The installed base of photovoltaic power plants in the United States has roughly doubled every 1 to 2 years between 2008 and 2015. The primary economic drivers of this are government mandates for renewable power, falling prices for all PV system components, 3rd party ownership models, and a generous tariff scheme known as net-metering. Other drivers include a desire for decreasing the environmental impact of electricity generation and a desire for some degree of independence from the local electric utility. The result is that in coming years, PV power will move from being a minor niche to a mainstream source of energy. As additional PV power comes online this will create challenges for the electric grid operators. We examine some problems related to large scale adoption of PV power in the United States. We do this by first discussing questions of reliability and efficiency at the PV system level. We measure the output of a fleet of small PV systems installed at Tucson Electric Power, and we characterize the degradation of those PV systems over several years. We develop methods to predict energy output from PV systems and quantify the impact of negatives such as partial shading, inverter inefficiency and malfunction of bypass diodes. Later we characterize the variability from large PV systems, including fleets of geographically diverse utility scale power plants. We also consider the power and energy requirements needed to smooth those systems, both from the perspective of an individual system and as a fleet. Finally we report on experiments from a utility scale PV plus battery hybrid system deployed near Tucson, Arizona where we characterize the ability of this system to produce smoothly ramping power as well as production of ancillary energy services such as frequency response.

The current trend in the wind power industry is to develop wind turbines of constantly increasing size and rated power, as well as wind farms of growing size and installed wind power. A careful study of the behaviour of the wind turbines during their operation is of crucial importance in the planning phase and in the design stage of a wind farm, in order to minimize the risks deriving from a non accurate prediction of their impact in the electric grid causing sensible faults of the system. To analyze the impact of the wind turbines in the system, motivates the development of accurate yet simple models. To be able to practically deal with this topics, a simple model of a wind turbine system is investigated and developed; it has the aim to describe the behaviour of a wind turbine in operation on a mechanical standpoint. The same reduced order simple model can also be employed for control system studies; the control system model that can’t be used in generation, can use the reduced model. Together with the analytical description of such model, is realized a MatLab code to numerically analyse the response of the system, and the results of the simulation through such code are presented. The objective of this thesis has been to provide a simple benchmark tool in MatLab for grid integration and control studies for interested researchers.

The increased integration of distributed and renewable energy resources (DERs) has motivated the evolution of new standards in grid interconnection requirements. New standards have the requirement for the DERs to remain connected during the transient grid fault conditions and to offer support to the grid. This requirement is known as the fault ride-through (FRT) capability of the inverter-based DERs and is an increasingly important issue. This dissertation presents the FRT capability of the DERs that employ a voltage control strategy in their control systems. The voltage control strategy is increasingly replacing the current control strategy in the DERs due to the fact that it provides direct voltage support. However, the voltage control technique limits the ability of direct control over the inverter current. This presents a challenge in addressing the FRT capability where the problem is originally formulated in terms of the current control. This dissertation develops a solution for the FRT capability of inverters that use a voltage control strategy. The proposed controller enables the inverter to ride through the grid faults and support the grid by injecting a balanced current with completely controlled real and reactive power components. The proposed controller is flexible and can be used in connection with various voltage control strategies. Stability analysis of the proposed control structure is performed based on a new linear time-invariant model developed in this dissertation. This model significantly facilitates the stability and design of such control loops. Detailed simulation, real-time and experimental results are presented to evaluate the performance of the proposed control strategy in various operating conditions. Desirable transient and steady-state responses of the proposed controller are observed. Furthermore, the newly established German and Danish grid fault ride-through standards are implemented in this research as two application examples and the effectiveness of the dissertation results are illustrated in the context of those two examples.

