Comparative thermoeconomic analyses and multi-objective particle swarm optimization of geothermal combined cooling and power systems

被引:26
|
作者
Habibollahzade, Ali [1 ]
Mehrabadi, Zahra Kazemi [2 ]
Markides, Christos N. [3 ]
机构
[1] Univ Tehran, Coll Engn, Sch Mech Engn, POB 11155-4563, Tehran, Iran
[2] Alzahra Univ, Fac Engn & Technol, Dept Mech Engn, Tehran, Iran
[3] Imperial Coll London, Dept Chem Engn, Clean Energy Proc CEP Lab, London SW7 2AZ, England
基金
英国工程与自然科学研究理事会;
关键词
Absorption power; Combined cooling and power; Ejector refrigeration; Geothermal; Multi-objective optimization; Thermoeconomic analysis; HIGH-TEMPERATURE HEAT; LOW-GRADE HEAT; EXERGOECONOMIC ANALYSIS; EJECTOR REFRIGERATION; EXERGY ANALYSIS; THERMODYNAMIC ANALYSIS; CYCLES; KALINA; RANKINE; ORC;
D O I
10.1016/j.enconman.2021.113921
中图分类号
O414.1 [热力学];
学科分类号
摘要
Comparative parametric and multi-objective optimization analyses of three novel geothermal systems are performed for combined cooling and power generation. The first (Configuration (a)) consists of an absorption power cycle and an ejector refrigeration cycle, the second (Configuration (b)) of a modified Kalina cycle and an absorption refrigeration cycle, and the third (Configuration (c)) of a double-flash power cycle and an ejector refrigeration cycle, in all cases for power generation and cooling, respectively. Both thermodynamic (energy, exergy) and economic criteria are compared to gain an understanding of the characteristics and performance of these systems, and to ascertain the most appropriate system for different scenarios. Results from the parametric study show that Configuration (a) has the highest power output and exergy efficiency, but lowest cooling capacity and overall (power plus cooling) thermal efficiency, while Configuration (b) has the highest cooling capacity and thermal efficiency, but lowest power output and exergy efficiency. From an exergoeconomic perspective, Configuration (a) has the lowest and Configuration (b) the highest total specific cost. Configuration (c) maintains, generally, a thermoeconomic performance in-between those of the other two systems. The optimization results indicate that if the thermal efficiency and total specific cost are considered competing objectives over a range of well conditions, the optimal solutions obtained by the LINMAP method for Configurations (a) to (c) have thermal efficiencies of 19.1%, 43.0%, 42.4%, exergy efficiencies of 57.6%, 23.6%, 33.1%, total cost rates of 436 $/h, 558 $/h, 596 $/h, and total specific costs of 29.7 $/GJ, 66.9 $/GJ, 43.5 $/GJ. If the exergy efficiency and total cost rate are considered competing objectives, the corresponding values are 13.0%/29.1%/ 10.5%, 67.3%/30.5%/37.3%, 362/353/384 $/h, and 24.9/67.5/42.7$/GJ, respectively.
引用
收藏
页数:24
相关论文
共 50 条
  • [21] THERMOECONOMIC ANALYSIS AND MULTI-OBJECTIVE OPTIMIZATION OF AN INTEGRATED SOLAR SYSTEM FOR HYDROGEN PRODUCTION USING PARTICLE SWARM OPTIMIZATION ALGORITHM
    Keykhah, Sajjad
    Assareh, Ehsanolah
    Moltames, Rahim
    Taghipour, Abbas
    Barati, Hassan
    [J]. JOURNAL OF THERMAL ENGINEERING, 2021, 7 (04): : 746 - 760
  • [22] Thermoeconomic Analysis and Multi-Objective Optimization of a Combined Cooling and Power System Using Ammonia-Water Mixture: Case Study
    Cao, Liyan
    Wang, Jiangfeng
    Chen, Liangqi
    Lou, Juwei
    Wang, Jianyong
    Dai, Yiping
    [J]. JOURNAL OF ENERGY ENGINEERING, 2018, 144 (03)
  • [23] A modified particle swarm optimization for multimodal multi-objective optimization
    Zhang, XuWei
    Liu, Hao
    Tu, LiangPing
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 95
  • [24] Robust optimization using multi-objective particle swarm optimization
    Ono S.
    Yoshitake Y.
    Nakayama S.
    [J]. Artificial Life and Robotics, 2009, 14 (2) : 174 - 177
  • [25] Multi-objective particle swarm optimization approach to portfolio optimization
    Mishra, Sudhansu Kumar
    Panda, Ganapati
    Meher, Sukadev
    [J]. 2009 WORLD CONGRESS ON NATURE & BIOLOGICALLY INSPIRED COMPUTING (NABIC 2009), 2009, : 1611 - 1614
  • [26] A Hybrid Algorithm Based on Simplified Swarm Optimization for Multi-Objective Optimizing on Combined Cooling, Heating and Power System
    Yeh, Wei-Chang
    Zhu, Wenbo
    Peng, Yi-Fan
    Huang, Chia-Ling
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (20):
  • [27] Multi-objective thermoeconomic optimisation for combined-cycle power plant using particle swarm optimisation and compared with two approaches: an application
    Abdalisousan, Ashkan
    Fani, Maryam
    Farhanieh, Bijan
    Abbaspour, Majid
    [J]. INTERNATIONAL JOURNAL OF EXERGY, 2015, 16 (04) : 430 - 463
  • [28] Multi-objective Reactive Power Optimization Based on Improved Particle Swarm Algorithm
    Cui, Xue
    Gao, Jian
    Feng, Yunbin
    Zou, Chenlu
    Liu, Huanlei
    [J]. 2017 3RD INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND MATERIAL APPLICATION (ESMA2017), VOLS 1-4, 2018, 108
  • [29] Multi-objective optimization of engineering systems using game theory and particle swarm optimization
    Annamdas, Kiran K.
    Rao, Singiresu S.
    [J]. ENGINEERING OPTIMIZATION, 2009, 41 (08) : 737 - 752
  • [30] An improved multi-objective particle swarm optimization algorithm
    Zhang, Qiuming
    Xue, Siqing
    [J]. ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS, 2007, 4683 : 372 - +