This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
The Prediction for Adjustable Ability of Electric Vehicle Aggregator Based on Deep-Belief-Network
Technical Paper
2023-01-0062
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
In recent years, one of the keys to achieving energy conservation and emission reduction and practicing sustainable development strategies is the wide-area access of large-scale electric vehicles. The charging behavior of large-scale electric vehicles has brought great challenges to the load management and adjustment capacity determination of the power system. Therefore, the prediction of adjustable ability of electric vehicle aggregator based on deep-belief-network is proposed in this paper. First of all, this paper selects the indicators related to the load of the electric bus station: including the arrival time, departure time, and daily mileage of the electric vehicle, from which the SOC variation trend and accurate charging demand of the single electric vehicle are obtained. Secondly, a deep belief network model for load forecasting is established, and the corresponding data set is extracted using historical data, and is used as input together with the load data of electric vehicle aggregators, so as to accurately predict the load situation of electric vehicle aggregators in the next 1 day. Finally, based on the prediction results, the schedulable capacity of the electric vehicle aggregator is obtained. The calculation example shows that the electric vehicle aggregator has a large and stable dispatchable capacity at night. During the day, the user behavior can affect the dispatchable capacity of the aggregator by affecting the state of charge of the electric vehicle. It can be seen that the proposed prediction model has high accuracy and comprehensive consideration.
Authors
Citation
Li, B., Wang, N., Li, Y., and Huang, Y., "The Prediction for Adjustable Ability of Electric Vehicle Aggregator Based on Deep-Belief-Network," SAE Technical Paper 2023-01-0062, 2023, https://doi.org/10.4271/2023-01-0062.Also In
References
- Gan , L. , Li , G. , and Zhou , M. Coordinated Planning of Large-Scale Wind Farm Integration System and Transmission Network[J] Csee Journal of Power & Energy Systems 2 1 2016 19 29
- Fernandez , L.P. , Roman , T.G.S. , Cossent , R. , Domingo , C.M. et al. Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks IEEE Trans. Power Syst. 26 1 Feb. 2011 206 213
- Hong , T. , Xie , J. Black , J. Global Energy Forecasting Competition 2017: Hierarchical Probabilistic Load Forecasting[J] International Journal of Forecasting 35 4 2019 1389 1399
- Hammad , M.A. , Jereb , B. , Rosi , B. et al. Methods and Models for Electric Load Forecasting: A Comprehensive Review[J] Logistics Supply Chain，Sustainability and Global Challenges 11 1 2020 1 76
- Ghanavati , A.K. , Afsharinejad , A. , Vafamand , N. et al. 2020
- Hongchuan , Chen , Cai , Xu , Guoqi , Sun et al. Similar Day Short-Term Load Forecasting Based on Intelligent Optimization Method[J] Power System Protection and Control 49 13 2021 121 127
- Jinjin , Zhang , Qian , Zhang , Yuan , Ma et al. Short-Term Load Frequency Domain Prediction Methodbased on Improved Random Forest and Density-Basedspatial Clustering of Applications with Noise[J] Control Theory & Applications 37 10 2020 2257 2265
- Guoqing , W. , Youbing , Z. , Jun , Q. et al. Evaluation of V2G Usable Capacity of Multi-Type Electric Vehicle Battery Clusters Participating in Microgrid Energy Storage Journal of Electrotechnical Technology 29 8 2014 36 45
- Goebel , C. and Callaway , D.S. Using ICT-Controlled Plug-in Electric Vehicles to Supply Grid Regulation in California at Different Renewable Integration Levels IEEE Trans. Smart Grid 4 2 Jun. 2013 729 740
- Bessa , R.J. and Matos , M.A. Oct. 2012
- Fan , P. , Ke , S. , Kamel , S. , Yang , J. et al. A Frequency and Voltage Coordinated Control Strategy of Island Microgrid Including Electric Vehicles Electronics 11 2022 17 https://doi.org/10.3390/electronics11010017
- Wen , Y. , Fan , P. , Hu , J. , Ke , S. et al. An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning Sustainability 14 16 2022 10351 https://doi.org/10.3390/su141610351
- Liang , Y. , Li Zhi , Y. , and Haiwei. Medium-Term Load Forecasting Method with Improved Deep Belief Network for Renewable Energy[J] Distributed Generation & Alternative Energy Journal 37 3 2022
- Nihuan , L. et al. Summary of Short-Term Load Forecasting Methods for Power System Power System Protection and Control 39 01 2011 147 152 CNKI:SUN:JDQW.0.2011-01-031
- Ping , L. et al. Design of BP Neural Network Prediction System Based on MATLAB Computer Applications and Software 04 2008 149 150+184 CNKI:SUN:JYRJ.0.2008-04-057