Model Predictive Load Scheduling Using Solar Power Forecasting

Abdulelah Habib

This is a joint project with Prof. Raymond A. de Callafon,.

Paper abstract: In this paper an algorithm is developed to solve the on/off scheduling of (non-linear) dynamics electric loads based on predictions of the power delivery of a (standalone) solar power source. Knowledge of variations in the solar power output is used to optimally select the timing and the combinations of a set of given electric loads, where each load has a desired dynamic power profile. The algorithm exploits the desired power profiles of the electric loads in terms of dynamic power ramp up/down and minimum time on/off of each load to track a finite number of load switching combinations over a moving finite prediction horizon. Subsequently, evaluation of a user-specified optimization function with possible power constraints is evaluated over the finite number of combinations to allow for real-time computation of the optimal timing and switching of loads. The approach is illustrated on electric loads with varying first order dynamics for on/off switching and solar data obtained from the Solar Resource Assessment & Forecasting Laboratory at UC San Diego.




1.¬†Abdulelah H Habib, Jan Kleissl and Raymond A. de Callafon, “Model Predictive Load Scheduling Using Solar Power Forecasting,” submitted to The American Control Conference 2016, Boston, USA. [link]