Renewable energy integration is increasing – alongside it, the main limiting factors of such sources of energy have to be considered. Each source of energy comes with its unique sets of challenges, namely the way that the generation curves behave. These patterns should be considered, if photovoltaics can contribute at a larger rate to the grid. The current non-renewable sources provide a high response rate and great control over voltage/frequency – key parameters of the grid. Proper utilization of renewable energies is key to sustainable systems of the future.The work considers the possibility of regulating the energy flow through the usage of electric vehicles (EV). The thesis proposes a model within which particle swarm optimization is used to derive EV charging rates, which contribute to the overall performance of a controlled household system. Three control strategies are considered – individual, bottom-up and top-down control. The methodologies are introduced and compared in the study.Top-down control proves to be the most stable and most efficient at reducing energy mismatch when compared to other control strategies. It should however be underlined that any control strategy proposed in the study leads to a greater utilization of renewable energy and can greatly benefit any system with EVs and PV energy present.

Uptake of electric vehicles (EVs) is accelerating as governments around the world aim to decarbonize transportation. While EV adoption is widely promoted in Canada, swift and widespread EV adoption will require some degree of controlled charging to mitigate the challenges that EV charging imposes onto the power system, such as increased cost and emissions from electricity generation. In this analysis, the potential benefits of utility controlled charging (UCC) are evaluated for the city of Regina, Saskatchewan, which aims to be 100% renewable by 2050. The flexibility that UCC can contribute, and its effectiveness for integrating variable renewables is tested in configurations with solar resources, wind resources, and a mix of both. A novel modelling methodology is developed to do so, which links a travel demand model (TASHA) and an electricity system production cost model (SILVER), using a novel intermediate charging model to simulate electric vehicle travel behaviour and utility controlled charging. The use of operational models allows for an accurate representation of both travel demand and electricity system operating costs and emissions at a high spatial and temporal resolution. By linking sectoral models in this way, the interactions between the two sectors - transportation and power – can be investigated simultaneously with detailed insight into the two individual sectors. Results show that uncontrolled charging will increase average emissions from the electricity grid, but controlled charging decreases both greenhouse gas emissions as well as operating costs. By shifting vehicle charging to times when renewable energy production is high, UCC reduces operating costs and emissions by 7% compared to uncontrolled charging, without requiring changes to travel scheduling and behaviour. The temporal characteristics of wind generation is found to be more compatible with controlled charging than solar PV, due to its longer generation periods and higher capacity factor in the winter, when demand is also high. / Graduate / 2022-11-19

As the world undertakes global climate goals to lower the emissions of greenhouse gases, more renewable energy sources are introduced in the electrical energy system, and fossil fuel driven combustion engines aims to be replaced by electric vehicles. New problems connected to the transition such as voltage fluctuations, production and consumption mismatch and high peak loads, requires new solutions. Such a solution could be to use smart charging algorithms and bidirectional charging for electrical vehicles in order to avoid further increased power demand during peak demand hours and to make the electric vehicle a resource more than solely as a mode of transportation. Such resource could be a battery storage for the home by using a vehicle to home strategy and for the electrical grid by using a vehicle to grid strategy. These strategies demand certain technology with only a few options available on the market today, but with a lot of recent and ongoing research on the way. In this thesis a smart charging algorithm is developed which aims to lower a households electricity cost by optimizing charging and discharging of an electric vehicle. The charging may come from the grid or the household’s photovoltaic system and the discharging may go to the house home appliances or back to the grid, utilizing vehicle to home and vehicle to grid strategies. The algorithm was tested in MATLAB for five different scenarios showcasing these possibilities and simulated in combination with five different user profiles. The results show that the charging peaks are moved to an area of less consumption and verify that the electricity costs can be lowered on daily and yearly basis.

The purpose of this bachelor thesis is to investigate different outcomes of the usage of photovoltaic (PV) power for electric vehicle (EV) charging adjacent to workplaces. In the investigated case, EV charging stations are assumed to be connected to photovoltaic systems as well as the electricity grid. The model used to simulate different scenarios is based on a goal of achieving constant power exchange with the grid by adjusting EV charging to a solar irradiance forecast. The model is implemented in MATLAB. This enables multiple simulations for varying input parameters. Data on solar irradiance are used to simulate the expected PV power generation. Data on driving distances are used to simulate hourly electricity demands of the EVs at the charging stations. A sensitivity analysis, based on PV irradiance that deviates from the forecast, is carried out. The results show what power the grid needs to have installed capacity for if no PV power system is installed. Furthermore, appropriate PV power installation sizes are suggested. The suggestions depend on whether the aim is to achieve 100 percent self-consumption of PV generated power or full PV power coverage of charging demands. For different scenarios, PV power installations appropriate for reducing peak powers on the grid are suggested. The sensitivity analysis highlights deviations caused by interference in solar irradiance.

