Integration of EVs and DGs into the Electric Power System for Grid Modernization

Electric power systems (EPSs) are rapidly becoming more complex. Penetration of distributed generators (DGs) are increasing rapidly. Among them, DG units with intermittent renewables resources, such as solar or wind, are attracting more attention. Moreover, plug in electric vehicles (EVs) are expected to be deployed in large numbers over the next decade. These changes present opportunities as well as challenges for reliable and efficient operation of EPS. Integrating EVs in large scale, would result in over-loading of EPS. Interconnection of DGs could impact adversely on the system operation including power quality and safety of the EPS. However, due to the growing number of EVs in the system, faster charging, shorter battery reaction time, and vehicle-to-grid services, EVs could be attractive sources for system operators (SOs) to improve system reliability while creating opportunity for EV owners to gain monetary benefits. In addition, the potential benefits of DG could be sustained in avoiding or shifting investment in transmission lines and/or transformers, minimizing ohmic losses, and protecting the environment. In this dissertation, potential benefits and challenges of EVs and DGs are explored. For some potential benefits, the dissertation develops systematic frameworks, in order to facilitate integration of EVs and DGs into the EPS. Also for some challenges, the dissertation presents solutions to analyze and overcome related difficulties. To study consequences of integrating EVs, a comprehensive model of EV operation is presented. The model covers different modes of operation and includes impact of battery degradation during the operation. The model is then extended to control a large group of EVs efficiently. Several possible ancillary services which could be offered by EVs, including voltage and frequency regulation services, are discussed. Several systematic frameworks are developed to engage EVs in provision of ancillary services, from economical and technical view points. Simulation results clearly indicate EVs ability to participate in ancillary services and possible revenue stream for EV owners. In terms of DGs, the dissertation addresses a common issue in most of utility companies and that is the risk of unintentional islanding of interconnected DGs. A systematic procedure is presented in this dissertation which can detect any possible operating conditions leading to an unintentional islanding of DGs. The developed procedure can serve utility companies as an analytical tool for any interconnection study, in a timely and costly efficient manner. The procedure is not dependent on the anti-islanding schemes nor DG technologies. Simulation results of different real case studies prove the generality and practicality of the procedure

