abstract: The government support towards green energy sources for the better future of the planet has changed the perspective of the people towards the usage of green energy. Among renewables, solar is one of the important and easily accessible resources to convert energy from the sun directly into electricity and this system has gained fame since the past three decades. SRP has set up a 6.36 kW PV and 19.4 kWh battery system on the rooftop of Engineering Research Center (ERC). The system is grid-connected and ASU (Arizona State University) has developed two load banks with a minimum step of 72 watts to simulate different residential load profiles and perform other research objectives. A customer benefit analysis is performed for residential customers with photovoltaic (PV) systems and energy storage particularly in the state of Arizona. By optimizing the use of energy storage device, the algorithm aims at maximizing the profit and minimizing utility bills in accordance with the demand charge algorithm of the local utility. This part of the research has been published as a conference paper in IEEE PES General Meeting 2017. A transient test is performed on the PV-battery during the on-grid mode and the off-grid mode to study the system behaviour during the transients. An algorithm is developed by the ASU research team to minimize the demand charge tariff for the residential customers. A statistical analysis is performed on the data collected from the system using a MATLAB algorithm. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2017

<div>As on 2017, US Energy Information Administration (US EIA) claims that 50 % of the total US energy consumption are contributed by Commercial and Industrial (C&I) end-users.</div><div>Most of the energy consumption by these users is in the form of the electric power. Electric utilities, who usually supply the electric power, tend to care about the power consumption profiles of these users mainly because of the scale of consumption and their significant contribution</div><div>towards the system peak. Predicting and managing the peaks of C&I users is crucial both for the users themselves and for utility companies.</div><div>In this research, we aim to understand and predict the daily peaks of individual C&I users. To empirically understand the statistical characteristics of the peaks, we perform an extensive exploratory data analysis using a real power consumption time series dataset. To accurately predict the peaks, we investigate indirect and direct learning approaches. In the indirect approach, daily peaks are identified after forecasting the entire time series for the day whereas in the direct approach, the daily peaks are directly predicted based on the historical data available for different users during different days of the week. The machine learning models used in this research are based on Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN).</div>

This thesis discusses the development of a naval ship distributed system architectural framework and related design tools that can be used during ship Concept and Requirements Exploration (CandRE). This architectural framework includes architectures for ship operations, the physical arrangement of Mission Power and Energy Systems (MPES) vital components within the ship, the logical relationship between MPES vital components, and simple energy and data models of MPES functions. This architectural framework is implemented through integrated Ship Behavior Interaction Models (SBIMs) that include the following: Warfighting Model (WM), Ship Operational Model (OM), Capability Model (CM), and Dynamic Architecture Flow Optimization (DAFO). These models provide a critical interface between logical and operational architectures, quantifying warfighting capabilities through system measures of performance at specific capability nodes. These models' interface with each other in the warfighting environment to guide the alignment of MPES vital systems using a DAFO. The integrated models quantify the performance of tasks enabled by capabilities through system measures of performance at specific capability nodes, enabling the simulation of the MPES configuration in operational situations. / Master of Science / This thesis discusses the development of a naval ship distributed system architectural framework and related design tools that can be used during ship Concept and Requirements Exploration (CandRE). This architectural framework includes architectures for ship operations, the physical arrangement of Mission Power and Energy Systems (MPES) within the ship, the logical relationship between MPES, and simple energy and data models of MPES. This architectural framework is implemented through integrated Ship Behavior Interaction Models (SBIMs) that include the following: Warfighting Model (WM), Ship Operational Model (OM), Capability Model (CM), and Dynamic Architecture Flow Optimization (DAFO). These models provide a critical interface between logical and operational architectures, quantifying warfighting capabilities through system measures of performance. These models' interface with each other in the warfighting environment to guide the alignment of MPES during operations. The integrated models quantify the performance of tasks enabled by capabilities through system measures of performance at specific capability nodes, enabling the simulation of the MPES configuration in operational situations.

