Solar Energy Forecasting Models for Grid Integration and Power Balancing

¹ Department of IT, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
² Department of Management, Uttaranchal Institute of Management, Uttaranchal University, Dehradun, Uttarakhand, India
³ Sri Sairam institute of Technology
⁴ College of technical engineering, The Islamic university, Najaf, Iraq .
⁵ Department of Electrical & Electronics Engineering, IES College of Technology, IES University, India 462044, Bhopal, Madhya Pradesh
⁶ Dharmesh Dhabliya, Professor, Department of Information Technology, Vishwakarma Institute of Information Technology, India Email: mailto:dharmesh.dhabliya@viit.ac.in, Pune, Maharashtra

^* b.sivadharshini_it@psvpec.in
^** dr12archana@gmail.com
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Abstract

This paper offers a comprehensive review of the advancements in the domain of solar energy forecasting models, emphasizing their significance for grid integration and power balancing. The increasing inclusion of renewable resources in global energy portfolios underscores the urgency for precise forecasting of variable resources like solar energy. Solar generation technologies have witnessed remarkable growth, leading to heightened grid penetration rates. However, the ground-level solar resource is characterized by high variability, primarily influenced by factors such as cloud cover changes, atmospheric aerosol levels, and certain atmospheric gases. This variability, especially at elevated grid penetration levels, introduces challenges related to reserve costs, dispatchable generation, and overall grid reliability. To address these challenges, there’s a pressing need for forecast systems with high accuracy across multiple time horizons, catering to regulation, dispatching, scheduling, and unit commitment. Furthermore, the variability of renewable energy stands as a significant barrier to its broader adoption. Energy storage emerges as a potential solution to mitigate power imbalances arising from the disparity between available renewable power and load demands. Through an analytical model, this review explores the potential of storage in reducing power imbalances and the requisite storage capacity to achieve this balance. The paper delves into the theory behind these forecasting methodologies and highlights successful applications in solar forecasting for utility-scale solar plants.