Performance enhancement and multi-objective optimization of a solar-driven setup with storage process using an innovative modification. (December 2020)

• Record Type: Journal Article
• Authors: Cao, Yan; Mohamed, Abdeliazim Mustafa; Dahari, Mahidzal; Delpisheh, Mostafa; Haghghi, Maghsoud Abdollahi
• Is Part Of: Journal of energy storage. Volume 32(2020)
• Publication Date: 2020-12
• Subjects: Energy storage, Parabolic trough solar collector -- Combined cooling, heating and power -- Modification process -- Efficiency -- Optimization; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2020.101956
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: An innovative modification process of a solar-driven system is implemented. A genetic algorithm is carried out to optimize the modified configuration. Three different solar radiation operational modes during a day are considered. The efficiencies and products significantly increase during the modification process. Abstract: The main objective of this study is to propose and design a novel modification process of a combined cooling, heating, and power production setup using parabolic trough solar collectors, contributing a higher performance. The base setup consists of a solar subsystem, an organic Rankine cycle for electricity generation, a heat exchanger and a single-effect absorption chiller that produce heating and cooling utilizing the organic Rankine cycle waste heat. The base setup is modified by adding two regenerative organic Rankine cycles, three heating process heat exchangers, and a double-effect absorption chiller. Both systems are studied and compared in three different modes, embracing solar mode (low radiation), solar and storage mode (high radiation), and storage mode (no radiation). Compared to the base setup, for the modified system, electricity production improves by 10.4%, 18.4% and 3.0% in the aforementioned operational modes, respectively. Also, for these modes, the electrical efficiency of the modified system is found to be 1.4 percent-point, 0.9 percent-point, and 0.3 percent-point higher than those of the base one, correspondingly. Eventually, a multi-objective optimization is conducted to optimize the modified system using a genetic algorithm.