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A comparative study of multi-agent control approaches for optimization of central cooling systems without significant storage

机译：多剂量控制方法优化中央冷却系统无明显储存的比较研究

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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.
机译：本文介绍了多剂量控制方法在大型冷水机构中的应用。该方法最初设想自动化控制器设计和降低建筑能源系统的工程成本，包括具有分布式共识的优化算法的多代理仿真框架。为了使这种情况适应这种情况，代表冷却设备物理分量的代理在框架中开发并结合在框架中以及两种替代优化方法：通过遗传算法（GA）集中，并行优化，以及GA的组合拟牛顿方法处理与物理组件行为相关的非线性平等约束。利用现有的冷却设备作为案例研究，以模拟在不同的操作条件下的方法的性能。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，气体表明，通过利用能够处理非凸面目标功能的算法和离散和连续变量的组合来实现显着的能量节省，这是中央冷却系统的特征。

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