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Jia Ying Yong - One of the best experts on this subject based on the ideXlab platform.
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Experimental Verification of Vehicle-to-Grid Charger for Demand Response Service
2019 International Conference on Electrical Electronics and Computer Engineering (UPCON), 2019Co-Authors: Kang Miao Tan, Jia Ying Yong, Vigna K. Ramachandaramurthy, Mohd TariqAbstract:This paper introduces a Vehicle-to-Grid charger and a Vehicle-to-Grid energy management algorithm for the power Grid demand response application. The Vehicle-to-Grid charger is designed to allow bidirectional power flow according to power demand. This charger is capable of adjusting the amount of power absorbed from and supplied to the Grid, while prioritizing the battery health and safety. Moreover, the proposed charger is utilized in the Vehicle-to-Grid energy management algorithm to integrate electric Vehicles to achieve demand response objectives. The Vehicle-to-Grid energy management algorithm controls the power flow of each Grid-connected electric Vehicle to achieve peak load shaving and load leveling services, while ensuring the electric Vehicle users' benefits by preventing over charging, over discharging and over battery depletion. A laboratory experiment was conducted to examine the feasibility of the Vehicle-to-Grid system. The experimental results showed that the Vehicle-to-Grid charger has accurately followed the dual directional power demand instructed by the energy management algorithm to realize the demand response services.
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Coordinated Vehicle-to-Grid Scheduling to Minimize Grid Load Variance
2019 International Conference on Electrical Electronics and Computer Engineering (UPCON), 2019Co-Authors: Mohd Syahmi Hashim, Jia Ying Yong, Kang Miao Tan, Vigna K. Ramachandaramurthy, Mohd TariqAbstract:This paper presents the Vehicle-to-Grid scheduling algorithm to minimize the Grid load variance by utilizing the Grid-connected electric Vehicle battery. The algorithm performs in two modes, which are load leveling and peak load shaving. In the load leveling mode, the Grid-connected electric Vehicle is charged from the power Grid and hence, increase the Grid loading. Meanwhile, the Grid loading is reduced in peak load shaving mode since electric Vehicle discharges energy from the battery to support the power Grid. Various constraints have been considered to ensure the practicality of this study. The Vehicle-to-Grid study was implemented in a commercial-residential area with electric Vehicle mobility of 1300. Both uncoordinated charging and coordinated Vehicle-to-Grid scheduling were performed and compared. The results showed that the uncoordinated charging of electric Vehicle will induce a new peak in the power Grid load profile. On the other hand, the results showed that the proposed coordinated Vehicle-to-Grid scheduling algorithm successfully minimized the Grid load variance while satisfying all the constraints and power Grid requirements.
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Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling
Energies, 2017Co-Authors: Kang Miao Tan, Jia Ying Yong, Lucian Mihet-popa, Sanjeevikumar Padmanaban, Vigna K. Ramachandaramurthy, Frede BlaabjergAbstract:The introduction of electric Vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric Vehicles and the power Grid has spurred the emergence of a smart Grid technology, denoted as Vehicle-to Grid-technology. Vehicle-to-Grid technology manages the energy exchange between a large fleet of electric Vehicles and the power Grid to accomplish shared advantages for the Vehicle owners and the power utility. This paper presents an optimal scheduling of Vehicle-to-Grid using the genetic algorithm to minimize the power Grid load variance. This is achieved by allowing electric Vehicles charging (Grid-to-Vehicle) whenever the actual power Grid loading is lower than the target loading, while conducting electric Vehicle discharging (Vehicle-to-Grid) whenever the actual power Grid loading is higher than the target loading. The Vehicle-to-Grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power Grid load and capability of the Grid-connected electric Vehicles. Hence, the performance of the proposed algorithm under various target load and electric Vehicles’ state of charge selections were analysed. The effectiveness of the Vehicle-to-Grid scheduling to implement the appropriate peak load shaving and load levelling services for the Grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric Vehicle historical data.
