Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

A Framework for Flexible Loads Aggregation

L'abstract è presente nell'allegato / the abstract is in the attachmen

Autonomous Demand Side Management of Electric Vehicles

There is an error in the table of content, where publication A and B have swiched places.Demand-side management approaches that exploit the temporal flexibility of electric vehicles have attracted much attention in recent years due to the increasing market penetration. These demand-side management measures contribute to alleviating the burden on the power system, especially in distribution grids where bottlenecks are more prevalent. Electric vehicles can be defined as an attractive asset for distribution system operators, which have the potential to provide grid services if properly managed. In this thesis, first, a systematic investigation is conducted for two typically employed demand-side management methods reported in the literature: A voltage droop control-based approach and a market-driven approach. Then a control scheme of decentralized autonomous demand side management for electric vehicle charging scheduling which relies on a unidirectionally communicated grid-induced signal is proposed. In all the topics considered, the implications on the distribution grid operation are evaluated using a set of time series load flow simulations performed for representative Austrian distribution grids. Droop control mechanisms are discussed for electric vehicle charging control which requires no communication. The method provides an economically viable solution at all penetrations if electric vehicles charge at low nominal power rates. However, with the current market trends in residential charging equipment especially in the European context where most of the charging equipment is designed for 11 kW charging, the technical feasibility of the method, in the long run, is debatable. As electricity demand strongly correlates with energy prices, a linear optimization algorithm is proposed to minimize charging costs, which uses next-day market prices as the grid-induced incentive function under the assumption of perfect user predictions. The constraints on the state of charge guarantee the energy required for driving is delivered without failure. An average energy cost saving of 30% is realized at all penetrations. Nevertheless, the avalanche effect due to simultaneous charging during low price periods introduces new power peaks exceeding those of uncontrolled charging. This obstructs the grid-friendly integration of electric vehicles.publishedVersio

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station

The increasing use of electric vehicles connected to the power grid gives rise to challenges in the vehicle charging coordination, cost management, and provision of potential services to the grid. Scheduling of the power in an electric vehicle charging station is a quite challenging task, considering time-variant prices, customers with different charging time preferences, and the impact on the grid operations. The latter aspect can be addressed by exploiting the vehicle charging flexibility. In this article, a specific definition of flexibility to be used for an electric vehicle charging station is provided. Two optimal charging strategies are then proposed and evaluated, with the purpose of determining which strategy can offer spinning reserve services to the electrical grid, reducing at the same time the operation costs of the charging station. These strategies are based on a novel formulation of an economic model predictive control algorithm, aimed at minimising the charging station operation cost, and on a novel formulation of the flexibility capacity maximisation, while reducing the operation costs. These formulations incorporate the uncertainty in the arrival time and state of charge of the electric vehicles at their arrival. Both strategies lead to a considerable reduction of the costs with respect to a simple minimum time charging strategy, taken as the benchmark. In particular, the strategy that also accounts for flexibility maximisation emerges as a new tool for maintaining the grid balance giving cost savings to the charging stations

