A stochastic–probabilistic energy and reserve market clearing scheme for smart power systems with plug-in electrical vehicles

Khazali, Amirhossein; Kalantar, Mohsen

doi:10.1016/j.enconman.2015.08.050

Cited by 22 publications

(7 citation statements)

References 37 publications

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“…DR is one of these paradigms, which encompasses the consumer management methods that lead to changes in the consumption level of costumers caused by the changes in electricity prices in the market. According to the United States Department of Energy, DR is defined as the empowerment of industrial, commercial, and residential users to improve electronic energy consumption, so that appropriate costs could be established and the network exploitation conditions could be improved [10]. In other words, DR could change the form of electronic energy consumption, so that the maximum system demand would reduce and consumptions would be transferred to non-peak hours.…”

Section: Response Demandmentioning

confidence: 99%

Risk management and participation of electric vehicle considering transmission line congestion in the smart grids for demand response

Beyzaee

Marvi

2020

FACTA U EE

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Demand response (DR) could serve as an effective tool to further balance the electricity demand and supply in smart grids. It is also defined as the changes in normal electricity usage by end-use customers in response to pricing and incentive payments. Electric cars (EVs) are potentially distributed energy sources, which support the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes, and their participation in time-based (e.g., time of use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Moreover, the smart scheduling of EV charging and discharging activities supports the high penetration of renewable energies with volatile energy generation. This article was focused on DR in the presence of EVs to assess the effects of transmission line congestion on a 33-bit grid. A random model from the standpoint of an independent system operator was used to manage the risk and participation of EVs in the DR of smart grids. The main risk factors were those caused by the uncertainties in renewable energies (e.g., wind and solar), imbalance between demand and renewable energy sources, and transmission line congestion. The effectiveness of the model in a 33-bit grid in response to various settings (e.g., penetration rate of EVs and risk level) was evaluated based on the transmission line congestion and system exploitation costs. According to the results, the use of services such as time-based DR programs was effective in the reduction of the electricity costs for independent system operators and aggregators. In addition, the results demonstrated that the participation of EVs in incentive-based DR programs with the park model was particularly effective in this regard.

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Section: Response Demandmentioning

confidence: 99%

Risk management and participation of electric vehicle considering transmission line congestion in the smart grids for demand response

Beyzaee

Marvi

2020

FACTA U EE

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“…In [24], a PEV aggregator model is used. PEV aggregators submit three forecasted amounts for the total available capacity, required state of charge, and the available power for one hour in an EV.…”

Section: 1literature Reviewmentioning

confidence: 99%

“…Indeed, it was not possible to find a work that focus solely on this issue. Nevertheless, the studied works [1,24,26,27,29] do not take into account the existence of a historic database, while aggregated representative fleets are considered, thus not obtaining individual EV's profiles. Estimating individual profiles can be important for energy resources scheduling in smart grids [32].…”

Section: Figurementioning

confidence: 99%

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Scenario generation for electric vehicles' uncertain behavior in a smart city environment

Soares¹,

Borges²,

Ghazvini³

et al. 2016

Energy

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This paper presents a framework and methods to estimate electric vehicles' possible states, regarding their demand, location and grid connection periods. The proposed methods use the Monte Carlo simulation to estimate the probability of occurrence for each state and a fuzzy logic probabilistic approach to characterize the uncertainty of electric vehicles' demand.Day-ahead and hour-ahead methodologies are proposed to support the smart grids' operational decisions. A numerical example is presented using an electric vehicles fleet in a smart city environment to obtain each electric vehicle possible states regarding their grid location.

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“…The former deals strongly with the real-time pricing and critical peak pricing. On the other hand, the latter is related to the incentive due to utilization of PHEVs and BEVs for frequency regulation and spinning reserve [20]. Pricing system in the electrical grid requires accurate prediction on both supply and demand sides.…”

Section: Introductionmentioning

confidence: 99%

Advanced Charging System for Plug-in Hybrid Electric Vehicles and Battery Electric Vehicles

Aziz¹

2017

Hybrid Electric Vehicles

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The increase of plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) results in higher electricity demand for their charging. In addition, the uncontrolled and timely concentrated charging is potential to decrease the quality of electricity. This condition has encouraged the development of advanced charging system, which is able to facilitate quick charging with minimum impacts on the electrical grid. This chapter explains some issues related to charging of PHEVs and BEVs including some available charging systems, charging behaviour and developed charging system employing battery for assistance during charging. In analysis of charging behaviour, the effect of ambient temperature to charging rate is clarified. Higher ambient temperature, such as during summer, leads to higher charging rate compared to one during winter. As advanced charging system, a battery-assisted charging system for PHEVs and BEVs is also described. The evaluation results in terms of their performance to facilitate a quick, simultaneous charging as well as reduce the stress of electrical grid due to massively uncontrolled charging are also provided. This system is considered as one of the appropriate solutions that can be adopted in the near future to avoid problems on electrical grid due to massive charging of PHEVs and BEVs.

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A stochastic–probabilistic energy and reserve market clearing scheme for smart power systems with plug-in electrical vehicles

Cited by 22 publications

References 37 publications

Risk management and participation of electric vehicle considering transmission line congestion in the smart grids for demand response

Risk management and participation of electric vehicle considering transmission line congestion in the smart grids for demand response

Scenario generation for electric vehicles' uncertain behavior in a smart city environment

Advanced Charging System for Plug-in Hybrid Electric Vehicles and Battery Electric Vehicles

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