Development of a Predictive Optimal Controller for Thermal Energy Storage Systems

This paper describes the development and simulation of a predictive optimal controller for thermal energy storage systems. The “optimal” strategy minimizes the cost of operating the cooling plant over the simulation horizon. The particular case of a popular ice storage system (ice-on-coil with internal melt) was investigated in a simulation environment. Various predictor models were analyzed with respect to their performance in forecasting cooling load data and information on ambient conditions (dry-bulb and wet-bulb temperatures). The predictor model provides load and weather information to the optimal controller in discrete time steps. An optimal storage charging and discharging strategy was planned at every time step over a fixed look-ahead time window utilizing newly available information. The first action of the optimal sequence of actions was executed over the next time step and the planning process was repeated at every following time step. The effect of the length of the planning horizon was investigated. It was found that short horizons of one day were only marginally suboptimal relative to one that is optimal over the entire simulation horizon of one week. Various utility rate structures were analyzed to cover a range of potential real-time pricing scenarios. The predictive optimal controller was then compared to conventional control heuristics. In summary, it was found that in the presence of complex rate structures the proposed controller had a significant performance benefit over the conventional controls, while requiring only a simple predictor.