Large-Scale Optimization among Photovoltaic and Concentrated Solar Power Systems: A State-of-the-Art Review and Algorithm Analysis

Yi’an Wang, Zhe Wu and Dong Ni ()
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Yi’an Wang: College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Zhe Wu: Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
Dong Ni: College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2024, vol. 17, issue 17, 1-38

Abstract: Large-scale optimization (LSO) problems among photovoltaic (PV) and concentrated solar power (CSP) systems are attracting increasing attention as they help improve the energy dispatch efficiency of PV and CSP systems to minimize power costs. Therefore, it is necessary and urgent to systematically analyze and summarize various LSO methods to showcase their advantages and disadvantages, ensuring the efficient operation of hybrid energy systems comprising different PV and CSP systems. This paper compares and analyzes the latest LSO methods for PV and CSP systems based on meta-heuristic algorithms (i.e., Particle Swarm Optimization, Genetic Algorithm, Enhanced Gravitational Search Algorithm, and Grey Wolf Optimization), numerical simulation and stochastic optimization methods (i.e., Constraint Programming, Linear Programming, Dynamic Programming Optimization Algorithm, and Derivative-Free Optimization), and machine learning-based AI methods (Double Grid Search Support Vector Machine, Long Short-Term Memory, Kalman Filter, and Random Forest). An in-depth analysis and A comparison of the essence and applications of these algorithms are conducted to explore their characteristics and suitability for PV and CSP or hybrid systems. The research results demonstrate the specificities of different LSO algorithms, providing valuable insights for researchers with diverse interests and guiding the selection of the most appropriate method as the solution algorithm for LSO problems in various PV and CSP systems. This also offers useful references and suggestions for extracting research challenges in LSO problems of PV and CSP systems and proposing corresponding solutions to guide future research development.

Keywords: large-scale optimization; photovoltaic system; concentrated solar power system; numerical simulation; meta-heuristic algorithm; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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