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. <div><br></div><div>This work presents a solution methodology for optimizing the operation of a campus CCHP system using a detailed network energy flow model solved by a hybrid approach combining mixed-integer linear programming (MILP) and nonlinear programming (NLP) optimization techniques. In the first step, MILP optimization is applied to a plant model that includes linear models for all components and a penalty for turning on or off the boilers and steam chillers. The MILP step determines which components need to be turned on and their respective load needed to meet the campus energy demand for the chosen time period (short, medium or long term) with one-hour resolution. Based on the solution from MILP solver as a starting point, the NLP optimization determines the actual hourly state of operation of selected components based on their nonlinear performance characteristics. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The chief benefits of this formulation are its ability to determine the optimal mix of equipment with on/off capabilities and penalties for startup and shutdown, consideration of cost from all auxiliary equipment and its applicability to large-scale energy systems with multiple heating, cooling and power generation units resulting in improved performance. </div><div><br></div><div>The case-study considered in this research work is the Wade Power Plant and the Northwest Chiller Plant (NWCP) located at the main campus of Purdue University in West Lafayette, Indiana, USA. The electricity, steam, and chilled water are produced through a CCHP system to meet the campus electricity, heating and cooling demands. The hybrid approach is validated with the plant measurements and then used with the assumption of perfect load forecasts to evaluate the economic benefits of optimal control subjected to different operational conditions and fuel prices. Example cost optimizations were performed for a 24-hour period with known cooling, heating, and electricity demand of Purdue’s main campus, and based on actual real-time prices (RTP) for purchasing electricity from utility. Three optimization cases were considered for analysis: MILP [no on/off switch penalty (SP)]; MILP [including on/off switch penalty (SP)] and NLP optimization. Around 3.5% cost savings is achievable with both MILP optimization cases while almost 10.7% cost savings is achieved using the hybrid MILP-NLP approach compared to the current plant operation. For the selected components from MILP optimization, NLP balances the equipment performance to operate at the state point where its efficiency is maximum while still meeting the demand. Using this hybrid approach, a high-quality global solution is determined when the linear model is feasible while still taking into account the nonlinear nature of the problem. </div><div><br></div><div>Simulations were extended for different seasons to examine the sensitivity of the optimization results to differences in electric, heating and cooling demand. All the optimization results suggest there are opportunities for potential cost savings across all seasons compared to the current operation of the power plant. For a large CCHP plant, this could mean significant savings for a year. The impact of choosing different time range is studied for MILP optimization because any changes in MILP outputs impact the solutions of NLP optimization. Sensitivity analysis of the optimized results to the cost of purchased electricity and natural gas were performed to illustrate the operational switch between steam and electric driven components, generation and purchasing of electricity, and usage of coal and natural gas boilers that occurs for optimal operation. Finally, a modular, generalizable, easy-to-configure optimization framework for the cost-optimal control of large-scale combined cooling, heating and power systems is developed and evaluated.</div>

<div> <div> <div> <p>Puerto Rico’s lack of effective and affordable energy substitutes after Hurricane Maria resulted in a mortality increase of 4,970 residents (Verma, Murray, and Mamdani, 2018). Puerto Rico’s Island dependency on electric power and no energy substitutes available have provoked a risk to human life after catastrophic events. The problem was measured by comparing Puerto Rico’s reliance on fossil fuels with accessible and economical renewable energy options. Solar photovoltaic (PV) technologies are the optimum alternative to transition from fossil fuel usage to renewable energy. Previous research has demonstrated the impact of using solar panels instead of an electric grid due to the constant solar radiation throughout the year. The analyzed data and projections showed a reduction in fossil fuels and carbon dioxide emissions by implementing solar photovoltaic technologies. The installation of PV systems in landfills, household roofs and transitioning to solar public lighting positively impacts the atmosphere carbon dioxide emissions. </p> </div> </div> </div>

<div>Due to the instabilities that may occur in dc power systems with regulated power electronic loads such as those used in aircraft, ships, as well as terrestrial vehicles, many analysis techniques and design methodologies have been developed to ensure stable operation following small disturbances starting from normal operating conditions. However, these techniques do not necessarily guarantee large-displacement</div><div>stability following major disturbances such as faults, regenerative operation, pulsed loads, and/or loss of generating capacity. In this thesis, a formal mathematical definition of large-displacement stability is described and the analytical conditions needed to guarantee large-displacement stability are investigated for a notional dc power system. It is shown possible to guarantee large-displacement stability for any piecewise continuous value of load power provided it is bounded by the peak rating of the dc source.</div>

