Behavioural Change in Green Transportation: Micro-Economics Perspectives and Optimization Strategies

The increasing demand for Electric Vehicle (EV) charging is putting pressure on the power grids and capacities of charging stations. This work focuses on how to use indirect control through price signals to level out the load curve in order to avoid the power consumption from exceeding these capacities. We propose mathematical programming models for the indirect control of EV charging that aim at finding an optimal set of price signals to be sent to the drivers based on price elasticities. The objective is to satisfy the demand for a given price structure, or minimize the curtailment of loads, when there is a shortage of capacity. The key contribution is the use of elasticity matrices through which it is possible to estimate the EV drivers’ reactions to the price signals. As real-world data on relating the elasticity values to the EV driver’s behaviour are currently non-existent, we concentrate on sensitivity analysis to test how different assumptions on elasticities affect the optimal price structure. In particular, we study how market segments of drivers with different elasticities may affect the ability of the operator to both handle a capacity problem and properly satisfy the charging needs.

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Impact of EV Charging Stations in Power Grids in Italy and its Mitigation Mechanisms

10.1109/eeeic/icpseurope51590.2021.9584782 ◽

2021 ◽

Author(s):

A. Samson Mogos ◽

S. Grillo

Keyword(s):

Power Grids ◽

Charging Stations ◽

Ev Charging

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Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data

Energies ◽

10.3390/en13164211 ◽

2020 ◽

Vol 13 (16) ◽

pp. 4211

Author(s):

Manu Lahariya ◽

Dries F. Benoit ◽

Chris Develder

Keyword(s):

Electric Vehicle ◽

Real World ◽

Synthetic Data ◽

Gaussian Mixture ◽

Real World Data ◽

Arrival Times ◽

World Data ◽

Data Generator ◽

Charging Stations ◽

Ev Charging

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potential, e.g., in deferring the load in time. To analyze such scenarios, realistic EV data are required, which are hard to come by. Therefore, in this article we define a synthetic data generator (SDG) for EV charging sessions based on a large real-world dataset. Arrival times of EVs are modeled assuming that the inter-arrival times of EVs follow an exponential distribution. Connection time for EVs is dependent on the arrival time of EV, and can be described using a conditional probability distribution. This distribution is estimated using Gaussian mixture models, and departure times can calculated by sampling connection times for EV arrivals from this distribution. Our SDG is based on a novel method for the temporal modeling of EV sessions, and jointly models the arrival and departure times of EVs for a large number of charging stations. Our SDG was trained using real-world EV sessions, and used to generate synthetic samples of session data, which were statistically indistinguishable from the real-world data. We provide both (i) source code to train SDG models from new data, and (ii) trained models that reflect real-world datasets.

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Placement and Sizing of EV Charging Stations According to Centrality of the Underlying Network

2020 Intermountain Engineering, Technology and Computing (IETC) ◽

10.1109/ietc47856.2020.9249203 ◽

2020 ◽

Author(s):

Hossein Parastvand ◽

Octavian Bass ◽

Mohammad A. S. Masoum ◽

Zeinab Moghaddam ◽

Stefan Lachowicz ◽

...

Keyword(s):

Underlying Network ◽

Charging Stations ◽

Ev Charging

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Optimal location of EV charging stations in a neighborhood considering a multi-objective approach

Electric Power Systems Research ◽

10.1016/j.epsr.2021.107391 ◽

2021 ◽

Vol 199 ◽

pp. 107391

Author(s):

Leonardo Bitencourt ◽

Tiago P. Abud ◽

Bruno H. Dias ◽

Bruno S.M.C. Borba ◽

Renan S. Maciel ◽

...

Keyword(s):

Optimal Location ◽

Multi Objective ◽

Charging Stations ◽

Ev Charging

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A Tale of Two Entities

ACM Transactions on Internet of Things ◽

10.1145/3437258 ◽

2021 ◽

Vol 2 (2) ◽

pp. 1-21

Author(s):

Hossam ElHussini ◽

Chadi Assi ◽

Bassam Moussa ◽

Ribal Atallah ◽

Ali Ghrayeb

Keyword(s):

Power Flow ◽

Power Grid ◽

Communication Protocols ◽

Charging Infrastructure ◽

Electric Vehicle Charging ◽

Public Data ◽

Simulation Based ◽

Charging Stations ◽

The Impact ◽

Ev Charging

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.

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