Stochastic Smart Charging of Electric Vehicles for Residential Homes with PV Integration

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1 Citation (Scopus)

Resumé

Using electric vehicles (EVs) in residential homes integrated with solar photovoltaic (PV) array can address many problems associated with environmental issues and energy demand. EVs are useful as long as they utilized with the smart energy system (SES) to optimally and smartly charge the EV batteries. In order to improve the SES performance for optimally and effectively charging the vehicle, the impact of uncertainties and random parameters have to be considered in the EV models. Furthermore, online control methods like model predictive control (MPC) should be used to eliminate the effect of the error uncertainties and random parameters model on the system performance during the actual operation. In this paper, the EV departure time and required energy consumption during driving are modeled by Markov chain and conditional probability. Also, the PV performance and home load demand are modeled by PVWatt model and adaptive neuro-fuzzy inference system (ANFIS) respectively. Afterward, an MPC is designed to optimally charge the EV, while maintaining the desired battery energy level. The simulation is performed and the results demonstrate the effectiveness and enhancement of the proposed method.
OriginalsprogEngelsk
TitelProceedings of 2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC)
Antal sider6
ForlagIEEE Press
Publikationsdatofeb. 2019
DOI
StatusUdgivet - feb. 2019
Begivenhed2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC) - Shiraz, Iran
Varighed: 12 feb. 201914 feb. 2019

Konference

Konference2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC)
LandIran
ByShiraz
Periode12/02/201914/02/2019

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Electric vehicles
Model predictive control
Fuzzy inference
Markov processes
Electron energy levels
Energy utilization
Uncertainty

Citer dette

Yousefi, M., Kianpoor, N., Hajizadeh, A., & N. Soltani, M. (2019). Stochastic Smart Charging of Electric Vehicles for Residential Homes with PV Integration. I Proceedings of 2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC) IEEE Press. https://doi.org/10.1109/PEDSTC.2019.8697231
Yousefi, Mojtaba ; Kianpoor, Nasrin ; Hajizadeh, Amin ; N. Soltani, Mohsen. / Stochastic Smart Charging of Electric Vehicles for Residential Homes with PV Integration. Proceedings of 2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC). IEEE Press, 2019.
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title = "Stochastic Smart Charging of Electric Vehicles for Residential Homes with PV Integration",
abstract = "Using electric vehicles (EVs) in residential homes integrated with solar photovoltaic (PV) array can address many problems associated with environmental issues and energy demand. EVs are useful as long as they utilized with the smart energy system (SES) to optimally and smartly charge the EV batteries. In order to improve the SES performance for optimally and effectively charging the vehicle, the impact of uncertainties and random parameters have to be considered in the EV models. Furthermore, online control methods like model predictive control (MPC) should be used to eliminate the effect of the error uncertainties and random parameters model on the system performance during the actual operation. In this paper, the EV departure time and required energy consumption during driving are modeled by Markov chain and conditional probability. Also, the PV performance and home load demand are modeled by PVWatt model and adaptive neuro-fuzzy inference system (ANFIS) respectively. Afterward, an MPC is designed to optimally charge the EV, while maintaining the desired battery energy level. The simulation is performed and the results demonstrate the effectiveness and enhancement of the proposed method.",
keywords = "Smart energy system, Stochastic Smart charging, Model predictive control, Actual operation",
author = "Mojtaba Yousefi and Nasrin Kianpoor and Amin Hajizadeh and {N. Soltani}, Mohsen",
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language = "English",
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Yousefi, M, Kianpoor, N, Hajizadeh, A & N. Soltani, M 2019, Stochastic Smart Charging of Electric Vehicles for Residential Homes with PV Integration. i Proceedings of 2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC). IEEE Press, Shiraz, Iran, 12/02/2019. https://doi.org/10.1109/PEDSTC.2019.8697231

Stochastic Smart Charging of Electric Vehicles for Residential Homes with PV Integration. / Yousefi, Mojtaba; Kianpoor, Nasrin; Hajizadeh, Amin; N. Soltani, Mohsen.

Proceedings of 2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC). IEEE Press, 2019.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - Stochastic Smart Charging of Electric Vehicles for Residential Homes with PV Integration

AU - Yousefi, Mojtaba

AU - Kianpoor, Nasrin

AU - Hajizadeh, Amin

AU - N. Soltani, Mohsen

PY - 2019/2

Y1 - 2019/2

N2 - Using electric vehicles (EVs) in residential homes integrated with solar photovoltaic (PV) array can address many problems associated with environmental issues and energy demand. EVs are useful as long as they utilized with the smart energy system (SES) to optimally and smartly charge the EV batteries. In order to improve the SES performance for optimally and effectively charging the vehicle, the impact of uncertainties and random parameters have to be considered in the EV models. Furthermore, online control methods like model predictive control (MPC) should be used to eliminate the effect of the error uncertainties and random parameters model on the system performance during the actual operation. In this paper, the EV departure time and required energy consumption during driving are modeled by Markov chain and conditional probability. Also, the PV performance and home load demand are modeled by PVWatt model and adaptive neuro-fuzzy inference system (ANFIS) respectively. Afterward, an MPC is designed to optimally charge the EV, while maintaining the desired battery energy level. The simulation is performed and the results demonstrate the effectiveness and enhancement of the proposed method.

AB - Using electric vehicles (EVs) in residential homes integrated with solar photovoltaic (PV) array can address many problems associated with environmental issues and energy demand. EVs are useful as long as they utilized with the smart energy system (SES) to optimally and smartly charge the EV batteries. In order to improve the SES performance for optimally and effectively charging the vehicle, the impact of uncertainties and random parameters have to be considered in the EV models. Furthermore, online control methods like model predictive control (MPC) should be used to eliminate the effect of the error uncertainties and random parameters model on the system performance during the actual operation. In this paper, the EV departure time and required energy consumption during driving are modeled by Markov chain and conditional probability. Also, the PV performance and home load demand are modeled by PVWatt model and adaptive neuro-fuzzy inference system (ANFIS) respectively. Afterward, an MPC is designed to optimally charge the EV, while maintaining the desired battery energy level. The simulation is performed and the results demonstrate the effectiveness and enhancement of the proposed method.

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KW - Model predictive control

KW - Actual operation

U2 - 10.1109/PEDSTC.2019.8697231

DO - 10.1109/PEDSTC.2019.8697231

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Yousefi M, Kianpoor N, Hajizadeh A, N. Soltani M. Stochastic Smart Charging of Electric Vehicles for Residential Homes with PV Integration. I Proceedings of 2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC). IEEE Press. 2019 https://doi.org/10.1109/PEDSTC.2019.8697231