Predictive scheduling for Electric Vehicles considering uncertainty of load and user behaviors

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for E...

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Published in:Proceedings of the ... IEEE Power Engineering Society Transmission and Distribution Conference pp. 1 - 5
Main Authors: Bin Wang, Rui Huang, Yubo Wang, Nazaripouya, Hamidreza, Qiu, Charlie, Chi-Cheng Chu, Gadh, Rajit
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.05.2016
Subjects:
Algorithms
Charging
Data models
Electric vehicles
Energy consumption
Estimation
EV Scheduling
IEC 61850
IEC Standards
Load modeling
Mathematical models
Modules
Optimization
Predictive Control
Real-time systems
Renewable Energy Integration
Stress concentration
Uncertainty
ISSN:2160-8563
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Abstract Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimization module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.
AbstractList Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimization module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.
Author Bin Wang
Qiu, Charlie
Gadh, Rajit
Chi-Cheng Chu
Nazaripouya, Hamidreza
Yubo Wang
Rui Huang
Author_xml – sequence: 1
  surname: Bin Wang
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  surname: Rui Huang
  fullname: Rui Huang
  organization: Dept. of Mech. Eng., Univ. of California, Los Angeles, Los Angeles, CA, USA
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  surname: Yubo Wang
  fullname: Yubo Wang
  organization: Dept. of Mech. Eng., Univ. of California, Los Angeles, Los Angeles, CA, USA
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  givenname: Hamidreza
  surname: Nazaripouya
  fullname: Nazaripouya, Hamidreza
  organization: Dept. of Mech. Eng., Univ. of California, Los Angeles, Los Angeles, CA, USA
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  givenname: Charlie
  surname: Qiu
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  organization: Dept. of Mech. Eng., Univ. of California, Los Angeles, Los Angeles, CA, USA
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  surname: Chi-Cheng Chu
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  givenname: Rajit
  surname: Gadh
  fullname: Gadh, Rajit
  email: gadh@ucla.edu
  organization: Dept. of Mech. Eng., Univ. of California, Los Angeles, Los Angeles, CA, USA
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Snippet Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system...
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SubjectTerms Algorithms
Charging
Data models
Electric vehicles
Energy consumption
Estimation
EV Scheduling
IEC 61850
IEC Standards
Load modeling
Mathematical models
Modules
Optimization
Predictive Control
Real-time systems
Renewable Energy Integration
Stress concentration
Uncertainty
Title Predictive scheduling for Electric Vehicles considering uncertainty of load and user behaviors
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