In order to reach climate goals regarding the reduction ofcarbon dioxide in the environment, the decarbonisation of the transport sector plays a crucial role. Along with this revolution, electric vehicles will most likely be a candidate to replace market shares from gasoline and diesel. The deployment of electric cars is now starting to increase, by over 2 millions electrical vehicles running on the streets world wide in year 2016, which is more than the double compared to previous year. This master thesis examines home charging and its impact on the distribution grid of two types, one in a smaller urban area and one in a rural area in the Swedish city Sala. Different integration levels of electric vehicles are examined regarding voltage drop, relative loading of conductors and transformers, and the voltage symmetry with charging only on one phase. The simulations are made in the NIS-based system dpPower based on charging data from Grahn et.al.(2013), and the study of powerpeaks is made in Matlab with the use of consumption data from customers. The results show that the transfomer is the only limitation in the grid of the urban area, which fails at an integration level of 50 percent at 11 kW of charging. Compared to the grid in the rural area which reacts more strongly of home charging. In this case it is most likely to fail at 30 percents integration at 3,7 kW of charging. Also a greater accuracy has to be taken into account here regarding the placing of one phase-chargers at different phases. This in order to not exceed the 2 percent limit of voltage symmetry. With the aim to reduce power peaks, the grid seems to benefit from high demand response and local battery storage, which reduce the power peaks to an extent that could be comparable to reducing the integration level of electric vehicles from 50 to 30 percent.

Due to the environmental issues, the amount of installed solar power increases. In the same time, the electric vehicle fleet is expanding rapidly. Those two growing technologies, if not controlled, can cause various unwanted effects for the electricity grid. In order to decrease their negative effects on the grid and benefit from it at the same time, these technologies have to work in synergy with each other. This synergy can be enabled through smart charging of electric vehicles. Therefore, the aim of this study is to develop a smart charging algorithm which uses solar production forecasts to charge the vehicles at a workplace. Furthermore, the goal is to examine how such control of the charging affects the self-consumption of solar power, self-sufficiency and the amount of energy imported from the grid as opposed to uncontrolled charging. To fulfill the goal, the algorithm was developed based on solar production forecasts. The forecasts were created through autoregressive models, AR and ARMA which were estimated using the actual solar production data collected at one of Uppsala regions solar production plants. Also, a case where ideal forecasts were used was applied. Furthermore, the charging need for various number of cars was simulated for every working day throughout an entire year in order to simulate the application of the algorithm and examine its performance but also to simulate the uncontrolled charging. The results, compared to the uncontrolled charging, show that the algorithm is able to increase the self-consumption of solar power by an average of 9,33 – 25,30 percentage points for 10 – 50 charging cars. It is also able to increase the selfsufficiency by an average of 42,65 – 31,28 percentage points for 10-50 cars respectively thus reducing the need of electricity imports from the grid. Furthermore, it was discovered that the results, the self-consumption and selfsufficiency, from the simulations with ideal forecasts differed only by up to 2 percentage points from the simulations where the forecasts were created through an AR(9) model (AR model of order 9). This allows a conclusion that a simple AR(9) model is completely sufficient to create forecasts that are good enough to produce satisfactory results. In general, it is concluded that the algorithm developed in this study is successful when it comes to increasing the self-consumption of the solar power, the selfsufficiency and decreasing the amount of energy needed from the electricity grid. This limits the negative impacts that the increasing solar power production and the growing electric vehicle fleet have on the electricity grid.