Sähköautojen lataus yhdistettynä kuormanhallintaan kaupallisissa rakennuksissa

This research studies electric vehicle (EV) charging integrated to commercial buildings. It compares smart EV charging in smart buildings with stand-alone EV charging in buildings without a building management system. In this research, two different commercial buildings with EV charging are studied. The intention of this research is to study what kind of technical and economic benefits are possible to achieve with a smart system, which consists of both smart charging and smart buildings, compared to a stand-alone system. To achieve this, both EV charging data and electricity consumption data for both buildings are studied heuristically by using Excel spreadsheet as a tool. The aim is to find out a way to organize the charging in the most optimized way within the constraints set by the building and by the EV charging system in each case. As result, smart charging integrated to a smart building management system can save up to 4 500 € yearly in buildings electricity costs compared to a stand-alone system. Although the investment cost for the smart system is more expensive, during the chargers expected lifetime, the smart system becomes more profitable due to the savings in the electricity usage. The benefits achieved with the smart system are strongly connected with the size of the commercial building's EV charging system. Especially with large EV charging systems, smart charging proves to be a more viable and profitable option.Detta arbete undersöker elbilsladdning integrerat till kommersiella byggnader. Arbetet jämför smart laddning i smarta byggnader med fristående laddning i byggnader utan belastningsstyrning. I arbetet studeras två olika kommersiella byggnader med elbilsladdning. Arbetets syfte är att undersöka de tekniska och ekonomiska fördelar som är möjliga att uppnå med ett intelligent system, som består av både smart laddning och smart byggnad, jämfört med ett fristående system. För att uppnå detta, både elbilsladdning och strömförbrukning i byggnader analyseras heuristiskt med hjälp av Excel. Målet är att hitta ett sätt att optimera laddningen inom de begränsningar som fastställts av byggnaden och elbilsladdningen i båda fallen. Som resultat, med smart laddning som är integrerat till ett smart byggnadsautomationssystem, kan man spara upp till 4 500 € per år i elförbrukningen jämfört med ett fristående system. Även om installationskostanderna för det smarta systemet är högre, de uppnådda besparingar i elförbrukningen under laddarnas förväntad livslängd gör det smarta systemet mer lönsamt. De uppnådda fördelarna med det smart systemet korrelerar starkt med storleken på den kommersiella byggnadens laddningssystem. Speciellt inom stora laddningssystem, smarta laddningen visar sig att vara mer fördelaktigt och förmånligt alternativ

System configuration, fault detection, location, isolation and restoration: a review on LVDC Microgrid protections

Low voltage direct current (LVDC) distribution has gained the significant interest of research due to the advancements in power conversion technologies. However, the use of converters has given rise to several technical issues regarding their protections and controls of such devices under faulty conditions. Post-fault behaviour of converter-fed LVDC system involves both active converter control and passive circuit transient of similar time scale, which makes the protection for LVDC distribution significantly different and more challenging than low voltage AC. These protection and operational issues have handicapped the practical applications of DC distribution. This paper presents state-of-the-art protection schemes developed for DC Microgrids. With a close look at practical limitations such as the dependency on modelling accuracy, requirement on communications and so forth, a comprehensive evaluation is carried out on those system approaches in terms of system configurations， fault detection, location, isolation and restoration

Battery Pack Cells Mon itoring for Intelligent Charging

This dissertation intends to create a system capable of cell charging, cell balancing or both at the same time for batteries with multiple cells connected in series. It also tries to understand why there is only few literature connected with cell balancing and cell charging at the same time. For that purpose, this dissertation presents a review on the state of the art of many concepts related both to balancing and charging in order to pick the right methods and equipment to achieve the objectives of this work. This dissertation includes literature review on batteries, cell balancing methods and topologies, cell charging methods and a small review on state of charge estimation methods. Later on, this document studies and explains hardware and software requirements and choices in order to understand the final developed circuit. Lastly, development difficulties, results and conclusions are presented.Esta dissertação pretende criar um sistema capaz de carregar, balancear ou ambos em simultâneo num pack com diversas células ligadas em série. Tenta ainda perceber a razão de haver tão pouca bibliografia que junte em simultâneo carregamento e balanceamento de baterias. Para alcançar estes objetivos, esta dissertação conta com uma revisão do estado da arte de vários temas relacionados tanto com balanceamento como com carregamento de forma a perceber os métodos e equipamentos mais adequados para implementar. A dissertação inclui revisão bibliográfica em baterias, métodos de balanceamento e suas topologias, métodos de carregamento de baterias e ainda alguma revisão sobre métodos de estimação de estado de carga. Posteriormente, este documento estuda e explica os requisitos de software e hardware e as escolhas feitas para o desenvolvimento do circuito. Finalmente apresentam-se as dificuldades de desenvolvimento encontradas, os resultados e ainda algumas conclusões