Optimal control versus conventional control strategies for ice-based thermal storage

M.Ing.Ice storage systems are used to store thermal energy in the form of ice build-up inside storage tanks. During off peak hours, the chiller is used to charge up the storage tank until it is full. During on peak hours, the storage is discharged to meet a certain fraction of the building cooling load. The control strategy employed determines the extent to which the storage compensates the chiller and visa versa. Given the way in which electricity rates are structured, ice storage systems become an effective energy management strategy. The objective of the study is to compare energy savings derived by using conventional control strategies versus optimal control. Conventional control strategies can be classified as chiller priority control, constant proportion control and storage priority control. In chiller priority control, the chiller meets the cooling load subject to a pre-set limit not being exceeded. Should the limit be exceeded, the remaining cooling load (at each time horizon) is compensated for by the storage. In constant proportion control, both the storage and chiller meets a constant proportion of the cooling load. Storage priority control attempts to discharge as much of the storage as possible, such that at the end of the planning horizon, the ice build up in the storage tank is just depleted. Optimal control employs dynamic programming to ensure that the integrated cost of energy, during the entire planning horizon, is minimal. A steady state ice storage plant model for analysing the performance of the control strategies is presented. The model computes the inlet and outlet temperatures into the various components of the air-conditioning plant, being the air-handling units, heat exchanger, ice storage tanks and chiller. The maximal possible discharge and charging rate at each time period (for the different control strategies) is determined using the model. Given the state of charge of the ice storage tank at each time period, it is then possible to calculate chiller power consumption. The power consumed by fans, fan coil units and pumps (in the air-conditioning plant) has not been calculated in the present analysis, however, the model can easily be extended to include such calculations. The ice storage plant model, enabled simulations of the different control strategies to be carried out over building cooling load profiles for summer and winter. Based on a 24-hour planning horizon, optimal control is found to be optimal and the only consistently performing strategy for all seasons. For the 5000 kWh ice storage plant investigated, optimal control yielded 25% energy savings in June and 12% in January, amounting to a potential of R 11 000 per month. Chiller priority control was near optimal in January but consumed 25% more energy than the base case (without storage) in June. Constant proportion control was optimal in January but poorer in June. Storage priority control is found to be optimal in June but the lowest performer in January. The drawback of optimal control and storage priority control, however, is that they require prediction of future cooling loads. The variance when using auto-regressive neural network to predict the load is expected to be in the region of 2% and thus considered acceptable. Chiller priority control and constant proportion control are instantaneous and simple to implement hence their popularity