Model Predictive Load Scheduling Using Solar Power Forecasting

TL;DR

This paper presents a model predictive control approach for scheduling on/off electric loads based on solar power forecasts, optimizing load switching to match dynamic power profiles in real-time.

Contribution

It introduces a novel optimization framework that uses solar power predictions to schedule non-linear loads with dynamic profiles, enabling real-time load management.

Findings

Effective load scheduling based on solar forecasts demonstrated in case study.

Optimized switching reduces power mismatch and improves system efficiency.

Real-time computation of load schedules achieved with the proposed method.

Abstract

In this paper a model is developed to solve the on/off scheduling of (non-linear) dynamic 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 optimization 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, 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. A case study for scheduling electric on/off loads with switching dynamics and solar forecast data at UC San Diego is carried out.