Two-Stage Fuzzy Logic Inference Algorithm for Maximizing the Quality of Performance under the Operational Constraints of Power Grid in Electric Vehicle Parking Lots

The widespread adoption of electric vehicles (EVs) has entailed the need for the parking lot operators to satisfy the charging and discharging requirements of all the EV owners during their parking duration. Meanwhile, the operational constraints of the power grids limit the amount of simultaneous c...

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Vydáno v:Energies (Basel) Ročník 13; číslo 18; s. 4634
Hlavní autoři: Hussain, Shahid, Lee, Ki-Beom, A. Ahmed, Mohamed, Hayes, Barry, Kim, Young-Chon
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2020
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ISSN:1996-1073, 1996-1073
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Abstract The widespread adoption of electric vehicles (EVs) has entailed the need for the parking lot operators to satisfy the charging and discharging requirements of all the EV owners during their parking duration. Meanwhile, the operational constraints of the power grids limit the amount of simultaneous charging and discharging of all EVs. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE represents a certain percentage of the EV battery required for its next trip distance; whereas, the QoP refers to the ratio of EVs with satisfied QoE to the total number of EVs during the operational hours of the parking lot. This paper proposes a two-stage fuzzy logic inference based algorithm (TSFLIA) to schedule the charging and discharging operations of EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The first stage fuzzy inference system (FIS) of TSFLIA is modeled based on the real-time arrival and departure probability density functions in order to calculate the aggregated charging and discharging energies of EVs according to their next trip distances. The second stage FIS evaluates several dynamic and uncertain input parameters from the electric grid and from EVs to distribute the aggregated energy among the EVs by controlling their charging and discharging operations through preference variables. The feasibility and effectiveness of the proposed algorithm are demonstrated through the IEEE 34-node distribution system.
AbstractList The widespread adoption of electric vehicles (EVs) has entailed the need for the parking lot operators to satisfy the charging and discharging requirements of all the EV owners during their parking duration. Meanwhile, the operational constraints of the power grids limit the amount of simultaneous charging and discharging of all EVs. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE represents a certain percentage of the EV battery required for its next trip distance; whereas, the QoP refers to the ratio of EVs with satisfied QoE to the total number of EVs during the operational hours of the parking lot. This paper proposes a two-stage fuzzy logic inference based algorithm (TSFLIA) to schedule the charging and discharging operations of EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The first stage fuzzy inference system (FIS) of TSFLIA is modeled based on the real-time arrival and departure probability density functions in order to calculate the aggregated charging and discharging energies of EVs according to their next trip distances. The second stage FIS evaluates several dynamic and uncertain input parameters from the electric grid and from EVs to distribute the aggregated energy among the EVs by controlling their charging and discharging operations through preference variables. The feasibility and effectiveness of the proposed algorithm are demonstrated through the IEEE 34-node distribution system.
Author Hayes, Barry
Lee, Ki-Beom
Hussain, Shahid
A. Ahmed, Mohamed
Kim, Young-Chon
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  fullname: Kim, Young-Chon
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Snippet The widespread adoption of electric vehicles (EVs) has entailed the need for the parking lot operators to satisfy the charging and discharging requirements of...
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SubjectTerms Algorithms
Alternative energy
Electric vehicles
Electricity distribution
Fuzzy logic
fuzzy logic inference
parking lot
Preferences
quality of experience
quality of performance
Scheduling
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Title Two-Stage Fuzzy Logic Inference Algorithm for Maximizing the Quality of Performance under the Operational Constraints of Power Grid in Electric Vehicle Parking Lots
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