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minimization of load variance in power Grids investigation on optimal Vehicle to Grid scheduling
Energies, 2017Co-Authors: Kang Miao Tan, Jia Ying Yong, Sanjeevikumar Padmanaban, Vigna K. Ramachandaramurthy, Lucian Mihetpopa, Frede BlaabjergAbstract:The introduction of electric Vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric Vehicles and the power Grid has spurred the emergence of a smart Grid technology, denoted as Vehicle-to Grid-technology. Vehicle-to-Grid technology manages the energy exchange between a large fleet of electric Vehicles and the power Grid to accomplish shared advantages for the Vehicle owners and the power utility. This paper presents an optimal scheduling of Vehicle-to-Grid using the genetic algorithm to minimize the power Grid load variance. This is achieved by allowing electric Vehicles charging (Grid-to-Vehicle) whenever the actual power Grid loading is lower than the target loading, while conducting electric Vehicle discharging (Vehicle-to-Grid) whenever the actual power Grid loading is higher than the target loading. The Vehicle-to-Grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power Grid load and capability of the Grid-connected electric Vehicles. Hence, the performance of the proposed algorithm under various target load and electric Vehicles’ state of charge selections were analysed. The effectiveness of the Vehicle-to-Grid scheduling to implement the appropriate peak load shaving and load levelling services for the Grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric Vehicle historical data.
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Integration of electric Vehicles in smart Grid: A review on Vehicle to Grid technologies and optimization techniques
Renewable and Sustainable Energy Reviews, 2016Co-Authors: Kang Miao Tan, Vigna K. Ramachandaramurthy, Jia Ying YongAbstract:Energy crisis and environmental issues have encouraged the adoption of electric Vehicle as an alternative transportation option to the conventional internal combustion engine Vehicle. Recently, the development of smart Grid concept in power Grid has advanced the role of electric Vehicles in the form of Vehicle to Grid technology. Vehicle to Grid technology allows bidirectional energy exchange between electric Vehicles and the power Grid, which offers numerous services to the power Grid, such as power Grid regulation, spinning reserve, peak load shaving, load leveling and reactive power compensation. As the implementation of Vehicle to Grid technology is a complicated unit commitment problem with different conflicting objectives and constraints, optimization techniques are usually utilized. This paper reviews the framework, benefits and challenges of Vehicle to Grid technology. This paper also summarizes the main optimization techniques to achieve different Vehicle to Grid objectives while satisfying multiple constraints.
Kang Miao Tan - One of the best experts on this subject based on the ideXlab platform.
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Experimental Verification of Vehicle-to-Grid Charger for Demand Response Service
2019 International Conference on Electrical Electronics and Computer Engineering (UPCON), 2019Co-Authors: Kang Miao Tan, Jia Ying Yong, Vigna K. Ramachandaramurthy, Mohd TariqAbstract:This paper introduces a Vehicle-to-Grid charger and a Vehicle-to-Grid energy management algorithm for the power Grid demand response application. The Vehicle-to-Grid charger is designed to allow bidirectional power flow according to power demand. This charger is capable of adjusting the amount of power absorbed from and supplied to the Grid, while prioritizing the battery health and safety. Moreover, the proposed charger is utilized in the Vehicle-to-Grid energy management algorithm to integrate electric Vehicles to achieve demand response objectives. The Vehicle-to-Grid energy management algorithm controls the power flow of each Grid-connected electric Vehicle to achieve peak load shaving and load leveling services, while ensuring the electric Vehicle users' benefits by preventing over charging, over discharging and over battery depletion. A laboratory experiment was conducted to examine the feasibility of the Vehicle-to-Grid system. The experimental results showed that the Vehicle-to-Grid charger has accurately followed the dual directional power demand instructed by the energy management algorithm to realize the demand response services.
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Coordinated Vehicle-to-Grid Scheduling to Minimize Grid Load Variance
2019 International Conference on Electrical Electronics and Computer Engineering (UPCON), 2019Co-Authors: Mohd Syahmi Hashim, Jia Ying Yong, Kang Miao Tan, Vigna K. Ramachandaramurthy, Mohd TariqAbstract:This paper presents the Vehicle-to-Grid scheduling algorithm to minimize the Grid load variance by utilizing the Grid-connected electric Vehicle battery. The algorithm performs in two modes, which are load leveling and peak load shaving. In the load leveling mode, the Grid-connected electric Vehicle is charged from the power Grid and hence, increase the Grid loading. Meanwhile, the Grid loading is reduced in peak load shaving mode since electric Vehicle discharges energy from the battery to support the power Grid. Various constraints have been considered to ensure the practicality of this study. The Vehicle-to-Grid study was implemented in a commercial-residential area with electric Vehicle mobility of 1300. Both uncoordinated charging and coordinated Vehicle-to-Grid scheduling were performed and compared. The results showed that the uncoordinated charging of electric Vehicle will induce a new peak in the power Grid load profile. On the other hand, the results showed that the proposed coordinated Vehicle-to-Grid scheduling algorithm successfully minimized the Grid load variance while satisfying all the constraints and power Grid requirements.
