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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of 2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC)
Number of pages6
PublisherIEEE Press
Publication dateFeb 2019
DOIs
Publication statusPublished - Feb 2019
Event2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC) - Shiraz, Iran, Islamic Republic of
Duration: 12 Feb 201914 Feb 2019

Conference

Conference2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC)
CountryIran, Islamic Republic of
CityShiraz
Period12/02/201914/02/2019

Fingerprint

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

Keywords

  • Smart energy system
  • Stochastic Smart charging
  • Model predictive control
  • Actual operation

Cite this

Yousefi, M., Kianpoor, N., Hajizadeh, A., & N. Soltani, M. (2019). Stochastic Smart Charging of Electric Vehicles for Residential Homes with PV Integration. In 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.",
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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. in Proceedings of 2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC). IEEE Press, 2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC), Shiraz, Iran, Islamic Republic of, 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.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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