Analysis of a Grid-Connected Solar PV System with Battery Energy Storage for Irregular Load Profile

Mohannad Alhazmi (), Abdullah Alfadda and Abdullah Alfakhri
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Mohannad Alhazmi: Electrical Engineering Department, College of Applied Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Abdullah Alfadda: Advanced Research Institute, Virginia Tech, Arlington, VA 22203, USA
Abdullah Alfakhri: Electrical Engineering Department at Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA

Energies, 2024, vol. 17, issue 14, 1-24

Abstract: In recent decades, Saudi Arabia has experienced a significant surge in energy consumption as a result of population growth and economic expansion. This has presented utility companies with the formidable challenge of upgrading their facilities and expanding their capacity to keep pace with future energy demands. In order to address this issue, there is an urgent need to implement energy-saving solutions such as energy storage systems (ESSs) and renewable energy sources, which can help to reduce demand during peak hours. To ensure optimal use of ESSs, it is crucial to integrate a load forecasting model with the ESS in order to control charging and discharging rates and schedules. The irregular load profile is a particularly significant consumer of energy, consuming approximately 2.5 GWh annually at the cost of USD 3 billion in Saudi Arabia. In light of this, this paper develops a load forecasting model for the irregular load profile with a high degree of accuracy: achieving 95%. One of the key applications of this model is load peak shaving. Given the region’s abundance of solar irradiation, the paper propose an integration of a solar PV system with a battery energy storage system (BESS) and analyzes various scenarios to determine the efficacy of the proposed approach. The results demonstrate significant savings when the proposed forecasting model is integrated with a BESS and PV system, with the potential to reduce monthly imported power by more than 22% during the summer season.

Keywords: load forecasting; machine learning; energy storage; peak shaving (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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