Optimal design of hybrid combined cooling, heating and power systems considering the uncertainties of load demands and renewable energy sources

The uncertainties such as loads and renewable energy sources have significant impacts on operational performances of hybrid combined cooling, heating, and power (CCHP) systems. A multi-objective stochastic optimization model of a hybrid CCHP system is proposed, which contains a gas turbine, photovoltaic/thermal collectors, an absorption chiller/heater, a ground source heat pump, and storage devices of battery and water tank. Energy hub models of energy converters and storage devices considering the off-design characteristics of the components are constructed. The uncertainties of solar irradiance and building loads are expressed in a parametric method with probability distributions. Considering the uncertainties with system reliability, the hybrid CCHP system is optimized to achieve the best energetic, economic, and environmental benefits using the non-dominated sorting genetic algorithm-II. The Pareto frontiers obtained from the case study indicated that a lower system reliability results in more energy-saving and greenhouse emission reduction benefits. When the system confidence level decreases from 0.99 to 0.50, the hybrid CCHP system compared to the conventional separate production system averagely saves 13.7% primary energy and reduces 8.0% carbon dioxide emission. However, the annual cost saving rate will be reduced with the decrease in system confidence level and increase in uncertainty. The sensitivity analysis of Pareto frontiers on key economic parameters is performed and the results demonstrated that the annual cost saving rate is more sensitive to natural gas price, and the investment cost of solar collectors has a stronger impact than gas turbine. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study presents a multi-objective stochastic optimization model of a hybrid CCHP system considering uncertainties and uses the non-dominated sorting genetic algorithm-II to optimize the system to achieve the best energy, economic, and environmental benefits. The results show that lower system reliability leads to more energy-saving and greenhouse emission reduction benefits, but the annual cost saving rate is affected by system confidence level and uncertainty. Sensitivity analysis demonstrates that the annual cost saving rate is more sensitive to natural gas price, while the investment cost of solar collectors has a stronger impact than gas turbine.