Technological advancements in PV-based energy storage methods

Abstract

Maximum energy demand exceeded 180,000 TWh in 2024, with renewable sources covering approximately 32%. Solar energy, notable for sustainability and economic viability, reached 1,250 GW installed capacity in 2024 and is projected to double to 2,500 GW by 2030. Despite advancements and cost reductions, the intermittent nature of photovoltaic (PV) generation, meeting 45% of demand on sunny days and less than 10% on cloudy days, poses significant challenges. Energy storage systems (ESS), critical for managing intermittency, achieved 70 GW capacity globally in 2024, expected to reach 300 GW by 2030. Lithium-ion batteries, with an 80% cost reduction and 40% improvement in energy density, along with thermal storage using phase change materials (PCM) and supercapacitors, significantly improved performance. Hybrid energy storage systems (HESS) further enhanced reliability, achieving around a 15% reduction in carbon emissions. Artificial intelligence (AI) and machine learning (ML) play crucial roles in optimizing energy management and efficiency in PV-integrated ESS. This research investigates recent technological developments in PV-integrated energy storage, assessing thermal, electrochemical, and hybrid storage solutions, and highlighting the significance of artificial intelligence (AI) and machine learning (ML) in optimizing energy management and enhancing overall system efficiency.