A Distributed Computing Algorithm for Electricity Carbon Emission Flow and Carbon Emission Intensity

The calculation of the indirect carbon emis-sion is essential for power system policy making,carbon market development,and power grid planning.The em-bedded carbon emissions of the electricity system are commonly calculated by carbon emission flow theory.However,the calculation procedure is time-consuming,especially for a country with 500-1000 thousand nodes,making it challenging to obtain nationwide carbon emis-sions intensity precisely.Additionally,the calculation procedure requires to gather all the grid data with high classified levels from different power grid companies,which can prevent data sharing and cooperation among different companies.This paper proposes a distributed computing algorithm for indirect carbon emission that can reduce the time consumption and provide privacy protection.The core idea is to utilize the sparsity of the nodes’flow matrix of the nationwide grid to partition the computing procedure into parallel sub-procedures exe-cuted in multiple terminals.The flow and structure data of the regional grid are transformed irreversibly for pri-vacy protection,when transmitted between terminals.A 1-master-and-N-slave layout is adopted to verify the method.This algorithm is suitable for large grid compa-nies with headquarter and branches in provinces,such as the State Grid Corporation of China.

Particulate matter emission by an isolated rotating wheel

Particulate matter emission from rotating wheels, which imparts a force to the contact surface and causes strong air currents, is one of the important pollutant sources on the road. This study investigates the particulate matter emission by measuring mass and size distributions of particulate matter near an isolated rotating wheel in a deliberately designed setup. Five rotating speeds from 0.7 m/s to 1.5 m/s are conducted to test its impact on the emission of particulate matter. Mass of particulate matter is measured at twenty-six sampling points around the rotating wheel under different experimental conditions. Experimental results show that the farther away from the wheel, the less of particulate matters deposited on the sampling points in general. Moreover, the emission factor increases from 0.12 g/vkt to 0.24 g/vkt when the rotating speed of the wheel increases from 0.7 m/s to 1.5 m/s. The number and proportion of PM2.5 and PM10 on different sampling points are also measured. The results show that the position of the highest number of PM2.5 and PM10 tends to move to a further and higher sampling point with the increasing of the speed. Moreover, the number proportions of PM2.5 and PM10 on the sampling points range from 19% to 97% and 61% to 100% at different speeds, respectively. This study is believed to be helpful to estimate particulate matter emission and make effective control strategies on targeted pollution.