Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Plug-in electric vehicle load modeling for charging scheduling strategies in microgrids
Date
2022-12-01
Author
Güzel, Saliha İven
Göl, Murat
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
223
views
0
downloads
Cite This
Utilization of plug-in electric vehicle (PEV) load models can improve the performance of smart charging strategies, which increase the reliability of the grid by harnessing the flexibility of PEV loads. This paper presents a method for utilizing personal PEV load models in real-time stochastic charging control with single and finite system-time horizons. First, the drivers' load models are found with Kernel Density Estimation (KDE). Second, a single system-time horizon coordinated charging control algorithm is devised to ensure each PEV is charged at least a critical amount given a feasible set of optimization constraints. The coordinated charging algorithm tackles the NP-hardness of single-deadline charging scheduling problems efficiently with a sorting algorithm utilizing the stochastic PEV load models. Third, we extend the single system-time horizon coordinated charging control algorithm to a scheduling algorithm considering a finite system-time horizon. This approach utilizes the stochastic PEV load models in a model predictive control based approach to decrease the complexity of stochastic online charging scheduling problem into a deterministic case. The scheduling algorithm makes assumptions about the future arrivals to the charging station, unlike the classical online EV charging scheduling algorithms, which optimize the load demand revealed at the current time but underestimate the load demand revealed in the future. Our findings suggest the individual load models complement smart charging algorithms' decision process by improving the fairness of charging time allocation and extending the degree of knowledge of future random data for the scheduling algorithm.
Subject Keywords
Plug-in electric vehicles
,
Load modeling
,
Kernel Density Estimation
,
Smart charging
,
EV charging scheduling
,
NETWORKS
URI
https://hdl.handle.net/11511/99121
Journal
SUSTAINABLE ENERGY GRIDS & NETWORKS
DOI
https://doi.org/10.1016/j.segan.2022.100819
Collections
Department of Electrical and Electronics Engineering, Article
Suggestions
OpenMETU
Core
Plug-in Electric Vehicle Load Modeling for Smart Charging Strategies in Microgrids
Güzel, Saliha İven; Göl, Murat (2021-01-01)
The widespread adoption of plug-in electric vehicles (PEVs) is a path to be taken towards a green energy future, yet the uncoordinated penetration of PEVs prompts overloadings and low voltage violations that the existing power grid is not capable of managing. This issue can be addressed by utilizing PEV load models in component selection and smart charging strategies. PEV load modeling researches focus on the aggregator's and system operator's perspectives, and consideration of individual PEV loads in charg...
Multi-objective charging scheduling utilizing electric vehicle load models
Güzel, İven; Göl, Murat; Department of Electrical and Electronics Engineering (2022-1)
Utilization of electric vehicle (EV) load models can improve the performance of smart charging strategies, which increase the reliability of the grid by harnessing the flexibility of EV loads. This thesis presents methods for utilizing EV load models in real time stochastic charging control with single and finite system-time horizons. First, the drivers’ load models are found with kernel density estimation. A single system time horizon coordinated charging control algorithm is devised to ensure each EV is c...
Assessment of Impacts of Electric Vehicles on LV Distribution Networks in Turkey
TEMIZ, Armagan; Güven, Ali Nezih (2016-04-08)
This study proposes a methodology to analyze the impacts of Electric Vehicles (EVs) on Low Voltage (LV) distribution networks based on probabilistic models developed for the charging process of EVs. In addition to the battery charging characteristics, Gaussian distribution function for EV plug-in times and Weibull distribution function for daily travel times are utilized in simulations. Monte Carlo based load flow simulations are performed in order to evaluate the response of the LV networks to various EV a...
Parameter Estimation of Electric Vehicles for Improved Range Prediction
Saglam, Berkay; Bostancı, Emine; Göl, Murat (2021-01-01)
© 2021 IEEE.In order to improve performance of range estimation of electric vehicles, parameters that affect energy consumption should be determined accurately. This paper presents a parameter estimation methodology for electric vehicles based on least squares method. In this study, the power and angular velocity of wheels are measured from the vehicle directly. In addition to those, the directional velocity data is extracted from the GPS signal, in order to avoid the parameter dependency between the angula...
Control Strategy of Permanent Magnet Synchronous Generator for Stand Alone Wind Power Generation System
Lachguer, Nora; Tahar Lamchich, Moulay (2011-09-10)
This paper presents the control strategy of the permanent magnet synchronous generator (PMSG) for stand-alone wind power generation system with battery energy storage management during wind speed and load variations. The complete system includes a PMSG connected to the load through a fully controlled back to back PWM converter and an intermediate DC link capacitor which is connected to a battery energy storage system through a bidirectional dc-dc converter. The control strategy to a stand-alone wind convers...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
S. İ. Güzel and M. Göl, “Plug-in electric vehicle load modeling for charging scheduling strategies in microgrids,”
SUSTAINABLE ENERGY GRIDS & NETWORKS
, vol. 32, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/99121.