The purpose of this bachelor thesis is to develop a smart charging algorithm for electric vehicles (EVs) and examine the potential of the smart charging scheme, compared to uncontrolled charging scheme at residential houses with an installed photovoltaic (PV) system. The thesis examines if smart charging can increase the photovoltaic self-consumption and self-sufficiency of houses. Also, the thesis will evaluate if the smart charging scheme can reduce the household peak loads compared to the uncontrolled charging scheme. The presented results show that the implementation of the proposed algorithm will reduce the household peak load on average by 38.64 percent at a house with an installed PV system. The self-consumption and self-sufficiency increased by 4.69 percent and 4.97 percent when the smart charging algorithm was applied. To increase the credibility of the developed model a sensitivity analysis considering a number of houses and vehicles was done.  From the results, it can be concluded that the proposed smart charging algorithm could be an option to reduce the household peak load and increase the usage of renewable energy sources.

Nickel cadmium and nickel metal hydride accumulators sets are currently used for supplying hand tools (screwdrivers,...). Problems of cheap hand tools are chargers, mostly of it doesn’t use any charging driving circuits. In consequence of it the accumulators are kept incorrectly, which causes lowernig of lifetime. This thesis suggest circuit design Main of intelligent charging system, which enable better battery management, increases battery lifetime and lower load of environment.

The rollout of electric vehicles (EV) occurring in parallel with the decarbonisation of the power sector can bring uncontested environmental benefits, in terms of CO2 emission reduction and air quality. This roll out, however, poses challenges to power systems, as additional power demand is injected in context of increasingly volatile supply from renewable energy sources. Smart EV charging services can provide a solution to such challenges. The development of effective smart charging services requires evaluating pre-emptively EV drivers’ response. The current practice in the appraisal of smart charging strategies largely relies on simplistic or theoretical representation of drivers’ charging and travel behaviour. We propose a random utility model for joint EV drivers’ activity-travel scheduling and charging choices. Our model easily integrates in activity-based demand modelling systems for the analyses of integrated transport and energy systems. However, unlike previous charging behaviour models used in integrated transport and energy system analyses, our model empirically captures the behavioural nuances of tactical charging choices in smart grid context, using empirically estimated charging preferences. We present model estimation results that provide insights into the value placed by individuals on the main attributes of the charging choice and draw implications charging service providers

Electric Vehicles (EVs) reduce dependency on oil and carbon emissions. An upsurge in demand for EVs could lead to negative impacts on the grid. However, charging strategies, such as supporting the grid using vehicle-to-grid (V2G) and smart charging technology, can go a long way to reducing the impacts on the electrical load curve. The thesis presents a number of aspects which relate to the interconnection between EVs and the electric grid for achieving an optimized integration. An adaptive algorithm has been developed to perform load peak shaving by V2G and smart charging, while a hypothetical case study containing several types of EVs in a local grid has been conducted. The aim is to examine the developed algorithm. In conclusion, by using the adaptive algorithm, written in C++, an optimum status has been achieved concerning the electric grid and EV batteries.

