Multiple-attribute optimization and assessment of CCHP systems with renewable energy: Optimal capacity planning under carbon trading and hybrid energy storage
Jia-Hua Li, Xinyu Ren, Qingyu Gao, Zhoujian An, En-kuo Xing, Zhihua Wang
Abstract
To promote the utilization of renewable energy and improve the economic, low-carbon, and energy efficiency of the integrated energy system. This study proposes an optimal capacity allocation and scheduling model for a combined cooling, heating, and power system with wind, solar, hydrogen, and carbon collaboration. The model couples new energy devices and energy storage devices, and integrates carbon capture, a ladder carbon trading mechanism, and diversified hydrogen utilization. Using ε-constraint algorithm and technique for order preference by similarity to ideal solution, in combination with the entropy weight method, are used to solve for the annual total cost (ATC), the annual carbon emissions (ACE), and the primary energy consumption (PEC) under six different scenarios optimize system configuration and operation scheduling strategies, and study the impact of each component on the overall performance of the system. The paper also discusses the sensitivity analysis of scenario 5 based on carbon price, hydrogen doping ratio, and storage cap, and the uncertainty analysis of efficiency fluctuations of photovoltaic thermoelectric modules. Simulation results show that the proposed model reduces 5981.57 tons of ACE and 959 000 m3 of PEC while achieving a lower ATC ($592 230) compared to the conventional system, with good stability and robustness of the system. These findings underscore the potential of the proposed system to accelerate decarbonization, improve operational efficiency, and support the transition to a more sustainable energy future.