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Thermodynamic And Thermoeconomic Optimization Of A Cooling Tower-Assisted Ground Source Heat Pump

机译:冷却塔辅助地源热泵的热力学和热经济性优化

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Thermodynamic and thermoeconomic optimization of a cooling tower-assisted ground source heat pump (CSHP) in a multi-objective optimization process is performed. A thermodynamic model based on energy and exergy analyses is presented, and an economic model of the hybrid GSHP (HGSHP) system is developed according to the total revenue requirement (TRR) method. The proposed hybrid cooling tower-assisted GSHP system, including 12 decision variables, is considered for optimization. Three optimization scenarios, including thermodynamic single objective, thermoeconomic single objective, and multi-objective optimizations, are performed. In multi-objective optimization, both thermodynamic and thermoeconomic objectives are simultaneously considered. An optimization process is performed using the genetic algorithm (GA). In the case of multi-objective optimization, an example of a decision-making process for selection of the final solution from the Pareto optimal frontier is presented. The results obtained using the various optimization approaches are compared and discussed. Further, the sensitivity of optimized systems to the interest rate, the annual number of operating hours in cooling mode, the electricity price, and the water price are studied in detail. It is shown that the thermodynamic optimization is focused on provision for the limited source of energy, whereas the thermoeconomic optimization only focuses on monetary resources. In contrast, the multi-objective optimization considers both energy and monetary. Further, it is found that thermodynamic optimization is economical when the operating time in cooling mode is long and/or the electricity price is high, and water prices variations have no marked impact on the total product cost.
机译:在多目标优化过程中,对冷却塔辅助地源热泵(CSHP)进行了热力学和热经济性优化。提出了基于能量和火用分析的热力学模型,并根据总收益需求(TRR)方法建立了混合GSHP(HGSHP)系统的经济模型。建议的混合冷却塔辅助GSHP系统,包括12个决策变量,被认为是最优化的。执行了三种优化方案,包括热力学单一目标,热经济单一目标和多目标优化。在多目标优化中,同时考虑热力学和热经济目标。使用遗传算法(GA)执行优化过程。在多目标优化的情况下,给出了一个从Pareto最优边界中选择最终解决方案的决策过程示例。比较和讨论了使用各种优化方法获得的结果。此外,详细研究了优化系统对利率的敏感性,制冷模式下的年工作小时数,电价和水价。结果表明,热力学优化集中在有限能源的供应上,而热经济优化只集中在货币资源上。相反,多目标优化同时考虑了能源和金钱。此外,发现当在冷却模式下的工作时间长和/或电价高并且水价变化对总产品成本没有显着影响时,热力学优化是经济的。

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