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Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling
Energies, 2017Co-Authors: Kang Miao Tan, Jia Ying Yong, Lucian Mihet-popa, Sanjeevikumar Padmanaban, Vigna K. Ramachandaramurthy, Frede BlaabjergAbstract:The introduction of electric Vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric Vehicles and the power Grid has spurred the emergence of a smart Grid technology, denoted as Vehicle-to Grid-technology. Vehicle-to-Grid technology manages the energy exchange between a large fleet of electric Vehicles and the power Grid to accomplish shared advantages for the Vehicle owners and the power utility. This paper presents an optimal scheduling of Vehicle-to-Grid using the genetic algorithm to minimize the power Grid load variance. This is achieved by allowing electric Vehicles charging (Grid-to-Vehicle) whenever the actual power Grid loading is lower than the target loading, while conducting electric Vehicle discharging (Vehicle-to-Grid) whenever the actual power Grid loading is higher than the target loading. The Vehicle-to-Grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power Grid load and capability of the Grid-connected electric Vehicles. Hence, the performance of the proposed algorithm under various target load and electric Vehicles’ state of charge selections were analysed. The effectiveness of the Vehicle-to-Grid scheduling to implement the appropriate peak load shaving and load levelling services for the Grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric Vehicle historical data.
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minimization of load variance in power Grids investigation on optimal Vehicle to Grid scheduling
Energies, 2017Co-Authors: Kang Miao Tan, Jia Ying Yong, Sanjeevikumar Padmanaban, Vigna K. Ramachandaramurthy, Lucian Mihetpopa, Frede BlaabjergAbstract:The introduction of electric Vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric Vehicles and the power Grid has spurred the emergence of a smart Grid technology, denoted as Vehicle-to Grid-technology. Vehicle-to-Grid technology manages the energy exchange between a large fleet of electric Vehicles and the power Grid to accomplish shared advantages for the Vehicle owners and the power utility. This paper presents an optimal scheduling of Vehicle-to-Grid using the genetic algorithm to minimize the power Grid load variance. This is achieved by allowing electric Vehicles charging (Grid-to-Vehicle) whenever the actual power Grid loading is lower than the target loading, while conducting electric Vehicle discharging (Vehicle-to-Grid) whenever the actual power Grid loading is higher than the target loading. The Vehicle-to-Grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power Grid load and capability of the Grid-connected electric Vehicles. Hence, the performance of the proposed algorithm under various target load and electric Vehicles’ state of charge selections were analysed. The effectiveness of the Vehicle-to-Grid scheduling to implement the appropriate peak load shaving and load levelling services for the Grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric Vehicle historical data.
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Integration of electric Vehicles in smart Grid: A review on Vehicle to Grid technologies and optimization techniques
Renewable and Sustainable Energy Reviews, 2016Co-Authors: Kang Miao Tan, Vigna K. Ramachandaramurthy, Jia Ying YongAbstract:Energy crisis and environmental issues have encouraged the adoption of electric Vehicle as an alternative transportation option to the conventional internal combustion engine Vehicle. Recently, the development of smart Grid concept in power Grid has advanced the role of electric Vehicles in the form of Vehicle to Grid technology. Vehicle to Grid technology allows bidirectional energy exchange between electric Vehicles and the power Grid, which offers numerous services to the power Grid, such as power Grid regulation, spinning reserve, peak load shaving, load leveling and reactive power compensation. As the implementation of Vehicle to Grid technology is a complicated unit commitment problem with different conflicting objectives and constraints, optimization techniques are usually utilized. This paper reviews the framework, benefits and challenges of Vehicle to Grid technology. This paper also summarizes the main optimization techniques to achieve different Vehicle to Grid objectives while satisfying multiple constraints.
