This study introduces a method to optimize charging strategies for Automated Guided Vehicles in warehouses and logistics centers, aiming to enhance efficiency and reduce downtime. Using Tecnomatix Plant Simulation, a model with two vehicles and two charging stations was created to simulate realistic delivery scenarios, generating data on order duration, energy consumption, and vehicle charging times. This data was optimized with CPLEX to determine the best order sequences and loading schedules. The key challenge addressed is optimizing Automated Guided Vehicle charging strategies to maximize operational readiness and energy efficiency. A supervised learning approach was used, where a neural network predicts if an Automated Guided Vehicle should charge based on its State of Charge and current order backlog. The model was developed in Python, using an 80-20 split for training and testing. The study demonstrates the effectiveness of machine learning in improving Automated Guided Vehicle fleet management, providing a data-driven solution for real-time decision-making.

In Kombination mit erneuerbaren Energien (EEG) werden Elektrofahrzeuge (EVs) als wichtiger Bestandteil einer Transformation hin zu nachhaltigen Energiesystemen angesehen. Obwohl EVs heute nur einen geringen Anteil an der Fahrzeugdurchdringung in Deutschland darstellen, ist es das Ziel der Bundesregierung, dass im Jahr 2030 sechs Millionen EVs auf deutschen Straßen fahren sollen. Die Realisierung dessen hätte aufgrund des daraus resultierenden zusätzlichen Strombedarfs erhebliche Auswirkungen auf das Stromsystem. Wie hoch diese sind, hängt maßgeblich von der Ladestrategie der Fahrzeuge ab und ist der Forschungsgegenstand dieser Arbeit. Die übergeordnete ökonomische Fragestellung lautet: Welche Auswirkungen haben unterschiedliche EV-Ladestrategien auf Strommengen und -preise in einem Stromsystem mit einem hohen Anteil an erneuerbaren Energien? Zur Beantwortung dessen wird zunächst der zeitabhängige Strombedarf von EVs bewertet. Im Anschluss, werden die EV-Ladestrategien unkontrolliertes Laden (UNC), kostengesteuertes Laden (DSM) und bidirektionales Laden (V2G) in einem europäischen Strommarktmodell umgesetzt und die Auswirkungen quantifiziert. Dadurch wurden folgende Erkenntnisse erlangt: EVs tragen zu einer besseren Integration der EEG bei, da alle drei Ladestrategien deren Abregelung reduzieren. Der zusätzliche Spitzenlastbedarf aufgrund von UNC wird je Millionen EVs im schlimmsten Fall auf 560 MW geschätzt. Entsprechend des Fahrverhaltens variiert die Stromnachfrage stark zwischen Werktagen und Wochenendtagen. An Werktagen sind die Spitzenwerte fast dreimal so hoch wie an Wochenendtagen. Wird durch UNC die Stromnachfrage erhöht, bedarf es des vermehrten Einsatzes von Spitzenlastkraftwerken, was zu steigenden Preisspitzen führt. Im Gegensatz dazu verschieben die beiden flexiblen Ladestrategien DSM und V2G die EV-Stromnachfrage in Zeiten mit geringer residualer Netzlast bzw. bei V2G deutlich zugunsten von Kraftwerken mit den niedrigsten Grenzkosten. Dies führt bei DSM zu einer Anhebung der Preise in Schwachlastzeiten. Bei V2G wird die Preisstruktur erheblich geglättet, indem Spitzenlastpreise reduziert und Schwachlastpreise deutlich erhöht werden. An Wochenenden ist dieser Effekt bei V2G noch stärker als an Werktagen, da ein großer Teil der EVs als stationärer Speicher genutzt werden kann. Neben ökonomischer Effizienz hat dies teilweise unerwünschte ökologische Nebenwirkungen. So werden im Fall von V2G bei niedrigen CO2-Preisen emissionsintensive Technologien wie Braunkohlekraftwerke begünstigt. Nichtsdestotrotz führen systemische Effekte, nämlich die Reduzierung von EEG-Abschaltungen, die Substitution von Spitzenlastkraftwerken und ein erhöhter Stromaustausch mit den Nachbarländern zu einer Gesamtreduktion der CO2-Emissionen. Bei hohen CO2-Preisen sind die Effekte durch V2G hinsichtlich der CO2-Emissionen und der ökonomischen Effizienz durchweg positiv. Begrenzt werden diese Vorteile von V2G durch wirtschaftliche Sättigungseffekte, welche bereits ab zwei Millionen Fahrzeugen deutlich werden. / In combination with renewable energy sources (RES), electric vehicles (EVs) are seen as an important element of a transformation towards sustainable energy systems. Although EVs currently represent only a small fraction of vehicle penetration in Germany, it is the goal of the German government to have six million EVs on German roads by 2030. The achievement of this would have a significant impact on the electricity system due to the resulting additional energy demand. How large these impacts are is the subject of this work. The overarching economic research question is: What effects do different EV charging strategies have on quantities and prices in a power system with a high share of RES? To answer this question, the time-dependent electricity demand of EVs is initially evaluated. Subsequently, the EV charging strategies uncontrolled charging (UNC), demand side management (DSM), in the sense of cost effective charging and bidirectional charging, i.e. vehicle-to-grid (V2G) are implemented in a European electricity market model and the impacts quantified. To summarize the findings: EVs contribute to the integration of RES, since all three charging strategies reduce curtailment. In the worst case scenario, the additional peak load demand due to UNC is estimated at 560 MW per million