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

Elektrifieringen av bilflottan sker i rasande takt. Även andra samhällssektorers efterfrågan på el väntas öka drastiskt under kommande decennier, vilket i kombination med en växande andel intermittenta energikällor trappar upp påfrestningarna på elnätet och ställer krav på anpassningar. En föreslagen dellösning till kraftsystemets kommande utmaningar är att utnyttja efterfrågeflexibiliteten i laddande elbilar genom att en aggregator styr ett stort antal elbilsladdare och säljer den sammanlagda kapaciteten på till exempel Svenska kraftnäts stödtjänstmarknader.  För att avgöra hur mycket flexibilitet som elbilsladdning kan bidra med behöver aggregatorn upprätta prognoser över hur mycket effekt som mest sannolikt finns tillgänglig vid en viss tidpunkt – en punktprognos – men också en uppskattning av vilken effektnivå man kan vara nästan säker på att utfallet överstiger – en kvantilprognos. I den här studien har en undersökning gjorts av hur prognosfelet förändras om gruppen av aggregerade elbilsladdare ökas, och hur mycket en aggregator på så sätt kan sänka sina marginaler vid försäljning av efterfrågeflexibiliteten för att med säkerhet kunna uppfylla sitt bud. Det gjordes genom att kvantifiera flexibiliteten för 1 000 destinationsladdare belägna vid huvudsakligen arbetsplatser, och genom att skala upp och ner datamängden genom slumpmässiga urval. För dessa grupper gjordes sedan probabilistiska prognoser av flexibiliteten med en rullande medelvärdes- och en ARIMA-modell. Utifrån prognoserna simulerades slutligen potentiella intäkter om aggregatorn skulle använda den flexibla kapaciteten för uppreglering till stödtjänsten FCR-D upp, vilket är en frekvensreserv som aktiveras vid störningar av nätfrekvensen.  Resultaten visar att en tiodubbling av antalet aggregerade elbilsladdare mer än halverar det relativa prognosfelet. De båda prognosmodellerna visade sig ha jämförbar precision, vilket talar för att använda sig av den rullande medelvärdesmetoden på grund av dess lägre komplexitet. Den ökade säkerheten i prognosen resulterade dessutom i högre intäkter per laddare.  De genomsnittliga intäkterna av att leverera flexibilitet från 1 000 aggregerade elbilsladdare till FCR-D uppgick till 6 900 kr per månad, eller 0,8 kr per session – siffror som troligen hade varit högre utan coronapandemins ökade hemarbete. En 99-procentig konfidensgrad för kvantilprognosen resulterade i en säkerhetsmarginal med varierande storlek, som i genomsnitt var runt 90 procent för 100 laddpunkter, 60 procent för 1 000 laddpunkter samt 30 procent för 10 000 laddpunkter. Mest flexibilitet fanns tillgänglig under vardagsförmiddagar då ungefär 600 kW fanns tillgängligt som mest för 1 000 laddpunkter.  Att döma av tio års nätfrekvensdata är den sammanlagda sannolikheten för att över 50 procent aktivering av FCR-D-budet skulle sammanfalla med att utfallet för den tillgängliga kapaciteten är en-på-hundra-låg i princip obefintlig – en gång på drygt 511 år. Att aggregatorn lägger sina bud utifrån en 99-procentig konfidensgrad kan alltså anses säkert. / The electrification of the car fleet is taking place at a frenetic pace. Additionally, demand for electricity from other sectors of the Swedish society is expected to grow considerably in the coming decades, which in combination with an increasing proportion of intermittent energy sources puts increasing pressure on the electrical grid and prompts a need to adapt to these changes. A proposed solution to part of the power system's upcoming challenges is to utilize the flexibility available from charging electric vehicles (EVs) by letting an aggregator control a large number of EV chargers and sell the extra capacity to, for example, Svenska kraftnät's balancing markets. To quantify how much flexibility charging EVs can contribute with, the aggregator needs to make forecasts of how much power that is most likely available at a given time – a point forecast – but also an estimate of what power level the aggregator almost certainly will exceed – a quantile forecast. In this study, an investigation has been made of how the forecast error changes if the amount of aggregated EV chargers is increased, and how much an aggregator can lower their margins when selling the flexibility to be able to deliver according to the bid with certainty. This was done by quantifying the flexibility of 1000 EV chargers located at mainly workplaces, and by scaling up and down the data through random sampling. For these groups, probabilistic forecasts of the flexibility were then made with a moving average forecast as well as an ARIMA model. Based on the forecasts, potential revenues were finally simulated for the case where the aggregator uses the available flexibility for up-regulation to the balancing market FCR-D up, which is a frequency containment reserve that is activated in the event of disturbances. The results show that a tenfold increase in the number of aggregated EV chargers more than halves the forecast error. The two forecast models proved to have comparable precision, which suggests that the moving average forecast is recommended due to its lower complexity compared to the ARIMA model. The increased precision in the forecasts also resulted in higher revenues per charger. The average income from delivering flexibility from 1000 aggregated electric car chargers to FCR-D amounted to SEK 6900 per month, or SEK 0.8 per session – figures that would probably have been higher without the corona pandemic's increased share of work done from home. A 99 percent confidence level for the quantile forecast resulted in a safety margin of varying size, which on average was around 90 percent for 100 chargers, 60 percent for 1000 chargers and 30 percent for 10,000 chargers. Most flexibility was shown to be available on weekday mornings when approximately 600 kW was available at most for 1000 chargers. By examining frequency data for the Nordic power grid from the past ten years, the joint probability that a more than 50 percent activation of the FCR-D bid would coincide with the outcome for the available capacity being one-in-a-hundred-low, was concluded to be nearly non-existent – likely only once in about 511 years. For the aggregator to place bids based on a 99 percent confidence level can thus be considered safe.

