Multi-objective evolutionary-based optimization of a ground source heat exchanger geometry using various optimization techniques

• Published in: Geothermics 2020-07, Vol.86, p.101861-11, Article 101861
• Main Authors: Keshavarzzadeh, Amir H., Maleki Zanjani, Atefeh, Gharali, Kobra, Dusseault, Maurice B.
• Format: Article
• Language: English
• Subjects: Algorithms; Boreholes; Configurations; Density distribution; Design parameters; Economic analysis; Economic models; Energy consumption; Evolutionary algorithms; Exergy; Exergy efficiency; Genetic algorithms; Geothermal power; GSHP; Heat exchangers; Heat pumps; Heat transfer; Mathematical models; Multi-objective optimization; Multiple objective analysis; Optimization; Optimization techniques; Pareto optimum; Refrigerants; Working fluids
• ISSN: 0375-6505; 1879-3576
• DOI: 10.1016/j.geothermics.2020.101861

[Display omitted] •Assessing various optimization methods for selected GSHP system;•Proposing a comprehensive model of the GSHP system with respect to the design parameters of the GHE;•Finding the optimum length of the GHE;•Obtaining the optimum value of the GHE design parameters;•Find the optimal refrigerant for the GSHP. Ground Source Heat Pumps (GSHP) are commonly implemented to reduce energy consumption. This study seeks to delineate optimum operating conditions and borehole geometry configurations for GSHPs on the basis of multi-objective evolutionary algorithms. A thermodynamic heat transfer model is proposed to describe the GSHP behavior, and an economic model is proposed to assess the GSHP total cost for the amount of energy provided. An exergoeconmic optimization is employed to estimate the Pareto optimal solution for the GSHP, along with the optimum operating conditions and borehole configuration. Three prominent points of the Pareto frontier (equilibrium point, total cost rate objective and exergy efficiency objective) are used to isolate and illustrate each objective’s performance. Scatter density distribution plots of design parameters are also obtained to find their trends, which can give more insight into GSHPs design parameters. The solutions from five different Evolutionary Algorithms (EAs), NSGA-II, GDE3, IBEA, SMPSO, and SPEA2, are compared. Three different refrigerants (R134-a, R123 and isobutene) are also compared to find the most suitable working fluid for the decision space delineated. This study attempts to find the optimal configuration of ground heat exchangers (GHEs) based upon the most reliable multi-objective evolutionary algorithm.