Mohsen Guizani - One of the best experts on this subject based on the ideXlab platform.
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Securing Vehicle-to-Grid communications in the smart Grid
IEEE Wireless Communications, 2013Co-Authors: Yan Zhang, Laurence Yang, Stein Gjessing, Huansheng Ning, Hong Liu, Mohsen GuizaniAbstract:Using Vehicle-to-Grid (V2G) services, battery Vehicles (BVs) may help the smart Grid alleviate peaks in power consumption. However, wireless communications infrastructure between BVs and the smart Grid also introduce severe and unprecedented security vulnerabilities. In this article, we discuss V2G network architectures and present state-of-the-art security, including different security challenges during V2G power and communications interactions. Then we report on our context-aware authentication solution for V2G communications in the smart Grid. Finally, we describe several open issues for secure V2G networks.
Omer Tatari - One of the best experts on this subject based on the ideXlab platform.
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boosting the adoption and the reliability of renewable energy sources mitigating the large scale wind power intermittency through Vehicle to Grid technology
Energy, 2017Co-Authors: Yang Lu-zhao, Mehdi Noori, Omer TatariAbstract:The integration of wind energy in the electricity sector and the adoption of electric Vehicles in the transportation sector both have the potential to significantly reduce greenhouse gas emissions individually as well as in tandem with Vehicle-to-Grid technology. This study aims to evaluate the greenhouse gas emission savings of mitigating intermittency resulting from the introduction of wind power through Vehicle-to-Grid technologies, as well as the extent to which the marginal electricity consumption from charging an electric Vehicle fleet may weaken this overall environmental benefit. to this end, the comparisons are conducted in seven independent system operator regions. The results indicate that, in most cases, the emission savings of a combination of wind power and Vehicle-to-Grid technology outweighs the additional emissions from marginal electricity generation for electric Vehicles. In addition, the fluctuations in newly-integrated wind power could be balanced in the future using EVs and V2G technology, provided that a moderate portion of EV owners is willing to provide V2G services. On the other hand, such a combination is not favorable if the Vehicle-to-Grid service participation rate is less than 5% of all electric Vehicle owners within a particular region.
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Boosting the adoption and the reliability of renewable energy sources: Mitigating the large-scale wind power intermittency through Vehicle to Grid technology
Energy, 2017Co-Authors: Yang Lu-zhao, Mehdi Noori, Omer TatariAbstract:The integration of wind energy in the electricity sector and the adoption of electric Vehicles in the transportation sector both have the potential to significantly reduce greenhouse gas emissions individually as well as in tandem with Vehicle-to-Grid technology. This study aims to evaluate the greenhouse gas emission savings of mitigating intermittency resulting from the introduction of wind power through Vehicle-to-Grid technologies, as well as the extent to which the marginal electricity consumption from charging an electric Vehicle fleet may weaken this overall environmental benefit. to this end, the comparisons are conducted in seven independent system operator regions. The results indicate that, in most cases, the emission savings of a combination of wind power and Vehicle-to-Grid technology outweighs the additional emissions from marginal electricity generation for electric Vehicles. In addition, the fluctuations in newly-integrated wind power could be balanced in the future using EVs and V2G technology, provided that a moderate portion of EV owners is willing to provide V2G services. On the other hand, such a combination is not favorable if the Vehicle-to-Grid service participation rate is less than 5% of all electric Vehicle owners within a particular region.