Electric Mobility: Smart Transportation in Smart Cities

2014 - 2015One of the mega trends over the past century has been humanity’s move towards cities. Public Administration and Municipalities are facing a challenging task, to harmonize a sustainable urban development offering to people in city the best living conditions. Smart cities are now considered a winning urban strategy able to increase the quality of life by using technology in urban space, both improving the environmental quality and delivering better services to the citizens. Mobility is a key element to support this new approach in the growth of the cities. In fact, transport produces several negative impacts and problems for the quality of life in cities, such as, pollution, traffic and congestion. Therefore, Sustainable Mobility is one of the most promising topics in smart city, as it could produce high benefits for the quality of life of almost all the city stakeholders. The boldest and imminent challenge awaiting mobility in smart cities is the introduction of the electricity as energy vector instead of fossil fuels, concerning both the collective and the private transports. Electric public transport include electric city buses, trolleybuses, trams (or light rail), passenger trains and rapid transit (metro/subways/undergrounds, etc.). Even though railway systems are the most energy efficient than other transport modes, the enhancement of energy efficiency is an important issue to reduce their contributions to climate change further as well as to save and enlarge competition advantages involved. One key means for improving energy efficiency is to deploy advanced systems and innovative technologies. Additionally, electrification of the private road transport has emerged as a trend to support energy efficiency and CO2 emissions reduction targets. According to the International Energy Agency, in order to limit average global temperature increases to 2°C - the critical threshold that scientists say will prevent dangerous climate change -, by 2050, 21% of carbon reductions must come from the transport sector. Full electric vehicles (EVs) use electric motor and battery energy for propulsion, which has higher efficiency and lower operating cost compared to the conventional internal combustion engine vehicle. Today, there are more than 20 models offered by different brands covering different range of sizes, styles, prices and powertrains to suit the wider range of consumers as possible. The continuous development of lithium ion battery and of fast charging technology will be the major facilitators for EVs roll out in the very near future. However, the present EVs industry meets many technical limitations, such as high initial price, long battery recharge time, limited charging facilities and driving range. Although it is desirable a fast development from the start of electric mobility, its impact on the existing power grid must be assessed beforehand to see if it is necessary prior an adjustment of power infrastructure or/and the introduction of new services in the power grid. In fact, the interconnection of EVs on the power grid for charging their batteries potentially introduces negative impacts on grid operation: uncontrolled charging can significantly increase average load in the existing power systems, with problems in terms of reliability and overloads. If uncontrolled EV charging is added to the system, this can have effects both at the distribution and at the generation level. Controlled or smart charging will allow a much greater number of cars in the cities, avoiding local overload and allowing a faster EVs penetration without requiring an imminent improvement of the electricity generating and grid capacity. Smart charging might also allow load balancing both at sub-station and at the grid level, particularly with charging at peak wind supply times. This kind of use of EV battery capacity for storing electric energy may ease the integration of large scale intermittent electricity sources such as renewable energy sources. The proposed PhD Dissertation is developed in the context just described, mainly focusing the attention on the impact that electric mobility will have on the power systems and the effectiveness of solutions aimed to increase the reliability and resilience in the smart grid. In particular, it is addressed a scenario analysis regarding the electric vehicles charging management and some innovative solutions to increase energy efficiency in electrified transport systems. The first chapter emphasizes on the key aspects related to the sustainable mobility in the smart cities of the future. It provides a brief overview on the transport sector energy consumption expected in the next years. In particular, the chapter shows the significant contribution that the electrification of urban transport may provide to the sustainable mobility, and the serious concerns related to its impact on existing power systems. Chapter 2 proposes a solution method for an optimal generation rescheduling and load-shedding (GRLS) problem in microgrids in order to determine a stable equilibrium state following unexpected outages of generation or sudden increase in demand. The chapter mainly focuses on the mathematical formulation of the GRLS problem and the proposed solution algorithm. Finally, simulations results carried out by using a real case study data are presented and discussed. In Chapter 3, a simple and effective methodology is proposed to analyze data acquired during the fulfillment of the COSMO research project, and to identify typical load pattern for the EVs charging. The chapter also presents a novel scheduling problem formulation, flattening the demand load profile and minimizing the EVs charging costs, according to the electricity prices during the day. Finally, some simulations results are discussed, showing the effectiveness of the proposed methodology. Chapter 4 introduces some innovative solutions for energy efficiency in urban railway systems focusing, in particular, on energy storage systems and eco-drive operations in metro networks. The mathematical formulation of these optimization problems and the proposed solution algorithms are illustrated and discussed. The obtained results are part of the activity carried out in the SFERE research project. Finally, Chapter 5 ends the Dissertation with some concluding remarks and further developments of the proposed research activity. [edited by author]Una delle grandi tendenze nel corso del secolo scorso è stata la concentrazione della popolazione nelle città. Attualmente, le Pubbliche Amministrazioni e i Comuni si trovano ad affrontare un compito impegnativo per armonizzare uno sviluppo urbano sostenibile e offrire agli abitanti delle città le migliori condizioni di vita. Le smart cities sono ormai considerate una strategia urbana vincente in grado di aumentare la qualità della vita utilizzando la tecnologia, sia per il miglioramento della qualità ambientale che per fornire servizi migliori ai cittadini. A tale scopo, la mobilità risulta essere un elemento chiave per sostenere questo nuovo