Dissertations / Theses on the topic '090607 Power and Energy Systems Engineering (excl. Renewable Power)'

Renewable energy (i.e., biomass, wind and solar) and Battery Energy Storage (BES) are emerging as sustainable solutions for electricity generation. In the last decade, the smart grid has been introduced to accommodate high penetration of such renewable resources and make the power grid more efficient, reliable and resilient. The smart grid is formulated as a combination of power systems, telecommunication communication and information technology. As an integral part of the smart grid, a smart integration approach is presented in this thesis. The main idea behind the smart integration is locating, sizing and operating renewable-based Distributed Generation (DG) resources and associated BES units in distribution networks strategically by considering various technical, economical and environmental issues. Hence, the aim of the thesis is to develop methodologies for strategic planning and operations of high renewable DG penetration along with an efficient usage of BES units. The first contribution of the thesis is to present three alternative analytical expressions to identify the location, size and power factor of a single DG unit with a goal of minimising power losses. These expressions are easily adapted to accommodate different types of renewable DG units for minimizing energy losses by considering the time-varying demand and different operating conditions of DG units. Both dispatchable and non-dispatchable renewable DG units are investigated in the study. Secondly, a methodology is also introduced in the thesis for the integration of multiple dispatchable biomass and nondispatchable wind units. The concept behind this methodology is that each nondispatchable wind unit is converted into a dispatchable source by adding a biomass unit with sufficient capacity to retain the energy loss at a minimum level. Thirdly, the thesis studies the determination of nondispatchable photovoltaic (PV) penetration into distribution systems while considering time-varying voltage-dependent load models and probabilistic generation. The system loads are classified as an industrial, commercial or residential type or a mix of them with different normalised daily patterns. The Beta probability density function model is used to describe the probabilistic nature of solar irradiance. An analytical expression is proposed to size a PV unit. This expression is based on the derivation of a multiobjective index (IMO) that is formulated as a combination of three indices, namely active power loss, reactive power loss and voltage deviation. The IMO is minimised in determining the optimal size and power factor of a PV unit. Fourthly, the thesis discusses the integration of PV and BES units considering optimal power dispatch. In this work, each nondispatchable PV unit is converted into a dispatchable source by adding a BES unit with sufficient capacity. An analytical expression is proposed to determine the optimal size and power factor of PV and BES units for reducing energy losses and enhancing voltage stability. A self-correction algorithm is then developed for sizing multiple PV and BES units. Finally, the thesis presents a comprehensive framework for DG planning. In this framework, analytical expressions are proposed to efficiently capture the optimal power factor of each DG unit with a standard size for minimising energy losses and enhancing voltage stability. The decision for the optimal location, size and number of DG units is obtained through a benefit-cost analysis over a given planning horizon. Here, the total benefit includes energy sales, loss reduction, network investment deferral and emission reduction, while the total cost is a sum of capital, operation and maintenance expenses. The study reveals that the time-varying demand and generation models play a significant role in renewable DG planning. Depending on the characteristics of demand and generation, a distribution system would accommodate up to an estimated 48% of the nondispatchable renewable DG penetration. A higher penetration level could be obtained for dispatchable DG technologies such as biomass and a hybrid of PV and BES units. More importantly, the study also indicates that optimal power factor operation could be one of the aspects to be considered in the strategy of smart renewable DG integration. A significant energy loss reduction and voltage stability enhancement can be achieved for all the proposed scenarios with DG operation at optimal power factor when compared to DG generation at unity power factor which follows the current standard IEEE 1547. Consequently, the thesis recommends an appropriate modification to the grid code to reflect the optimal or near optimal power factor operation of DG as well as BES units. In addition, it is shown that inclusion of energy loss reduction together with other benefits such as network investment deferral and emission reduction in the analysis would recover DG investments faster.

Wireline telecommunications infrastructure in the customer access network or CAN is undergoing a veritable technological and commercial revolution. The paired-copper CAN is being modernised with optical fibre deployed ever closer to customers, culminating soon with fibre-to-the-home networks or some variant thereof. Although bandwidth ceases to be a scarce commodity, the underlying natural monopoly will most likely be strengthened. National competition policy desires open access to multiple service providers yet commercial pressure calls for closure. This has been the recent experience with the hybrid fibre coaxial networks delivering pay television and Internet access. This research asks the question: What are the factors that prevent open access to the broadband services of next generation wireline infrastructure? How can these obstacles be overcome? A particular focus is given to non-price considerations which come to the fore due to the unique strategic and technological characteristics of optical fibre in the access network. The methodological approach involves data gathering via three case studies - that of the Telstra/Foxtel pay television network, the TransACT broadband network and fibre-to-the-home networks in general. Although the ultimate focus is on the research question above, these cases are discussed in a holistic way with consideration of a number of contextual factors. The research also examines the relationship between the concepts of ‘open access’ and ‘network neutrality’, visiting the concept of ‘common carriage’ in doing so. Several findings are reached that illuminate the field of telecommunications access regulation as applied to infrastructure capable of delivering truly next generation broadband services. Since 1993, our politicians have only paid lip service to the importance of competition and have deferred to the demands of the dominant builder of telecommunications infrastructure. From the viewpoints of end-users and access seekers, the access regime is found to be incapable of dealing with the technical and commercial bottlenecks arising from optical fibre in the CAN. It is concluded that communication between users should be recognised as the prime purpose of telecommunications and that the regulatory regime should not reward discriminatory practices detracting from the development of a networked information economy. It is also concluded that dominant players should never be rewarded with access holidays which could otherwise entrench market dominance through the creation of new bottlenecks. Access regulation is ill-equipped to cope with optical fibre in the CAN until it also recognizes the strategic potential of such infrastructure.

