The intermittent nature of renewable energy, variations in energy demand, and fluctuations in oil and gas prices have all contributed to variable demand for power generation from coal-burning power plants. The varying demand leads to load-follow and on/off operations referred to as cycling. Cycling causes transients of properties such as pressure and temperature within various components of the steam generation system. The transients can cause increased damage because of fatigue and creep-fatigue interactions shortening the life of components. The data-driven model based on artificial neural networks (ANN) is developed for the first time to estimate properties of the steam generator components during cycling operations of a power plant. This approach utilizes data from the Coal Creek Station power plant located in North Dakota, USA collected over 10 years with a 1-hour resolution. Cycling characteristics of the plant are identified using a time-series of gross power. The ANN model estimates the component properties, for a given gross power profile and initial conditions, as they vary during cycling operations. As a representative example, the ANN estimates are presented for the superheater outlet pressure, reheater inlet temperature, and flue gas temperature at the air heater inlet. The changes in these variables as a function of the gross power over the time duration are compared with measurements to assess the predictive capability of the model. Mean square errors of 4.49E-04 for superheater outlet pressure, 1.62E-03 for reheater inlet temperature, and 4.14E-04 for flue gas temperature at the air heater inlet were observed.

Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO₂-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO₂ mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO₂ concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.

<p>Fast charging is crucial to the proliferation of electric vehicles. Fast charging is limited by lithium plating, which is the deposition of lithium metal on the anode surface instead of intercalation of lithium into the anode. Lithium plating causes capacity fade, increases cell resistance, and presents safety issues. A fast charging strategy was implemented using a battery management system (BMS) that avoided lithium plating by predicting the anode impedance. Commercial pouch cells modified with a reference electrode were cycled with and without the BMS. Cells cycled with the BMS avoided lithium plating but experienced significant degradation at the cathode. Cells cycled without the BMS underwent extensive lithium plating at the anode. Capacity loss was differentiated into irreversible and irretrievable capacity to understand electrode-based degradation mechanisms. Post-mortem analysis on harvested electrodes showed that the BMS cycled cells exhibited minimal anode degradation and had a two-times higher capacity loss on the cathode. The cells cycled without the BMS had extensive anode degradation caused by lithium plating and a seven-times higher capacity loss on the anode. </p> <p> </p> <p>Understanding and preventing the aging mechanisms of lithium-ion batteries is necessary to prolong battery life. Traditional full cell measurements are limited because they cannot differentiate between degradation processes that occur separately on anode and cathode. A reference electrode was inserted into commercial cylindrical lithium-ion cells to deconvolute the anode and cathode performance from the overall cell performance. Two configurations of the reference electrode placement inside the cell were tested to find a location that was stable and had minimal interference on the full cell performance. The reference electrode inside the mandrel of the cylindrical cell had stable potential measurements for 80 cycles and at different C-rates and had minimal impact on the full cell performance.<b></b></p>

Increase in the awareness environmental conservation is leading the automotive industry into the adaptation of alternatively fueled vehicles. Electric, Fuel-Cell as well as Hybrid-Electric vehicles focus on this research area with aim to efficiently utilize vehicle powertrain as the first step. Energy and Power Management System control strategies play vital role in improving efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used in the given system. A kinematic mathematical model for Plug-in Hybrid Electric Vehicle (PHEV) has been developed in this study and is further optimized by determining optimal power management strategy for minimal fuel consumption as well as NOx emissions while executing a set drive cycle. A multi-objective optimization using weighted sum formulation is needed in order to observe the trade-off between the optimized objectives. Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the trade-off curve between fuel and NOx. In performing these optimizations, the control signal consisting of engine speed and reference battery SOC trajectory for a 2-hour cycle is used as the controllable decision parameter input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours, giving slightly less than 2.5 minutes per point, noting that the values used in the model are interpolated between the points for each time step. With the control signal consisting of 2 distinct signals, speed and SOC trajectory, as 50 element time variant signals, a multidimensional problem was formulated for the optimizer. Novel approaches to balance the optimizer exploration and convergence, as well as seeding techniques are suggested to solve the optimal control problem. The optimization of each involved individual runs at 5 different weight levels with the resulting cost populations being compiled together to visually represent with the help of Pareto front development. The obtained results of simulations and optimization are presented involving performances of individual components of the PHEV powertrain as well as the optimized PMS strategy to follow for given drive cycle. Observations of the trade-off is discussed in the case of Multi-Objective Optimizations.

