Renewable energy management system for Saudi Arabia: Methodology and preliminary results
Chamarthi, Phani Kumar
Beegum, Naseema S.
Permanent link to this recordhttp://hdl.handle.net/10754/679206
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AbstractA Renewable Energy Management System (REMS) has been developed for Saudi Arabia to provide the system operator with the daily day-ahead electrical power generation forecasts. The core models included in REMS are: (i) adapted weather research and forecasting (WRF-solar) model that predicts weather conditions and solar irradiance components based on the aerosol (natural and anthropogenic) properties from CHIMERE chemistry transport model, (ii) renewable power prediction schemes that predict electrical power for specific solar technologies (photovoltaic and concentrating solar power plants) at different scales (plant, substation, area, and country land), and (iii) cutting-edge web-interface and geospatial technologies that enable interactive data visualisation on screens of the power plant characteristics and predicted variables over a four-day time frame (two days prior, current day, and a day ahead). The performance of the REMS was evaluated using the actual power output data from Masdar City 10 MW solar photovoltaic power plant and Shams 100 MW concentrated solar power plant in Abu Dhabi, the United Arab Emirates (UAE). The obtained results show the applicability and accuracy of the modelling framework. Currently, REMS simulates the performance of any set of renewable energy scenarios considering daily day-ahead forecasts. But the eventual use of the system in daily operations coupled with decision-making procedures will ensure efficient monitoring and management of renewable assets over time.
CitationGherboudj, I., Zorgati, M., Chamarthi, P.-K., Tuomiranta, A., Mohandes, B., Beegum, N. S., … Ghedira, H. (2021). Renewable energy management system for Saudi Arabia: Methodology and preliminary results. Renewable and Sustainable Energy Reviews, 149, 111334. doi:10.1016/j.rser.2021.111334
SponsorsThis project was funded by the King Abdullah City for Atomic and Renewable Energy (KACARE), Riyadh, Saudi Arabia, and developed at Khalifa University of Science and Technology, Abu-Dhabi, United Arab Emirates. We acknowledge the Elia Grid International for their support in this project. The support and resources from the High-Performance Computing Cluster King Abdullah University for Science and Technology (KAUST) and Khalifa University of Science and Technology are also gratefully acknowledged. We would like to thank Dr Sergio Martinez for his helpful support in developing this research, Dr Stephanie Clarke and Russ Jones for the proof-reading the manuscript, Dr Miguel Frasquet Herraiz and Dr Mercedes Ibarra for their technical support, and Mohammed Al Suhaibani and for their valuable comments.