The Role of Mechanical Energy Storage Systems Based on Artificial Intelligence Techniques in Future Sustainable Energy Systems
Keywords:Compressed-Air Energy Storage, Pumped Hydro Energy Storage Systems, Flywheel Energy Storage Systems, Artificial Intelligence Techniques, Smart Power Grids
The utilization of fossil fuels has played a substantial role in climate change and the progression of global warming. Consequently, there is an increasing demand for environmentally sustainable and renewable alternatives to address these issues. It is widely acknowledged that renewable energy resources represent the optimal choice for replacing fossil fuels in the foreseeable future. In this context, mechanical energy storage systems (MESS) continue to present substantial challenges to smart power grids (PGs). The MESS model can be purposefully designed to offer exceptional flexibility to smart PGs engaged in the intricate task of balancing energy resources and demand loads. MESS not only holds the potential for significant economic advantages but also ensures the reliability of smart PG supplies while delivering sustainability and maintaining a high level of power quality. Furthermore, it enables electrical grids to fully harness the benefits of a potent combination of distributed renewable energy resources (RER). The primary goal of this article is to facilitate the adoption of innovative MESS technologies that synergize with improved efficiency, energy conservation, and rapid response capabilities. The integration empowers smart PG to effectively employ intelligent operations management techniques. Thus, the utilization of artificial intelligence (AI) techniques in the smart PG domain is progressively manifesting its importance including Expert Systems, Supervised learning, Supervised learning, Reinforcement Learning, and Ensemble methods. This comprehensive survey provides a systematic analysis of the existing research endeavors employing various prevalent AI techniques in load forecasting, PG stability assessment, fault detection, and addressing security concerns within smart PG. Additionally, it delineates forthcoming research challenges that necessitate attention to fully actualize AI techniques in the creation of authentically smart PG systems. Ultimately, this survey underscores the potential for applying AI to tackle issues within smart PG systems, underscoring that the incorporation of AI techniques has the potential to significantly elevate and enhance the reliability and resilience of these smart PG systems.