A review of the population-based and individual-based approaches for electric vehicles in network energy studies

Borray, Andrés Felipe Cortés; Merino, Julia; Mazón, Javier

doi:10.1016/j.epsr.2020.106785

Cited by 6 publications

(3 citation statements)

References 77 publications

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“…Fahmy et al proposes a grid-aware distributed control using alternating direction method of multipliers to mitigate grid security and power quality concerns caused by aggregated EV charging stations. Reference [35] presents a systematic review of EV aggregation modeling offering considerations of individual based, population-based, and hybrid approaches. The role of EV aggregators in transactive energy systems, local flexibility markets, and priced-based approaches is examined as justification for the necessity of regulatory mechanisms enabling them.…”

Section: ) Vehicle-to-grid Chargingmentioning

confidence: 99%

Assessing the Need and State of Power System and Transportation System Co-Simulation

2023

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According to the Environmental Protection Agency, the power and transport systems contribute over 50% of greenhouse gas emissions in the United States. The combination of renewable energy resources and electrified transportation play a crucial role in helping countries meet their carbon neutrality goals. Coordinated planning of the power and transportation systems is necessary to ensure reliable, efficient, and economic operation of both systems. This paper surveys applications of large-scale transportation electrification, vehicle-to-grid charging, prosumer behavior, sector coupling, route optimization, and charging demand and estimation. The co-simulation frameworks available for coupling the power and transport systems to further study these applications are also surveyed. Most of the existing literature utilizes stochastic transportation models or ad-hoc co-simulation approaches which do not promote scalability, extensibility, and interoperability. It is proposed to extend the Hierarchical Engine for Large-Scale Infrastructure Cosimulation to communicate with an agent based transportation simulator for examining these interconnected applications.

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Section: ) Vehicle-to-grid Chargingmentioning

confidence: 99%

Assessing the Need and State of Power System and Transportation System Co-Simulation

2023

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“…Mathematically, the proposed model is posed as a mixed-integer robust linear problem in which binary variables are used to consider the charging, discharging or idle status of the aggregator. This work assumes a population-based EV aggregation [42] in which all EVs have the same battery and efficiency. Although it would be straightforward to include several models of EV cars merged by battery type, as in [35], only one is considered in order to present a clear formulation.…”

Section: Model Descriptionmentioning

confidence: 99%

A Robust Optimization Model to the Day-Ahead Operation of an Electric Vehicle Aggregator Providing Reliable Reserve

Jiménez-Marín

Pérez-Ruiz

2021

Energies

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This paper presents a robust optimization model to find out the day-ahead energy and reserve to be scheduled by an electric vehicle (EV) aggregator. Energy can be purchased from, and injected to, the distribution network, while upward and downward reserves can be also provided by the EV aggregator. Although it is an economically driven model, the focus of this work relies on the actual availability of the scheduled reserves in a future real-time. To this end, two main features stand out: on one hand, the uncertainty regarding the EV driven pattern is modeled through a robust approach and, on the other hand, a set of non-anticipativity constraints are included to prevent from unavailable future states. The proposed model is posed as a mixed-integer robust linear problem in which binary variables are used to consider the charging, discharging or idle status of the EV aggregator. Results over a 24-h case study show the capability of the proposed model.

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“…Optimization model of an EV aggregator can be individualbased or population-based [9]. Most research uses the former, having a battery state and control signal per each EV [10], [11], [12], [13], [14], [15], [16].…”

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confidence: 99%

Aggregated Representation of Electric Vehicles Population on Charging Points for Demand Response Scheduling

Kovačević

Vašak

2023

IEEE Trans. Intell. Transport. Syst.

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Charging electric vehicles (EVs), whose number is increasing, is a great challenge for the power grid due to the charging load variability. Coordinated charging and schedule optimization with seized demand response opportunities are wellknown conceptual solutions to that. Still, the main challenge is to adequately predict availability and parameters of electric vehicles which is crucial for determining the charging schedule and the demand response potential. We propose a method to represent a population of electric vehicles that on the one hand enables prediction via machine learning and on the other it enables an accurate optimization of the charging schedule and demand response ability. The method essence is to use five discrete-time signals spanned over a prediction horizon period which are related to envelopes of feasible charging power and charging states for the EV population on that horizon. We also introduce a robust conversion of any sequence of these signals into individual EVs data. It enables to pose and solve the optimization problem of charging scheduling with included demand response for a predicted population in the introduced representation. The proposed method is validated by schedule optimization using first the original data and then using reconstructed population data. The validation results show that the proposed EV population representation method preserves the valuable information needed for the charging schedule optimization and demand response.

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A review of the population-based and individual-based approaches for electric vehicles in network energy studies

Cited by 6 publications

References 77 publications

Assessing the Need and State of Power System and Transportation System Co-Simulation

Assessing the Need and State of Power System and Transportation System Co-Simulation

A Robust Optimization Model to the Day-Ahead Operation of an Electric Vehicle Aggregator Providing Reliable Reserve

Aggregated Representation of Electric Vehicles Population on Charging Points for Demand Response Scheduling

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