Improving the operational efficiency of chillers and science-based planning the cooling load distribution between the chillers and ice tank are core issues to achieve low-cost and energy-saving operations of ice storage air-conditioning systems. In view of the problems existing in centralized control architecture applied in heating, ventilation, and air conditioning, a distributed multi-objective particle swarm optimization improved by differential evolution algorithm based on a decentralized control structure was proposed. The energy consumption, operating cost, and energy loss were taken as the objectives to solve the chiller’s hourly partial load ratio and the cooling ratio of ice tank. A large-scale shopping mall in Xi’an was used as a case study. The results show that the proposed algorithm was efficient and provided significantly higher energy-savings than the traditional control strategy and particle swarm optimization algorithm, which has the advantages of good convergence, high stability, strong robustness, and high accuracy.

Practical application: The end equipment of the electromechanical system is the basic component through the building operation. Based on this characteristic, taken electromechanical equipment as the computing unit, this paper proposes a distributed multi-objective optimization control strategy. In order to fully explore the economic and energy-saving effect of ice storage system, the optimization algorithm solves the chillers operation status and the load distribution. The improved optimization algorithm ensures the diversity of particles, gains fast optimization speed and higher accuracy, and also provides a better economic and energy-saving operation strategy for ice storage air-conditioning projects.

提高制冷机的运行效率和基于科学的计划，制冷机和冰柜之间的制冷负荷分配是实现冰蓄冷空调系统的低成本和节能运行的核心问题。针对集中控制结构在采暖，通风，空调中存在的问题，提出了一种基于分散控制结构的差分进化算法改进的分布式多目标粒子群算法。以能耗，运行成本和能量损失为目标，以解决冷水机组的小时分担负荷率和冰柜冷却率问题。以西安某大型购物中心为例。

实际应用：机电系统的终端设备是通过楼宇运营的基本组成部分。基于这一特点，以机电设备为计算单元，提出了一种分布式多目标优化控制策略。为了充分发掘储冰系统的经济和节能效果，优化算法解决了冷水机组的运行状态和负荷分布问题。改进的优化算法确保了粒子的多样性，获得了更快的优化速度和更高的精度，还为储冰空调项目提供了更好的经济和节能的运行策略。