One of the biggest challenges of today is to mitigate climate change and adjust our way of living in accordance with sustainability. To reduce the climate impact of the transport sector the electrification of the road transport sector is commonly seen as having a key role to play. The rate of increase in number of electric cars has increased dramatically the recent years. Substantial electrification of the transport sector highlights the need of efficient integration of EVs with the electricity grid in order to handle the extra electricity demand. A potentia lway of efficiently integrating EVs to the grid could be to apply the concept of vehicle to grid (V2G). V2G simply means that the battery within an EV is seen as a storage component of the electricity grid that can be charged and discharged. Hence, in this thesis, the potential of V2G is explored. This thesis comprises of two parts. The first part investigates this potential impact of V2G in a future Swedish energy system. The second part investigates the optimized economic value, a car sharing company can achieve using different charging modes as well as the potential for participation in Swedish ancillary service markets. For the first part, the dispatch model EnergyPLAN was used to simulate a future energy system in Sweden in 2045. For the second part, an optimization model was designed using Python Optimization Modeling Objects (PYOMO) to optimize the charging and V2G usage of a car sharing fleet. Additionally, the battery degradation cost due to V2G was calculated as well as the potential income from participation in the FCR-D up and FCR-D down market. For both parts of the thesis different scenarios were developed. Scenarios with different electrification rate of the transport sector, V2G compatibility as well as different electricity production mix were considered for energy systems model of Sweden. For participation of shared cars in ancillary services market, scenarios related to different charging modes, rated charging power and the impact of including or excluding tax on sold electricity were created. While analysing the impact of V2G on Swedish energy system in future, it was observed that V2G has a marginal system impact on an annual basis, regardless of transport electrification rate, V2G compatibility and energy mix. The analysis of optimization algorithm for participation of shared pool of 255 cars resulted in economic savings when implementing smart charging and V2G. Due to battery degradation, the savings from integrating V2G in the system were marginal compared to the smart charging annual cost. For both the FCR-D up market and FCR-D down market, the revenue for participation was higher than electricity arbitrage through V2G. Both parts of the methodology, highlight the need for in centives inorder to make V2G an attractive business model and for electric cars to be able to provide flexibility services in a future Swedish energy system. / En av dagens största utmaningar är att begränsa klimatförändringarna och anpassa vårt sätt att leva i enlighet med hållbarhet. För att minska transportsektorns klimatpåverkan anses elektrifieringen av vägtransportsektorn allmänt ha en nyckelroll att spela. ökningstakten i antalet elbilar har ökat dramatiskt de senaste åren. En betydande elektrifiering av transportsektorn belyser behovet av en effektiv integrering av elfordon med elnätet för att hantera den extra efterfrågan på el. Ett potentiellt sätt att effektivt integrera elfordon i nätet skulle kunna vara att tillämpa begreppet fordon till nät (V2G). V2G innebär helt enkelt att batteriet i en elbil ses som en lagringskomponent i elnätet som kan laddas och laddas ur. Därför undersöks potentialen för V2G i detta examensarbete. Detta examensarbete består av två delar. Den första delen undersöker den potentiella påverkan av V2G i ett framtida svenskt energisystem. Den andra delen undersöker det optimerade ekonomiska värde som ett bildelningsföretag kan uppnå med olika laddningslägen samt potentialen för deltagande på svenska stödtjänstmarknader. För den första delen användes modeller-ingsverktyget EnergyPLAN för att simulera ett framtida energisystem i Sverige 2045. För den andra delen gjordes en optimeringsmodell med hjälp av Python Optimization Modeling Objects (PYOMO) för att