Metaheuristic multi-objective optimization with artificial neural networks surrogate modeling for optimal energy-economic performance for CSP technology

Abstract

• Evaluated LFR CSP performance under various climatic conditions and design parameters. • Developed ANN surrogate models to predict annual LFR capacity factor. • Proposes regularization methods to improve ANN model robustness and accuracy. • Used MOPSO and MOGWO for optimizing energy and cost criteria effectively. • Identified optimal Site (site 4) with 51.05% capacity factor and $5246.71/kW cost. Among CSP technologies, the linear Fresnel reflector (LFR) can provide reliable carbon-neutral electricity for large-scale applications. In this study, the performance of a large solar LFR power plant under varying climatic conditions and the dependency of the performance on major plant design specifications, such as solar multiple and full-load thermal storage hours, were examined. Next, artificial neural network (ANN) surrogate models were introduced to predict the annual capacity factor of 100 MWe power plants operating with LFR technology. Single-hidden-layer ANN models with different numbers of neurons in the hidden layer were used and the Levenberg–Marquardt training algorithm was adopted. To overcome overfitting, validation and Bayesian Regularization approaches were compared. As training and testing data, 36 geographical sites with various combinations of design parameters were used. Through multi-objective optimization techniques, including the Multi-Objective Particle-Swarm Optimizer and Multi-Objective Grey Wolf Optimizer coupled with ANN surrogate modeling, this study navigates the trade-offs to identify Pareto-optimal solutions for large-scale LFR-based CSP integration based on the energy and cost criteria. The study also identified Site 4 (S4) as a promising candidate for optimal balance between the capacity factor (51.05%) and specific cost (5246.71$/kW), showcasing the practical implications of the research for sustainable and efficient CSP plant implementation.