Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm

Abstract Determining the optimal sizing of a solar power tower system (SPTS) with a thermal energy storage system is subject to finding the optimum values of design parameters including the solar multiple (SM), design direct normal irradiance (DNI) and thermal storage hours. These design parameters are determined for each station separately and have remarkable effects on the thermo-economic performance of the system. This paper aims to demonstrate how artificial intelligence (AI) techniques may play an important role in addressing the above-mentioned need and help determine the optimum design parameters for different stations. For this purpose, we developed a thermo-economic model of a 100 MW SPTS with a molten salt storage system for five stations (two stations in India, and one each in Bangladesh, Pakistan, and Afghanistan). A method-based AI is utilized in this paper to ascertain the design parameters of the system. Additionally, a novel hybrid method based on adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching-learning-based optimization algorithm (ANFIS-GATLBO) is employed. The input parameters are latitude, longitude, design point DNI and SM, while the annual energy produced, levelized cost of energy and capacity factor are the target variables. The results of the study show that although the annual energy produced by SPTS rises by increasing the SM and decreasing design point DNI, optimum design parameters should be determined by the economic factors. In addition, it was found that the ANFIS-GATLBO method used in this study successfully predicted the targets with a correlation coefficient close to 1.

[1]  V. Chandramohan,et al.  Influence of thermal energy storage system on flow and performance parameters of solar updraft tower power plant: A three dimensional numerical analysis , 2019, Journal of Cleaner Production.

[2]  Ricardo Nicolau Nassar Koury,et al.  Thermo-economic analysis and sizing of the components of an ejector expansion refrigeration system , 2018 .

[3]  F. Lippke,et al.  Direct steam generation in parabolic trough solar power plants : Numerical investigation of the transients and the control of a once-through system , 1996 .

[4]  Chitralekha Mahanta,et al.  A novel approach for ANFIS modelling based on full factorial design , 2008, Appl. Soft Comput..

[5]  Victor César Pigozzo Filho,et al.  Direct steam generation in linear solar concentration: Experimental and modeling investigation – A review , 2018, Renewable and Sustainable Energy Reviews.

[6]  Cristina Prieto,et al.  Review of commercial thermal energy storage in concentrated solar power plants: Steam vs. molten salts , 2017 .

[7]  Ricardo Nicolau Nassar Koury,et al.  Energy, exergy and economic analysis of a hybrid renewable energy with hydrogen storage system , 2018 .

[8]  Nicolas Bayer Botero,et al.  Heliostat field layout optimization for high-temperature solar thermochemical processing , 2011 .

[9]  A. Khellaf,et al.  Contribution to the modeling and simulation of solar power tower plants using energy analysis , 2014 .

[10]  Ricardo Nicolau Nassar Koury,et al.  Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms , 2018 .

[11]  Gabriel López,et al.  Daily solar irradiation estimation over a mountainous area using artificial neural networks , 2008 .

[12]  Torsten Fransson,et al.  A Comparative Thermoeconomic Study of Hybrid Solar Gas-Turbine Power Plants , 2013 .

[13]  Yıldız Koç,et al.  Designing and exergetic analysis of a solar power tower system for Iskenderun region , 2019, International Journal of Exergy.

[14]  Jabar Yousif,et al.  A Comparison Study Based on Artificial Neural Network for Assessing PV/T Solar Energy Production , 2019, Case Studies in Thermal Engineering.

[15]  Fahad A. Al-Sulaiman,et al.  Energy and exergy analyses of solar tower power plant driven supercritical carbon dioxide recompression cycles for six different locations , 2017 .

[16]  Torsten Fransson,et al.  Micro Gas-Turbine Design for Small-Scale Hybrid Solar Power Plants , 2013 .

[17]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[18]  J. Ji,et al.  A novel approach to thermal storage of direct steam generation solar power systems through two-step heat discharge , 2019, Applied Energy.

[19]  K. Mathioudakis,et al.  Simulation models for supporting the solar thermal power plant operator , 2019, Energy.

[20]  S. Liao,et al.  Determination of key parameters for sizing the heliostat field and thermal energy storage in solar tower power plants , 2018, Energy Conversion and Management.

[21]  Amin Shahsavar,et al.  Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models , 2019, Energy Conversion and Management.

[22]  J. Gómez-Hernández,et al.  Maximizing the power block efficiency of solar tower plants: Dual-pressure level steam generator , 2018, Applied Thermal Engineering.

