Smart Energy Management System for Residential Homes Regarding Uncertainties of Photovoltaic Array and Plug-in Electric Vehicle

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

Resumé

smart energy management approaches can improve the economy and performance of residential homes integrated with photovoltaic array (PV) and plug-in electric vehicle (PEV). The key novelty of this paper is improving the real-time operation of the smart home using advanced stochastic forecast techniques and stochastic control methods. In this paper, an optimal model predictive control is formulated for a smart home to minimize the electricity cost under time-varying electricity price signals. In addition, the PEV charging and home power demand requirements have to be satisfied in a smart and optimal way. Stochastic forecast model is developed for PV, home load demand and PEV to consider the effect of the different uncertainties on their performance. Furthermore, a fundamental trade-off between PEV lithium-ion battery aging and economic performance of the energy management system is implemented through an appropriate cost function formulation. In this paper, the EV departure time and required energy consumption during driving are modeled by Markov chain and conditional probability. In addition, 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 minimize the cost of energy as well as make increase the lifetime of the PEV battery by avoiding unnecessary charging/discharging schemes. The results demonstrate the effectiveness and enhancement of the proposed method.
OriginalsprogEngelsk
TitelIEEE proceeding : 28th International Symposium on Industrial Electronics (ISIE)
Antal sider6
Sider1-6
StatusAfsendt - 28 feb. 2019

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Energy management systems
Electricity
Model predictive control
Energy management
Fuzzy inference
Stochastic models
Cost functions
Markov processes
Costs
Energy utilization
Aging of materials
Uncertainty
Plug-in electric vehicles
Economics

Citer dette

Yousefi, M., Kianpoor, N., Hajizadeh, A., & N. Soltani, M. (2019). Smart Energy Management System for Residential Homes Regarding Uncertainties of Photovoltaic Array and Plug-in Electric Vehicle. Manuskript afsendt til publicering. I IEEE proceeding: 28th International Symposium on Industrial Electronics (ISIE) (s. 1-6)
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title = "Smart Energy Management System for Residential Homes Regarding Uncertainties of Photovoltaic Array and Plug-in Electric Vehicle",
abstract = "smart energy management approaches can improve the economy and performance of residential homes integrated with photovoltaic array (PV) and plug-in electric vehicle (PEV). The key novelty of this paper is improving the real-time operation of the smart home using advanced stochastic forecast techniques and stochastic control methods. In this paper, an optimal model predictive control is formulated for a smart home to minimize the electricity cost under time-varying electricity price signals. In addition, the PEV charging and home power demand requirements have to be satisfied in a smart and optimal way. Stochastic forecast model is developed for PV, home load demand and PEV to consider the effect of the different uncertainties on their performance. Furthermore, a fundamental trade-off between PEV lithium-ion battery aging and economic performance of the energy management system is implemented through an appropriate cost function formulation. In this paper, the EV departure time and required energy consumption during driving are modeled by Markov chain and conditional probability. In addition, 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 minimize the cost of energy as well as make increase the lifetime of the PEV battery by avoiding unnecessary charging/discharging schemes. The results demonstrate the effectiveness and enhancement of the proposed method.",
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year = "2019",
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Smart Energy Management System for Residential Homes Regarding Uncertainties of Photovoltaic Array and Plug-in Electric Vehicle. / Yousefi, Mojtaba; Kianpoor, Nasrin; Hajizadeh, Amin; N. Soltani, Mohsen.

IEEE proceeding: 28th International Symposium on Industrial Electronics (ISIE). 2019. s. 1-6.

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

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T1 - Smart Energy Management System for Residential Homes Regarding Uncertainties of Photovoltaic Array and Plug-in Electric Vehicle

AU - Yousefi, Mojtaba

AU - Kianpoor, Nasrin

AU - Hajizadeh, Amin

AU - N. Soltani, Mohsen

PY - 2019/2/28

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N2 - smart energy management approaches can improve the economy and performance of residential homes integrated with photovoltaic array (PV) and plug-in electric vehicle (PEV). The key novelty of this paper is improving the real-time operation of the smart home using advanced stochastic forecast techniques and stochastic control methods. In this paper, an optimal model predictive control is formulated for a smart home to minimize the electricity cost under time-varying electricity price signals. In addition, the PEV charging and home power demand requirements have to be satisfied in a smart and optimal way. Stochastic forecast model is developed for PV, home load demand and PEV to consider the effect of the different uncertainties on their performance. Furthermore, a fundamental trade-off between PEV lithium-ion battery aging and economic performance of the energy management system is implemented through an appropriate cost function formulation. In this paper, the EV departure time and required energy consumption during driving are modeled by Markov chain and conditional probability. In addition, 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 minimize the cost of energy as well as make increase the lifetime of the PEV battery by avoiding unnecessary charging/discharging schemes. The results demonstrate the effectiveness and enhancement of the proposed method.

AB - smart energy management approaches can improve the economy and performance of residential homes integrated with photovoltaic array (PV) and plug-in electric vehicle (PEV). The key novelty of this paper is improving the real-time operation of the smart home using advanced stochastic forecast techniques and stochastic control methods. In this paper, an optimal model predictive control is formulated for a smart home to minimize the electricity cost under time-varying electricity price signals. In addition, the PEV charging and home power demand requirements have to be satisfied in a smart and optimal way. Stochastic forecast model is developed for PV, home load demand and PEV to consider the effect of the different uncertainties on their performance. Furthermore, a fundamental trade-off between PEV lithium-ion battery aging and economic performance of the energy management system is implemented through an appropriate cost function formulation. In this paper, the EV departure time and required energy consumption during driving are modeled by Markov chain and conditional probability. In addition, 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 minimize the cost of energy as well as make increase the lifetime of the PEV battery by avoiding unnecessary charging/discharging schemes. The results demonstrate the effectiveness and enhancement of the proposed method.

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Yousefi M, Kianpoor N, Hajizadeh A, N. Soltani M. Smart Energy Management System for Residential Homes Regarding Uncertainties of Photovoltaic Array and Plug-in Electric Vehicle. I IEEE proceeding: 28th International Symposium on Industrial Electronics (ISIE). 2019. s. 1-6