Journal Home > Volume 1 , Issue 1

The matching between dynamic supply of renewable power generation and flexible charging demand of the Electric Vehicles (EVs) can not only increase the penetration of renewables but also reduce the load to the state electric power grid. The challenges herein are the curse of dimensionality, the multiple decision making stages involved, and the uncertainty of both the supply and demand sides. Event-Based Optimization (EBO) provides a new way to solve large-scale Markov decision process. Considering different spatial scales, we develop a bi-level EBO model in this paper which can both catch the changes on the macro and micro levels. By proper definition, the size of event space stays fixed with the scale of the problem, which shows good scalability in online optimization. Then a bi-level Q-learning method is developed to solve the problem iteratively. We demonstrate the performance of the method by numerical examples. Our method outperforms other methods both in performance and scalability.


menu
Abstract
Full text
Outline
About this article

Matching Uncertain Renewable Supply with Electric Vehicle Charging Demand-A Bi-Level Event-Based Optimization Method

Show Author's information Teng LongQing-Shan Jia*( )
Center For Intelligent and Networked Systems (CFINS), Department of Automation, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China

Abstract

The matching between dynamic supply of renewable power generation and flexible charging demand of the Electric Vehicles (EVs) can not only increase the penetration of renewables but also reduce the load to the state electric power grid. The challenges herein are the curse of dimensionality, the multiple decision making stages involved, and the uncertainty of both the supply and demand sides. Event-Based Optimization (EBO) provides a new way to solve large-scale Markov decision process. Considering different spatial scales, we develop a bi-level EBO model in this paper which can both catch the changes on the macro and micro levels. By proper definition, the size of event space stays fixed with the scale of the problem, which shows good scalability in online optimization. Then a bi-level Q-learning method is developed to solve the problem iteratively. We demonstrate the performance of the method by numerical examples. Our method outperforms other methods both in performance and scalability.

Keywords: electric vehicle, event-based optimization, cyber physical energy system

References(36)