The global market for photovoltaics (PV) has increased rapidly: during 2014, 44 times more was installed than in 2004, partly due to a price reduction of 60-70% during the same time period. Economic support schemes that were needed to make PV competitive on the electricity market have gradually decreased and self-consumption of PV electricity is becoming more interesting internationally from an economic perspective. This licentiate thesis investigates self-consumption of residential PV electricity and how more PV power can be allowed in and injected into a distribution grid. A model was developed for PV panels in various orientations and showed a better relative load matching with east-west-oriented compared to south-oriented PV panels. However, the yearly electricity production for the east-west-system decreased, which resulted in less self-consumed electricity. Alternatives for self-consumption of PV electricity and reduced feed-in power in a community of detached houses were investigated. The self-consumption increased more with shared batteries than with individual batteries with identical total storage capacity. A 50% reduction in feed-in power leads to losses below 10% due to PV power curtailment. Methodologies for overvoltage prevention in a distribution grid with a high share of PV power production were developed. Simulations with a case with 42% of the yearly electricity demand from PV showed promising results for preventing overvoltage using centralized battery storage and PV power curtailment. These results show potential for increasing the self-consumption of residential PV electricity with storage and to reduce stress on a distribution grid with storage and power curtailment. Increased self-consumption with storage is however not profitable in Sweden today, and 42% of the electricity from PV is far more than the actual contribution of 0.06% to the total electricity production in Sweden in 2014.

During the 21st century there has been a tremendous increase in grid-connected photovoltaic (PV) capacity globally, due to falling prices and introduction of economic incentives. PV systems are in most cases small-scale, installed on residential dwellings, which means that the power production is widely distributed and close to the end-user of electricity. In this licentiate thesis the distributed PV in the built environment is studied. A methodology for assessing short-term (sub-minute) solar variability was developed, which in the continuation of this PhD project could be used to study the aggregated impact on the local distribution grid from dispersed PV systems. In order to identify potential locations for PV systems in a future scenario, methodology was developed to assess the rooftop topography on both local level using LiDAR data and nationally through building statistics. Impacts on the distribution grid were investigated through a case study on a rural municipality in Sweden. It was found that the hosting capacity, i.e. the amount of PV power generation that can be integrated in the grid without exceeding certain power quality measures, is high, at least 30%. However, the hosting capacity on transmission level needs further investigation. As a first step a methodology was developed in order to model scenarios for hourly solar power generation, aggregated over wide areas, here applied to the whole Swedish power system. The model showed high correlation compared to PV power production reported to the Swedish transmission system operator (TSO). Furthermore, it was used to model scenarios of high PV penetration in Sweden, which give some indications on the impact on the power system, in terms of higher frequency of extreme ramps.

During the 21st century there has been a rapid increase in grid-connected photovoltaic (PV) capacity globally, due to falling system component prices and introduction of various economic incentives. To a large extent, PV systems are installed on buildings, which means they are widely distributed and located close to the power consumer, in contrast to conventional power plants. The intermittency of solar irradiance poses challenges to the integration of PV, which may be mitigated if properly assessing the solar resource. In this thesis, methods have been developed for solar variability and resource assessment in the built environment on both national and local level, and have been applied to grid integration studies. On national level, a method based on building statistics was developed that reproduces the hourly PV power generation in Sweden with high accuracy; correlation between simulated and real power generation for 2012 and 2013 were 0.97 and 0.99, respectively. The model was applied in scenarios of high penetration of intermittent renewable energy (IRE) in the Nordic synchronous power system, in combination with similar models for wind, wave and tidal power. A mix of the IRE resources was sought to minimise the variability in net load (i.e., load minus IRE, nuclear and thermal power). The study showed that a fully renewable Nordic power system is possible if hydropower operation is properly planned for. However, the contribution from PV power would only be 2-3% of the total power demand, due to strong diurnal and seasonal variability. On local level, a model-driven solar resource assessment method was developed based on low-resolution LiDAR (Light Detection and Ranging) data. It was shown to improve the representation of buildings, i.e., roof shape, tilt and azimuth, over raster-based methods, i.e., digital surface models (DSM), which use the same LiDAR data. Furthermore, the new method can provide time-resolved data in contrast to traditional solar maps, and can thus be used as a powerful tool when studying the integration of high penetrations of PV in the distribution grid. In conclusion, the developed methods fill important gaps in our ability to plan for a fully renewable power system.

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.

