Electricity trading is an effective measure to minimize carbon emissions and alleviate the imbalance between reverse distribution of regional energy resources and power load. However, the effects of China’s electricity trading on carbon emissions have not been fully explored due to lack of complete and balanced inter-provincial power transmission data. Therefore, the electricity generation–consumption downscaling model, logarithmic mean Divisia index (LMDI) model, and random forest clustering algorithm within a general framework were used in the present study to explore the effect of electricity trading on level of carbon emissions. Comprehensive inter-provincial electricity transmission data were generated, driving factors including electricity imports and exports were decomposed at the national and provincial scales, and clustered provincial policy implications were evaluated. The results revealed that: (i) although economic activities were the main driving factor for increase in carbon emissions at the national level, 382.95 million tons carbon emissions were offset from 2005 to 2019 due to inter-provincial electricity importation, whereas electricity export increased carbon emission by 230.30 million tons; (ii) analysis at the provincial level showed that electricity exports from Sichuan and Yunnan provinces accounted for more than 20% of the nation’s total electricity flow. Notably, this high level of exports did not significantly increase carbon emissions in these provinces owing to the abundant hydropower resources; (iii) emission reductions were only observed at the national level if the carbon intensity of the exporting provinces was lower compared with that of importing provinces, or if the electricity trading was generated from renewable sources; (iv) the effect of electricity import on emissions reduction was markedly higher relative to the effect of electricity export in most provinces, which reflected the actual situation of sustaining optimization of electricity generation structure in provincial grids of China. These findings provide a basis for decision makers to understand the contributions of electricity trading to the changes in carbon emissions from electricity generation, as well as form a foundation to explore practicable carbon emission mitigation strategies in the power industry.

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The pressure drop of a main steam and reheat steam system should be optimized during the design and operation of a thermal power plant to minimize operation costs. In this study, the pressure drop of the main steam pipe and reheat steam pipe of a 1000 MW secondary reheat unit are optimized by modulating the operation parameters and the cost of operation is explored. Optimal pipe specifications were achieved by selecting a bend pipe and optimizing the pipe specifications. The pressure loss of the main steam pipeline was optimized to 2.61% compared with the conventional pressure drop (5%), the heat consumption of steam turbine was reduced by about 0.63 kJ/(kW·h), the standard coal consumption was minimized by about 0.024 g/(kW·h), and the total income in 20 years is approximated to be CNY 217,700. The primary reheat system was optimized to 4.88%, the steam turbine heat consumption was reduced by about 7.13 kJ/(kW·h), the standard coal consumption decreased by about 0.276 g/(kW·h), and the total income in 20 years is projected to be CNY 20.872 million after the optimization of the pressure drop. The secondary reheat system was optimized to 8.13%, the steam turbine heat consumption was reduced by about 7.86 kJ/(kW·h), the standard coal consumption decreased by about 0.304 g/(kW·h), and the total income in 20 years is projected to be CNY 22.7232 million after the optimization of the pressure drop. The research results of the present study provide a guide for the design and operation of secondary reheat units to achieve an effective operation and minimize costs.