Abstract
This paper presents the development of a controlled load estimator for a water pumping system (WPS) using the data generated from a PSCAD simulation model. A neural network (NN) is trained, using the generated data, to estimate the power demand of the WPS as a function of the control variables. This NN-based load model is then incorporated into the WPS energy management system to determine the optimal operational schedules of the pumps with the objective of minimizing the energy consumption costs and charges associated with peak power demand. Modeling related uncertainties are captured through a novel recursive mechanism for NN retraining, while operational uncertainties are accounted for by applying a receding horizon model predictive control technique. Simulation results indicate notable savings in total energy costs for the WPS facility after applying the proposed strategy as a result of operational schedules optimization and uncertainties mitigation.
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