Wind generating resources have been increasing more rapidly than any other renewable generating resources. Wind power forecasting is an important issue for deploying higher wind power penetrations on power grids. The existing work on power output forecasting for wind farms has focused on the temporal issues. As wind farm outputs depend on natural wind resources that vary over space and time, spatial analysis and modeling is also needed. Predictions about suitability for locating new wind generating resources can be performed using spatial modeling. In this dissertation, we propose a new approach to spatial prediction of wind farm outputs for grid integration based on Kriging techniques. First, we investigate the characteristics of wind farm outputs. Wind power is variable, uncontrollable, and uncertain compared to traditional generating resources. In order to understand the characteristics of wind power outputs, we study the variability of wind farm outputs using correlation analysis. We estimate the Power Spectrum Density (PSD) from empirical data. Following Apt[1], we classify the estimated PSD into four frequency ranges having different slopes. We subsequently focus on phenomena relating to the slope of the estimated PSD at a low frequency range because our spatial prediction is based on the period over daily to monthly timescales. Since most of the energy is in the lower frequency components (the second, third, and fourth slope regions have much lower spectral density than the first), the conclusion is that the dominant issues regarding energy will be captured by the low frequency behavior. Consequently, most of the issues regarding energy (at least at longer timescales) will be captured by the first slope, since relatively little energy is in the other regions. We propose the slope estimation model of new wind farm production. When the existing wind farms are highly correlated and the slope of each wind farm is estimated at a low frequency range, we can predict the slope with low frequency components of a new wind farm through the proposed spatial interpolation techniques. Second, we propose a new approach, based on Kriging techniques, to predict wind farm outputs. We introduce Kriging techniques for spatial prediction, modeling semivariograms for spatial correlation, and mathematical formulation of the Kriging system. The aim of spatial modeling is to calculate a target value of wind production at unmeasured or new locations based on the existing values that have already been measured at locations considering the spatial correlation relationship between measured values. We propose the multivariate spatial approach based on Co-Kriging to consider multiple variables for better prediction. Co-Kriging is a multivariate spatial technique to predict spatially distributed and correlated variables and it adds auxiliary variables to a single variable of interest at unmeasured locations. Third, we develop the Augmented Kriging-based Model, to predict power outputs at unmeasured or new wind farms that are geographically distributed in a region. The proposed spatial prediction model consists of three stages: collection of wind farm data for spatial analysis, performance of spatial analysis and prediction, and verification of the predicted wind farm outputs. The proposed spatial prediction model provides the univariate prediction based on Universal Kriging techniques and the multivariate prediction based on Universal and Co-Kriging techniques. The proposed multivariate prediction model considers multiple variables: the measured wind power output as a primary variable and the type or hub height of wind turbines, or the slope with low frequency components as a secondary variable. The multivariate problem is solved by Co-Kriging techniques. In addition, we propose $p$ indicator as a categorical variable considering the data configuration of wind farms connected to electrical power grids. Although the interconnection voltage does not influence the wind regime, it does affect transmission system issues such as the level of curtailments, which, in turn, affect power production. Voltage level is therefore used as a proxy to the effect of the transmission system on power output. The Augmented Kriging-based Model (AKM) is implemented in the R system environments and the latest Gstat library is used for the implementation of the AKM. Fourth, we demonstrate the performance of the proposed spatial prediction model based on Kriging techniques in the context of the McCamey and Central areas of ERCOT CREZ. Spatial prediction of ERCOT wind farms is performed in daily, weekly, and monthly time scales for January to September 2009. These time scales all correspond to the lowest frequency range of the estimated PSD. We propose a merit function to provide practical information to find optimal wind farm sites based on spatial wind farm output prediction, including correlation with other wind farms. Our approach can predict what will happen when a new wind farm is added at various locations. Fifth, we propose the Augmented Sequential Outage Checker (ASOC) as a possible approach to study the transmission system, including grid integration of wind-powered generation resources. We analyze cascading outages caused by a combination of thermal overloads, low voltages, and under-frequencies following an initial disturbance using the ASOC. / text