EVs. The demand for electricity varies greatly between working days and weekend days, depending on the driving patterns. On working days, the peak demand is almost three times as high as on weekend days. Overall, UNC leads to the increased use of peak load power plants, which leads to rising price peaks. In contrast, the two flexible charging strategies DSM and V2G shift the EVs' electricity demand in times of low residual grid load or, in the case of V2G, significantly in favour of the power plants with the lowest marginal costs. With DSM, this results in an increase in prices during off-peak periods. With V2G, the price structure is considerably smoothed by reducing peak load prices and significantly increasing off-peak prices. On weekend days this effect is even stronger with V2G than on working days, since a large part of the EVs can be used as stationary storage. In addition to economic effciency, this has partly undesirable ecological side effects. In the case of V2G, emission-intensive technologies such as lignite-fired power plants are promoted at low CO2 prices. Nevertheless, systemic effects, namely the reduction of RES curtailment, the substitution of peak load power plants, and an increased electricity exchange with neighboring countries, lead to an overall reduction of the CO2 emissions. These benefits of V2G are limited due to economic saturation effects, which are already noticeable starting at two million vehicles.

Electric vehicles (EVs) are increasing in popularity and play an important role in decarbonizing the transport sector. However, a growing EV fleet can cause problems for power grids as the grids are not initially designed for EV charging. The potential of a power grid to accommodate EV loads can be assessed through hosting capacity (HC) analysis. The HC is grid specific and varies, therefore it is necessary to conduct analysis that reflects local conditions and covers uncertainties and correlations over time. This theses aims to investigate the HC for EVs in existing residential power grids, and to gain a better understanding of how it varies based on how the EVs are implemented and charged. The work is in collaboration with a distribution system operator (DSO) and is based on two case studies using real-life data reflecting conditions in Swedish grids. Combinations of different HC assessment methods have been used and the HC is evaluated based on cable loading, transformer loading and voltage deviation. Additionally, the study investigated three distinct charging strategies: charging on arrival, evenly spread charging over whole connection period, and charging at the lowest spot price.  The results show that decisions on acceptable voltage deviation limit can have a large influence on the HC as well as the charging strategy used. A charging strategy based on energy prices resulted in the lowest HC, as numerous EVs charging simultaneously caused high power peaks during low spot price periods. Charging on arrival was the second worst strategy, as the peak power coincided with household demand. The best strategy was to evenly spread out the charging, resulting in fewer violations for 100% EV implementation compared to the other two strategies for 25% EV implementation.  The findings underscore the necessity for coordinated charging controls for EV fleets or diversified power tariffs to balance power on a large scale in order to use the grids efficiently.

Electric vehicles (EVs) are increasing in popularity and play an important role in decarbonizing the transport sector. However, a growing EV fleet can cause problems for power grids as the grids are not initially designed for EV charging. The potential of a power grid to accommodate EV loads can be assessed through hosting capacity (HC) analysis. The HC is grid specific and varies, therefore it is necessary to conduct analysis that reflects local conditions and covers uncertainties and correlations over time. This theses aims to investigate the HC for EVs in existing residential power grids, and to gain a better understanding of how it varies based on how the EVs are implemented and charged. The work is in collaboration with a distribution system operator (DSO) and is based on two case studies using real-life data reflecting conditions in Swedish grids. Combinations of different HC assessment methods have been used and the HC is evaluated based on cable loading, transformer loading and voltage deviation. Additionally, the study investigated three distinct charging strategies: charging on arrival, evenly spread charging over whole connection period, and charging at the lowest spot price.  The results show that decisions on acceptable voltage deviation limit can have a large influence on the HC as well as the charging strategy used. A charging strategy based on energy prices resulted in the lowest HC, as numerous EVs charging simultaneously caused high power peaks during low spot price periods. Charging on arrival was the second worst strategy, as the peak power coincided with household demand. The best strategy was to evenly spread out the charging, resulting in fewer violations for 100% EV implementation compared to the other two strategies for 25% EV implementation.  The findings underscore the necessity for coordinated charging controls for EV fleets or diversified power tariffs to balance power on a large scale in order to use the grids efficiently.

