Phase change materials in solar energy storage: Recent progress, environmental impact, challenges, and perspectives

The escalating global energy demand, coupled with the urgent need to combat climate change, underscores the necessity for effective and sustainable energy storage solutions. Phase change materials (PCMs) have emerged as a viable technology for thermal energy storage, particularly in solar energy applications, due to their ability to efficiently store and release thermal energy during phase transitions while maintaining a near-constant temperature. This paper addresses the limitations of traditional thermal energy storage systems and explores the advancements in PCM integration within various solar energy systems. We discuss innovative methods to enhance heat transfer rates and thermal conductivity, including modifications of extended surfaces, heat pipes, cascading PCMs, encapsulation techniques, and the incorporation of nanoparticles. These enhancements can improve system performance by up to 73 %, with nanoparticle dispersion identified as the most economically viable solution. Additionally, we provide a comprehensive overview of the implementation of the artificial intelligence approach in optimizing PCM-based thermal energy storage systems, emphasizing the effectiveness of ensemble learning frameworks for accurate modeling. The review also highlights the development of nano-PCMs, which demonstrate significant improvements—25.6 % in charging and 23.9 % in discharging rates—compared to conventional PCMs. Furthermore, we analyze the economic and environmental implications of PCM-based systems, focusing on critical issues such as carbon emissions, waste minimization, biodegradability, and alignment with circular economy principles. Finally, we discuss the major challenges and future research directions necessary for advancing PCM-based thermal energy storage systems. It is hoped that this article will update readers and experts working in this area on the recent advancements in PCM-based TES systems and provide an in-depth understanding of ML potentials in revolutionizing PCM-based solar energy storage systems.