[23]  Qiang Yu,et al.  Modeling and simulation of 1 MW DAHAN solar thermal power tower plant , 2011 .

[24]  M. Wagner.  Simulation and predictive performance modeling of utility-scale central receiver system power plants , 2008 .

[25]  G. Barigozzi,et al.  Thermal performance prediction of a solar hybrid gas turbine , 2012 .

[26]  Fahad A. Al-Sulaiman,et al.  Performance comparison of different supercritical carbon dioxide Brayton cycles integrated with a solar power tower , 2015 .

[27]  Markus Eck,et al.  Modelling and Design of Direct Solar Steam Generating Collector Fields , 2005 .

[28]  Xiaoze Du,et al.  Dynamic simulation of steam generation system in solar tower power plant , 2019, Renewable Energy.

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

[30]  L. Machado,et al.  Prediction of pressure drop during evaporation of R407C in horizontal tubes using artificial neural networks , 2018 .

[31]  Zhao Xin-gang,et al.  The economic performance of concentrated solar power industry in China , 2018, Journal of Cleaner Production.

[32]  Sanna Syri,et al.  Thermodynamic and economic analysis of a hybrid ocean thermal energy conversion/photovoltaic system with hydrogen-based energy storage system , 2019, Energy.

[33]  Meng Lin,et al.  Modeling and design guidelines for direct steam generation solar receivers , 2018 .

[34]  Ibrahim Dincer,et al.  Design and analysis of a solar tower based integrated system using high temperature electrolyzer for hydrogen production , 2016 .

[35]  P. A. González-Gómez,et al.  Thermo-economic optimization of molten salt steam generators , 2017 .

[36]  Jean-Jacques Bezian,et al.  Control systems for direct steam generation in linear concentrating solar power plants – A review , 2016 .

[37]  J. Gómez-Hernández,et al.  Influence of the steam generator on the exergetic and exergoeconomic analysis of solar tower plants , 2018 .

[38]  Christian A. Gueymard,et al.  Clear-sky solar luminous efficacy determination using artificial neural networks , 2007 .

[39]  Francisco J. Collado,et al.  Two-stages optimised design of the collector field of solar power tower plants , 2016 .

[40]  D. Yogi Goswami,et al.  A computationally efficient method for the design of the heliostat field for solar power tower plant , 2014 .

[41]  Mamdouh El Haj Assad,et al.  Comparison of artificial intelligence methods in estimation of daily global solar radiation , 2018, Journal of Cleaner Production.

[42]  María José Montes,et al.  Performance analysis of an Integrated Solar Combined Cycle using Direct Steam Generation in parabolic trough collectors , 2011 .

[43]  Ibrahim Dincer,et al.  Thermodynamic assessment of an integrated solar power tower and coal gasification system for multi-generation purposes , 2013 .

[44]  Xiaowei Zhao,et al.  An artificial intelligence approach for thermodynamic modeling of geothermal based-organic Rankine cycle equipped with solar system , 2019, Geothermics.

[45]  Zhifeng Wang,et al.  Energy and exergy analysis of solar power tower plants , 2011 .

[46]  Robert Pitz-Paal,et al.  Steam temperature stability in a direct steam generation solar power plant , 2011 .

[47]  Luisa F. Cabeza,et al.  Thermal energy storage evaluation in direct steam generation solar plants , 2018 .

[48]  Reiner Buck,et al.  Assessment of Improved Molten Salt Solar Tower Plants , 2014 .

[49]  Birinchi Bora,et al.  Performance prediction of PV module using electrical equivalent model and artificial neural network , 2018, Solar Energy.

[50]  Inmaculada Pulido-Calvo,et al.  Modeling water vapor impacts on the solar irradiance reaching the receiver of a solar tower plant by means of artificial neural networks , 2018, Solar Energy.

[51]  A. Khosravi,et al.  Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil , 2018 .

[52]  George S. Young,et al.  The all-seeing eye: Using multi-pyranometer arrays and neural networks to estimate direct normal irradiance , 2015 .

[53]  Zhifeng Wang,et al.  A new method for the design of the heliostat field layout for solar tower power plant , 2010 .

[54]  R. Venkata Rao,et al.  Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems , 2012, Inf. Sci..

[55]  Garvin A. Heath,et al.  Molten Salt Power Tower Cost Model for the System Advisor Model (SAM) , 2013 .