Multi-objective optimization of combined cooling, heating, and power systems with supercritical CO2 recompression Brayton cycle - Yang, Yiping; Huang, Yulei; Jiang, Peixue; Zhu, Yinhai (Applied energy, 2020)
•A Novel system based on supercritical CO2 recompression Brayton cycle was proposed.•Simulation of system performance using thermodynamic and economic models.•Use of multi-objective optimization methods for performance estimation.•Optimum comprehensive performance in the cooling-to-power ratio range of 1.37–1.53. The supercritical CO2 Brayton power cycle is considered as a promising primary mover of the combined cooling, heating, and power system to potentially provide higher efficiency. This paper presents a novel combined cooling, heating, and power system with a supercritical CO2 Brayton cycle as the primary mover, wherein a part of heat that was originally released in the low temperature recuperator and a part of heat that was originally released to the environment are recovered for heating or cooling. The comprehensive performance is evaluated by identifying the Pareto frontier through the multi-objective genetic algorithm method and determining the optimal solution through the Technique for Order Preference by Similarity to Ideal Solution method. Results demonstrate that it is better to locate the heating/cooling heat exchanger before the flow split for both the heating and cooling modes. The two combined cooling, heating, and power systems always outperformed the basic system in terms of economic and comprehensive performances in heating mode. In particular, the comprehensive performance index of the proposed novel system was up to 31% higher than that of the basic system. We found that the supercritical CO2 recompression Brayton system should not be combined with the heat–driven cooling cycle in most circumstances. The proposed combined cooling, heating, and power system gave a better comprehensive performance only in the range of the cooling to power ratio 1.37–1.53 when taking the electricity efficiency and total product unit cost as the optimization objectives with a weight of (0.5, 0.5).