PEV-based combined frequency and voltage regulation for smart grid

Wu, Chenye; Mohsenian‐Rad, Hamed; Huang, Jianwei; Jatskevich, Juri

doi:10.1109/isgt.2012.6175758

Cited by 54 publications

(39 citation statements)

References 31 publications

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“…Yes [26,[38][39][40] introduce 1 million EVs and 1 million heat pumps to reduce the excess of energy by 30% from a hypothetical scenario of 30 GW of solar capacity in the area. Overall, the literature examines the extent of renewables integration, namely wind and PV energy, which EVs can accommodate in the power system in order to reduce CO 2 emissions.…”

Section: Referencesmentioning

confidence: 99%

“…In terms of voltage regulation, this objective is achieved by proper scheduling of the reactive power or by controlling the load demand in order to reduce the voltage drop. Wu et al [38] examined the potential of a proper selection of current phase angle by EV charger in order to compensate capacitive and inductive reactive power. Leemput et al [39] discussed the impact of the reactive support by EVs charging in a low-voltage residential distribution network.…”

Section: Non-spinning Reservementioning

confidence: 99%

See 1 more Smart Citation

Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects

Hu¹,

Morais²,

Sousa

et al. 2016

Renewable and Sustainable Energy Reviews

370

197

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a b s t r a c tElectric vehicles can become integral parts of a smart grid, since they are capable of providing valuable services to power systems other than just consuming power. On the transmission system level, electric vehicles are regarded as an important means of balancing the intermittent renewable energy resources such as wind power. This is because electric vehicles can be used to absorb the energy during the period of high electricity penetration and feed the electricity back into the grid when the demand is high or in situations of insufficient electricity generation. However, on the distribution system level, the extra loads created by the increasing number of electric vehicles may have adverse impacts on grid. These factors bring new challenges to the power system operators. To coordinate the interests and solve the conflicts, electric vehicle fleet operators are proposed both by academics and industries. This paper presents a review and classification of methods for smart charging (including power to vehicle and vehicle-to-grid) of electric vehicles for fleet operators. The study firstly presents service relationships between fleet operators and other four actors in smart grids; then, modeling of battery dynamics and driving patterns of electric vehicles, charging and communications standards are introduced; after that, three control strategies and their commonly used algorithms are described; finally, conclusion and recommendations are made.

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Section: Referencesmentioning

confidence: 99%

Section: Non-spinning Reservementioning

confidence: 99%

Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects

Hu¹,

Morais²,

Sousa

et al. 2016

Renewable and Sustainable Energy Reviews

370

197

View full text Add to dashboard Cite

show abstract

“…For example, an aggregator can motivate EV customers to sell excess energy during peak hours in [113], [114]. The latter case is usually envisioned in a mature V2G market, where EVs are capable of providing auxiliary services to the smart grid [99], [100], [104], [115], [116] (several research works [99], [100], [104] which propose price-based approaches for frequency regulation and voltage regulation have been investigated in Section. III-B and Section.…”

Section: B Indirect Coordinated Controlmentioning

confidence: 99%

Smart Charging for Electric Vehicles: A Survey From the Algorithmic Perspective

Wang

et al. 2016

IEEE Commun. Surv. Tutorials

223

113

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Abstract-Smart interactions among the smart grid, aggregators and EVs can bring various benefits to all parties involved, e.g., improved reliability and safety for the smart gird, increased profits for the aggregators, as well as enhanced self benefit for EV customers. This survey focus on viewing this smart interactions from an algorithmic perspective. In particular, important dominating factors for coordinated charging from three different perspectives are studied, in terms of smart grid oriented, aggregator oriented and customer oriented smart charging. Firstly, for smart grid oriented EV charging, we summarize various formulations proposed for load flattening, frequency regulation and voltage regulation, then explore the nature and substantial similarity among them. Secondly, for aggregator oriented EV charging, we categorize the algorithmic approaches proposed by research works sharing this perspective as direct and indirect coordinated control, and investigate these approaches in detail. Thirdly, for customer oriented EV charging, based on a commonly shared objective of reducing charging cost, we generalize different formulations proposed by studied research works. Moreover, various uncertainty issues, e.g., EV fleet uncertainty, electricity price uncertainty, regulation demand uncertainty, etc., have been discussed according to the three perspectives classified. At last, we discuss challenging issues that are commonly confronted during modeling the smart interactions, and outline some future research topics in this exciting area.

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“…the waiting time of a PEV at a station. 6 Then the mean waiting time of a PEV at charging station i is given by…”

Section: ) Traveling Costmentioning

confidence: 99%

Competitive charging station pricing for plug-in electric vehicles

Yuan

Huang

Zhang

2014

2014 IEEE International Conference on Smart Grid Communications (SmartGridComm)

Self Cite

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This paper considers the problem of charging station pricing and plug-in electric vehicles (PEVs) station selection. When a PEV needs to be charged, it selects a charging station by considering the charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs' charging station selection decisions and the other station's pricing decision, in order to maximize its profit. To obtain insights of such a highly coupled system, we consider a one-dimensional system with two competing charging stations and Poisson arriving PEVs. We propose a multi-leader-multi-follower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in Stage I, and the PEVs (followers) make their charging station selections in Stage II. We show that there always exists a unique charging station selection equilibrium in Stage II, and such equilibrium depends on the charging stations' service capacities and the price difference between them. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in Stage I. We also develop a low complexity algorithm that efficiently computes the pricing equilibrium and the subgame perfect equilibrium of the two-stage Stackelberg game.

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PEV-based combined frequency and voltage regulation for smart grid

Cited by 54 publications

References 31 publications

Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects

Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects

Smart Charging for Electric Vehicles: A Survey From the Algorithmic Perspective

Competitive charging station pricing for plug-in electric vehicles

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