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Light-duty electric Vehicles to improve the integrity of the electricity Grid through Vehicle-to-Grid technology: Analysis of regional net revenue and emissions savings
Applied Energy, 2016Co-Authors: Mohammed Noori, Stephanie Gardner, Nuri Cihat Onat, Yang Lu-zhao, Omer TatariAbstract:Vehicle to Grid technologies utilize idle EV battery power as a Grid storage tool to meet fluctuating electric power demands. Vehicle to Grid systems are promising substitutes for traditional gas turbine generators, which are relatively inefficient and have high emissions impacts. The purpose of this study is to predict the future net revenue and life cycle emissions savings of Vehicle to Grid technologies for use in ancillary (regulation) services on a regional basis in the United States. In this paper, the emissions savings and net revenue calculations are conducted with respect to five different Independent System Operator/Regional Transmission Organization regions, after which future EV market penetration rates are predicted using an Agent-Based Model designed to account for various uncertainties, including regulation service payments, regulation signal features, and battery degradation. Finally, the concept of Exploratory Modeling and Analysis is used to estimate the future net revenue and emissions savings of integrating Vehicle to Grid technology into the Grid, considering the inherent uncertainties of the system. The results indicate that, for a single Vehicle, the net revenue of Vehicle to Grid services is highest for the New York region, which is approximately $42,000 per Vehicle on average. However, the PJM region has an approximately $97 million overall net revenue potential, given the 38,200 Vehicle to Grid-service-available electric Vehicles estimated to be on the road in the future in the PJM region, which is the highest among the studied regions.
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A hybrid life cycle assessment of the Vehicle-to-Grid application in light duty commercial fleet
Energy, 2015Co-Authors: Yang Lu-zhao, Omer TatariAbstract:The Vehicle-to-Grid system is an approach utilizing the idle battery capacity of electric Vehicles while they are parked to provide supplementary energy to the power Grid. As electrification continues in light duty Vehicle fleets, the application of Vehicle-to-Grid systems for commercial delivery truck fleets can provide extra revenue for fleet owners, and also has significant potential for reducing greenhouse gas emissions from the electricity generation sector. In this study, an economic input-output based hybrid life cycle assessment is conducted to analyze the potential greenhouse gas emissions emission savings from the use of the Vehicle-to-Grid system, as well as the possible emission impacts caused by battery degradation. A Monte Carlo simulation was performed to address the uncertainties that lie in the electricity exchange amount of the Vehicle-to-Grid service as well as the battery life of the electric Vehicles. The results of this study showed that extended range electric Vehicles and battery electric Vehicles are both viable regulation service providers for saving greenhouse gas emissions from electricity generation if the battery wear-out from regulation services is assumed to be minimal, but the Vehicle-to-Grid system becomes less attractive at higher battery degradation levels.
Vigna K. Ramachandaramurthy - One of the best experts on this subject based on the ideXlab platform.
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Experimental Verification of Vehicle-to-Grid Charger for Demand Response Service
2019 International Conference on Electrical Electronics and Computer Engineering (UPCON), 2019Co-Authors: Kang Miao Tan, Jia Ying Yong, Vigna K. Ramachandaramurthy, Mohd TariqAbstract:This paper introduces a Vehicle-to-Grid charger and a Vehicle-to-Grid energy management algorithm for the power Grid demand response application. The Vehicle-to-Grid charger is designed to allow bidirectional power flow according to power demand. This charger is capable of adjusting the amount of power absorbed from and supplied to the Grid, while prioritizing the battery health and safety. Moreover, the proposed charger is utilized in the Vehicle-to-Grid energy management algorithm to integrate electric Vehicles to achieve demand response objectives. The Vehicle-to-Grid energy management algorithm controls the power flow of each Grid-connected electric Vehicle to achieve peak load shaving and load leveling services, while ensuring the electric Vehicle users' benefits by preventing over charging, over discharging and over battery depletion. A laboratory experiment was conducted to examine the feasibility of the Vehicle-to-Grid system. The experimental results showed that the Vehicle-to-Grid charger has accurately followed the dual directional power demand instructed by the energy management algorithm to realize the demand response services.
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Coordinated Vehicle-to-Grid Scheduling to Minimize Grid Load Variance
2019 International Conference on Electrical Electronics and Computer Engineering (UPCON), 2019Co-Authors: Mohd Syahmi Hashim, Jia Ying Yong, Kang Miao Tan, Vigna K. Ramachandaramurthy, Mohd TariqAbstract:This paper presents the Vehicle-to-Grid scheduling algorithm to minimize the Grid load variance by utilizing the Grid-connected electric Vehicle battery. The algorithm performs in two modes, which are load leveling and peak load shaving. In the load leveling mode, the Grid-connected electric Vehicle is charged from the power Grid and hence, increase the Grid loading. Meanwhile, the Grid loading is reduced in peak load shaving mode since electric Vehicle discharges energy from the battery to support the power Grid. Various constraints have been considered to ensure the practicality of this study. The Vehicle-to-Grid study was implemented in a commercial-residential area with electric Vehicle mobility of 1300. Both uncoordinated charging and coordinated Vehicle-to-Grid scheduling were performed and compared. The results showed that the uncoordinated charging of electric Vehicle will induce a new peak in the power Grid load profile. On the other hand, the results showed that the proposed coordinated Vehicle-to-Grid scheduling algorithm successfully minimized the Grid load variance while satisfying all the constraints and power Grid requirements.
