Dynamic performance and control strategy of a combined heat and power system driven by geothermal energy considering the building multi-load requirements

The combined heat and power (CHP) system driven by geothermal energy has been confirmed as a promising technology due to its high energy conversion efficiency and environmental benefits. However, the operation of the geothermal CHP system is affected by the external disturbances and energy demand of users. In this paper, a geothermal CHP system coupling the organic Rankine cycle (ORC)with heat pump cycle was developed to supply heat and electricity simultaneously. Meanwhile, a model of the building multi-load based on back propagation neural network was established to acquire the energy demand of users. The dynamic models of the geothermal CHP system were established to explore the effect of external disturbances on the system dynamic performance. Besides, a prediction-feedback control strategy was developed to improve the stability of the geothermal CHP system under fluctuant energy demands. Results show that the heat output of the geothermal CHP system can be improved by increasing the mass flow rate of working fluid in heat pump cycle, and the electrical output can be improved by increasing the mass flow rate of working fluid in ORC and geothermal water simultaneously. Moreover, the shorter settling time indicates that the prediction-feedback control strategy can enable the geothermal CHP system to match the energy demand quickly.