For achieving higher energy transferring efficiency from the resources to the load, the Combined Cooling, Heating, and Power (CCHP) systems have been widely researched and applied as an efficient approach. The key idea of this study is designing a novel structure of a hybrid CCHP system and evaluating its performance. In this research, there is a hybrid energy storage unit enhancing the whole system’s operation flexibility while supplying cooling, heating, and power. An ORC system is integrated into the CCHP system which takes responsibility of absorbing the low-temperature heat source for electricity generation. There are a few research studies focusing on the CCHP systems’ performance with this structure. In order to evaluate the integrated system’s performance, investigation and optimisation work has been conducted with the approaches of experimental studies and modelling simulation. The integrated system’s configuration, the model building process of several key components, the optimisation method, and the case studies are discussed and analysed in this study. The design of the integrated system and the control strategy are displayed in detail. Several sets of dynamic energy demand profiles are selected to evaluate the performance of the integrated system. The simulation study of the system supplying selected scenarios of loads is conducted. A comprehensive evaluation report indicates that the system’s efficiency during each study process differs while supplying different loads. The results include the power supplied by each component, the energy consumed by each type of load, and the efficiency improvements. It is found that the integrated system fully satisfies the selected domestic loads and various selected scenarios of loads with high efficiency. Compared to conventional power plants or CHP systems, the system efficiency enhancement comes from higher amount of recovery waste heat. Especially, the ORC system can absorb the low-temperature heat source for electricity generation. Compared to the original following electrical load (FEL) control strategy, the optimisation process brings overall efficiency improvements. The system’s overall efficiency was increased by from 3%, 3.18%, 2.85%, 17.11%, 8.89%, and 21.7% in the second case studies. Through the whole study, the main challenge lies within the design and the energy management of the integrated system.

Complete characteristic curves of a pump turbine are fundamental for improving the modeling accuracy of the pump turbine in a pump turbine governing system. In view of the difficulty in modeling the “S” characteristic region of the complete characteristic curves in the pump turbine, a novel Autoencoder and partial least squares regression based extreme learning machine model (AE-PLS-ELM) was proposed to describe the pump turbine characteristics. First, a mathematical model was formulated to describe the flow and moment characteristic curves. The improved Suter transformation was employed to transfer the original curves into WH and WM curves. Second, the ELM-Autoencoder technique and the partial least squares regression (PLSR) method were introduced to the architecture of the original ELM network. The ELM-Autoencoder technique was employed to obtain the initial weights of the Autoencoder based extreme learning machine (AE-ELM) model. The PLS method was exploited to avoid the multicollinearity problem of the Moore-Penrose generalized inverse. Lastly, the effectiveness of the proposed AE-PLS-ELM model has been verified using real data from a pumped storage unit in China. The results demonstrated that the AE-PLS-ELM model can obtain better modeling accuracy and generalization performance than the traditional models and, thus, can be exploited as an effective and sufficient approach for the modeling of pump turbine characteristics.