Application of intelligent optimization algorithm in capacity configuration of CSP power plants

This study conducted an in-depth analysis and optimization of the capacity configuration of CSP power plants by applying an improved intelligent optimization algorithm. An actual CSP power plant located in a high sunshine area was selected as a case, and optimization analysis was performed using solar radiation data, meteorological data, and power demand data. By comparing the performance of genetic algorithm (GA), particle swarm optimization algorithm (PSO), and ant colony optimization algorithm (ACO), it was found that the PSO algorithm performed best in terms of convergence speed and optimal fitness, and the GA and ACO algorithms also showed high stability in terms of final fitness values. The effectiveness and applicability of intelligent optimization algorithms are shown from the actual case analysis, and the optimized scheme of capacity configuration will obviously promote the efficiency of the power plant operation. The comparison of the annual change characteristics of the data and the optimization results shows that the operating efficiency of the CSP power plant can be significantly improved by reasonably selecting and improving the intelligent optimization algorithm, indicating the practical application value and potential broad application prospects of the intelligent optimization algorithm in the capacity configuration optimization of CSP power plants.