Regular Article

Research on optimization method of CCHP system coupled with renewable energy

¹ School of Civil Engineering and Architecture, Hebei University of Science and Technology, 26 Yuxiang Street, Yuhua District, Shijiazhuang 050018, PR China
² School of Energy, Power and Mechanical Engineering, North China Electric Power University, 2 Beinong Road, Huilongguan, Changping District, Beijing 102206, PR China

^* Corresponding author: ligaikang@hebust.edu.cn

Received: 5 March 2024
Accepted: 16 August 2024

Abstract

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.