A Study on Power Demand Prediction of the Apartment Housing Considering Charging Usage Pattern of EV Charging Infrastructure Demonstration Complex in Jeju

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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Research on optimal power allocation strategy based on power demand prediction for electro-mechanical transmission

2016 35th Chinese Control Conference (CCC) ◽

10.1109/chicc.2016.7554735 ◽

2016 ◽

Author(s):

Yulong Zhao ◽

Wenjun Chen ◽

Weida Wang ◽

Changle Xiang ◽

Kun Huang

Keyword(s):

Power Allocation ◽

Mechanical Transmission ◽

Optimal Power Allocation ◽

Power Demand ◽

Allocation Strategy ◽

Demand Prediction ◽

Optimal Power

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Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis

Energies ◽

10.3390/en11071869 ◽

2018 ◽

Vol 11 (7) ◽

pp. 1869 ◽

Cited By ~ 13

Author(s):

Alexandre Lucas ◽

Giuseppe Prettico ◽

Marco Flammini ◽

Evangelos Kotsakis ◽

Gianluca Fulli ◽

...

Keyword(s):

Urban Areas ◽

Energy Demand ◽

Energy Use ◽

Spatial Density ◽

Carbon Intensity ◽

Impact Indicator ◽

Charging Infrastructure ◽

Smart Charging ◽

The Impact ◽

Ev Charging

Electric vehicle (EV) charging infrastructure rollout is well under way in several power systems, namely North America, Japan, Europe, and China. In order to support EV charging infrastructures design and operation, little attempt has been made to develop indicator-based methods characterising such networks across different regions. This study defines an assessment methodology, composed by eight indicators, allowing a comparison among EV public charging infrastructures. The proposed indicators capture the following: energy demand from EVs, energy use intensity, charger’s intensity distribution, the use time ratios, energy use ratios, the nearest neighbour distance between chargers and availability, the total service ratio, and the carbon intensity as an environmental impact indicator. We apply the methodology to a dataset from ElaadNL, a reference smart charging provider in The Netherlands, using open source geographic information system (GIS) and R software. The dataset reveals higher energy intensity in six urban areas and that 50% of energy supplied comes from 19.6% of chargers. Correlations of spatial density are strong and nearest neighbouring distances range from 1101 to 9462 m. Use time and energy use ratios are 11.21% and 3.56%. The average carbon intensity is 4.44 gCO2eq/MJ. Finally, the indicators are used to assess the impact of relevant public policies on the EV charging infrastructure use and roll-out.

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An expert system for unit commitment and power demand prediction using fuzzy logic and neural networks

Expert Systems ◽

10.1111/j.1468-0394.1996.tb00281.x ◽

1996 ◽

Vol 13 (1) ◽

pp. 29-40 ◽

Cited By ~ 5

Author(s):

Sameer Bataineh ◽

Adnan Al-Anbuky ◽

Salem Al-Aqtash

Keyword(s):

Neural Networks ◽

Fuzzy Logic ◽

Expert System ◽

Unit Commitment ◽

Power Demand ◽

Demand Prediction

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Determining Equipment Capacity of Electric Vehicle Charging Station Operator for Profit Maximization

Energies ◽

10.3390/en11092301 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2301 ◽

Cited By ~ 6

Author(s):

Se Baik ◽

Young Jin ◽

Yong Yoon

Keyword(s):

Capital Investment ◽

Storage System ◽

Profit Maximization ◽

Energy Storage System ◽

Auxiliary Equipment ◽

Charging Infrastructure ◽

Physical Constraints ◽

Charging Station ◽

Study Results ◽

Ev Charging

Related to global efforts to reduce greenhouse gases, numerous electric vehicles (EVs) are expected to be integrated to the power grid. However, the introduction of EVs, particularly in Korea, is still marginal due to the lack of EV charging infrastructure, even though various supportive policies exist. To address this shortage of EV charging stations, the EV charging business needs to be profitable. As with any business, the profitability of the EV charging business is significantly affected by the initial capital investment related to EV chargers and auxiliary equipment such as power conditioning system (PCS), battery energy storage system (BESS), and on-site photovoltaic (PV) generation system. Thus, we propose a formulation to determine the number of EV chargers and the capacity of auxiliary equipment with the objective of a charging station operator (CSO) maximizing profit under regulatory, economic, and physical constraints. The effectiveness of the proposed method is verified with simulations considering various EV charging patterns. The study results will help improve the EV charging infrastructure by encouraging individuals and companies to participate in EV charging business.

