An integrated program of a stand-alone parabolic trough solar thermal power plant: Code description and test. (September 2018)

• Record Type: Journal Article
• Authors: Mohammad, Sanan T.; Al-Kayiem, Hussain H.; Assadi, Morteza K.; Sabir, Osama.; Khlief, Ayad K.
• Is Part Of: Case studies in thermal engineering. Volume 12(2018)
• Page Start: 26
• Page End: 37
• Publication Date: 2018-09
• Subjects: Direct normal radiation -- Direct steam generation -- Parabolic trough solar thermal power plant; Heat engineering -- Case studies -- Periodicals; 621.40205
• DOI: 10.1016/j.csite.2018.02.006
• Languages: English
• ISSNs: 2214-157X

Abstract

Abstract: Solar thermal systems produce steam after being energized by solar parabolic trough concentrators, are incorporated with a steam turbine-generator assembly to produce electricity. This study presents a code for prediction of performance, while under-taking preliminary plant-sizing for a variety of parabolic trough solar fields operating under nominal conditions. The code, named as PTPPPP (Parabolic Trough Power Plant Performance Predictor) consists of four blocks. The code allows prediction of variables including: heat loss coefficient, U L, aperture effective direct normal irradiance, I, heat gain, Q gain, and the thermal efficiency of stand-alone parabolic trough solar thermal power plant in commerce, ηp . The conceptual design of the stand-alone parabolic trough solar involves: selection and sizing of system components, power generation cycles, working fluid types, and power block sizing. The input weather parameters and the operational parameters to the code have been acquired from in-situ measurements. The prediction results of the code have been found in good agreement with literature data with mean error of 0.18% in prediction of output power. In addition, this code is able to provide a flexibility in terms of temperature, heat transfer, and pressure range. Graphical abstract: A solar thermal power plant model, based on parabolic trough concentrators has been proposed as above. The plant has been modelled mathematically and in-site weather measured data have been input to the model to assist in sizing and analysis of the process parameters.