Online coordination of plug-in electric vehicles considering grid congestion and smart grid power quality
© 2018 MDPI AG. All rights reserved. This paper first introduces the impacts of battery charger and nonlinear load harmonics on smart grids considering random plug-in of electric vehicles (PEVs) without any coordination. Then, a new centralized nonlinear online maximum sensitivity selection-based ch...
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| Format: | Journal Article |
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M D P I AG
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/72351 |
| _version_ | 1848762727448707072 |
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| author | Deilami, Sara |
| author_facet | Deilami, Sara |
| author_sort | Deilami, Sara |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2018 MDPI AG. All rights reserved. This paper first introduces the impacts of battery charger and nonlinear load harmonics on smart grids considering random plug-in of electric vehicles (PEVs) without any coordination. Then, a new centralized nonlinear online maximum sensitivity selection-based charging algorithm (NOL-MSSCA) is proposed for coordinating PEVs that minimizes the costs associated with generation and losses considering network and bus total harmonic distortion (THD). The aim is to first attend the high priority customers and charge their vehicles as quickly as possible while postponing the service to medium and low priority consumers to the off-peak hours, considering network, battery and power quality constraints and harmonics. The vehicles were randomly plugged at different locations during a period of 24 h. The proposed PEV coordination is based on the maximum sensitivity selection (MSS), which is the sensitivity of losses (including fundamental and harmonic losses) with respect to the PEV location (PEV bus). The proposed algorithm uses the decoupled harmonic power flow (DHPF) to model the nonlinear loads (including the PEV chargers) as current harmonic sources and computes the harmonic power losses, harmonic voltages and THD of the smart grid. The MSS vectors are easily determined using the entries of the Jacobian matrix of the DHPF program, which includes the spectrums of all injected harmonics by nonlinear electric vehicle (EV) chargers and nonlinear industrial loads. The sensitivity of the objective function (fundamental and harmonic power losses) to the PEVs were then used to schedule PEVs accordingly. The algorithm successfully controls the network THDv level within the standard limit of 5% for low and moderate PEV penetrations by delaying PEV charging activities. For high PEV penetrations, the installation of passive power filters (PPFs) is suggested to reduce the THDv and manage to fully charge the PEVs. Detailed simulations considering random and coordinated charging were performed on the modified IEEE 23 kV distribution system with 22 low voltage residential networks populated with PEVs that have nonlinear battery chargers. Simulation results are provided without/with filters for different penetration levels of PEVs. |
| first_indexed | 2025-11-14T10:52:10Z |
| format | Journal Article |
| id | curtin-20.500.11937-72351 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:52:10Z |
| publishDate | 2018 |
| publisher | M D P I AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-723512021-01-05T08:07:07Z Online coordination of plug-in electric vehicles considering grid congestion and smart grid power quality Deilami, Sara © 2018 MDPI AG. All rights reserved. This paper first introduces the impacts of battery charger and nonlinear load harmonics on smart grids considering random plug-in of electric vehicles (PEVs) without any coordination. Then, a new centralized nonlinear online maximum sensitivity selection-based charging algorithm (NOL-MSSCA) is proposed for coordinating PEVs that minimizes the costs associated with generation and losses considering network and bus total harmonic distortion (THD). The aim is to first attend the high priority customers and charge their vehicles as quickly as possible while postponing the service to medium and low priority consumers to the off-peak hours, considering network, battery and power quality constraints and harmonics. The vehicles were randomly plugged at different locations during a period of 24 h. The proposed PEV coordination is based on the maximum sensitivity selection (MSS), which is the sensitivity of losses (including fundamental and harmonic losses) with respect to the PEV location (PEV bus). The proposed algorithm uses the decoupled harmonic power flow (DHPF) to model the nonlinear loads (including the PEV chargers) as current harmonic sources and computes the harmonic power losses, harmonic voltages and THD of the smart grid. The MSS vectors are easily determined using the entries of the Jacobian matrix of the DHPF program, which includes the spectrums of all injected harmonics by nonlinear electric vehicle (EV) chargers and nonlinear industrial loads. The sensitivity of the objective function (fundamental and harmonic power losses) to the PEVs were then used to schedule PEVs accordingly. The algorithm successfully controls the network THDv level within the standard limit of 5% for low and moderate PEV penetrations by delaying PEV charging activities. For high PEV penetrations, the installation of passive power filters (PPFs) is suggested to reduce the THDv and manage to fully charge the PEVs. Detailed simulations considering random and coordinated charging were performed on the modified IEEE 23 kV distribution system with 22 low voltage residential networks populated with PEVs that have nonlinear battery chargers. Simulation results are provided without/with filters for different penetration levels of PEVs. 2018 Journal Article http://hdl.handle.net/20.500.11937/72351 10.3390/en11092187 http://creativecommons.org/licenses/by/4.0/ M D P I AG fulltext |
| spellingShingle | Deilami, Sara Online coordination of plug-in electric vehicles considering grid congestion and smart grid power quality |
| title | Online coordination of plug-in electric vehicles considering grid congestion and smart grid power quality |
| title_full | Online coordination of plug-in electric vehicles considering grid congestion and smart grid power quality |
| title_fullStr | Online coordination of plug-in electric vehicles considering grid congestion and smart grid power quality |
| title_full_unstemmed | Online coordination of plug-in electric vehicles considering grid congestion and smart grid power quality |
| title_short | Online coordination of plug-in electric vehicles considering grid congestion and smart grid power quality |
| title_sort | online coordination of plug-in electric vehicles considering grid congestion and smart grid power quality |
| url | http://hdl.handle.net/20.500.11937/72351 |