When the low voltage electric grid is dimensioned electric loads are predicted by analyzing the area by certain factors such as geographical data, customer type, heating method etc. So far, the charging of Plugin Electric Vehicles (PEVs) is not considered as one of these factors. Approximately 30% of the distribution grid in Sweden is projected to need reinforcements due to the increased loads from PEVs during winters if the charging isn’t controlled. In addition to this Stockholm face the problem of capacity shortage from the transmission grid, limiting the flow of electricity into the city. This research is therefore conducted to analyze the impact that the increase of PEVs will have on the distribution grid in the future. This thesis simulates the electric grid for three substations located in Hammarby Sjöstad by using power flow analysis and electric grid data from 2016. To approach this problem a method to disaggregate the total power consumption per substation into power consumption responding to each building was developed. In addition to this the number of PEVs in the future was projected. Nine different scenarios were used to compare different outcomes for the future, namely the years of 2025 and 2040. In order to simulate the worst possible case for the electric grid all the PEVs were assumed to be charged at the same time, directly when arriving home on the Sunday when the power demand peaks in 2016. The results indicate that PEVs can have a considerable impact on the components of the low voltage distribution network and controlled charging should be implemented. By examining the impact on the simulated electric grid from the different scenarios the limit of PEV penetration is found. In the area of Hammarby this limit seems to be around 30 % of the total cars if there is no controlled charging. Without any controlled charging the peak power demand increases by 30% with a 30% share of PEVs, which is projected to happen in 2025. In 2040 when share of PEVs is projected to be about 95% the peak power is instead increased by more than 100% which shows the impact that PEVs can exert on the electric grid. Utilizing a simple method of controlled charging where the PEVs are instead charged during the night when the power demand is low, the peak power is not increased at all. This also results in the small cost benefit for PEV owners since the electricity is cheaper during the night and controlled charging can therefore save about 15% of the electricity charging cost. However, the main savings are for the grid owners since the need to reinforce the grid is heavily reduced. In addition to this the power losses are reduced heavily from about 14% down to 5% in the electric grid that is simulated. / När dimensioneringen av distributionsnätet utförs analyseras området genom att räkna med elektriska laster som till exempel kan bero på geografiska data, typ av konsument, uppvärmningsmetod etcetera. Än så länge har laddningen av elbilar (PEVs) inte varit en av dessa faktorer trots den förväntade tillväxten av elbilar. Ungefär 30% av Sveriges distributionsnät förväntas behöva förstärkningar på grund av den ökade elkonsumtionen från elbilar under vintrarna om laddningen inte kontrolleras. Utöver detta står Stockholm inför problemet med effektbrist från elöverföringsnätet. Denna uppsats genomförs således för att analysera påverkan från elbilar på fördelningsnätet i framtiden. Denna masteruppsats simulerar det elektriska nätet för tre nätstationer i Hammarby Sjöstad genom en analys av effektflödet. En metod för att disaggregera elkonsumtionen per nätstation ned till elkonsumtionen per byggnad utvecklades och antalet elbilar i framtiden uppskattades. För att utvärdera elbilars påverkan skapades nio olika scenarion för framtiden genom att undersöka hur det kommer att se ut år 2025 och år 2040. Genom att anta att laddningen av alla elbilar i området sker samtidigt, samma tid som den maximala förbrukningen av el sker under en söndag 2016, analyseras det värsta möjliga scenario för det elektriska nätet. Resultaten visar att elbilar kan ha enorm påverkan på de maximala lasterna för ett lågspänningsnät och därför kommer kontroll av laddningen behövas. Genom att undersöka elnätets påverkan i de olika scenariona uppskattades gränsen för hur många elbilar det modellerade elnätet klarar av. I Hammarby Sjöstad ligger denna gräns på ungefär 30% elbilar. Utan kontrollerad laddning ökar maxlasten med 30% år 2025 då antalet elbilar förväntas vara 30% av alla bilar i Hammarby Sjöstad. År 2040 då antalet elbilar uppnår ungefär 95 % av alla bilar ökar maxlasterna med mer än 100% vilket visar den enorma påverkan elbilar kan ha på elnätet. Genom att använda en simpel modell av kontrollerad laddning som består av att flytta laddningen från eftermiddagen till natten, då förbrukningen av elektricitet är låg, ökar inte maxlasten för dygnet alls jämfört med scenariot utan elbilar. Detta resulterar också i besparingen av elektricitetskostnad för elbilsägaren med cirka 15% eftersom elektriciteten ofta är billigare under natten jämfört med kvällens elpriser. Detta är dock små summor jämfört med besparingar elnätsägarna kan göra då elnätet inte behöver förstärkas lika mycket som skulle behövas utan kontroll av laddningen. Utöver detta så sänks även förlusterna av elektricitet i det simulerade nätet från 14% ned till 5% genom att utnyttja denna modell av kontrollerad laddning.

