A Survey on the Electrification of Transportation in a Smart Grid Environment

Su, Wencong; Rahimi-Eichi, Habiballah; Zeng, Wente; Chow, Mo-Yuen

doi:10.1109/tii.2011.2172454

Cited by 689 publications

(332 citation statements)

References 68 publications

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“…Many countries are making great efforts to increase the penetration of EVs into the market [4]. The Chinese government has also laid down policies such as "The 12th Five-Year Plan on Developing EV Technology" to promote the EV industrialization and improve the integration of EVs into power systems.…”

Section: Introductionmentioning

confidence: 99%

Integrated Energy Exchange Scheduling for Multimicrogrid System With Electric Vehicles

Wang

Guan

et al. 2016

IEEE Trans. Smart Grid

149

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Abstract-Electric vehicles (EVs) can be considered as flexible mobile battery storages in microgrids. For multiple microgrids in an area, coordinated scheduling on charging and discharging are required to avoid power exchange spikes between the multimicrogrid system and the main grid. In this paper, a two-stage integrated energy exchange scheduling strategy for multimicrogrid system is presented, which considers EVs as storage devices. Then several dual variables, which are representative of the marginal cost of proper constraints, are utilized to form an updated price, thereby being a modification on the original electricity price. With this updated price signal, a price-based decentralized scheduling strategy is presented for the Microgrid Central Controller (MGCC). Simulation results show that the two-stage scheduling strategy reduces the electricity cost and avoids frequent transitions between battery charging/discharging states. With the proposed decentralized scheduling strategy, each microgrid only needs to solve its local problem and limits the total power exchange within the safe range.Index Terms--Microgrid, electric vehicle, energy exchange, dual variable, updated price signal, decentralized scheduling strategy. I. NOMENCLATURE Index:i Index for microgrid,, ( ) i s B t Lower bound of the aggregate batteries' remaining energy in microgrid i during t in scenario s (kWh).Upper bound of the aggregate batteries' remaining energy in microgrid i during t in scenario s (kWh). ( ) C tOriginal electricity price during t (CNY/kWh).

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Section: Introductionmentioning

confidence: 99%

Integrated Energy Exchange Scheduling for Multimicrogrid System With Electric Vehicles

Wang

Guan

et al. 2016

IEEE Trans. Smart Grid

149

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“…The power network has as goal to distribute electricity to all users. Due to the excessive increasing of the electricity demand caused by diverse factors such as the birth of new and diverse consumers like the EVs, the smart grid has to promote its capacity and increase its production by deploying decentralized and fluctuant energy sources such as wind and solar power [8][9][10]. All this requires a new and complete vision of the electricity network to quickly overcome peak demand of electricity and intermittency of energy sources.…”

Section: Smart Grid Architecturementioning

confidence: 99%

Bayesian game model to analyze electric vehicles and smart grid interaction

Jahromi¹,

Hamedi²,

Ahmadi³

et al. 2018

SGCE

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In this paper, a novel Bayesian game model is developed to estimate the behavior of the Electric Vehicles (EVs) and smart grid. The proposed model focus on EVs as consumers and how to combine them efficiently into the smart grid. We first briefly introduce the smart grid and its structure. Then, the proposed model is presented and the effect of different variables such as load demand, energy price, and battery charge are investigated. Finally, the simulation is carried out on a test case and the obtained results demonstrate that our models help to efficiently combine the EVs into the smart grid.

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“…Some papers [30,83,86] propose DLC methods that centrally control each appliance. These methods decrease the security and comfort of customers, and based on the authors' opinion, it is another reason that discourages the customers' participation in DSM.…”

Section: Decision Authority Of Customers (D2)mentioning

confidence: 99%

“…Here, each EV has a bidirectional charger, which can charge the battery from the grid or discharge the battery for supplying power to the grid. Since vehicles are in use for only about 4% of the time and parked during the other 96% of the time [30], a bidirectional charger and EVs' batteries can provide almost all the benefits of ESSs for the power grid. Therefore, this new extra load can rather benefit the SG.…”

Section: Vehicle To Grid (V2g)mentioning

confidence: 99%

Electrical Market Management Considering Power System Constraints in Smart Distribution Grids

Divshali

Choi

2016

Energies

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Rising demand, climate change, growing fuel costs, outdated power system infrastructures, and new power generation technologies have made renewable distribution generators very attractive in recent years. Because of the increasing penetration level of renewable energy sources in addition to the growth of new electrical demand sectors, such as electrical vehicles, the power system may face serious problems and challenges in the near future. A revolutionary new power grid system, called smart grid, has been developed as a solution to these problems. The smart grid, equipped with modern communication and computation infrastructures, can coordinate different parts of the power system to enhance energy efficiency, reliability, and quality, while decreasing the energy cost. Since conventional distribution networks lack smart infrastructures, much research has been recently done in the distribution part of the smart grid, called smart distribution grid (SDG). This paper surveys contemporary literature in SDG from the perspective of the electricity market in addition to power system considerations. For this purpose, this paper reviews current demand side management methods, supply side management methods, and electrical vehicle charging and discharging techniques in SDG and also discusses their drawbacks. We also present future research directions to tackle new and existing challenges in the SDG.

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A Survey on the Electrification of Transportation in a Smart Grid Environment

Cited by 689 publications

References 68 publications

Integrated Energy Exchange Scheduling for Multimicrogrid System With Electric Vehicles

Integrated Energy Exchange Scheduling for Multimicrogrid System With Electric Vehicles

Bayesian game model to analyze electric vehicles and smart grid interaction

Electrical Market Management Considering Power System Constraints in Smart Distribution Grids

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