Optimization of hybrid solar chimney power plants (HSCPPs): A review of multi-objective approaches

This comprehensive review examines the state-of-the-art developments in multi-objective optimization approaches for hybrid solar chimney power plants. The study examines practically relevant challenges of hybrid solar chimney power plant systems where the deployment of multi-objective optimization is critical. In terms of techniques used for multi-objective optimization, the presented review article chronologically discusses the evolution of optimization algorithms from scalarization to Pareto front-based evolutionary algorithms, and finally the integration of multi-objective optimization with machine learning frameworks. Through a detailed examination of 185 research works, this review identifies critical optimization challenges and emerging solutions in the field. This review highlights that while conventional optimization techniques have proven effective, modern hybrid algorithms that integrate evolutionary computation with machine learning methods offer enhanced performance in addressing the complex trade-offs involved in the design and operation of hybrid solar chimney power plants. Furthermore, the study identifies key areas for future research and offers insights on potential advancements, including real-time optimization and integration with smart grid systems. This work provides valuable information for both researchers and practitioners, keeping them informed on the latest developments in solar chimney-based energy systems and the role of multi-objective optimization in optimizing plant operations within the context of renewable energy applications.