ANN-based optimization of a parabolic trough solar thermal power plant

Abstract

Design and optimization of a solar power plant are very complex and require many calculations, data and time. From this point of view, artificial neural network (ANN) models are desired options to determine techno-economic performances of this kind of plants. Therefore, the objective of this study is to investigate the feed-forward back-propagation learning algorithm with three different variants; Levenberge Marguardt (LM), Scaled Conjugate Gradient (SCG), and Pola-Ribiere Conjugate Gradient (CGP), used in the ANN to find the best approach for prediction and techno-economic optimization of parabolic trough solar thermal power plant (PTSTPP) integrated with fuel backup system and thermal energy storage. The obtained statistical parameters showed that LM algorithm with 38 neurons in a single hidden layer looks as the best ANN model to predict the annual power generation (PGnet) and levelized cost of electricity (LCOE) of the presented PTSTPP. Moreover, the obtained weights from this topology were used in the LCOE analysis for determining the optimum system design. It is therefore available to get a minimum LCOE of 8.88Cent/kWh from the new optimized plant when the plant characteristics are; 34°C and 850W/m2 for both design ambient temperature and solar radiation, 23m for row spacing, 1.7 for solar multiple, and 2.5h number of hours for the storage system.