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 ( PG net ) 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 8.88 Cent/kWh from the new optimized plant when the plant characteristics are; 34 °C and 850 W/m2 for both design ambient temperature and solar radiation, 23 m for row spacing, 1.7 for solar multiple, and 2.5 h number of hours for the storage system.

[1]  Oguz Arslan,et al.  ANN based optimization of supercritical ORC-Binary geothermal power plant: Simav case study , 2011 .

[2]  O. Arslan,et al.  ANN-based Determination of Optimum Working Conditions of Residential Combustors with Respect to Optimum Insulation , 2014 .

[3]  K. Ravi Kumar,et al.  Solar collector field design and viability analysis of stand-alone parabolic trough power plants for Indian conditions , 2012 .

[4]  O. Arslan,et al.  ANN Modeling of an ORC-Binary Geothermal Power Plant: Simav Case Study , 2014 .

[5]  Robert Pitz-Paal,et al.  Trough integration into power plants : a study on the performance and economy of integrated solar combined cycle systems , 2004 .

[6]  Tugce Bekat,et al.  PREDICTION OF THE BOTTOM ASH FORMED IN A COAL-FIRED POWER PLANT USING ARTIFICIAL NEURAL NETWORKS , 2012 .

[7]  Mariano Martín,et al.  Optimal year-round operation of a concentrated solar energy plant in the south of Europe , 2013 .

[8]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[9]  Henry Price,et al.  A Parabolic Trough Solar Power Plant Simulation Model , 2003 .

[10]  Taqiy Eddine Boukelia,et al.  Estimation of direct solar irradiance intercepted by a solar concentrator in different modes of tracking (case study: Algeria) , 2015 .

[11]  V. Poghosyan,et al.  Techno-economic assessment of substituting natural gas based heater with thermal energy storage system in parabolic trough concentrated solar power plant , 2015 .

[12]  M. Valdés,et al.  Solar multiple optimization for a solar-only thermal power plant, using oil as heat transfer fluid in the parabolic trough collectors , 2009 .

[13]  Soteris A. Kalogirou,et al.  Solar thermoelectric power generation in Cyprus: Selection of the best system , 2013 .

[14]  Mariano Luque,et al.  Optimization of the size of a solar thermal electricity plant by means of genetic algorithms , 2011 .

[15]  A. Ramos,et al.  Strategies in tower solar power plant optimization , 2012 .

[16]  Germain Augsburger,et al.  Thermoeconomic optimization of a combined-cycle solar tower power plant , 2012 .

[17]  Soteris A. Kalogirou,et al.  Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .

[18]  Rodrigo Escobar,et al.  Performance model to assist solar thermal power plant siting in northern Chile based on backup fuel consumption , 2010 .

[19]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[20]  Oguz Arslan,et al.  Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycl , 2011 .

[21]  Soteris A. Kalogirou,et al.  Artificial neural networks for modelling the starting-up of a solar steam-generator , 1998 .

[22]  Eduardo Zarza,et al.  Parabolic-trough solar thermal power plant simulation scheme, multi-objective genetic algorithm calibration and validation , 2012 .

[23]  Paul Gauché,et al.  A comparison of solar aided power generation (SAPG) and stand-alone concentrating solar power (CSP): A South African case study , 2013 .

[24]  Santanu Bandyopadhyay,et al.  Optimization of concentrating solar thermal power plant based on parabolic trough collector , 2015 .

[25]  Antonio L. Avila-Marin,et al.  Evaluation of the potential of central receiver solar power plants: Configuration, optimization and trends , 2013 .

[26]  Ricardo Vasquez Padilla,et al.  Simplified Methodology for Designing Parabolic Trough Solar Power Plants , 2011 .