Present a multi-criteria modeling and optimization (energy, economic and environmental) approach of industrial combined cooling heating and power (CCHP) generation systems using the genetic algorithm, case study: A tile factory

Hamed Ershadi and Arash Karimipour

Energy, 2018, vol. 149, issue C, 286-295

Abstract: In this study, an industrial combined cooling, heat and power (CCHP) generation system in a tile factory was simulated and optimized by the genetic algorithm approach taking into account electricity, heating and cooling loads. Modeling and optimization were performed based on thermodynamic, environmental and economic analyzes. A multi-criteria function (energy, economic, and environmental) called relative annual benefit (RAB) with a gas engine (with partial load operation) as the prime mover was used in the optimization process. The analysis was performed for three different scenarios of the possibility of selling (selling scenario or SS) and impossibility of selling electricity (no-selling scenario or NS) to the grid and the possibility of selling electricity with similar capacities. The designing variables including the number of prime movers, nominal capacity of movers, backup boiler capacity and the capacity of compression and absorption chillers were optimized. The CCHP system for the tile factory showed the better performance of selling scenario using a gas engine with a capacity of 5000 and 700. However, the nominal capacity of the prime movers in the selling scenario was higher than that in the no-selling strategy. The results showed that the relative annual benefit decreased by choosing a similar capacities.

Keywords: Combined cooling heating and power (CCHP); Industrial scale; Relative annual benefit (RAB); Genetic algorithm (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:149:y:2018:i:c:p:286-295

DOI: 10.1016/j.energy.2018.02.034

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