approccio nella crescita delle città. Infatti, i sistemi di trasporto urbano producono diversi effetti negativi sulla qualità della vita urbana, come ad esempio, inquinamento, traffico e congestione. Pertanto, la mobilità sostenibile è uno degli argomenti più interessanti per le smart cities, in quanto in grado produrre elevati benefici per la qualità della vita di quasi tutte le parti interessate degli agglomerati urbani. La sfida più audace e imminente per la mobilità nelle smart cities del futuro è l'introduzione dell'elettricità come vettore energetico al posto dei combustibili fossili, per quanto riguarda sia il trasporto collettivo che quello privato. I mezzi per il trasporto pubblico comprendono autobus elettrici, filobus, tram, treni passeggeri e trasporto rapido (metropolitane, etc.). Anche se i sistemi di trasporto su ferro sono più efficienti rispetto ad altri modi di trasporto, l’incremento dell'efficienza energetica è un tema importante per ridurre ulteriormente il loro contributo alle emissioni inquinanti e al consumo di energia. Le più promettenti soluzioni per migliorarne l'efficienza energetica consistono nell’implementazione di sistemi avanzati per il recupero dell’energia di frenata e tecnologie di controllo innovative. D’altro canto, l'elettrificazione del trasporto individuale su strada è emersa come una tendenza finalizzata a sostenere gli obiettivi di efficienza energetica e di riduzione delle emissioni di CO2. Secondo l'Agenzia Internazionale per l'Energia, al fine di limitare, entro il 2050, l'aumento della temperatura media globale a 2 °C - la soglia critica che gli scienziati suggeriscono di non superare per evitare pericolosi cambiamenti climatici -, il 21% delle riduzioni di biossido di carbonio deve provenire dal settore trasporti. I veicoli elettrici (EV) utilizzano un motore elettrico e l'energia accumulata nelle batterie per la propulsione, in modo da avere una maggiore efficienza e minori costi operativi rispetto ai veicoli convenzionali con motore a combustione interna. Oggi, esistono in commercio più di 20 modelli offerti da diverse case produttrici che coprono una ampia gamma di modelli che differiscono per dimensione, stile, prezzo e motorizzazione in modo da soddisfare il maggior numero di consumatori possibile. Il continuo sviluppo delle batterie al litio e delle tecnologie di ricarica rapida saranno i principali fattori abilitanti per la diffusione degli EV in un futuro molto prossimo. Tuttavia, l'attuale industria dei veicoli elettrici incontra molte limitazioni tecnico-economiche, come elevati costi, autonomia e tempi di ricarica della batteria, capillarità delle infrastrutture di ricarica. Sebbene sia auspicabile un rapido sviluppo della mobilità elettrica, il suo impatto sulla rete elettrica esistente deve essere investigato a fondo per verificare la necessità di potenziamenti delle infrastrutture e/o l'introduzione di nuovi servizi nella rete elettrica. Infatti, l'interconnessione dei veicoli elettrici con la rete di distribuzione dell’energia necessaria per la ricarica delle batterie può causare effetti negativi sul normale funzionamento del sistema elettrico: una ricarica degli EV non controllata può aumentare significativamente il carico medio negli impianti esistenti, introducendo problemi di affidabilità e sovraccarico. La ricarica intelligente o controllata degli EV consente, invece, di gestire un numero molto maggiore di autovetture elettriche nelle città, riducendo le possibilità di sovraccarico locale e di velocizzare la penetrazione della mobilità elettrica senza che rendere necessari imminenti potenziamenti dei sistemi di produzione di energia elettrica e incrementi della capacità di rete. La ricarica intelligente, inoltre, può anche influire sul bilanciamento del carico sia a livello della sottostazione elettrica che a livello di rete di distribuzione, in particolare quando si verificano molte sessioni di ricarica nelle ore di punta. Infatti, l’utilizzo della capacità della batteria degli EV per l’accumulo di energia elettrica può facilitare l'integrazione su larga scala delle fonti di energia non programmabili, come quelle rinnovabili. Il lavoro di tesi si sviluppa nel contesto di riferimento appena descritto, focalizzando l'attenzione soprattutto sull'impatto che la mobilità elettrica ha sui sistemi elettrici e sull'efficacia di nuove soluzioni finalizzate all’incremento dell'affidabilità nelle smart grids. In particolare, viene proposta un'analisi di scenario per quanto riguarda la gestione intelligente delle ricariche dei veicoli elettrici e alcune soluzioni innovative per aumentare l'efficienza energetica nei sistemi di trasporto elettrificati. Il primo capitolo sottolinea gli aspetti chiave relativi alla mobilità sostenibile nelle smart cities del futuro e fornisce una breve panoramica sul consumo energetico del settore trasporti previsto nel prossimo futuro. In particolare, vengono evidenziate da un lato il significativo contributo che l'elettrificazione dei trasporti urbani può fornire alla causa della mobilità sostenibile, e dall’altro, le gravi preoccupazioni legate all’impatto sui sistemi elettrici esistenti di un notevole incremento della domanda. Il Capitolo 2 propone un metodo per la soluzione del problema congiunto di scheduling dei generatori e load shedding (GRLS) all’interno di microgrids portando in conto l’incertezza sia sulla domanda che lato generazione. Il fine è determinare un nuovo stato di equilibrio stabile in seguito a guasti, riduzione della generazione da fonte rinnovabile o improvviso aumento della domanda. Il capitolo si concentra principalmente sulla formulazione matematica del problema GRLS e sull'algoritmo di soluzione proposto. Infine, sono presentati e commentati i risultati di simulazione basati su un caso studio reale. Nel Capitolo 3, è proposta una metodologia semplice ed efficace per identificare profili di carico tipico relativi alla ricarica di veicoli elettrici: in particolare, l’analisi condotta si basa sull’analisi dei dati acquisiti durante lo svolgimento del progetto di ricerca COSMO. Il capitolo, inoltre, introduce una formulazione matematica del problema dello scheduling delle ricariche dei veicoli elettrici, che garantisce un appiattimento del profilo di carico e riduce allo stesso tempo il costo della ricarica per gli utenti. Infine, sono commentati i risultati delle simulazioni eseguite dimostrando l'efficacia della metodologia proposta. Il Capitolo 4 introduce alcune soluzioni innovative per l'efficienza energetica nei sistemi di trasporto urbani: l’attenzione viene posta, in particolare, sui sistemi di accumulo dell’energia e sulla condotta di guida Eco-Drive in reti metropolitane. In dettaglio, nel capitolo, vengono introdotti e commentati la formulazione matematica dei problemi di ottimizzazione proposti e i rispettivi algoritmi di soluzione. I risultati ottenuti fanno parte delle attività svolte nell’ambito del progetto di ricerca SFERE. Infine, il Capitolo 5 conclude la tesi con alcune osservazioni finali e con i possibili sviluppi dell'attività di ricerca proposta. [a cura dell'autore]XIV n.s