<p>Recent developments in semiconductor device technology have seen the advent of wide-bandgap (WBG) based devices that enable operation at high switching frequencies. These devices, such as silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs), are becoming a favored choice in inverters for electric drive systems because of their lower switching losses and higher allowable operating temperature. However, the fast switching of such devices implies increased voltage edge rates (high <i>dv/dt</i>) that give rise to various undesirable effects including large common-mode currents, electromagnetic interference, transient overvoltages, insulation failure due to the overvoltages, and bearing failures due to</p> <p>microarcs. With increased use of these devices in transportation and industrial applications, it is imperative that accurate models and efficient simulation tools, which can predict these high-frequency effects and accompanying system losses, be established. This research initially focuses on establishing an accurate wideband model of a surface-mount permanent-magnet</p> <p>ac machine supplied by a WBG-based inverter. A new multirate simulation framework for predicting the transient behavior and estimating the power losses is then set forth. In this approach,</p> <p>the wideband model is separated into high- and low-frequency models implemented using two different computer programs that are best suited for the respective time scales. Repetitive execution of the high-frequency model yields look-up tables for the switching losses in the semiconductors, electric machine, and interconnecting cable. These look-up tables are then incorporated into the low-frequency model that establishes the conduction</p> <p>losses. This method is applied to a WBG-based electric drive comprised of a SiC inverter and permanent-magnet ac machine. Comparisons of measured and simulated transients are provided.</p>

<p>CO₂ concentration in the atmosphere poses a great threat to life on earth as we know it. The reduction of CO₂ concentration is key to avoid the critical turning point of 1.5^oC temperature increase highlighted by Intergovernmental Panel on Climate Change (IPCC). Gasification using CO₂ as reacting agent can potentially reduce the CO₂ concentration in the atmosphere. Naturally, biomass such as corn, uses great amounts of CO₂ for photosynthesis and produces O₂; hence, energy and fuel production using biomass can potentially be classified as carbon neutral. Moreover, if CO₂ is used as the gasifying agent, gasification can effectively be carbon-negative and collaborate to the reduction of CO2 in the atmosphere.</p> <p>The major setback of using CO₂ biomass gasification is the energy-intensive reaction between C + CO₂ -> 2CO. This reaction at atmospheric pressure and room temperature is heavily tilted towards producing char and CO2. The current investigation describes efforts to favor the right hand side of the reaction by using simple impregnation techniques and cost-effective catalysts to reduce the energy requirements of the reaction. Also, parameters such as pressure are explored to tilt the balance towards the production of CO. Corn stover is selected as the biomass for the present research due to its wide use and availability in the US.</p> <p>The results show that by using catalysts such as iron nitrate and sodium aluminate, the temperature required to achieve substantial char conversion is reduced. Also, increasing the pressure of the reactor, the temperature can be substantially decreased by 100 K and 150 K. The structure and chemical composition of the chars is studied to explain the differences in the reaction rate between chars. Further, chemical kinetics are calculated to compare the present work with previous work in the literature. Finally, data-driven analysis of the gasification data is explored. The appendices provide supplementary information on the application of deep learning to CO₂ recycling using turbulent flames and efforts to reduce the flame spread rate over a pool of Jet A by using Multi Walled Carbon Nanotubes (MWCNTS).</p>

The demand for electrical power is growing due to factors such as population growth, urbanisation, and the transition from fossil fuels to renewable energy sources. To be able to keep up with the changes in electricity demand, the Swedish power grid must connect more renewable power generation, but also  increase its transmission capacity. Traditionally, power grids are expanded to increase the transmission capacity which requires a lot of time and investments. In order not to hinder the electrification of society, it is important to adequately estimate the current transmission capacity and plan the expansions accordingly. In the past, the generation of electrical power was primarily based on dispatchable energy sources, and the planning of new connections to the grid was assessed according to the stable and controllable nature of the electricity supply. However, renewable sources like solar and wind power are affected by weather variations. Therefore, the traditional methods of planning the power grid are no longer sufficient. Instead, there is a need to develop and implement new methods that account for the variable nature of renewable energy sources, and also the possible complementarity between different renewable power sources. This can possibly allow more connection of renewable power generation to the grid, without the need of expanding it. The aim of this thesis is to investigate two different methods for analysing how much renewable power generation that can be connected to the power grid, so-called hosting capacity methods. The first method is a deterministic method which is traditionally used in power system analyses since it is a fast, simple and conservative method. This method does neither consider the intermittent nature of solar and wind power, nor any complementarity. The second method is a time series method which considers the complementarity and intermittency of solar and wind power but requires much data. The methods are compared in regards to assessed hosting capacities, risks and reliability of results. The study is performed on a regional grid case in the middle of Sweden. Solar and wind power plants with different capacities are modeled at ten buses in the power grid. The power grid is analysed in PSS/E with loading of lines and voltage levels determining the assessed hosting capacities. A correlation map presenting the temporal correlations of solar and wind power over the grid case area is also created in order to evaluate the complementarity in the area and its possible effects on the assessed hosting capacities.  The results show that the time series method is more reliable than the deterministic method. This is due to the difficulties in identifying accurate worst case hours that are used for the deterministic method. The time series method is also preferred as it considers complementarity between solar and wind power. However, the correlation map argues that the grid case area has weakly positive correlations, meaning low complementarity between solar and wind power. This suggests that the differences in hosting capacity between the two methods are more likely dependent on the temporal variations in existing load and power generation. The differences in assessed hosting capacity between the ten buses in the power grid are probably not due to the local complementarity either, but rather the structural differences of the grid in terms of components, local loads and existing power generation.

In this research, an attempt is made to evaluate alternative model-predictive microgrid control approaches and to understand the trade-offs that emerge between model complexity and the ability to achieve real-time optimized system performance. Three alternative controllers are considered and their computational and optimization performance compared. In the first, nonlinearities of the generators are included within the optimization. Subsequently, an approach is considered wherein alternative (non-traditional) states and inputs of generators are used which enables one to leverage linear models with the model predictive control (MPC). Nonlinearities are represented outside the control in maps between MPC inputs and the physical inputs. Third, a recently proposed linearized trajectory (LTMPC) is considered. Finally, the performance of the controllers is examined utilizing alternative models of the synchronous machine that have been proposed for power system analysis.

<p>As energy demands for the world increase, the necessity for alternate sources of energy are critical. Just in the United States alone, 92 quadrillion British thermal units (Btu) were used in 2020. As political and geographical pressures surrounding oil increase, along with the growing concern for climate, the drive to explore alternative and renewable means for harvesting energy is on the rise. Solar cells, also known as photovoltaics (PVs), are an attractive renewable source and have been developed as an alternative energy means for over 60 years. When considering losses due to atmospheric absorption and scattering, the Earth’s surface gets about 1000 W/m2 of energy from the sun, which is why there are research efforts around the world trying to maximize the efficiency of solar cells.</p> <p>Organic-inorganic halide perovskites provide for ideal absorbing layers that feature long carrier lifetime and diffusion lengths, strong photoluminescence, and promising tunability. Furthermore, the solution-processing methods used to make these perovskites ensure that the solar cells will remain low-cost and have easy scale-up possibilities. The main problem perovskites is that they degrade in the presence of water, thus leading to decreased device performance.</p> <p>In this work two approaches are investigated to increase moisture stability. The first investigates incorporation of thiols as pseudohalides into the 2D perovskite structure. Instead of the theorized perovskite, two novel 2D compounds were created, Pb₂X(S-C₆H₅)₃ (X= I, Br, Cl) and PbI_1.524(S-C₆H₅)_0.476. While not perovskites, this study gives insight into the effect that the thiol may have on determining structure when comparing –S-C₆H₅ with –SCN groups. Future work will explore more electronegative thiols that will be used to make moisture resistant, tunable 2D perovskites.</p> <p>The second approach is to incorporate longer organic ammonium cations into the perovskite structure to produce quasi-2D perovskite films fabricate them into devices. Adding in electronically insulating ligands leads to a stricter requirement for vertically aligned 2D films and special care must be taken to have efficient charge collection. The current field has successfully incorporated short ligands such as butylammonium (BA) into PVs, however the extension to larger and more beneficially hydrophobic ligands has been very scarce. In this work, a novel solvent engineering system is developed to create vertically aligned quasi-2D perovskite absorbing layers based off of a bithiophene ligand (2T). These absorbing layers are then characterized and incorporated into efficient PV devices. Generalizations to solvent conditions related to ligand choice is discussed herein, creating deep insights into incorporating more conjugated ligands into devices.</p>

Access to electricity is an important factor for rural development as many needs and services such as education, health care and water supply all have energy requirements. The aim of this study was to develop a sustainable electrification system based on renewable energy for the remote village Keteira on Moala Island, Fiji. Keteira does not currently have any reliable electricity supply, but the Fijian Government has set ambitious goals regarding electricity access and the renewable share in the power generation which led to the conclusion that Keteira in the near future will have access to electricity. The daily electricity demand profile for the village has been estimated based on consumption patterns available from other communities in similar living standards. The renewable energy sources available to Moala island have been identified as solar, wind and biomass energy, and the potentials of those sources were calculated based on global data libraries available online. Six different electrification system alternatives were developed, based on the aforementioned energy resources, either as single energy source-based systems or hybrid energy system solutions.These system alternatives were evaluated analytically and optimised for Levelized Cost of Electricity (LCOE) using the software HOMER Pro. The results showed that the optimal LCOE was 0.516 USD/kWh for the hybrid energy system which consisted of biomass, wind, solar and battery storage designed to supply the maximum power demand and daily energy demand in the village. Capital investment cost (CAPEX) was estimated as 480,500 USD for installation of the optimum system. However, it should be taken into account that no field study could be conducted in Keteira due to covid-19 and that the resulting system therefore might not be the most optimal for Keteira’s real conditions. / Tillgång till elektricitet är en viktig faktor för utveckling av landsbygden eftersom många behov och tjänster såsom utbildning, hälsovård och vattenförsörjning har energikrav. Syftet med denna studie var att utveckla ett hållbart elektrifieringssystem baserat på förnybar energi för den avlägset belägna byn Keteira på Moala Island, Fiji. Keteira har för närvarande ingen pålitlig elförsörjning, men den Fijianska regeringen har satt upp ambitiösa mål gällande tillgång till elektricitet och den förnybara andelen i kraftproduktionen vilket ledde till slutsatsen att Keteira kommer att få tillgång till elektricitet inom en snar framtid. Den dagliga elbehovsprofilen för byn har uppskattats baserat på tillgängliga konsumtionsmönster från andra samhällen med liknande levnadsstandard. De förnybara energikällor som finns på ön Moala har identifierats som sol-, vind- och biomassaenergi, där potentialen för dessa källor beräknades baserat på globala databibliotek tillgängliga online. Sex olika elektrifieringssystemsalternativ utvecklades baserat på de tidigare nämnda energiresurserna, antingen som systemlösningar bestående av en energikälla eller som hybrid-energisystemlösningar. Dessa systemalternativ utvärderades analytiskt och optimerades för Levelized Cost of Electricity (LCOE) med hjälp av programvaran HOMER Pro. Resultaten visade att den optimala LCOE var 0,516 USD / kWh för hybridenergisystemet vilket bestod av biomassa, vind, sol och batterilagring utformat för att tillgodose det maximala kraftbehovet och det dagliga energibehovet i byn. Kapitalinvesteringskostnaden (CAPEX) beräknades till 480 500 USD för installation av det optimala systemet. Det bör dock beaktas att ingen fältstudie kunde genomföras i Keteira på grund av covid-19 och att det resulterande systemet därför kanske inte är det mest optimala för Keteiras verkliga förhållanden.