The Swedish energy sector is undergoing transformational changes. Along with a rapid growth of renewables and a shift towards electromobility, the transformation is expected to bring challenges to the power system in terms of grid instability and capacity deficiency. Integrating distributed renewable electricity production into the electric vehicle (EV) charging infrastructure is a promising solution to overcome those challenges. The feasibility of implementing such a charging infrastructure system in northern Sweden is however uncertain, as the solar resources are scarce in the long winter period. This study aims to maximise the value of a solar powered EV charging station, placed in a workplace environment in Umeå. An integrated system model of the charging station is developed, comprising separate models of a solar PV system, a battery energy storage system (BESS), the workplace EV fleet and the building Växthuset, onto which the charging station will be installed. Three scenarios are developed to study the charging station’s system performance under different EV charging strategies and BESS dispatch strategies. Two additional scenarios are developed to study the potential grid services that the charging station can provide in the winter period. A techno-economic assessment is performed on each scenario’s simulation results, to measure their effect on the charging station’s value. It involves analysing the charging station’s profitability and how well the BESS is utilised by the end of a ten-year project period. The charging station’s grid impact is further assessed by its self-consumption of solar power, peak power demand and the grid energy exchange. The assessed charging station values indicate that the overall grid impact was reduced with dynamic EV charging strategies and that the BESS capacity utilisation was strongly influenced by its dispatch strategy. The charging station further implied a net capital loss under the explored scenarios, even while the dynamic charging strategies brought by a slightly increased economic value. Moreover, the studied winter scenarios showed a great potential for the charging station to provide ancillary services to the local distribution grid while maintaining an efficient BESS capacity utilisation. The winter period’s peak power demand was significantly reduced by optimising the BESS operation to shift peaks in the building’s load profile, and peaks caused by the additional EV charging demand and the EV heaters, to off-peak hours. On this basis, future research is recommended for improved simulations of the charging station operation and to study additional value-added features that the solar driven charging station can bring. / Sveriges energisystem genomgår en omfattande omställning. Förändringar i form av en ökad andel förnybar elproduktion och elektrifieringen av transportsektorn förväntas medföra stora utmaningar för elsystemets nätstabilitet och överföringskapacitet. Att integrera in distribuerad, förnybar elproduktion som en del av laddinfrastrukturen för elfordon ställer sig som en lovande lösning för att möta de väntande utmaningarna. Möjligheterna att tillämpa en sådan lösning i norra Sverige är däremot mindre självklara, då solresurserna är knappa under vintertid. Det här examensarbetet syftar till att maximera nyttan av en soldriven laddstation för elbilar, placerad på ett arbetsplatsområde i Umeå. En integrerad energisystemmodell av laddstationen har skapats, bestående av systemmodeller av solpaneler, ett batterienergilager, arbetsplatsens elbilsflotta samt byggnaden Växthuset, som laddstationen ska anslutas till. Tre scenarier har utformats för att undersöka hur laddstationens prestanda förändras beroende på olika laddstrategier för elbilarna och batterienergilagrets styrning. Ytterligare två scenarier har utvecklats för att utforska möjliga nättjänster som laddstationen kan bistå med under vintertid. Laddstationens värde har vidare bedömts utifrån systemets prestanda i de olika scenarierna. Bedömningen grundar sig på laddstationens lönsamhet och hur välutnyttjat batterienergilagret är efter en kalkylperiod på 10 år, samt på specifika påverkansfaktorer på elnätet. Faktorerna omfattar konsumtionen av egenproducerad el, toppeffektuttaget och nätöverföringarna orsakade av laddstationen. Från värderingen av laddstationen framgår det att de dynamiska laddstrategierna ledde till en, överlag, minskad påverkan på elnätet samt att styrningen av batterienergilagret hade stor inverkan på dess utnyttjandegrad. Laddstationens nettonuvärde förblev negativt i de tre scenarierna, även om de dynamiska laddstrategierna, ökade dess ekonomiska värde till en viss del. Vidare tyder simuleringen av vinterscenarierna på att det finns en stor potential för laddstationen att erbjuda tjänster för lokalnätet och samtidigt nyttiggöra sig av batterienergilagret. Växthusets toppeffektuttag reducerades märkbart genom att optimera batteristyrningen till att flytta effekttoppar orsakade av Växthusets ellastkurva eller elbilarnas laddning och uppvärmning, till de timmar där lasten var lägre. Med detta i bakgrund föreslås vidare studier som fokuserar på den integrerade energisystemmodellen för att förbättra simuleringarna, samt att undersöka möjligheterna till att erbjuda fler nättjänster, som ökar laddstationens mervärde.