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Microgrids: Planning, Protection and Control

This Special Issue will include papers related to the planning, protection, and control of smart grids and microgrids, and their applications in the industry, transportation, water, waste, and urban and residential infrastructures. Authors are encouraged to present their latest research; reviews on topics including methods, approaches, systems, and technology; and interfaces to other domains such as big data, cybersecurity, human–machine, sustainability, and smart cities. The planning side of microgrids might include technology selection, scheduling, interconnected microgrids, and their integration with regional energy infrastructures. The protection side of microgrids might include topics related to protection strategies, risk management, protection technologies, abnormal scenario assessments, equipment and system protection layers, fault diagnosis, validation and verification, and intelligent safety systems. The control side of smart grids and microgrids might include control strategies, intelligent control algorithms and systems, control architectures, technologies, embedded systems, monitoring, and deployment and implementation

Electric Bus Demand Management through Unidirectional Smart Charging

The difficulty of controlling the charging of electric buses (EBs), managing the EBs missions requirements with the ingoing and outgoing timetables and its effects on network demand are discussed in this study. The solutions suggest a call for worldwide, complex infrastructures that manage EVs and EBs equally. Additionally, the Distribution Network (DN) must be prepared for an increased prevalence of reverse power flow caused by widespread distributed renewable generation. This thesis focuses exclusively on EBs since they have higher capacity and predictable charging patterns, which makes them more significant for the DN in the context of a transition to complete vehicle electrification and technologies that are mature enough to be hosted. The proposed algorithm employs the Day-Ahead Energy Market (DAEM) in the Smart Charging (SC) to forecast the network operating circumstances, or to design a charging station specifications according to the networks requirements. Additionally, the technique makes it possible for distributed photovoltaic (PV) generation, allowing network demand to be referenced depending on net demand. It also optimises individual charger current per vehicle and per-time-step with load-levelling or peak-shaving as its primary goal. The final real demand demonstrates that a coarse correction of the demand is possible. According to the analysis on a typical European DN synthesised with a 13-Node Feeder with a radial topology, by using the DN voltage profile and associated line losses as references, it was found that the ideal node position location of the CS is dependent on PV penetration. In addition, the study points out the possibility for future updates of the algorithm in order to host real-time control of the reverse power flow and reactive power control, without overturn the charging system Infrastructure.The difficulty of controlling the charging of electric buses (EBs), managing the EBs missions requirements with the ingoing and outgoing timetables and its effects on network demand are discussed in this study. The solutions suggest a call for worldwide, complex infrastructures that manage EVs and EBs equally. Additionally, the Distribution Network (DN) must be prepared for an increased prevalence of reverse power flow caused by widespread distributed renewable generation. This thesis focuses exclusively on EBs since they have higher capacity and predictable charging patterns, which makes them more significant for the DN in the context of a transition to complete vehicle electrification and technologies that are mature enough to be hosted. The proposed algorithm employs the Day-Ahead Energy Market (DAEM) in the Smart Charging (SC) to forecast the network operating circumstances, or to design a charging station specifications according to the networks requirements. Additionally, the technique makes it possible for distributed photovoltaic (PV) generation, allowing network demand to be referenced depending on net demand. It also optimises individual charger current per vehicle and per-time-step with load-levelling or peak-shaving as its primary goal. The final real demand demonstrates that a coarse correction of the demand is possible. According to the analysis on a typical European DN synthesised with a 13-Node Feeder with a radial topology, by using the DN voltage profile and associated line losses as references, it was found that the ideal node position location of the CS is dependent on PV penetration. In addition, the study points out the possibility for future updates of the algorithm in order to host real-time control of the reverse power flow and reactive power control, without overturn the charging system Infrastructure

