a significant impact on this energy cost. This paper proposes a multiple objective design optimization method for optimal design of chilled water central plants. The method integrates whole system models with multi-objective genetic algorithm optimization solver to minimize the annual energy cost, initial cost, the life cycle cost, or any combination of those costs. The design variables considered are chilled water and condenser water piping diameters, chilled water supply temperature, and condenser and chilled water temperature differences. The proposed approach combines cooling load analysis and head and energy calculations integrated with whole chilled water plant model. The pump head calculations including piping, all fittings, valves, and devices are achieved by developed chilled and condenser water flow model. The energy calculations are done by using generic chiller, fan, and pump models. The method is tested on an existing three-story, eighty-eight thousand square foot building. The annual energy cost vs. initial cost, and initial cost vs. life cycle cost were selected as two objective functions to be solved by two-objective GA optimization algorithm to obtain a set of solutions for better design decisions. A whole building energy simulation model is used to generate the hourly cooling loads and then the optimal design variables are found to minimize the two objective functions. The testing results show this approach will achieve better results than rules-of-thumb or traditional design procedures. The life cycle cost saving could be up to 8% depending on project specifications and locations.