This research focuses on developing strategies for the optimal control of large-scale Combined Cooling, Heating and Power (CCHP) systems to meet electricity, heating, and cooling demands, and evaluating the cost savings potential associated with it. Optimal control of CCHP systems involves the determination of the mode of operation and set points to satisfy the specific energy requirements for each time period. It is very complex to effectively design optimal control strategies because of the stochastic behavior of energy loads and fuel prices, varying component designs and operational limitations, startup and shutdown events and many more. Also, for large-scale systems, the problem involves a large number of decision variables, both discrete and continuous, and numerous constraints along with the nonlinear performance characteristic curves of equipment. In general, the CCHP energy dispatch problem is intrinsically difficult to solve because of the non-convex, non-differentiable, multimodal and discontinuous nature of the optimization problem along with strong coupling to multiple energy components.

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.

The power transmission network is stretched thin in Western North America. When generators or substations fault, the resultant cascading failures can diminish transmission capabilities across wide regions of the continent. This thesis examined several methods of

This thesis primarily used particle swarm optimization (PSO) with inertia to determine a well-adapted set of parameters. The parameters included up to three thresholds for indices calculated from frequency. The researchers found that the best method for distinguishing between one or more phase faults used thresholds on two Fourier indices. Future lines of research regarding one-shot controls were considered.
threshold for control, and a positive threshold to reject control. The negative threshold for the moving average is met frequently during any large transient event. An additional index must be used to distinguish loss of generation events. This index is the maximum value of the moving average up to the present time and it is good for distinguishing loss of
generation events from transient swings caused by other events.


This thesis found that using local modes is generally useful to diagnose and apply one-shot controls when instability is caused by one or more phase faults, while when disconnected generators or reduced loads cause instability in the system, the local modes did not distinguish between loss of generation capacity events and reduced load events. Instead, differentiating based on the rate of change of frequency and an initial upward deflection of frequency or an initial downward deflection of frequency did distinguish between these types of events.

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 dv/dt) 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

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

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,

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

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.

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.

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.

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.

The pandemic scenario caused by COVID-19 is an event with no precedent. Therefore, it
is a phenomenon that can be studied to observe how electricity loads have changed during the stayat-home order weeks. The data collection process was done through online surveys and using
publicly available data. This study is focusing on analyzing household energy units such as
appliances, HVAC, lighting systems. However, collecting this data is expensive and timeconsuming since dwellings would have to be studied individually. As a solution, previous studies
have shown success in characterizing residential electricity using surveys with stochastic models.
This characterized electricity consumption data allows the researchers to generate a predictive
model, make a regression and understand the data. In that way, the data collection process will not
be as costly as installing measuring instruments or smart meters. The input data will be the
behavioral characteristics of each participant; meanwhile, the output of the analysis will be the
estimated electricity consumption "kWh." After generating the "kWh" target, a sensitivity analysis
will be done to observe the electricity consumption through time and examine how people evolved
their load during and after the stay-at-home order.
This research can help understand the change in electricity consumption of people who
worked at home during the pandemic and generate energy indicators and costs such as home office
electricity cost kWh/year. In addition to utilities and energy, managers can benefit from having a
clear understanding of domestic consumers during emergency scenarios as pandemics.

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.

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.

The transportation sector consumes around 70% of all petroleum in the US. In recent years, there have been improvements in the efficiency of the vehicles, and hybrid techniques that have been used to improve efficiency for conventional combustion vehicles. Hydraulic systems have been used as an alternative to conventional electric regenerative systems with good results. It has been proven that hydraulic systems can improve energy consumption in conventional combustion vehicles and in refuse collection vehicles. The control strategy has a large impact on the performance of the system and studies have shown the control strategy selection should be optimized and selected based on application. The performance of a hydraulic accumulator was compared with the performance of a set of ultracapacitors with the same energy storage capacity. The energy efficiency for the ultracapacitor was around 79% and the energy efficiency of the hydraulic accumulator was 87.7%. The power/mass ratio in the set of ultracapacitors was 2.21 kW/kg and 2.69 kW/kg in the hydraulic accumulator. The cost/power ratio is 217 US$/kW in the ultracapacitors and 75 US$/kW in the hydraulic accumulator. Based on these results, the hydraulic accumulator was selected as the energy storage device for the system. A testbench was designed, modeled, implemented to test the energy storage system in different conditions of operation. The experimental results of the testbench show how system can be actively controlled for different operating conditions. The operating conditions in the system can be adjusted by changing the number of rheostats connected to the electric generator. Different variables in the system were measured such as the angular shaft speed in the hydraulic pump, the torque and speed in the hydraulic motor, the pressure in the system, the flow rate, and the current and voltage in the electric generator. The control algorithm was successfully implemented, the results for the pressure in the system and the angular speed in the electric generator show how the control system can follow a desired reference value. Two different controllers were implemented: one controller for the pressure in the system, and one controller for the speed.

