Recently, different techniques have been proposed for the scheduling and loading problems in cooling plants with chillers in a parallel configuration. Two broad groups can be considered: the online control-based group and the offline optimization-based group. The first group is exemplified by Model Predictive Control, where the selection of control moves provides a solution to both scheduling and loading. The second group includes Optimal Chiller Loading and Optimal Chiller Sequencing algorithms. They usually derive operating plans with some lead time in a batch-like fashion for long horizons. Both groups use forecasts of important factors such as the cooling demand and ambient conditions; hence, they have to deal with inaccuracies in the forecasts. In this paper, a comparison among these two groups is made considering demand uncertainties. The severity of the uncertainty is shown to play a role in the results as well as the controller tuning in the case of the predictive approach. The results are favorable to OCS with respect to overall consumption (up to 15%) but uses more on/off changes in the chiller’s operation (double in some cases).
Satué et al. (2026) studied this question.