With the current trend in cloud computing and virtualization, more organizations are moving their systems from a physical host to a virtual server. Although this can significantly reduce hardware, power, and administration costs, it can increase the cost of analyzing performance problems. With virtualization, there is an initial performance overhead, and as more virtual machines are added to a physical host the interference increases between various guest machines. When this interference occurs, a virtualized guest application may not perform as expected. There is little or no information to the virtual OS about the interference, and the current performance tools in the guest are unable to show this interference. We examine the interference that has been shown in previous research, and relate that to existing tools and research in root cause analysis. We show that in virtualization there are additional layers which need to be analyzed, and design a framework to determine if degradation is occurring from an external virtualization layer. Additionally, we build a virtualization test suite with Xen and PostgreSQL and run multiple tests to create I/O interference. We show that our method can distinguish between a problem caused by interference from external systems and a problem from within the virtual guest.

Diplomová práce se zabývá dopadem nabíjení elektrických vozidel a bateriových úložišť na schopnost distribučních sítí nízkého napětí absorbovat fotovoltaické systémy. Převážně venkovské, příměstské a převážně městské regiony s různými stupni penetrace nekontrolovaně i kontrolovaně nabíjených elektromobilů jsou analyzovány Monte Carlo simulacemi. Hostingová kapacita je také analyzována, jestliže jsou elektrická vozidla jak nahrazena, tak doplněna domácími bateriovými úložišti. Práce je zakončena krátkou analýzou využitelnosti BESS.

<p>As the future of energy looks increasingly electrified, the development of safe and sustainable battery technologies has never been more relevant. This is particularly critical for applications in stationary energy storage and transportation, where batteries must be produced and stored at large scale. Sustainability is necessary to meet the volume of demand at reasonable cost without straining resources. Safety is also paramount since fires can easily spread from one cell to the next and result in catastrophe when batteries are stored in proximity for large power banks or EVs. The focus of this thesis is thus to design and engineer materials for rechargeable batteries, which improve safety and sustainability while still enhancing the electrochemical performance. Towards this end, polymers play a central role throughout this thesis work due to their tunable chemical and physical properties.</p>

The purpose of this paper was to use the Overall Equipment Effectiveness productivity formula in plant manufacturing and convert it to measuring productivity for forklifts. Productivity for a forklift was defined as being available and picking up and moving containers at port locations in Seattle and Alaska. This research uses performance measures in plant manufacturing and applies them to mobile equipment in order to establish the most effective means of analyzing reliability and productivity. Using the Internet of Things to collect data on fifteen forklift trucks in three different locations, this data was then analyzed over a six-month period to rank the forklifts’ productivity from 1 – 15 using the Operating Equipment Effectiveness formula (OPEE). This ranking was compared to the industry standard for utilization to demonstrate how this approach would yield a better performance analysis and provide a more accurate tool for operations managers to manage their fleets of equipment than current methods. This analysis was shared with a fleet operations manager, and his feedback indicated there would be considerable value to analyzing his operations using this process. The results of this research identified key areas for improvement in equipment reliability and the need for additional operator training on the proper use of machines and provided insights into equipment operations in remote locations to managers who had not visited or evaluated those locations on-site.

