Multi-time scale electric vehicle charging load forecasting considering constant current charging and parallel computing. (November 2022)

• Record Type: Journal Article
• Authors: Bian, Haihong; Guo, Zhengyang; Zhou, Chengang; Peng, Shan
• Is Part Of: Energy reports. Volume 8(2022)Supplement 13
• Page Start: 722
• Page End: 732
• Publication Date: 2022-11
• Subjects: Load forecasting -- Constant current charging process -- Electric vehicle -- Monte Carlo method -- Parallel computing; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.08.034
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: At present, the concept of ecological civilization has been widely recognized by the whole world, and a series of policies that guarantees the evolution of the electric vehicle (EV) industry has been implemented by multiple countries. Therefore, it is significant to predict the charging load of EVs to solve the challenges of power system planning and operation. The Monte Carlo (MC) method is preferred by many scholars in EV charging load prediction because it is very suitable for describing random characteristics with a good prediction effect. To obtain more reliable and efficient prediction results, this paper analyzes the application of parallel computing technology in MC simulation. Firstly, EVs in the region are classified according to their battery capacity. Based on the voltage change curve of lithium-ion power batteries in the process of constant current charging under different capacities, the charging power, charging time, and state of charge (SOC) of EVs are investigated. Secondly, the behavior characteristics of users and the driving parameter characteristics of EVs are studied respectively, and the probability distribution model of multi-source information is established. Thirdly, parallel computing technology in the computer field is introduced, and an improved MC method is proposed based on the multi-core CPU architecture. After fully considering the complex constant current charging process after fitting, the charging load of EVs in a region of East China is simulated. Finally, the time cost and the load forecasting results of serial and parallel methods are compared and analyzed, and the progressiveness and effectiveness of the parallel method are verified. Results show that the charging load has four peaks in a day, taxis are the main source of the peak load of the power grid, the charging load of buses fluctuates the most, and private cars are the main backup capacity to participate in V2G dispatching in the future. In addition, under the experimental conditions set in this paper, compared with the traditional serial MC method, the improved MC method based on parallel computing shows good performance with the acceleration effect improved by 7 to 12 times.