Les ventes de Véhicules Électriques (VE) ont fortement augmenté ces dernières années. Si les processus de charge de ces VE ne sont pas gérés de manière intelligente, ils risquent de surcharger les réseaux électriques. Inversement, les VE pourraient représenter une opportunité pour ces réseaux en tant qu'unités de stockage distribuées.Cette thèse se propose d’étudier l’intégration intelligente des véhicules rechargeables dans les réseaux électriques d’un point de vue technique, réglementaire et économique. Dans un premier temps, le cadre général nécessaire au développement de ces solutions est passé en revue : les domaines d’application et scenarios de référence sont décrits, les acteurs principaux listés, et les défis principaux analysés.Ensuite, l’accent est mis sur les services système, et plus particulièrement sur le réglage de fréquence. Les conditions règlementaires permettant la participation d’une flotte de véhicules électriques à ce service sont étudiées à partir d’une revue des règles de gestionnaires de réseau de transport existants. De nombreuses simulations techniques et économiques sont réalisées, pour différentes règles de marché.Les services réseau locaux sont ensuite considères. Un éco-quartier est modélisé : il comprend différentes unités de consommation et des sources de production distribuées. Un gestionnaire énergétique local est proposé : son rôle est de contrôler les taux de charge / décharge des véhicules électriques de l’éco-quartier dans l’objectif de limiter les surcharges subies par le transformateur électrique de l’éco-quartier. Des conséquences économiques sont tirées des résultats techniques.Enfin, des résultats expérimentaux sont présentés. Le comportement de deux VE est analysé, dont un dispose de capacités bidirectionnelles. Les preuves de concept expérimentales confirment les capacités théoriques des véhicules électriques : il s’agit d’unités à temps de réponse très court (même en considérant l’architecture TIC complète) et ils sont capables de réagir à des signaux réseau très précisément. / Electric vehicles (EVs) penetration has been rapidly increasing during the last few years. If not managed properly, the charging process of EVs could jeopardize electric grid operations. On the other hand, Grid Integrated Vehicles (GIVs), i.e. vehicles whose charging and discharging patterns are smartly controlled, could turn into valuable assets for the electrical grids as distributed storage units.In this thesis, GIVs are studied from a technical, regulatory, and economics perspectives. First, the general framework for a smart grid integration of EVs is reviewed: application areas and benchmark scenarios are described, the main actors are listed, and the most important challenges are analyzed.Then, the emphasis is put on system wide services, and more particularly on frequency control mechanisms. The regulatory conditions enabling the participation of GIV fleets to this service are studied based on an intensive survey of existing transmission system operator rules. Several economics and technical simulations are performed for various market designs.Then, local grid services are investigated. A representative eco-district is modeled, considering various consumption units and distributed generation. A local energy management system is proposed; it is responsible for controlling the charging / discharging patterns of the GIVs which are located in the district in order to mitigate the overloading conditions of the eco-district transformer. Economic consequences are derived from this technical analysis.At last, some experimental results are presented. They show the behavior of two GIVs, including one with bidirectional capabilities. The experimental proof of concepts confirm the theoretical abilities of GIVs: they are very fast responding units (even considering the complete required IT architecture) and are able to follow grid signals very accurately.

Les ventes de Véhicules Electriques ont été en constante augmentation ces dix dernières années, stimulées par l’adoption de politique publique favorisant la décarbonation du secteur automobile. Dans un contexte d’accroissement des énergies renouvelables dans le mix énergétique, entraînant des besoins plus important en flexibilité, la diffusion massive des véhicules électriques pourrait constituer une nouvelle source de contrainte pour les gestionnaire de réseaux d'électricité si la recharge n’est pas gérée de manière intelligente.La gestion de la recharge des flottes de Véhicules Electriques peut aussi constituer une opportunité pour apporter cette flexibilité, en particulier si les véhicules sont équipés de chargeurs bidirectionnels, capables de réinjecter de l’électricité dans le système pour équilibrer les réseaux.La recherche s’est principalement intéressée à la conception d’algorithmes permettant cette recharge « intelligente », qui prennent en compte les besoins en mobilité des utilisateurs, tout en fournissant différents services de flexibilité.Cette thèse s’attache à aller au-delà de l’aspect algorithmique, en balayant l’ensemble des aspects qui permettraient d’aboutir à un modèle d’affaire viable, et en se focalisant sur la fourniture d’un type de service : la réserve primaire (Frequency Containment Reserve), qui constitue le service identifié comme ayant la plus forte valeur pour des flottes de Véhicules équipés de chargeurs bidirectionnels. / Transport industry being one the first CO2 emitters, there is an urgent need to decarbonize this sector, which could be achieved by the conjunction of the electrification of the vehicles and decarbonization of the electricity generation mix. In conjunction with increasing flexibility needs to support the introduction of Renewable Energy Sources, the development of Electric Vehicles could add new constraints for System Operators if charging process is not managed in a smart way.However, considering mobility requirements, there is a flexibility in the charging pattern of the vehicles, which could be used to offer flexibility services to System Operators, using smart-charging algorithms. Moreover, this flexibility could increase with the possibility to have reverse flow from the battery to the grid.Research focused mainly, during last years, on the design of algorithms to provide services with electric vehicles, taking into account mobility needs of users. In this thesis, we try to go beyond this design of algorithms, going through the different steps to elaborate a viable business model. We focus on the provision of one service – Frequency Containment Reserve – identified as the most valuable for Electric Vehicles equipped with bidirectional chargers.