Configuration optimization of the novel cogeneration heating system with multi turbine units

摘要

At present, the 300 MW extraction condensing turbine units are still the dominant heat sources of cogeneration heating in China. With a rapid increment of heating scale, combined heating with multi turbine units has applied extensively in cogeneration plants. The novel cogeneration heating system can achieve efficient utilization of the condensed waste heat (CWH) of cogeneration plants with multi turbine units, through organically combining the technical elements, such as improving the backpressure of turbine units, using the absorption heat pumps (AHPs), and lowering the return water temperature of heating network. This article takes the 300 MW watercooling turbine units as analysis objects, adopts the method of the equivalent electricity of heating for energy consumption evaluation, and aims at minimizing the heating energy consumption of the system. Considering the influence on system economy is dominated by the heating energy consumption, the optimization of system configuration is guided by the heating energy consumption analysis. The elaborate and intensive analysis focuses on the optimum system configuration under different numbers of turbine units and different return water temperature. According to the application environment, the system configuration is discussed on the flexible high-backpressure heating mode (FHBM) and the restricted high-backpressure heating mode (RHBM). Through analyzing the variation regularities of the optimum crucial parameters under different conditions, it reveals the internal causes of energy consumption change of the heating system. On this basis, from the perspective of system design, this article summarizes the applicability of the novel high backpressure heating system (NHBS) and the novel absorption heat pump heating system (NAHPS). Furthermore, it refines the configuration optimization principles for the novel cogeneration heating system with multi turbine units. The conclusions are expected to indicate the optimization directions of system design.