Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

AbstractThe mission to move from fossil to renewable energy sources is accompanied and enabled by the digitalization of our energy systems. With the introduction of information and communication technologies, the widespread integration of distributed, renewable sources, even in the distribution grid, are enabled. New use cases such as fast EV charging, local energy communities and dynamic energy tariffs are also enabled. However, this move toward digitalization also increases the exposure of the energy systems for cybercrime and raises concerns regarding the privacy of personal data. In this article, we address the issue of privacy in smart energy systems and give an overview of current methods to enhance privacy.

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Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

10.20944/preprints201703.0107.v1 ◽

2017 ◽

Author(s):

Bharatiraja Chokkalingam ◽

Sanjeevikumar Padmanaban ◽

Pierluigi Siano ◽

Ramesh Krishnamoorthy ◽

Raghu Selvaraj

Keyword(s):

Real Time ◽

Low Cost ◽

Waiting Times ◽

Data Communication ◽

Charging Infrastructure ◽

Charging Station ◽

Smart Energy Systems ◽

Range Anxiety ◽

Charging Stations ◽

Scheduling Management

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

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Smart energy systems: A critical review on design and operation optimization

Sustainable Cities and Society ◽

10.1016/j.scs.2020.102369 ◽

2020 ◽

Vol 62 ◽

pp. 102369 ◽

Cited By ~ 7

Author(s):

Yizhe Xu ◽

Chengchu Yan ◽

Huifang Liu ◽

Jin Wang ◽

Zhang Yang ◽

...

Keyword(s):

Critical Review ◽

Energy Systems ◽

Operation Optimization ◽

Smart Energy Systems

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A real-time abnormal operation pattern detection method for building energy systems based on association rule bases

Building Simulation ◽

10.1007/s12273-021-0791-x ◽

2021 ◽

Author(s):

Chaobo Zhang ◽

Yang Zhao ◽

Yangze Zhou ◽

Xuejun Zhang ◽

Tingting Li

Keyword(s):

Real Time ◽

Association Rule ◽

Detection Method ◽

Energy Systems ◽

Building Energy ◽

Pattern Detection ◽

Operation Pattern ◽

Building Energy Systems ◽

Rule Bases ◽

Abnormal Operation

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The Architecture Design of Electrical Vehicle Infrastructure Using Viable System Model Approach

Systems ◽

10.3390/systems9010019 ◽

2021 ◽

Vol 9 (1) ◽

pp. 19

Author(s):

Mahdi Boucetta ◽

Niamat Ullah Ibne Hossain ◽

Raed Jaradat ◽

Charles Keating ◽

Siham Tazzit ◽

...

Keyword(s):

Urban Space ◽

Electric Vehicles ◽

Reference Model ◽

System Model ◽

Ecosystem Structure ◽

Viable System Model ◽

Charging Station ◽

Viable System ◽

Station Location ◽

Ev Charging

Exponential technological-based growth in industrialization and urbanization, and the ease of mobility that modern motorization offers have significantly transformed social structures and living standards. As a result, electric vehicles (EVs) have gained widespread popularity as a mode of sustainable transport. The increasing demand for of electric vehicles (EVs) has reduced the some of the environmental issues and urban space requirements for parking and road usage. The current body of EV literature is replete with different optimization and empirical approaches pertaining to the design and analysis of the EV ecosystem; however, probing the EV ecosystem from a management perspective has not been analyzed. To address this gap, this paper develops a systems-based framework to offer rigorous design and analysis of the EV ecosystem, with a focus on charging station location problems. The study framework includes: (1) examination of the EV charging station location problem through the lens of a systems perspective; (2) a systems view of EV ecosystem structure; and (3) development of a reference model for EV charging stations by adopting the viable system model. The paper concludes with the methodological implications and utility of the reference model to offer managerial insights for practitioners and stakeholders.

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