[1]
J. Y. Zhang and Y. H. Wu, A stochastic logical model-based approximate solution for energy management problem of HEVs, Sci. Chin. Inf. Sci., vol. 61, no. 7, p. 70207, 2018.
[2]
M. Muratori, Impact of uncoordinated plug-in electric vehicle charging on residential power demand, Nat. Energy, vol. 3, no. 3, pp. 193-201, 2018.
[3]
K. Y. Lee, S. H. Tsao, C. W. Tzeng, and H. J. Lin, Influence of the vertical wind and wind direction on the power output of a small vertical-axis wind turbine installed on the rooftop of a building, Appl. Energy, vol. 209, pp. 383-391, 2018.
[4]
Y. Yang, Q. S. Jia, G. Deconinck, X. H. Guan, Z. F. Qiu, and Z. C. Hu, Distributed coordination of EV charging with renewable energy in a microgrid of buildings, IEEE Trans. Smart Grid, vol. 9, no. 6, pp. 6253-6264, 2018.
[5]
S. Z. Zhao, X. J. Lin, and M. H. Chen, Robust online algorithms for peak-minimizing EV charging under multistage uncertainty, IEEE Trans. Automat. Contr., vol. 62, no. 11, pp. 5739-5754, 2017.
[6]
H. Z. Fang, Y. B. Wang, and J. Chen, Health-aware and user-involved battery charging management for electric vehicles: Linear quadratic strategies, IEEE Trans. Contr. Syst. Technol., vol. 25, no. 3, pp. 911-923, 2017.
[7]
H. H. Alhelou and M. E. H. Golshan, Hierarchical plug-in EV control based on primary frequency response in interconnected smart grid, presented at the 24th Iranian Conf. Electrical Engineering (ICEE), Shiraz, Iran, 2016.
DOI
[8]
M. X. Liu, P. K. Phanivong, Y. Shi, and D. S. Callaway, Decentralized charging control of electric vehicles in residential distribution networks, IEEE Trans. Contr. Syst. Technol., vol. 27, no. 1, pp. 266-281, 2019.
[9]
Z. J. Ma, N. Yang, S. L. Zou, and Y. F. Shao, Charging coordination of plug-in electric vehicles in distribution networks with capacity constrained feeder lines, IEEE Trans. Contr. Syst. Technol., vol. 26, no. 5, pp. 1917-1924, 2018.
[10]
B. Sun, Z. Huang, X. Q. Tan, and D. H. K. Tsang, Optimal scheduling for electric vehicle charging with discrete charging levels in distribution grid, IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 624-634, 2018.
[11]
C. R. Jin, J. Tang, and P. Ghosh, Optimizing electric vehicle charging: A customer’s perspective, IEEE Trans. Veh. Technol., vol. 62, no. 7, pp. 2919-2927, 2013.
[12]
T. Rui, C. G. Hu, G. L. Li, J. S. Tao, and W. X. Shen, A distributed charging strategy based on day ahead price model for PV-powered electric vehicle charging station, Appl. Soft Comput., vol. 76, pp. 638-648, 2019.
[13]
L. L. Guo, B. Z. Gao, Y. Li, and H. Chen, A fast algorithm for nonlinear model predictive control applied to HEV energy management systems, Sci. Chin. Inf. Sci., vol. 60, no. 9, p. 092201, 2019.
[14]
Q. L. Huang, Q. S. Jia, and X. H. Guan, Robust scheduling of EV charging load with uncertain wind power integration, IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 1043-1054, 2018.
[15]
R. Moghaddass, O. A. Mohammed, E. Skordilis, and S. Asfour, Smart control of fleets of electric vehicles in smart and connected communities, IEEE Trans. Smart Grid, vol. 10, no. 6, pp. 6883-6897, 2019.
[16]
S. Y. Wang, S. Z. Bi, Y. J. A. Zhang, and J. W. Huang, Electrical vehicle charging station profit maximization: Admission, pricing, and online scheduling, IEEE Trans. Sustain. Energy, vol. 9, no. 4, pp. 1722-1731, 2018.
[17]
Y. Y. Sun, Z. Q. Chen, Z. Y. Li, W. Tian, and M. Shahidehpour, EV charging schedule in coupled constrained networks of transportation and power system, IEEE Trans. Smart Grid, vol. 10, no. 5, pp. 4706-4716, 2019.
[18]
Q. S. Jia and T. Long, A review on charging behavior of electric vehicles: Data, model, and control, Contr. Theory Technol., vol. 18, no. 3, pp. 217-230, 2020.
[19]
J. J. Hu, H. Morais, T. Sousa, and M. Lind, Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects, Renew. Sustain. Energy Rev., vol. 56, pp. 1207-1226, 2016.
[20]
X. R. Cao, Event-based optimization of Markov systems, in Stochastic Learning and Optimization, Boston, MA, USA: Springer, 2007, pp. 387-454.
DOI
[21]
Q. S. Jia, On solving event-based optimization with average reward over infinite stages, IEEE Trans. Automat. Contr., vol. 56, no. 12, pp. 2912-2917, 2011.
[22]
Q. L. Huang, Q. S. Jia, L. Xia, and X. H. Guan, Event-based optimization for stochastic matching EV charging load with uncertain renewable energy, in Proc. 11th World Congress on Intelligent Control and Automation, Shenyang, China, 2014, pp. 794-799.
DOI
[23]
Q. L. Huang, Q. S. Jia, and X. H. Guan, A multi-timescale and bilevel coordination approach for matching uncertain wind supply with EV charging demand, IEEE Trans. Autom. Sci. Eng., vol. 14, no. 2, pp. 694-704, 2017.
[24]
Y. Yang, Q. S. Jia, X. H. Guan, X. Zhang, Z. F. Qiu, and G. Deconinck, Decentralized EV-based charging optimization with building integrated wind energy, IEEE Trans. Autom. Sci. Eng., vol. 16, no. 3, pp. 1002-1017, 2019.
[25]
Q. L. Huang, Q. S. Jia, Z. F. Qiu, X. H. Guan, and G. Deconinck, Matching EV charging load with uncertain wind: A simulation-based policy improvement approach, IEEE Trans. Smart Grid, vol. 6, no. 3, pp. 1425-1433, 2015.
[26]
S. I. Vagropoulos and A. G. Bakirtzis, Optimal bidding strategy for electric vehicle aggregators in electricity markets, IEEE Trans. Power Syst., vol. 28, no. 4, pp. 4031-4041, 2013.
[27]
M. H. K. Tushar, C. Assi, M. Maier, and M. F. Uddin, Smart microgrids: Optimal joint scheduling for electric vehicles and home appliances, IEEE Trans. Smart Grid, vol. 5, no. 1, pp. 239-250, 2014.
[28]
K. Grogg, Harvesting the wind: The physics of wind turbines, Physics and Astronomy Comps Papers, vol. 7, 2005.
[29]
J. Dutka, The incomplete Beta function-a historical profile, Arch. Hist. Exact Sci., vol. 24, no. 1, pp. 11-29, 1981.
[30]
T. W. Edward Lau and Y. C. Ho, Universal alignment probabilities and subset selection for ordinal optimization, J. Optim. Theory Appl., vol. 93, no. 3, pp. 455-489, 1997.
[31]
T. Long, J. X. Tang, and Q. S. Jia, Multi-scale event-based optimization for matching uncertain wind supply with EV charging demand, presented at 13th IEEE Conf. Automation Science and Engineering (CASE), Xi’an, China, 2017.
DOI
[32]
L. Xia, Q. S. Jia, and X. R. Cao, A tutorial on event-based optimization-A new optimization framework, Discrete Event Dyn. Syst., vol. 24, no. 2, pp. 103-132, 2014.
[33]
Sany, http://www.sanyhi.com/, 2020.
[34]
Beijing transport annual report 2013, Beijing Transportation Research Center, Beijing, China, 2013.
[35]
T. K. Lee, Z. Bareket, T. Gordon, and Z. S. Filipi, Stochastic modeling for studies of real-world PHEV usage: Driving schedule and daily temporal distributions, IEEE Trans. Veh. Technol., vol. 61, no. 4, pp. 1493-1502, 2012.
[36]
H. C. Zhang, Z. C. Hu, Z. W. Xu, and Y. H. Song, Optimal planning of PEV charging station with single output multiple cables charging spots, IEEE Trans. Smart Grid, vol. 8, no. 5, pp. 2119-2128, 2017.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 04 February 2021
Accepted: 22 February 2021
Published: 30 April 2021
Issue date: March 2021

Copyright

© The author(s) 2021

Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (No. 2016YFB0901900), the National Natural Science Foundation of China (Nos. 62073182 and 61673229), and the 111 International Collaboration Project of China (No. BP2018006).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return