Electric vehicles (EV) in the transportation sector will play a major role in achieving low-carbon emissions from the mobility of vehicles. With a future increase in electric vehicles and autonomous electric vehicles, the demand for charging increases and this comes with new problems that require modern solutions. The integrity of the power grid infrastructure can be at risk as grid congestion and power mismatch can cause problems as the act of charging the vehicles adds an extra burden on the power grid. Today, there exist several scenarios on how a vehicle can be charged and a number of technical solutions that are advantageous based on different aspects such as user's need, price of the electricity, and electricity network.   This thesis focuses on identifying different charging scenarios in mapping the information that is required and studying the effects of these charging scenarios through a smart charging algorithm. A smart charging algorithm to optimise for charging and discharging of the electric vehicle is developed and tested in a MATLAB environment with the aim to achieve a balanced grid load profile. The simulation results verify the potential of the algorithm to reduce the adverse effects of electric vehicle charging. Additionally, a model to map autonomous electric vehicles to charging station in accounts of a low state of charge is also developed in MATLAB

Reliable electricity supply remains a major problem in rural India nowadays. Renewable off-grid solutions have been applied in the last decades to increase power supply reliability but often failed to be feasible due to their high energy costs compared to the national grid. Grid Integrated Mini-grids with Storage (GIMS) can provide reliable power supply at an affordable price by combining mini-grids and national grid facilities. However, research on the techno-economic feasibility of these systems in the country is very limited and unavailable in the public sphere. This research project analysed three different aspects of the GIMS feasibility. First, the feasibility of the use of hybrid wind and solar Photovoltaic (PV) systems in GIMS was analysed by comparing the Levelised Cost of Electricity (LCOE) and Net Present Cost (NPC) of solar PV and hybrid PV/Wind GIMS systems. Second, the potential savings GIMS can offer due to the possibility of selling power to the grid were quantified by comparing the LCOE and NPC of the system with and without grid export. Lastly, the cost of reliability of the power supply was represented by the influence of the allowed percentage of capacity shortage on the total cost of the system. The analysis was carried out by means of the software HOMER and was based on three case studies in India. The results of this analysis showed that the use of hybrid systems could generate savings of up to 17% of the LCOE of the GIMS system in comparison to solar mini-grids. Moreover, power sales to the grid enabled LCOE savings up to 35% with respect to mini-grid without power sell-back possibility. In addition, the LCOE could be reduced in between 28% and 40% in all cases by enabling up to a 5% of capacity shortage in the system. / En tillförlitlig elförsörjning är ett stort problem på landsbygden i Indien. Elnätslösningar baserade på förnybara energikällor har undersökts under de senaste decennierna för att öka tillförlitligheten men har ofta misslyckats i genomförandefasen på grund av höga energikostnader jämfört med i det nationella nätet. Nätintegrerade mini-grids med energilagring (GIMS) kan ge tillförlitlig strömförsörjning till ett överkomligt pris genom att kombinera mini-grids och nationella elnätsanläggningar. Forskningen om den tekniskekonomiska genomförbarheten av dessa system i landet är emellertid mycket begränsad och otillgänglig inom den offentliga sfären. I den här studien analyseras tre olika aspekter av GIMS-genomförbarheten. För det första analyserades genomförbarheten av att använda hybrida vind- och solcellssystem i GIMS genom att jämföra ”Levelised Cost of Electricity” (LCOE) nivån och nuvärdeskostnaden (NPC) för solcellssystem (PV) och hybrid PV/Vind GIMS-system. För det andra kan de potentiella besparingar GIMS erbjuder, genom möjligheten att sälja elenergi till nätet, kvantifieras genom att jämföra LCOE och NPC i systemet med och utan ”nätexport”. Slutligen studeras kostnaden för tillförlitligheten hos strömförsörjningen i förhållande till accepterad kapacitetsbrist med avseende på systemets totala kostnad. Analysen har utförts med hjälp av mjukvaran HOMER och grundas på tre fallstudier i Indien. Resultaten av denna analys visar att användningen av hybridsystem skulle kunna generera besparingar på upp till 17% av LCOE i GIMS-systemet i jämförelse med enbart PV-baserade mini-grids. Försäljning av elenergi till nätet möjliggör LCOE-besparingar på upp till 35% med i förhållande till mini-grids utan möjlighet till export. Slutligen: LCOE kunde reduceras mellan 28% och 40% i samtliga fall genom att tillåta upp till 5% kapacitetsbrist i systemet.