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
  • [1] Multi-objective particle swarm optimization of binary geothermal power plants
    Clarke, Joshua
    McLeskey, James T., Jr.
    [J]. APPLIED ENERGY, 2015, 138 : 302 - 314
  • [2] Comparative exergy, multi-objective optimization, and extended environmental assessment of geothermal combined power and refrigeration systems
    Musharavati, Farayi
    Khanmohammadi, Shoaib
    Tariq, Rasikh
    [J]. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2021, 156 : 438 - 456
  • [3] Stochastic combined heat and power dispatch based on multi-objective particle swarm optimization
    Wang, Lingfeng
    Singh, Chanan
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2008, 30 (03) : 226 - 234
  • [4] Stochastic combined heat and power dispatch based on multi-objective particle swarm optimization
    Wang, Lingfeng
    Singh, Chanan
    [J]. 2006 POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS 1-9, 2006, : 2362 - +
  • [5] Multi-objective combined heat and power unit commitment using particle swarm optimization
    Anand, Himanshu
    Narang, Nitin
    Dhillon, J. S.
    [J]. ENERGY, 2019, 172 : 794 - 807
  • [6] Multi-objective optimization of combined cooling, heating and power system integrated with solar and geothermal energies
    Ren, Fukang
    Wang, Jiangjiang
    Zhu, Sitong
    Chen, Yi
    [J]. ENERGY CONVERSION AND MANAGEMENT, 2019, 197
  • [7] Continuous power generation through a novel solar/geothermal chimney system: Technical/cost analyses and multi-objective particle swarm optimization
    Habibollahzade, Ali
    Houshfar, Ehsan
    Ashjaee, Mehdi
    Ekradi, Khalil
    [J]. JOURNAL OF CLEANER PRODUCTION, 2021, 283
  • [8] Multi-Objective Particle Swarm Optimization Applied to the Design of Wireless Power Transfer Systems
    Hasan, Nazmul
    Yilmaz, Tuba
    Zane, Regan
    Pantic, Zeljko
    [J]. 2015 IEEE WIRELESS POWER TRANSFER CONFERENCE (WPTC), 2015,
  • [9] Multi-objective approach for a combined heat and power geothermal plant optimization
    Marty, Fabien
    Sochard, Sabine
    Serra, Sylvain
    Reneaume, Jean-Michel
    [J]. CHEMICAL PRODUCT AND PROCESS MODELING, 2021, 16 (03): : 205 - 228
  • [10] Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization
    Li, Zeyu
    Xiong, Junhua
    [J]. ENERGIES, 2024, 17 (10)