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Renewable energy is widely used in combined cooling, heating and power (CCHP) systems. This is important for building a low-carbon, flexible, multi-energy complementary energy system. However, coupling different renewable energy sources can have a somewhat differentiated impact on the performance of the system. In this study, an approach combining a long short-term memory (LSTM) network with multiple optimization algorithms is proposed. Comparative performance analysis of CCHP systems coupling solar and wind subsystems is conducted. Firstly, the renewable energy output is predicted by LSTM. Then, the Pareto frontiers of the coupled renewable energy CCHP system are generated by the Non-dominated Genetic Sorting Algorithm. The results are fed into the distance between superior and inferior solution methods to arrive at a decision, completing the multi-objective optimization of the system. Results show that the CCHP system coupling photovoltaic (PV) and solar collector (ST) is superior to the CCHP system coupling photovoltaic-photovoltaic-thermal integrated device. The system performance can be further improved by adding wind turbines to the integrated system coupling PV and ST.

Considering the limitation of system optimization and feed-in tariff policy research on combined cooling, heating and power (CCHP) system, a novel multi-objective CCHP system optimization model using linear search method was proposed. The analytic hierarchy process was used to determine the weight factors of three objectives: economic, energy and environmental. The system optimization of an office building in Sino-Singapore Eco-City located in Tianjin was analyzed with two different feed-in tariff policies. Results show that compared with the no feed-in tariff policy, the feed-in tariff policies could be beneficial for economic and environment performance of CCHP system. The comprehensive performance of CCHP system with two feed-in tariff policies are approximately the same in optimal configuration but there was a huge gap in primary energy consumption. According to the sensitivity analysis of the feed-in tariff price, the carbon emissions reduction of CCHP system changed slightly with the variation of the feed-in tariff. The cost saving grew steadily while the primary energy saving decreased dramatically with the increasing of feed-in tariff. And when the feed-in tariff price ratio is more than 0.9 under the fixed feed-in tariff policy, the primary energy consumption would be sharply increased compared with separate system. [ABSTRACT FROM AUTHOR]