optimera laddningen och V2G-användningen av en bildelningsflotta. Dessutom beräknades batterinedbrytningskostnaden på grund av V2G samt de potentiella intäkterna från deltagande på FCR-D upp och FCR-D ned-marknaden. För båda delarna av examensarbetet utvecklades olika scenarier. I den första delen jämförs scenarier med olika elektrifieringstakter inom transportsektorn, V2G-kompatibilitet samt olika elproduktionsmixar. För deltagande av bildelningsbilar på stödtjänstemarknader, skapades scenarion kopplat till olika laddningslägen, nominell laddningseffekt och effekterna av att inkludera eller exkludera skatt på såld el. Vid analys av V2G:s inverkan på det svenska energisystemet i framtiden observerades det att V2G har en marginell systempåverkan på årsbasis, oavsett transporteltrifikationshastighet, V2G-kompatibilitet och energimix. Analysen av optimeringsalgoritm för deltagande av delad bilpool med 255 bilar resulterade i ekonomiska besparingar vid implementering av smart laddning och V2G. På grund av batteriförsämring, blev besparingarna frĂĽn att integrera V2G i systemet marginella jämfört med den årliga kostnaden för smart laddning. För både FCR-D upp och FCR-D ned marknaderna var intäkterna för deltagande högre än el arbitrage genom V2G. Båda delarna av metodiken belyser behovet av incitament i för att göra V2G till en attraktiv affärsmodell och för att elbilar ska kunna tillhandahålla flexibilitetstjänster i ett framtida svenskt energisystem.

Transportsektorn står inför en omställning från förbränningsfordon till eldrivna fordon. Detta är en åtgärd för att minska koldioxidutsläppet inom transportsektorn och därmed reducera klimatpåverkan. Syftet med studien är att undersöka hur en ökad effektanvändning i form av elbilsladdning påverkar Gävles lokala elnät samt hur olika laddtekniker påverkar elnätet. Bakgrunden till studien grundar sig att elnätsföretaget vill öka medvetenheten om hur elnätets beredskap ser ut för en ökad elbilsladdning. Att undersöka elbilsladdningens påverkan på elnätet är av stor nytta för elnätsföretaget, men även andra som undersöker elbilsladdnings påverkan i elnätet kan ha användning för studien. Ämnet elbilsladdning är mycket aktuellt och många studier undersöker olika delar som berör elbilsladdning. Tidigare studier undersöker även olika typer av laddtekniker och hur smart laddning minska påverkan i elnätet. Smart laddning kan anpassa elbilsladdningen genom att styra den efter olika styrsignaler och sammankoppla hela elnätet. Denna studie undersöker delar av Gävles lokala elnät genom att simulera befintliga mätvärden lågspänningsnätet samt olika typer av elbilsladdning. Studien analyserar effektanvändningen av befintliga mätdata samt belastningsström och spänningsfall i elnätet med varierande lastprofiler i fyra olika områden. Resultatet för denna studie visar att elbilsladdning påverkar elnätet, vilket beror på vilken typ av laddteknik som används samt dimensioneringen av elnätet. Studien visar att elanvändningen i området idag har effekttoppar på eftermiddag och kväll när kunderna består av villakunder men att effekttoppen kan vara mitt på dagen där det finns industrier. Med elbilsladdning ökar belastningen samt spänningsfallet i nätet och en del av säkringarna i nätet löser ut. Laddning med 11 kW mellan kl. 16:00-19:00 samt laddning med effektvakt på 13,8 kW ger störst belastning och spänningsfall. Laddning utan styrning är den laddteknik som påverkar elnätet mest men laddning med effektvakt orsakar också problem. Laddning med 5,5 kW mellan kl. 23:00-06:00 samt när endast 50% av alla kunder laddar med 11 kW mellan kl. 16:00-19:00 är de scenarion som påverkar elnätet minst. Laddning med en låg effekt under natten när grundlasten är som lägst är den laddteknik som är mest gynnsam för elnätet. Studien visar även att nätet klarar en högre belastning av elbilsladdning inom en snar framtid om endast en del av kunderna i nätet använder elfordon. / The transport sector is facing a transition from combustion engine vehicles to electric vehicles. Through this action the carbon dioxide emissions in the transport sector can be reduced. The purpose of this study is to observe how an increased power use from electric vehicle charging (EVC) affects the local electricity grid in Gävle. The study also addresses how different