An Overview of Bidirectional Electric Vehicles Charging System as a Vehicle to Anything (V2X) Under Cyber–Physical Power System (CPPS)

Nowadays, EVs are rapidly increasing in popularity, and are accepted as the vehicles of the future all over the world. The most important components are their battery and charging systems. The energy capacity of EVs’ batteries has a significant potential to supply different energy requirements. Therefore, EVs must be designed in accordance with bidirectional power flow, and Electric Vehicle Supply Equipment (EVSE) should be upgraded as Electric Vehicle Power Exchange Equipment (EVPE). This power exchange infrastructure can be called Vehicle-to-Anything (V2X). V2X will also be the key solution for energy grids of the future that will turn into a much larger and smarter system with the help of emerging digitalization technologies, such as Artificial Intelligence (AI), Distributed Ledger Technology (DLT), and the Internet of Things (IoT). This study introduces a multi-layer Cyber–Physical Power Systems (CPPS) framework to explore the potential of V2X technologies allowing bidirectional charging. In addition, the impact of e-mobility is discussed from the V2X perspective. V2X has the potential to provide more practical use of electric vehicles and to bring advantages to the user in terms of both economy and comfort, thus accelerating the transformation of e-mobility and making it easier to accept

Smart Home Laboratory for Smart Grid Infrastructure in Turkey

Akıllı şebekeler konusunda yapılan çalışmalarda, özellikle enerji verimliliğinin etkin bir şekilde sağlanması için temel olarak evlerdeki elektrik enerjisi tüketimi ele alınmaktadır. Bu tüketim değerleri ile enerji üretim tesislerinin kontrolü ve izlenmesi ile enerji akışının verimli bir şekilde gerçekleştirilebileceği belirtilmektedir. Bu amaçla yapılan çalışmalarda üzerinde en çok durulan konulardan birisi de akıllı şebekelerin temelini oluşturan “akıllı evler (smart homes)”dir. Akıllı evlerin yerel şebekedeki diğer tüketicilerle ve evlerde bulunan elektrikli cihazlarla haberleşmesi sayesinde elektrik enerji sistemi daha verimli bir şekilde işletilebilir. Bu sayede, tüketim değerlerinin mevcut enerji üretimine oldukça başarılı bir şekilde adapte edilebilmesi mümkün hale gelmektedir. Bu çalışmada İstanbul Kalkınma Ajansı (ISTKA)’nın desteğiyle Yıldız Teknik Üniversitesi bünyesinde kurulan akıllı şebekeler alt yapısına uygun Akıllı Ev ele alınacaktır. Kurulan evde tüketicinin enerji ihtiyacı hem yenilenebilir enerji kaynaklarından sağlanabilmekte hem de geliştirilen enerji yönetim algoritmalarıyla enerjinin etkin kullanımı üzerine araştırmalar yapılmaktadır. In smart grid related studies ,mainly domestic electrical energy consumption is discussed in order to provide a better energy efficiency. It is stated that, control and observation of both domestic consumption rates and power plants will provide more effective energy flow. One of the core point of these studies are “smart homes” which can be considered as principal component of smart grids. The communication of smart homes with other consumers in the grid and the electrical appliances in the home provides a more efficient management of electrical energy system. Therefore it becomes possible to adapt consumption rates to the existent energy production in a succesfull way. In this study, a smart grid compatible smart home, established with the support of ISTKA (Istanbul Development Agency) in Yildiz Technical University, will be evaluated. In the constructed smart home, the user’s energy demand is provided by the renewable energy resources and research is being conducted for energy management algorithms for more effective use of electrical energy