In the US, seven regional transmission organizations (RTOs) operate wholesale electricity markets within three largely independent transmission systems, the largest of which includes five RTO regions and many vertically integrated utilities.

RTOs operate a day-ahead and a real-time market. In the day-ahead market, generation and demand-side resources are optimally scheduled based on bids and offers for the next day. Those schedules are adjusted according to actual operating conditions in the real-time market. Both markets involve a unit commitment calculation, a mixed integer program that determines which generators will be online, and an economic dispatch calculation, an optimization determines the output of each online generator for every interval and calculates locational marginal prices (LMPs).

The use of LMPs for the management of congestion in RTO transmission systems has brought efficiency and transparency to the operation of electric power systems and provides price signals that highlight the need for investment in transmission and generation. Through this work, we aim to extend these efficiency and transparency gains to the coordination across RTOs. Existing market-based inter-regional coordination schemes are limited to incremental changes in real-time markets.

We propose a multi-regional unit-commitment that enables coordination in the day-ahead timeframe by applying a distributed approach to approximate a system-wide optimal commitment and dispatch while allowing each region to largely maintain their own rules, model only internal transmission up to the boundary, and keep sensitive financial information confidential. A heuristic algorithm based on an extension of the alternating directions method of multipliers (ADMM) for the mixed integer program is applied to the unit commitment.

The proposed coordinated solution was simulated and compared to the ideal single-market scenario and to a representation of the current uncoordinated solution, achieving at least 58% of the maximum potential savings, which, in terms of the annual cost of electric generation in the US, could add up to nearly $7 billion per year. In addition to the coordinated day-ahead solution, we develop a distributed solution for financial transmission rights (FTR) auctions with minimal information sharing across RTOs that constitutes the first known work to provide a viable option for market participants to seamlessly hedge price variability exposure on cross-border transactions.

The potential use of enzymes in industrial synthesis of epoxidized soybean oil has been limited through the high cost of the enzyme catalyst, in this work we evaluate the effectiveness of chemo enzymatic epoxidation of high oleic soybean oil (HOSBO) using lipase B from Candida antarctica (CALB) on immobilization support Immobead 150 and H₂O₂ in a solvent-free system. Additionally, we evaluated the production decay rates for hydrolytic activity and epoxide product formation over consecutive batches to determine half-life of the enzyme catalyst.

Batch epoxidation of HOSBO using CALB on 4wt% loading shows yields higher than 90% after 12 hrs. of reaction, and with a correlation to the consumption of double bonds suggesting that the reaction is selective and limiting side product reactions. Non-selective hydrolysis of oil was not found beyond the initial hydrolysis degree of raw HOSBO. Evaluations of decay given by epoxide product formation and released free fatty acids shows a half-life of the enzyme catalyst on these activities is of 22 ad 25 hrs. respectively. Finally, we evaluated the physical parameters influencing this decay, and found that H₂O₂ presence is the most important parameter of enzyme inactivation with no significant effect from its slowed addition. We propose a new reactor configuration for the analysis of the specific steps on epoxide formation through peracid intermediates.

In this work, we have collaborated with scientists industry and academia to explain the origin of the increased cell efficiency of bulk solution-processed perovskite cells. We also explored the possibility of enhancing the efficiency of the c-Si and perovskite cells by using them in a tandem configuration. To improve the intrinsic reliability, we have investigated 2D-perovskite cells with slightly lower efficiency but longer lifetime. We interpreted the behavior of the 2D-perovskite cells using randomly stacked quantum wells in the absorber region. We studied the reliability issues of c-Si modules and correlated series resistance of the modules directly to the solder bond failure. We also found out that finger thinning of the contacts at cell level manifests as a fake shunt resistance but is distinguishable from real shunt resistance by exploring the reverse bias or efficiency vs. irradiance. Then we proposed a physics-based model to predict the energy yield and lifetime of a module that suffers from solder bond failure using real field data by considering the statistical nature of the failure at module level. This model is part of a more comprehensive model that can predict the lifetime of a module that suffers from more degradation mechanisms such as yellowing, potential induced degradation, corrosion, soiling, delamination, etc. simultaneously. This method is called forward modeling since we start from environmental data and initial information of the module, and then predict the lifetime and time-dependent energy yield of a solar cell technology. As the future work, we will use our experience in forward modeling to deconvolve the reliability issues of a module that is fielded since each mechanism has a different electrical signature. Then by calibrating the forward model, we can predict the remaining lifetime of the fielded module. This work opens new pathways to achieve 2030 Sunshot goals of LCOE below 3c/kWh by predicting the lifetime that the product can be guaranteed, helping financial institutions regarding the risk of their investment, or national laboratories to redefine the qualification and reliability protocols.