Reliability of Dynamic Load Scheduling with Solar Forecast Scenarios

TL;DR

This paper develops and evaluates an optimal load scheduling algorithm that uses short-term solar power forecasts, including cloud advection predictions, to improve the efficiency and reliability of photovoltaic-powered systems.

Contribution

It introduces a real-time optimal scheduling algorithm that incorporates short-term solar forecasts and dynamic load models, enhancing system performance under forecast errors.

Findings

Advection-based forecasts reduce load-solar mismatches.

Increasing decision horizon increases scheduling errors.

Energy reserves are necessary for forecast inaccuracies.

Abstract

This paper presents and evaluates the performance of an optimal scheduling algorithm that selects the on/off combinations and timing of a finite set of dynamic electric loads on the basis of short term predictions of the power delivery from a photovoltaic source. In the algorithm for optimal scheduling, each load is modeled with a dynamic power profile that may be different for on and off switching. Optimal scheduling is achieved by the evaluation of a user-specified criterion function with possible power constraints. The scheduling algorithm exploits the use of a moving finite time horizon and the resulting finite number of scheduling combinations to achieve real-time computation of the optimal timing and switching of loads. The moving time horizon in the proposed optimal scheduling algorithm provides an opportunity to use short term (time moving) predictions of solar power based on advection of clouds detected in sky images. Advection, persistence, and perfect forecast scenarios are used as input to the load scheduling algorithm to elucidate the effect of forecast errors on mis-scheduling. The advection forecast creates less events where the load demand is greater than the available solar energy, as compared to persistence. Increasing the decision horizon leads to increasing error and decreased efficiency of the system, measured as the amount of power consumed by the aggregate loads normalized by total solar power. For a standalone system with a real forecast, energy reserves are necessary to provide the excess energy required by mis-scheduled loads. A method for battery sizing is proposed for future work.