Centralised and decentralised control of active distribution systems: models, algorithms and applications.

Power system were traditionally planned and designed by assuming unidirectional power flows from power stations to loads. Nowadays, several factors (e.g., liberalization of the electricity market, need of increased reliability, and environmental issues) lead to a situation where electricity is produced also downstream the transmission level. Connecting generators to the distribution networks could provide several benefits to the whole system, but also technical and safety problems that must be faced. On the other hand, the loads are changing: new loads like electric vehicles and electric pumps are appearing in the network and they are going to modify the electricity consumption; while traditional loads are designed in order to be more efficient, but with additional functions or special features that require more energy. For all these reasons, since 2005, the interest on Smart Grid (electricity network that can intelligently integrate the actions of all users connected to it – generators, consumers – in order to efficiently deliver sustainable, economic and secure electricity supplies) increased. In this framework different techniques to control, operate and thereby integrate distributed energy resources into the network have been analysed and developed. The first technique designed is a centralised control, characterised by a central controller (Distribution Management System) that gathers information like the measures of the main electric parameters, energy price and indicates to DERs (Active Loads, Generators, Energy Storage) the optimal set points minimizing the system cost, subject to technical and economical constraints. The second technique developed is a decentralised control using Multi Agent Systems (MAS). This type of control has been designed and developed for the direct control of active demand and plug-in electric vehicles, managed by the Aggregator, entrusted by the end users to change their consumption habits according to their needs. Moreover, the proposed decentralised MAS, with the active participation of small consumers in the electricity system, support the integration of the Electric Vehicles in the LV distribution network and reduce its harmful impact on voltage regulation. The techniques and the algorithms proposed by the author are analysed and applied in representative Italian Distribution networks, by taking into account the development of the distribution system according to the load profile evolution, providing several examples to underline the importance of the Active Management for deferring the reinforcement of the existing grid infrastructures, increasing the hosting capacity of the networ

Centralised and decentralised control of active distribution systems: models, algorithms and applications.

Power system were traditionally planned and designed by assuming unidirectional power flows from power stations to loads. Nowadays, several factors (e.g., liberalization of the electricity market, need of increased reliability, and environmental issues) lead to a situation where electricity is produced also downstream the transmission level. Connecting generators to the distribution networks could provide several benefits to the whole system, but also technical and safety problems that must be faced. On the other hand, the loads are changing: new loads like electric vehicles and electric pumps are appearing in the network and they are going to modify the electricity consumption; while traditional loads are designed in order to be more efficient, but with additional functions or special features that require more energy. For all these reasons, since 2005, the interest on Smart Grid (electricity network that can intelligently integrate the actions of all users connected to it – generators, consumers – in order to efficiently deliver sustainable, economic and secure electricity supplies) increased. In this framework different techniques to control, operate and thereby integrate distributed energy resources into the network have been analysed and developed. The first technique designed is a centralised control, characterised by a central controller (Distribution Management System) that gathers information like the measures of the main electric parameters, energy price and indicates to DERs (Active Loads, Generators, Energy Storage) the optimal set points minimizing the system cost, subject to technical and economical constraints. The second technique developed is a decentralised control using Multi Agent Systems (MAS). This type of control has been designed and developed for the direct control of active demand and plug-in electric vehicles, managed by the Aggregator, entrusted by the end users to change their consumption habits according to their needs. Moreover, the proposed decentralised MAS, with the active participation of small consumers in the electricity system, support the integration of the Electric Vehicles in the LV distribution network and reduce its harmful impact on voltage regulation. The techniques and the algorithms proposed by the author are analysed and applied in representative Italian Distribution networks, by taking into account the development of the distribution system according to the load profile evolution, providing several examples to underline the importance of the Active Management for deferring the reinforcement of the existing grid infrastructures, increasing the hosting capacity of the networ

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems