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Due to the increased demand for electricity in recent years and the estimated demand in the future it has become clear that the capacity of the Swedish electrical grid is insufficient, and the grid is not able deliver the amount of power that is demanded by the market. The crucial points in the grid where the grid capacity is insufficient could be several. It could be in the transmission system but also at locations in the distribution system, such as congestion points for residential areas for example. For the grid to be able to meet the new demands large investments are needed to renew electricity grid. This will cost a lot of money and it will take several years before the grid capacity is up to scale. A problem is that the grid must be dimensioned for the highest power peaks that could be expected even though these might occur just a few times every year. As a response to this a new market for flexibility solutions in the grid has arisen. An example of a flexibility solution is a method called peak-shaving. Peak-shaving is a method that levels out the overall load-profile and so on reduces the highest power-peaks. The aim of this thesis was to investigate how integrated power systems with battery storageinstalled in household villas could implement peak-shaving with the purpose of providing a flexibility-service to the grid. The study includes theoretical simulations, as well as practical experiments and performance analyses of installed systems. The study also includes an investigation how the current price model and tax rules create incentives for costumers to provide this flexibility service. Firstly, a theoretical simulation model in MATLAB was developed that could make a virtual simulation of the result from implementation of peak-shaving based on historical data of the power profile of the household. After this, experimental tests were conducted for three households where peak-shaving was implemented. With the data from the experiments the performance and potential deficiency of the installed systems could be analysed. With the recorded data from the experiments and with collected data of the hourly based price for electricity it could be studied what impact peak-shaving has on the costs for the household and so on what incentives the price model and tax rules creates to implement peak-shaving.What could be concluded in the study was that peak-shaving has a good potential to reduce power-peaks and provide flexibility to the grid. The results from the tests showed that the power-peaks were able to be reduced but the power systems did however have some observed deficiencies that reduced their performance. It could also be concluded that the current price model and tax rules often increases the costs when implementing peak-shaving and so on create poor incentives to provide this flexibility service.

The dispersion model was used to study mixing levels in a full scale aeration tank. The effect of air flow rate, water flow rate and diffuser type was investigated. The peak time technique proved satisfactory in predicting the theoretical tracer response curve generated using the dispersion model. The dispersion model adequately described the longitudinal mixing that occurred in a full scale aeration tank equipped with fine and coarse bubble air diffusers. Response curves from two tanks-in-series were also obtained. / Thesis / Master of Engineering (ME)

The question of finding the optimal size for battery energy storage systems (BESS) to be used for energy arbitrage and peak shaving has gained more and more interest in recent years. This is due to the increase in variability of electricity prices caused by the increase of renewable but also variable electricity production units in the electricity grid. The problem of finding the optimal size for a BESS is of high complexity. It includes many factors that affect the usefulness and the economic value of a BESS. This study includes a thorough literature study regarding different methods and techniques used for finding optimal size (both capacity and power) for a BESS. From the literature study two meta-heuristic algorithms were found to have been used with success for similar problems. The two algorithms were Genetic algorithm (GA) and Firefly algorithm (FF). These algorithms have in this thesis been tested in a case study optimizing the BESS capacity and power to either maximising the net present value (NPV) of investing in a Li-ion BESS of the LPF type or minimizing the levelized cost of storage (LCOS) for the BESS, with a project lifetime of 10 years. The BESS gains monetary value from energy arbitrage by being a middleman between a large residential house complex seen as the "user" with a predefined hourly electricity load demand and the electricity grid. For the case study a simplified charge and discharge dispatch schedule was implemented for the BESS with the focus of maximising the value of energy arbitrage. The case study was divided into 3 different cases, the base case where no instalment of a BESS was done. Case 2 included the instalment of the BESS whilst case 3 included installing both a BESS and an electrical heater (ELH). The electrical heater in case 3 was implemented to shift a heating load from the user to an electrical load, to save money as well as reduce CO2 emissions from a preinstalled gas heater used in the base case. The results showed that overall GA was a better optimization algorithm for the stated problem, having lower optimization time overall between 60%-70% compared to FF and depending on the case. For case 2, GA achieves the best LCOS with a value of 0.225 e/kWh, being 11.4% lower compared to using FF. Regarding NPV for case 2, FF achieves the best solutions at the lowest possible value in the search space for the capacity and power (i.e., 0.1 kWh for capacity and 0.1 kW for power), with an NPV at -51.5e, showing that for case 2 when optimizing for NPV an investment in a BESS is undesirable. GA finds better solutions for case 3 for both NPV and LCOS at 954,982e and 0.2305 e/kWh respectively, being 35.7% larger and 9.1% lower respectively compared to using FF. For case 3 it was shown that the savings from installing the ELH stands for a large portion of the profits, leading to a positive NPV compared to case 2 when it was not implemented. Finally, it was found that the GA can be a useful tool for finding optimal power and capacity for BESS instalments, compared to FF that got stuck at local optimums. However, it was seen that the charge and discharge dispatch schedule play an important role regarding the effectiveness of installing a BESS. As for some cases the BESS was only used 17% of all hours during a year (case 2, when optimizing for NPV). Therefore, further research is of interest into the schedule function and its role regarding finding the optimal BESS size. / Frågan angående hur man hittar den optimal storleken på en energilagringsenhet av batteritypen (BESS) som skall användas för energiarbitrage samt "peak shaving" har fått mer och mer uppmärksamhet de senaste åren. Detta sker på grund av en ökning av variabiliteten av elpriser, vilket i sig delvis kommer från ett ökat installerande av förnyelsebar, men då också variabla energiproduktionsenheter till elnätet. Problemet med att hitta den optimala storleken för en BESS är på grund av komplexitet i frågan. Det innehåller många faktorer som påverkar effektiviteten samt det ekonomiska värdet av en BESS. Denna avhandling innehåller en litteraturstudie om olika tekniker och metoder som används för att hitta den optimal lösningen för optimal storlek (kapacitet och kraft) på en BESS. Från litteraturstudien hittades två meta-heuristiska algoritmer som använts med succés på liknande problem. De två algoritmerna var "Genetic algorithm" (GA) och "Firefly algorithm (FF). Dessa algoritmer har i denna avhandling blivit testade i en fallstudie för att optimera kapacitet och kraft för en BESS genom att antingen maximera nettonuvärdet (NPV) som fås av att investera i en Li-ion BESS av typen LPF eller att minimera "levelized cost of storage" (LCOE) för en BESS med en livstid på 10 år. Detta genom att man får monetärt värde från att använda en BESS för energiarbitrage genom att vara en mellanhand mellan ett stort bostadskomplex som ses vara en "användare" med ett förbestämt elanvändningsmönster och elnätet. För fallstudien användes en simpel metodologi för laddnings- och urladdninsgschema för att maximera energiarbitrage. Fallstudien delades upp i tre olika fall, ett basfall där ingen installation av en BESS gjordes. I fall 2 installerades bara en BESS medans för fall 3 installerades både en BESS samt en elektrisk värmare (ELH) för att omvandla användarens termiska energianvändning till mer elektrisk energianvändning. Genom detta kan monetära besparingar göras samt reducera mängden CO2 utsläpp som annars hade kommit från en redan installerade gasvärmare, i basfallet.  Resultatet visade att totalt sätt var GA en bättre optimeringsalgoritm för det specifika problemet, med lägre optimeringstid på 60%-70% jämfört med FF och beroende på fall. För fall 2 hittar GA det lägsta värdet på LCOS på 0.225 e/kWh, och var då 11.4% lägre jämfört med FF. Angående NPV för fall 2 hittar FF den bästa lösningen på det minsta möjliga värdet på kraft och kapacitet i sökutrymmet (det vill säga 0.1 kWh för kapacitet och 0.1 kW för kraft), med ett NPV värde på -51.5e, vilket visar att för fall 2 när man optimerar för NPV så finns ingen ekonomisk vinning av att investera i en BESS. GA hittar den bästa lösningen för fall 3, både för NPV och LCOS på 954,982e och 0.2305 e/kWh respektivt, vilket är 35.7% större och 9.1% lägre respektivt jämfört när man använder FF. För fall 3 visade resultaten att besparingarna från att installera en ELH stod för den större delen av alla vinster, vilket ledde till positiva värden för NPV. Slutligen visade resultaten att GA kan vara ett användbart verktyg för att hitta den optimala lösningen för storleken på en BESS, jämfört med FF som fastande på lokal optimala lösningar. Dock kunde resultaten också visa att laddnings- och urladdninsgschemat använt i fallstudien spelade en viktig roll angående effektiviteten med att installera en BESS. I vissa fall så användes BESS:en så lite som 17% av alla timmar på ett år (fall 2, optimering av NPV). Därför är det ett stort intresse att göra fortsatt forskning på andra laddnings- och urladdninsgscheman och dess roll med att hitta en optimal storlek på en BESS.

This dissertation explores the idea of inducing grid peak demand curtailment by turning commercial buildings into interactive assets for building owners during the demand control period. The work presented here is useful for both ab initio design of new sites and for existing or retrofitted sites. An analytical hierarchy process (AHP)-based framework is developed to curtail the thermal load effectively across a group of commercial buildings. It gives an insight into the amount of peak demand reduction possible for each building, subject to indoor thermal comfort constraints as per ASHRAE standards. Furthermore, the detailed operation of buildings in communion with the electric grid is illustrated through case studies. This analysis forms an outline for the assessment of transactive energy opportunities for commercial buildings in distribution system operations and lays the foundation for a seamless building-to-grid integration framework. The contribution of this dissertation is fourfold – (a) an efficient method of developing high-fidelity physics-based building energy models for understanding the realistic operation of commercial buildings, (b) identification of minimal dataset to achieve a target accuracy for the building energy models (c) quantification of building peak demand reduction potential and corresponding energy savings across a stipulated range of thermostat setpoint temperatures and (d) AHP-based demand curtailment scheme. By careful modeling, it is shown that commercial building models developed using this methodology are both accurate and robust. As a result, the proposed approach can be extended to other commercial buildings of diverse characteristics, independent of the location. The methodology presented here takes a holistic approach towards building energy modeling by accounting for several building parameters and interactions between them. In addition, parametric analysis is done to identify a useful minimal dataset required to achieve a specified accuracy for the building energy models. This thesis describes the concept of commercial buildings as interactive assets in a transactive grid environment and the idea behind its working. / Doctor of Philosophy / This dissertation titled "An Expert-based Approach for Grid Peak Demand Curtailment using HVAC Thermostat Setpoint Interventions in Commercial Buildings" tackles two important challenges in the energy management domain: –electric grid peak demand curtailment and energy savings in commercial buildings. The distinguishing feature of the proposed solution lies in addressing these challenges solely through demand-side management (DSM) strategies, which include HVAC thermostat setpoint interventions and lighting control. We present a methodology for developing highly accurate building energy models that serve as digital twins of actual buildings. These digital replicas can be used to quantify the impact of various interventions and reflect the realistic operation of commercial buildings across varied conditions. This enables building owners to control demand intelligently and transact energy effectively in the electricity market. The development of Internet of Things (IoT) market and advanced technologies such as smart meters and smart thermostats allows for the design of novel strategies that address traditional challenges faced by electric grid operators. This dissertation elaborates on how smart buildings can leverage IoT-based solutions to participate in the electricity market during demand control periods. We also developed an expert opinion-based demand curtailment allocation scheme resulting in grid peak demand reduction. The numerical results obtained reinforce the effectiveness of the proposed solution across varied climatic conditions.

No / The kinetics of the dissociation is fundamental to the formation and the in vivo performance of cyclodextrin supramolecules. The individual determination of the apparent dissociation rate constant (kd,app) using high performance affinity chromatography (HPAC) is a tedious process requiring numerous separate studies and massive data fitting. In this study, the multianalyte approach was employed to simultaneously measure the kd,app values of three drugs through one injection based on the investigation of the dependence of drug-cyclodextrin interaction kinetics on the mobile phase composition. As a result, the kd,app values increased when decreasing the ion strength, increasing the ionization of drugs and adding extra organic solvents. The values of kd,app for acetaminophen, phenacetin and S-flurbiprofen estimated by the multianalyte approach were 8.54+/-1.81, 5.36+/-0.94 and 0.17+/-0.02s(-1), respectively, which were in good agreement with those determined separately (8.31+/-0.58, 5.01+/-0.42 and 0.15+/-0.01s(-1)). For both of the single and multiple flow rate peak profiling methods, the results of the multianalyte approach were statistically equivalent with that of the single compound analysis for all of the three drugs (p>0.05). The multianalyte approach can be employed for the efficient evaluation of the drug-cyclodextrin kinetics with less variance caused by cyclodextrin column bleeding.

This thesis investigates the opportunities and challenges of using repurposed electric car batteries, so called Second-life Battery (SlB), in a residential building as an energy storage. The performance of SlBs is compared to a First-life Battery (FlB) by identifying two potential scenarios and using a battery degradation model. The first scenario involved the batteries providing ancillary services to the grid, while in the second scenario the batteries were used for peak shaving. The battery degradation model is based on typical usage from the scenarios. The thesis findings indicate that the SlB degrades at a slower rate than the FlB, but can perform fewer cycles. Economic performance varies based on the application and initial assumptions. Both batteries proved to be profitable in the two scenarios studied, with some of the SlB configurations outperforming FlB configurations and vice versa. The yearly compensation received, when providing ancillary services with a 1000 kWh battery, ranged between 3-8 MSEK, while the yearly compensation received, when peak shaving with a 200-300 kWh battery, ranged between 20-35 thousand SEK. The main challenges identified included reduced lifespan, security risks, potential price increases, and space constraints. On the other hand, the main opportunities identified included potential price decreases and sustainability benefits such as carbon footprint reduction and grid stability. These results may provide valuable insights for informed decision-making regarding investments in FlBs and SlBs on the Swedish market.