charging techniques affect the electricity grid. The background of this study is to the increase awareness of the capacity of the electricity grid. There is a need from the electricity grid company to look over the impact on the grid from EVC. This could also be useful for others looking over the impact on the electricity grid from EVC. This is a hot topic and lots of other studies look over the different aspects of EVC. Previous studies also examine different types of charging techniques and how smart charging reduces the negative impact on the electricity grid. Smart charging is a way to adjust the EVC by regulating it after different parameters and connecting the entire electrical grid. This study simulates existing measured values of the low-voltage grid in Gävle and various types of EVC. This study examines the power use of existing measurement data as well as load current and voltage drops in the electricity grid with different load profiles in four different areas. Results from this study shot that EVC affects the electricity grid, to what extent depends on the type of charging technology used and the dimensions of the electricity grid. The study shows that electricity use in the area has power peaks in the afternoon and evening with residential customers, but power peaks tend to be in the middle of the day if there are industries in the area. EVC increase the load on the electricity grid, causes voltage drops and a few fuses in the grid to be triggered. Charging with 11 kW between 16:00-19:00 and charging with a power monitor of 13.8 kW create the greatest voltage drops and highest load on the grid. Charging without means of control affects the electricity grid the most but charging with a power monitor also creates problems. Charging with 5.5 kW between 23:00-06:00 as well as when only 50 % of all customers charge with 11 kW between 16:00-19:00 impacts the grid the least. Charging with low power during the night when the base load is at its lowest is the charging technology that is most favorable for the electricity grid. Results also show that the grid can handle a higher load of EVC in the near future if only some of the customers in the network start using electric vehicles.

Controlling the balance of production and consumption of electricity will become increasingly challenging as the transport sector gradually converts to electric vehicles along with a growing share of wind power in the Swedish electric power system. This puts greater demand on resources that maintain the balance to ensure stable grid operation. The balancing act is called frequency regulation which historically has been performed almost entirely by hydropower. As the power production becomes more intermittent with renewable energy sources, frequency regulation will need to be performed in higher volumes on the demand side by having a more flexible consumption. In this report, the electrification of 17 buses Svealandstrafiken bus depot in Västerås has been studied. The aim has been to assess different charging strategies to efficiently utilize the available time and power but also to investigate if Svealandstrafiken can participate in frequency regulation. A smart charging model was created that demonstrated how smart charging can be implemented to optimize the charging in four different cases. The simulated cases were: charging with load balancing, reduced charging power, frequency regulation, and electrifying more buses. The results show that the power capacity limit will be exceeded if the buses are being charged directly as they arrive at the depot and without scheduling the charging session. By implementing smart charging, Svealandstrafiken can fully charge the 17 buses within the power capacity limit of the depot with 82 minutes to spare. By utilizing this 82-minute margin in the four different charging strategies, it was found that Svealandstrafiken can save 88 200SEK per year by load balancing, save 30 000 SEK per year by reducing the charging power by 10 %, earn 111 900 SEK per year by frequency regulation or electrify five more buses. Reducing the charging power may also increase the lifetime of the batteries but quantifying this needs further studies. Conclusively, there is economic potential for Svealandstrafiken for implementing smart charging.