Optimal Scheduling of Energy Storage for Power System with Capability of Sensing Short-term Future PV Power Production

TL;DR

This paper presents an optimal scheduling approach for energy storage in hybrid power systems with PV, utilizing machine learning predictions of PV output and load demand to reduce electricity costs significantly.

Contribution

It introduces a machine learning-based scheduling method that predicts PV and load variations, optimizing energy storage use in hybrid power systems for cost efficiency.

Findings

Machine learning model achieves high prediction accuracy with R2 of 0.999379.

Cost reduction of 37.5% with PV and grid, 43.06% with storage included.

Effective scheduling improves renewable energy integration and reduces electricity costs.

Abstract

Constant rise in energy consumption that comes with the population growth and introduction of new technologies has posed critical issues such as efficient energy management on the consumer side. That has elevated the importance of the use of renewable energy sources, particularly photovoltaic (PV) system and wind turbine. This work models and discusses design options based on the hybrid power system of grid and battery storage. The effects of installed capacity on renewable penetration (RP) and cost of electricity (COE) are investigated for each modality. For successful operation of hybrid power system and electricity trading in power market, accurate predictions of PV power production and load demand are taken into account. A machine learning (ML) model is introduced for scheduling, and predicting variations of the PV power production and load demand. Fitness of the ML model shows, when employing a linear regression model, the mean squared error (MSE) of 0.000012, root mean square error (RMSE) of 0.003560 and R2 of 0.999379. Using predicted PV power production and load demand, reduction of electricity cost is 37.5 % when PV and utility grid are utilized, and is 43.06% when PV, utility grid, and storage system are utilized.