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Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling
Energies, 2017Co-Authors: Kang Miao Tan, Jia Ying Yong, Lucian Mihet-popa, Sanjeevikumar Padmanaban, Vigna K. Ramachandaramurthy, Frede BlaabjergAbstract:The introduction of electric Vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric Vehicles and the power Grid has spurred the emergence of a smart Grid technology, denoted as Vehicle-to Grid-technology. Vehicle-to-Grid technology manages the energy exchange between a large fleet of electric Vehicles and the power Grid to accomplish shared advantages for the Vehicle owners and the power utility. This paper presents an optimal scheduling of Vehicle-to-Grid using the genetic algorithm to minimize the power Grid load variance. This is achieved by allowing electric Vehicles charging (Grid-to-Vehicle) whenever the actual power Grid loading is lower than the target loading, while conducting electric Vehicle discharging (Vehicle-to-Grid) whenever the actual power Grid loading is higher than the target loading. The Vehicle-to-Grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power Grid load and capability of the Grid-connected electric Vehicles. Hence, the performance of the proposed algorithm under various target load and electric Vehicles’ state of charge selections were analysed. The effectiveness of the Vehicle-to-Grid scheduling to implement the appropriate peak load shaving and load levelling services for the Grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric Vehicle historical data.
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minimization of load variance in power Grids investigation on optimal Vehicle to Grid scheduling
Energies, 2017Co-Authors: Kang Miao Tan, Jia Ying Yong, Sanjeevikumar Padmanaban, Vigna K. Ramachandaramurthy, Lucian Mihetpopa, Frede BlaabjergAbstract:The introduction of electric Vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric Vehicles and the power Grid has spurred the emergence of a smart Grid technology, denoted as Vehicle-to Grid-technology. Vehicle-to-Grid technology manages the energy exchange between a large fleet of electric Vehicles and the power Grid to accomplish shared advantages for the Vehicle owners and the power utility. This paper presents an optimal scheduling of Vehicle-to-Grid using the genetic algorithm to minimize the power Grid load variance. This is achieved by allowing electric Vehicles charging (Grid-to-Vehicle) whenever the actual power Grid loading is lower than the target loading, while conducting electric Vehicle discharging (Vehicle-to-Grid) whenever the actual power Grid loading is higher than the target loading. The Vehicle-to-Grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power Grid load and capability of the Grid-connected electric Vehicles. Hence, the performance of the proposed algorithm under various target load and electric Vehicles’ state of charge selections were analysed. The effectiveness of the Vehicle-to-Grid scheduling to implement the appropriate peak load shaving and load levelling services for the Grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric Vehicle historical data.
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Integration of electric Vehicles in smart Grid: A review on Vehicle to Grid technologies and optimization techniques
Renewable and Sustainable Energy Reviews, 2016Co-Authors: Kang Miao Tan, Vigna K. Ramachandaramurthy, Jia Ying YongAbstract:Energy crisis and environmental issues have encouraged the adoption of electric Vehicle as an alternative transportation option to the conventional internal combustion engine Vehicle. Recently, the development of smart Grid concept in power Grid has advanced the role of electric Vehicles in the form of Vehicle to Grid technology. Vehicle to Grid technology allows bidirectional energy exchange between electric Vehicles and the power Grid, which offers numerous services to the power Grid, such as power Grid regulation, spinning reserve, peak load shaving, load leveling and reactive power compensation. As the implementation of Vehicle to Grid technology is a complicated unit commitment problem with different conflicting objectives and constraints, optimization techniques are usually utilized. This paper reviews the framework, benefits and challenges of Vehicle to Grid technology. This paper also summarizes the main optimization techniques to achieve different Vehicle to Grid objectives while satisfying multiple constraints.
