The distribution of exergy destruction is different in power plant systems with water cooled condenser and air cooled condenser. A detailed comparison based on a conventional Rankine cycle with two different cooling configurations is carried out in this paper in an exergy perspective. The inefficiency of the overall systems is analyzed in the same amount of heat transfer capacity condition, it shows that the distribution of exergy destruction among components is similar in both conditions. And this study of energy consumption is benefit for realization of auto-efficient-working power plants in future.

Large coal-fired power unit is a complex nonlinear system with more uncertainty to address, evaluate and optimize. It is essential and difficult to determine the key features contributing to the energy consumption of power units, especially considering the varying boundary constraints, operation conditions and system characteristics. In this paper idea of big data analytics is employed to clean the historian operation data efficiently and select the key energy-consumption features with less information losses. The result shows that the resultant key features reflect the exterior factors and system behavior. It makes great reference for the modeling and optimization of large thermal power units.

Considering the varying operation conditions and ambient constraints, the in-depth energy conservation of thermal power units is confronting new challenges. Based on the already made ‘energy-consumption benchmark state’ concept, the description of energy-consumption benchmark state was obtained in this paper to describe the economic performance of coal-fired power thermal system with the varying operation boundary, operation conditions and equipment performance. Breaking the limitations of traditional modelling which always make statistic analysis and mechanism analysis isolate, hybrid modeling method synthesizing the merit of the mechanism analysis and statistical method was proposed. Considering the heat transfer characteristics of thermal system, this model make the energy-consumption of unit correspondence with parameter sets of thermal system. Optimized parameter sets were gained with the fuel specific consumption setting as the optimization objective, thus obtain the energy-consumption benchmark state in thermal system of coal-fired units. The results show that the method for determining energy-consumption benchmark state in the thermal system of coal-fired units based on hybrid model makes significant reference for the energy-saving diagnosis and operation optimization of thermal power units under overall working conditions.

It is of great significance to determine an optimal condenser vacuum for energy-saving diagnosis, for the vacuum means a lot to the safe and economic operation of thermal power units. The key parameters were calculated by the practical data, such as the cleanliness factor. The condenser heat transfer coefficient is affected by both the dirty of condenser water side and other factors on the basis of the method of adjusting the circulating-water flow unilaterally to get the optimal vacuum of condenser in this paper. The impacts of the exhausting steam resistance, the oxygen content of condensate caused by the change of the circulating-water flow were considered in this paper. The practical operation data was analysed with the results from HEI. The simulations were examined in the comparison of heat transfer coefficient. The impacts of unit energy consumption characteristics under overall working conditions caused by condenser vacuum were obtained in the approach based on the theory of energy specific fuel consumption (ESFC). The variation of auxiliary specific consumption as the temperature of circulating-water changing was obtained. The results indicated that the optimal condenser vacuum determined by the method aiming at maximum output power and many factors under overall working conditions accounted for played an important role in the energy saving diagnosis of thermal power units.

Energy-saving management is playing increasingly important parts in the energy conservation of thermal power generation. The economic performance indexes were decomposed and clarified to set a delicacy energy-saving management system. With the great volume of operation data, an fuzzy rough set (FRS) –based big data analytics were introduced to build the intelligent energy-saving decision-making model. Based on such energy-saving management system, the operation optimization practice was performed on a 600MW thermal power unit to determine the optimum working state under specific operation conditions. The result shows that the proposed energy-saving management can makes great guidelines for the operation optimization and energy-saving diagnosis of thermal power units.