Factors Analysis And Diagnostic Model Of Energy Efifciency Management In Thermal Power Enterprises

As a large industrial energy consumption and emissions, coal-fired electricitypower enterprise companies hold sway, whose electricity generation accounts fornearly80%of the total thermal power generating capacity. In the thermal powerproduction process, every step of the process is likely to be the source of energyloss. In order to reduce unnecessary losses, it is imperative to improve thermal powerperformance, improve plant energy efficiency, analyze factors affecting and diagnoseenergy efficiency.Currently, most of the theoretical research in this area are focus on aspects ofelectricity production, mainly on the economic performance of the production process,thermal unit for analysis and modeling, but in practice management, human factors,technological transformation, the external environment have an impact on energyefficiency for long-term development of the plant, research in this area is still verysmall. Energy efficiency of electricity production processes is affected by all aspectsof the subject, from small subtle changes of the device temperature, to efforts ofimplement the national energy policy within the plant, therefore, how tocomprehensive analyze the thermal process, extract the most influential, controllableand controllable factor is the paper first and main problem. Furthermore, afterdetermining factors, how to use the most appropriate model to diagnose the energyefficiency, obtain the most effectiveness results, and provide a reference to the actualenergy conservation for plant, which are also the difficulties of the article.Aiming at those problems above, this paper elaborates the major subsystems andhosting of the electricity production of thermal power enterprises involved in theprocess, and on this basis, analyzes the building of influence factors, queries therelevant data, determining the various factors impact of changes in energy efficiency,making indicator system more convincing; uses quantitative conversion for qualitativeindicators by cloud model and applies them to the C2R-based Data EnvelopmentAnalysis, making the comprehensive diagnostic model more perfect; applying aboveanalysis to the practice of thermal power enterprises Hebei Guohua Huanghua PowerPlant, Kunming second power plant and Zhongfu power plant as an example threeempirical studiein energy efficiency through the diagnosis, we can get the energy efficiency of sorting plant, and put forward measures to improve the energy efficiencyof the analysis results. This study allows a better understanding of plant actualconditions, conducive for energy efficiency backward enterprises to identify problemsand implement measures to improve energy efficiency in the future management ofthe actual work.