Solar Power Tower System

Ali Khosravi, Mohammad Malekan, Juan Jose Garcia Pabon, Mamdouh El Haj Assad

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

The solar power tower system (SPTS) has been successfully demonstrated as a favorable candidates to replace fossil fuel energy systems. Although several investigations have been developed to assess the SPTS, the detailed, fundamentals-based, long-term transient simulation capability is limited. Research presented in this chapter is intended to indicate how artificial intelligence (AI) techniques may play an important role to address this need. For this purpose, a method of AI is developed to simulate a solar power tower direct steam (SPTDS) system. As a matter of fact, this hybrid method is developed through the combination of adaptive neurofuzzy inference system with biogeography-based optimization algorithm. The power losses from the receiver, power absorbed by the receiver, receiver thermal efficiency, field optical focus fraction, field optical efficiency, and the cycle electrical power output are simulated and appraised through the intelligent method. A set of input parameters including the meteorological data, solar angles, central receiver features, and heliostat deploy angle are selected as independent variables for sensitivity analysis. The results depict that the intelligent model can successfully recognize the intricate relationship between parameters to predict the targets in a SPTDS system.
Original languageEnglish
Title of host publicationDesign and performance optimization of renewable energy systems
PublisherElsevier
Chapter5
Pages61-83
ISBN (Print)9780128216026
DOIs
Publication statusPublished - 2021
MoE publication typeA3 Part of a book or another research book

Keywords

  • solar power tower system
  • adaptive neuro-fuzzy inference system
  • biogeography-based optimization algorithm
  • artificial intelligence
  • energy modeling

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