Cybersecurity and Digital Privacy Aspects of V2X in the EV Charging Structure

With the advancement of green energy technology and rising public and political acceptance, electric vehicles (EVs) have grown in popularity. Electric motors, batteries, and charging systems are considered major components of EVs. The electric power infrastructure has been designed to accommodate the needs of EVs, with an emphasis on bidirectional power flow to facilitate power exchange. Furthermore, the communication infrastructure has been enhanced to enable cars to communicate and exchange information with one another, also known as Vehicle-to-Everything (V2X) technology. V2X is positioned to become a bigger and smarter system in the future of transportation, thanks to upcoming digital technologies like Artificial Intelligence (AI), Distributed Ledger Technology, and the Internet of Things. However, like with any technology that includes data collection and sharing, there are issues with digital privacy and cybersecurity. This paper addresses these concerns by creating a multi-layer Cyber-Physical-Social Systems (CPSS) architecture to investigate possible privacy and cybersecurity risks associated with V2X. Using the CPSS paradigm, this research explores the interaction of EV infrastructure as a very critical part of the V2X ecosystem, digital privacy, and cybersecurity concerns

An overview of bidirectional electric vehicles charging system as a Vehicle to Anything (V2X) under Cyber–Physical Power System (CPPS)

Nowadays, EVs are rapidly increasing in popularity, and are accepted as the vehicles of the future all over the world. The most important components are their battery and charging systems. The energy capacity of EVs’ batteries has a significant potential to supply different energy requirements. Therefore, EVs must be designed in accordance with bidirectional power flow, and Electric Vehicle Supply Equipment (EVSE) should be upgraded as Electric Vehicle Power Exchange Equipment (EVPE). This power exchange infrastructure can be called Vehicle-to-Anything (V2X). V2X will also be the key solution for energy grids of the future that will turn into a much larger and smarter system with the help of emerging digitalization technologies, such as Artificial Intelligence (AI), Distributed Ledger Technology (DLT), and the Internet of Things (IoT). This study introduces a multi-layer Cyber–Physical Power Systems (CPPS) framework to explore the potential of V2X technologies allowing bidirectional charging. In addition, the impact of e-mobility is discussed from the V2X perspective. V2X has the potential to provide more practical use of electric vehicles and to bring advantages to the user in terms of both economy and comfort, thus accelerating the transformation of e-mobility and making it easier to accept

A comprehensive review of recent advances in optimal allocation methods for distributed renewable generation

Abstract Distributed generation (DG) has a key role in enlarging the implementation of renewable energy resources (RES). However, the intermittent and uncontrollable nature of RES can lead to several severe power quality‐related issues. Therefore, many efforts have been made to overcome these issues by optimizing DG sizes and locations. Hence, optimal DG allocation (ODGA) is significant for DG performance and provides advantages to the power system, such as improved power quality, voltage stability, reliability, and profitability. This study reviews recent ODGA studies eliminating the main DG integration problems. Often used ODGA methods have been categorized, and the main differences have been discussed, giving the details of features of optimization methods such as convergence performance and computational burden. A deep analysis for categorizing the objectives of ODGA has been done. In addition, optimization methods applied in ODGA studies have been presented by comparing the superiorities of algorithms and validated test network models. The objectives and significant findings of the ODGA applications are summarized with the advantages and disadvantages. It can be concluded that ODGA has a critical role in RES integration on the DG side and in reducing carbon emissions. This paper leads and provides a perspective for researchers working on recent ODGA methods

Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting

The solar photovoltaics (PV) energy resources have become more important with their significant contribution to the current power grid among renewable energy resources. However, the integration of the solar PV causes reliability issues in the power grid due to its high dependence on the weather condition. The predictability and stability of forecasting are critical for fully utilizing solar power. This study presents an Artificial Neural Network (ANN)-based solar PV power generation forecasting using a public dataset to form a basis experimental testbed to demonstrate analysis and impact of deceptive data attacks with adversarial machine learning. In addition, it evaluates the algorithms’ performance using the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Average Error (MAE) metrics for two main cases, i.e., with and without adversarial machine learning attacks. The results show that the ANN-based models are vulnerable to adversarial attacks.publishedVersio