The rapidly evolving Nordic Power System demands enhanced flexibility and robustness in electricity production. The traditional role of hydropower plants in regulating the grid frequency has been challenged by new criteria for dynamic stability, which some units struggle to meet due to their relatively poor dynamic performance. This study addresses this challenge by investigating the potential of integrating optimal energy storage systems with hydropower plants. This study aimed to develop a tool that could streamline the process of converting a traditional hydropower plant into a hybrid unit using an optimal energy storage system. The problem is complex and requires an innovative approach that combines electrical engineering expertise with cutting-edge machine-learning algorithms. A comprehensive hydropower plant model, including governor control and mechanical and hydraulic subsystems, was developed and integrated with an energy storage system model to form a hybrid unit. This model was validated using real power plant data. Three distinct XGBoost Regressor models were trained using data samples generated from the optimized hybrid unit. These models aim to predict power and energy requirements for an optimal energy storage solution, including an estimation of wear and tear reduction. The XGBoost Power Regressor achieved a prediction accuracy of 92 % and the XGBoost Energy Regressor demonstrated a 95 % accuracy. The XGBoost Movement Regressor, indicating wear and tear, boasted an accuracy greater than 99 %. The integration of energy storage systems can significantly mitigate wear and tear on a hydropower plant, with reductions of up to 85 % or more. The results indicate that integrating energy storage systems with hydropower units can substantially enhance the dynamic performance, reduce wear and tear and enable the plants to meet the demanding requirements of providing frequency regulation services in the Nordic Power System. The findings of this study culminate in a robust and user-friendly tool capable of accurately estimating optimal energy storage requirements for any hydropower plant tasked with meeting frequency regulation service demands. / Det nordiska kraftsystemet är under snabb förändring och skiftar alltmera till elproduktion med krav på ökad flexibilitet och tillförlitlighet. Vattenkraftverkens traditionella roll som källa till reglering och stabilisering av nätfrekvensen, utmanas nu av nya krav på dynamisk prestanda och stabilitet. På grund av sina relativt dåliga prestanda har vissa vattenkraftverk svårigheter att uppfylla dessa nya krav. Detta examensarbete behandlar denna utmaning genom att undersöka möjligheterna att integrera optimala energilagringssystem med vattenkraftverk. Syftet med arbetet var att utveckla ett verktyg som skulle kunna effektivisera processen för att omvandla ett traditionellt vattenkraftverk till ett hybridkraftverk med hjälp av ett optimalt energilagringssystem. Detta är ett komplext problem som kräver ett innovativt tillvägagångssätt som kombinerar elkraftteknik med avancerade algoritmer för maskininlärning. En omfattande modell utvecklades för att simulera ett vattenkraftverk med styrsystem, mekaniska och hydrauliska system. Denna kraftverksmodell integrerades med en modell för ett energilagringssystem för att tillsammans bilda en hybridenhet. Modellens validitet verifierades med hjälp av verkliga testdata. Med hjälp av data från simuleringar av den optimerade hybridenheten kunde tre XGBoost-regressionsmodeller skapas för att estimera både effekt och energibehov för ett optimalt energilagringssystem. Utöver detta kunde även en uppskattning av minskning av slitage presenteras. XGBoost Power Regressor uppnådde en träffsäkerhet på 92 % och XGBoost Energy Regressor uppvisade en träffsäkerhet på 95 %. XGBoost Movement Regressor, som indikerar slitage, hade en noggrannhet på högre än 99 %. Integrering med energilagringssystem kan avsevärt minska slitaget på ett vattenkraftverk, med minskningar på upp till 85 % eller mer. Resultaten visar att integrering av energilagringssystem och vattenkraftverk väsentligt kan förbättra den dynamiska prestandan, minska slitage och göra det möjligt för kraftverken att uppfylla kraven för att bidra med frekvensregleringstjänster i det nordiska kraftsystemet. Resultaten av denna studie kulminerar i ett robust och användarvänligt verktyg som kan uppskatta ett optimalt energilagringsystem för ett vattenkraftverk som ska uppfylla kraven för frekvensreglering.

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This study delves into the comparative operational effectiveness of non-coordinated, bottom-up, and top-down coordinated control models within Distributed Energy Storage Systems (DESS) and Electric Vehicle (EV) networks. Employing meticulous data analysis, this research evaluates power demand and supply dynamics within the infrastructure and buildings, aiming to optimize energy usage and storage. The analysis involves comprehensive steps: descriptive statistical breakdown, understanding energy patterns across buildings, and a comparative assessment of the control models. Visual representations and graphs aid in depicting energy patterns, emphasizing the distinctive characteristics and effectiveness of each control model. The findings reinforce the superiority of the top-down coordinated control model in managing supply-demand imbalances, echoing established literature.

High electricity prices have become the title of several news articles recently in Sweden and the prices have experienced large sudden fluctuations during certain periods. In this thesis work, a smart control model for the electricity usage in three different households has been developed with the main purpose to minimize the electricity cost. This has been implemented by using mixed-integer linear programming (MILP) to optimize the cost 24 hours ahead, and by forecasting two of the main inputs; the load and the electricity spot prices for bidding zone three (SE3) in Sweden. The units included in the model are the photovoltaic system, the batteries, the electricity consumption in the house and the electric vehicles. However, the main task of the smart control was to determine when and in which amount the energy should flow from one unit to another, or to/from the grid. In other words, it decides the charging/discharging of the batteries, the selling/buying of electricity and the charging of the electric vehicle (EV). Different amounts of cost savings/profits have been obtained when applying the smart control on the three houses, which have different annual consumption, capacities of the components, heating systems and more. The results showed that it is most optimal to run the model between the time interval 13.00-00.00, when the spot prices for the next day are known, in order to avoid the remarkable impact accompanied with the use of forecasted electricity prices as input to the model. The forecasting of the load is, on the other hand, required to run the model, but this thesis showed that the effect of the uncertainties in this forecast is relatively small. Three types of machine learning methods were implemented to perform the forecasts, namely linear regression (LR), decision tree regression and random forest regression. After measuring especially the mean absolute error (MAE) to validate the results, the random forest regression showed the least error and the other methods showed close results when looking at the electric load prognosis.