China is facing severe climate pollution, thus the CO2 emissions of thermal power plants which consume a lot of fossil energy, need to be strictly monitored. At the same time, the thermal power plants and the government will face brand new environment, where the exactly appropriate monitoring approach of CO2 emission remains ambiguous. This study aims to distinguish monitoring approaches between Continuous Emission Monitoring System (CEMS) and factor-based approach on the basis of the operation features of China’s thermal power plants, analyzing the extension of CEMS. We review the major reducing greenhouse gas initiative in China—carbon market, and different emission monitoring approaches at first. We present the prospects of extension in CEMS’s technical features by analyzing an example of two generations using coal and gas, respectively, finding that CEMS is more accurate and dynamic. This study also presents the challenges by analyzing the refinement of factor-based monitoring approach. However, In contrast to many previous studies, we consider different influence in prospect and challenge from the market itself, the application experience and equipment installation basis. We finally draw an important conclusion that the factor-based monitoring approach is more suitable for China’s thermal power plants currently, but CEMS is more promising.

The current EU carbon trading market and the RGGI carbon market are relatively successful. Therefore, after studying the EU and RGGI thermal power units participating in carbon trading, it is of positive significance for formulating emission patterns in line with China’s thermal power units. Through the international experience of the carbon trading market, we will build China’s carbon trading market.

As China has been the biggest carbon dioxide emissions country in the world and taken electric power industry as a breakthrough to build carbon market, it is necessary to promote the use of carbon continuous emission monitoring system to improve the accuracy of carbon emissions accounting. Carbon emission model is built to convert CO2 flow rate and concentration to mass. The CO2 emissions per gas inflow is a relatively stable value with a random fluctuation and will be affected by gas inflow, which is called per CO2 emissions. Based on the BP neural network algorithm, we take per CO2 emissions, gas inflow, gas turbine load, steam turbine load as input layers to obtain a real value prediction interval of per CO2 emission, and conduct carbon emission abnormal data screening. In the case study, it is proved that the neural network algorithm give an efficient way to screen big CO2 emission abnormal data.

No abstract

No abstract