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Drivers of EV Charging Infrastructure Entrepreneurship in India

10.4018/978-1-7998-6858-3.ch011 ◽

2022 ◽

pp. 208-219

Author(s):

Mohd Yasir Arafat ◽

Imran Saleem ◽

Thoudam Prabha Devi

Keyword(s):

Developing Countries ◽

Electric Vehicles ◽

Crucial Role ◽

Entrepreneurship Policy ◽

Charging Infrastructure ◽

Personal Attitude ◽

Self Confidence ◽

Policy Measures ◽

Ev Charging

The existing research advocating entrepreneurship as an important way to increase the uptake of electric vehicles (EVs) in developing countries and EV charging business is also playing a crucial role in increasing the adoption of EVs. EV charging is important for EV adoption, and entrepreneurship is also important for EV adoption; therefore, it is important that we must understand what mobilizes or prevents EV charging entrepreneurship. This chapter aimed at explaining drivers of EV charging entrepreneurship. A survey of 121 potential entrepreneurs shows that personal attitude, self-confidence, and opportunity perceptions shape the decision to engage in EV charging entrepreneurship. Policy measures to boost EV charging entrepreneurship have been suggested.

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A Comprehensive Review of Blockchain Technology Implementation in the EV Charging Infrastructure

10.4018/978-1-7998-6858-3.ch003 ◽

2022 ◽

pp. 38-67

Author(s):

Toni Zhimomi ◽

Mohammad Saad Alam ◽

Hafiz Malik

Keyword(s):

Technology Implementation ◽

Security And Privacy ◽

Comprehensive Review ◽

Charging Infrastructure ◽

Blockchain Technology ◽

Key Factor ◽

Communication Flow ◽

Electric Vehicle Adoption ◽

Charging Stations ◽

Ev Charging

Charging infrastructure is a key factor in successful electric vehicle adoption. Charging stations are still a fragmented market in terms of ownership, lack of standards, and charging protocols. The increasing decentralised grid has made energy and communication flow bi-directional. Challenges arise in maintaining the increasing decentralised structure, security, and privacy of the network. Blockchain facilitates the interconnectedness of such a distributed and decentralised network. Blockchain's versatility lies in its transparent and immutable decentralized architecture that enables direct transactions between users without the need of a middleman. It provides powerful safeguards against cyberattacks with its advanced cryptography enabling privacy-preserving authentication. This chapter presents a comprehensive review on the application of blockchain technology in EV charging infrastructure such as facilitating the peer-to-peer energy exchange, increased security and privacy, immutable transactions, and mitigating trust issues among the participants in the charging infrastructure.

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Trends in Electric Vehicles, Distribution Systems, EV Charging Infrastructure, and Microgrids

Electric Vehicle Integration in a Smart Microgrid Environment ◽

10.1201/9780367423926-1 ◽

2021 ◽

pp. 1-6

Author(s):

April Bolduc

Keyword(s):

Electric Vehicles ◽

Distribution Systems ◽

Charging Infrastructure ◽

Ev Charging

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Controllability Evaluation of EV Charging Infrastructure Transformed from Gas Stations in Distribution Networks with Renewables

Energies ◽

10.3390/en12081577 ◽

2019 ◽

Vol 12 (8) ◽

pp. 1577

Author(s):

Shuang Gao ◽

Jianzhong Wu ◽

Bin Xu

Keyword(s):

Power Distribution ◽

Queuing Theory ◽

Distribution Networks ◽

Temporal And Spatial Distribution ◽

Charging Infrastructure ◽

Optimal Dispatch ◽

Gas Stations ◽

Charging Stations ◽

The Impact ◽

Ev Charging

A considerable market share of electric vehicles (EVs) is expected in the near future, which leads to a transformation from gas stations to EV charging infrastructure for automobiles. EV charging stations will be integrated with the power grid to replace the fuel consumption at the gas stations for the same mobile needs. In order to evaluate the impact on distribution networks and the controllability of the charging load, the temporal and spatial distribution of the charging power is calculated by establishing mapping the relation between gas stations and charging facilities. Firstly, the arrival and parking period is quantified by applying queuing theory and defining membership function between EVs to parking lots. Secondly, the operational model of charging stations connected to the power distribution network is formulated, and the control variables and their boundaries are identified. Thirdly, an optimal control algorithm is proposed, which combines the configuration of charging stations and charging power regulation during the parking period of each individual EV. A two-stage hybrid optimization algorithm is developed to solve the reliability constrained optimal dispatch problem for EVs, with an EV aggregator installed at each charging station. Simulation results validate the proposed method in evaluating the controllability of EV charging infrastructure and the synergy effects between EV and renewable integration.

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