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<br><div>determining one-shot controls based on frequency decline in electrical generators to reduce the effect of one or more phase faults and tripped generators. These methods included criteria based on indices calculated from frequency measured at the controller location. These indices included criteria based on local modes and the rate of change of frequency.</div><br>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.<br><div><br></div><div>A method that distinguished nearby tripped generators from one or more phase faults and load change events was proposed. This method used a moving average, a negative<br></div>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<br>generation events from transient swings caused by other events.<br><br><div>This thesis further demonstrated how well a combination of controls based on both rate of change of frequency and local modes reduces instability of the network as determined by both a reduction in RMSGA and control efficiency at any time after the events.</div><br>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.

<div>Combining rooftop solar with energy storage for off-grid residential operation is restrictively expensive. Historically, operating off-grid requires an 'isolated self-consumption' operating strategy where any excess generation is wasted and to ensure reliability you must install costly, polluting generators or a large amount of energy storage. With the advent of Blockchain technology residents can come together and establish transactive microgrids which have two possible operating strategies: Centralized Energy Sharing (CES) and Interconnected Energy Sharing (IES). The CES strategy proposes that all systems combine their photovoltaic (PV) generation and energy storage systems (ESS) to meet their loads. IES strategy establishes an energy trading system between stand-alone systems which allows buying energy when battery capacity is empty and selling energy when battery capacity is full. Transactive microgrids have been investigated analytically by several sources, none of which consider year-round off-grid operation.</div><div> </div><div>A simulation tool was developed through MATLAB for comparing the three operating strategies: isolated self-consumption, CES, and IES. This simulation tool could easily be incorporated into existing software such as HOMER. </div><div><br></div><div>The effect of several variables on total cost was tested including interconnection type, initial charge, load variability, starting month, number of stand-alone systems, geographic location, and required reliability.</div><div> </div><div>It was found that the CES strategy improves initial cost by 7\% to 10\% compared to the baseline (isolated self-consumption) and IES cases in every simulation. The IES case consistently saved money compared to the baseline, just by a very small amount (less than 1\%). Initial charge was investigated for March, July, and November and was only found to have an effect in November. More research should be done to show the effect of initial charge for every month of the year. Load variability had inconsistent results between the two geographic locations studied, Indianapolis and San Antonio. This result would be improved with an improved load simulation which includes peak shifting. The number of systems did not have a demonstrable effect, giving the same cost whether there were 2 systems or 50 involved in the trading strategies. It may be that only one other system is necessary to receive the benefits from a transactive microgrid. Geographic locations studied (Indianapolis, Indiana; Phoenix, Arizona; Little Rock, Arkansas; and Erie, Pennsylvania) showed a large effect on the total cost with Phoenix being considerably cheaper than any other location and Erie having the highest cost. This result was expected due to each geographic location's load and solar radiation profiles. Required reliability showed a consistent and predictable effect with cost going down as the requirement relaxed and more hours of outage were allowed. </div><div><br></div><div>In order to accomplish off-grid operation with favorable economics it is likely that a system will need to reduce its reliability requirement, adopt energy saving consumption habits, choose a favorable geographic location, and either establish a transactive microgrid or include secondary energy generation and/or storage. </div>

<div>In this thesis, we propose a model to predict the interfacial delamination in a flexible solar cell. The interface in a multilayer Copper Indium Gallium Diselenide (CIGS) flexible solar cell was studied applying the principles of fracture mechanics to a fixed-arm-peel test. </div><div>The principles of fracture mechanics ( J-integral and cohesive model) were implemented in a finite element software to compare the experimental with the numerical peeling force. A fixed-arm-peel test was used to obtain the peeling force for different peeling angles. This peel force and material properties from the CIGS solar cell were processed in several non-linear equations, so the energy required to start the delamination was obtained.The accuracy of the model was compared by fitting the experimental and numerical peeling force, which had a difference of 0.08 %. It is demonstrated that the peeling process for 90-degree could be replicated in COMSOL® software for a CIGS solar cell.</div>

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.