M-R algorithm based multi-level feeder queue optimization charging model of electric vehicle and its implementation in Energy Internet

摘要

Recently, issues of energy shortage and environment pollution of mankind society become more and more serious. Energy Internet which can properly utilize cleaning resources provides a new idea for mankind to solve this kind of issues. Electric vehicle is an important part of Energy Internet. But the problem is that largescale electric vehicles put into operation and connected to the grid is a major challenge to the security and stability of power grid. This paper references the job scheduling algorithm in computer operator system and presents a multi-level feeder queue optimization charging model with comprehensive consideration of the grid-side power load and charging fairness. According to this model we charge for the electric vehicles in regional grid, on the basis of ensuring fairness, realizing optimized charging, to ensure grid security and stability and improve the resource utilization rate. The implementation of multi-level feeder queue optimization charging model of electric vehicles in regional grid requires the fusion of power grid, cars networking, charging station networking and other information. With the development of the industry, the integration of multiple information sources will produce massive heterogeneous data, showed a trend of big data, and its storage and calculating will become a bottleneck. Hadoop open source cloud computing platform can set computing cluster to implement such a big data parallel processing. In this paper, I implement the model in the cloud computing platform through designing the model's HBase distributed data storage and M-R parallel computing mode.