Electric Vehicle Mobility in Transmission-Constrained Hourly Power Generation Scheduling

Khodayar, Mohammad E.; Wu, Lei; Li, Zuyi

doi:10.1109/tsg.2012.2230345

Cited by 100 publications

(59 citation statements)

References 19 publications

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“…The result shows that the operational cost is reduced by smart charging of PEV. Khodayar et al minimised hourly [73] and dayhead [74] generation cost considering PEV charging and discharging, wind and hydro power. Govardhan and Roy [75] analysed the economic cost of unit commitment with wind generation, PEV charging and discharging as well as the emergency demand response programme.…”

Section: Cost Minimisationmentioning

confidence: 99%

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Yang

Foley

2015

Renewable and Sustainable Energy Reviews

182

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Section: Cost Minimisationmentioning

confidence: 99%

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Yang

Foley

2015

Renewable and Sustainable Energy Reviews

182

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“…Off-Limit (1) In view of the charging and discharging behaviors of EVs, the voltage over the end-terminal node of the distribution network is more likely to exceed the acceptable range, and the line flow in the beginning of the branch is easy to exceed the transmission power limit. (2) Different price mechanisms will affect the safety risks of the distribution network.…”

Section: Case 2 Case 3 Time (H)mentioning

confidence: 99%

“…In this situation, the EV industry is booming due to the incentives of government policies and market requirements [1]. However, regarding when all those EVs are connected to the distribution network, the uncertainties of the charging and discharging demands could lead to risks in the safety of the distribution network [2,3].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of Distribution Networks Considering the Charging-Discharging Behaviors of Electric Vehicles

et al. 2016

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Electric vehicles (EVs) have received wide attention due to their higher energy efficiency and lower emissions. However, the random charging and discharging behaviors of substantial numbers of EVs may lead to safety risk problems in a distribution network. Reasonable price incentives can guide EVs through orderly charging and discharging, and further provide a feasible solution to reduce the operational risk of the distribution network. Considering three typical electricity prices, EV charging/discharging load models are built. Then, a Probabilistic Load Flow (PLF) method using cumulants and Gram-Charlier series is proposed to obtain the power flow of the distribution network including massive numbers of EVs. In terms of the risk indexes of node voltage and line flow, the operational risk of the distribution network can be estimated in detail. From the simulations of an IEEE-33 bus system and an IEEE 69-bus system, the demonstrated results show that reasonable charging and discharging prices are conducive to reducing the peak-valley difference, and consequently the risks of the distribution network can be decreased to a certain extent.

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“…Prominent examples include: cloud computing services and Internet data centers [7]- [10] and electric transportation systems [11]- [14]. Unlike traditional responsive loads (such as HVACs), here electricity consumption is coupled with another form of constrained (congestible) network resource to serve users.…”

Section: Introductionmentioning

confidence: 99%

“…Prior art regarding electricity price design for priceresponsive infrastructures: The authors in [14] consider the case where the operator tracks the mobility of large fleets of EVs and their energy consumption and designs optimal multi-period Vehicle-to-Grid strategies. In our previous work in [15], [16], we studied how non-profit transportation and power system operators can collaborate together to consider an interconnection introduced between their systems through EVs.…”

Section: Introductionmentioning

confidence: 99%

Optimal Electricity Pricing for Societal Infrastructure Systems

Alizadeh

Wai

Goldsmith³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Abstract-We develop a general framework for pricing electricity in order to optimally manage the electricity load of societal infrastructures that interact with power systems through their price-responsive electricity load. In such infrastructure systems, electricity is not the sole resource needed to serve users' needs. Examples include cloud computing infrastructure or electric transportation networks. In these cases, other shared networked resources such as charging stations or communication links and data centers are also required to serve users. Hence, pricing of electricity becomes intertwined to managing other congestible resources not priced by the power system operator, leading to a complex economic dispatch problem. For brevity of notation, our analysis is performed under a static setting. We discuss how the power system operator should model the effects of the mobility of loads and congestion in the infrastructure in the economic dispatch. We numerically study the performance of our algorithms using the example of a simple electric transportation network.

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Electric Vehicle Mobility in Transmission-Constrained Hourly Power Generation Scheduling

Cited by 100 publications

References 19 publications

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Risk Assessment of Distribution Networks Considering the Charging-Discharging Behaviors of Electric Vehicles

Optimal Electricity Pricing for Societal Infrastructure Systems

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