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Optimal Scheduling Strategy of Distribution Network Based on Electric Vehicle Forecasting

1
Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2
Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3
State Key Laboratory of Power Grid Safety and Energy Conservation, China Electric Power Research Institute, Haidian District, Beijing 100192, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(7), 816; https://doi.org/10.3390/electronics8070816
Received: 18 June 2019 / Revised: 18 July 2019 / Accepted: 19 July 2019 / Published: 22 July 2019
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Abstract

Based on the Monte Carlo method, this paper simulates, predicts the load, and considers the travel chain of electric vehicles and different charging methods to establish a predictive model. Based on the results of electric vehicle simulation prediction, an optimal scheduling model of the distribution network considering the demand response side load is established. The firefly optimization algorithm is used to solve the optimal scheduling problem. The results show that the prediction model proposed in this paper has a certain reference value for the prediction of an electric vehicle load. The electric vehicle is placed in the optimal scheduling resource of the distribution network, which increases the dimension of the scheduling resources of the network and improves the economics of the distribution network operation. View Full-Text
Keywords: Monte Carlo; electric vehicles (EV); load forecasting; distribution network optimization; demand response (DR) Monte Carlo; electric vehicles (EV); load forecasting; distribution network optimization; demand response (DR)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, F.; Dou, C.; Xu, S. Optimal Scheduling Strategy of Distribution Network Based on Electric Vehicle Forecasting. Electronics 2019, 8, 816.

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