Optimisation of a renewable cooling and heating system using an integer-based genetic algorithm, response surface method and life cycle analysis

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Energy Conversion and Management


A new simulation-based strategy for optimal design of a renewable cooling and heating system that includes a desiccant wheel, a photovoltaic/thermal-solar air collector (PV/T-SAC) and a thermal storage unit using phase change material (PCM) is presented in this study. The strategy is formulated by integrating an integer-based genetic algorithm (IGA) with the response surface method (RSM), in order to tackle the system nonlinearity with manageable computational costs. The PV/T-SAC is used to provide thermal energy for desiccant wheel regeneration and space heating in cooling and heating seasons, respectively. The thermal storage unit is used to mitigate the intermittency of solar energy. The twenty-year system life cycle cost (LCC) is employed as the optimisation objective. The influence of four economic factors, i.e. PV/T price, PCM price, electricity purchase price and electricity sale price on the optimal design and LCC of this system is also investigated. The results showed that the system LCC using the design identified by the IGA-RSM decreased by 32.4% and 31.2% respectively, when comparing to two other design cases. The system operating cost could be negative if the components are properly sized, which indicated that the benefit from the electricity exported to the electrical grid could cover the cost of the electricity purchase from the grid. It was also found that the optimisation results and system LCC were highly influenced by the electricity sale price. The computational cost of the optimisation was significantly reduced when using the proposed strategy, as compared to that using the conventional genetic algorithm.

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