This paper presents the application of a multi-agent control methodology to large chiller plants. The approach, originally conceived to automate controller design and reduce engineering costs in building energy systems, consisted of a multi-agent simulation framework with distributed consensus-based optimization algorithms. To adapt the approach to this scenario, agents representing physical components of a cooling plant were developed and incorporated in the framework along with two alternative optimization methods: centralized, parallel optimization with a genetic algorithm (GA), and a combination of the GA with a quasi-newton method to handle non-linear equality constraints associated to physical component behavior. An existing cooling plant was utilized as case study to simulate the performance of the methods under different operating conditions. The results demonstrated the difficulty of the consensus-based algorithms to find optimal solutions. The GAs, on the other hand, showed that significant energy savings can be achieved through the implementation of multi-agent control with algorithms capable of handling non-convex objective functions and a combination of discrete and continuous variables, which are characteristic of central cooling systems.

本文介绍了多代理控制方法在大型冷水机组中的应用。该方法最初旨在自动化控制器设计并降低建筑能源系统中的工程成本，该方法由具有基于分布式共识的优化算法的多主体仿真框架组成。为了使这种方法适应这种情况，开发了代表冷却设备物理组件的代理，并将其与两种替代性优化方法结合到了框架中：集中化，遗传算法（GA）并行优化，以及遗传算法和遗传算法的组合。拟牛顿法来处理与物理组件行为相关的非线性相等约束。利用现有的冷却设备作为案例研究来模拟在不同操作条件下该方法的性能。结果表明，基于共识的算法难以找到最佳解决方案。另一方面，遗传算法表明，通过采用能够处理非凸目标函数以及离散和连续变量组合的算法来实现多主体控制，可以实现显着的节能，这是中央冷却系统的特征。