Sunlight to hydrogen conversion: Design optimization and energy management of concentrated photovoltaic (CPV-Hydrogen) system using micro genetic algorithm. (15th March 2016)

• Record Type: Journal Article
• Authors: Burhan, Muhammad; Chua, Kian Jon Ernest; Ng, Kim Choon
• Is Part Of: Energy. Volume 99(2016)
• Page Start: 115
• Page End: 128
• Publication Date: 2016-03-15
• Subjects: CPV -- Optimization -- Genetic algorithm -- Hydrogen storage -- Concentrated photovoltaic -- Electrolyser; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.01.048
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Owing to the intermittent solar irradiance from cloud cover in the diurnal period and unavailability at night time, the practical design of a solar system requires energy backup storage for an uninterrupted supply or for off-grid operation. However, for highly efficient CPV (concentrated photovoltaic) system, the literature is lacking for energy management and optimization algorithm and tool for standalone operation. In this paper, a system with CPV and electrolyser is presented where beam irradiance of sunlight is harnessed to convert the instantaneously generated electricity into useful Hydrogen/Oxygen gas, where they can be stored and re-used for downstream applications such as the fuel cells, etc. The multi-variable design and multi-objective optimization strategies are proposed and presented for a standalone operation of the CPV-Hydrogen system as well as their system performances, particularly electrical rating of CPV based upon the real weather data of Singapore. Highlights: Design modelling and energy management strategy is proposed for CPV-Hydrogen system. Micro GA does multi-variable and multi-objective optimization for standalone operation. Design is verified and analysed for minimum cost, zero PSFT and optimal storage. Performance of each component is presented for different real weather data conditions. Proposed design approach is applicable in all regions with low and high DNI.