A non-cooperative game-based distributed optimization method for chiller plant control

The heating, ventilation, and air-conditioning (HVAC) systems account for about half of the building energy consumption. The optimization methodology access to optimal control strategies of chiller plant has always been of great concern as it significantly contributes to the energy use of the whole HVAC system. Given that conventional centralized optimization methods relying on a central operator may suffer from dimensionality and a tremendous calculation burden, and show poorer flexibility when solving complex optimization issues, in this paper, a novel distributed optimization approach is presented for chiller plant control. In the proposed distributed control scheme, both trade-offs of coupled subsystems and optimal allocation among devices of the same subsystem are considered by developing a double-layer optimization structure. Non-cooperative game is used to mathematically formulate the interaction between controlled components as well as to divide the initial system-scale nonlinear optimization problem into local-scale ones. To solve these tasks, strategy updating mechanisms (PSO and IPM) are utilized. In this way, the approximate global optimal controlled variables of devices in the chiller plant can be obtained in a distributed and local-knowledge-enabled way without neither global information nor the central workstation. Furthermore, the existence and effectiveness of the proposed distributed scheme were verified by simulation case studies. Simulation results indicate that, by using the proposed distributed optimization scheme, a significant energy saving on a typical summer day can be obtained (1809.47 kW center dot h). The deviation from the central optimal solution is 3.83%.