<p>This thesis proposes different scheduling algorithm to be implemented on the Roadside Units in ITS environment. Both variable and constant bit rate cases are considered.</p> / <p>Vehicular Ad-hoc Networks (VANETs) will be an integral part of future Intelligent Trans- portation Systems (ITS). In highway settings where electrical power connections may not be available, road-side infrastructure will often be powered by renewable energy sources, such as solar power. For this reason, energy efficient designs are desirable.</p> <p>This thesis considers the problem of energy efficient downlink scheduling for road- side infrastructure. In the first part of the thesis, the constant bit rate (CBR) air interface case is investigated. Packet-based and timeslot-based scheduling models for the theoretical minimum energy bound are considered. Timeslot-based scheduling is then formulated as a Mixed Integer Linear Program (MILP). Following this, three energy efficient online scheduling algorithms with varying complexity are introduced. Results from a variety of experiments show that the proposed scheduling algorithms perform well when compared to the energy lower bounds.</p> <p>In the second part of the thesis, the variable bit rate (VBR) air interface option is considered. Offline scheduling formulations are derived that provide lower bounds on the energy required to fufill vehicle requests. An integer linear program (ILP) is introduced which can be solved to find optimal offline VBR schedules. Two flow graph based models are then introduced. The first uses Generalized Flow (GF) graphs and the second uses time expanded graphs (TEGs) to model the scheduling problem. Four online scheduling algorithms with varying energy efficiency, fairness and computational complexities are developed. The proposed algorithms’ performance is examined under different traffic scenarios and they are found to perform well compared to the lower bound.</p> / Doctor of Philosophy (PhD)

This work explores the design, construction, and analysis of a novel DC-DC converter which incorporates combinations of switching capacitors and inductors to achieve an integer voltage divider function, without the need for a feedback loop controller to achieve the desired output voltage. The proposed Hybrid Voltage Divider additionally provides zero voltage switching (ZVS) at turn on transitions which yields improved overall efficiency of the converter. Besides a proof-of-concept via computer simulations, another primary goal of this thesis is to demonstrate the functionalities of the proposed Zero Voltage Switching Hybrid Voltage Divider (ZVS-HVD) through hardware prototyping. The proposed ZVS-HVD was designed and constructed to provide a 2:1 division with 24V input voltage at 120W maximum output power utilizing 500kHz switching frequency. Findings from simulations and hardware tests verify that the converter effectively provides the desired 12V output at varying loads with less than 5% voltage ripple. The efficiency of the converter reaches 95.02% at full load and peak efficiency of 96.33% at 55% load. Moreover, the converter consistently maintains the ZVS operations across all switches under varying loads. Overall, results verify the feasibility of the proposed ZVS HVD converter as an alternative solution in providing high efficiency DC voltage division without the need for complex feedback circuitry.

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.

There is a growing interest in electric transportation, and the number of electric vehicles (EVs) is increasing. The resulting increase in EV charging power demand has an adverse impact on the existing power grids, especially the distribution transformers. The repeated and continued overloading caused by EV charging can significantly reduce their operational life. This dissertation aims to comprehensively study the adverse impacts of EV charging on distribution transformers and provide robust and practical solutions to mitigate it. A typical North American secondary distribution system with different EV penetration levels and four realistic residential EV charging scenarios are used for the analyses. The IEEE Standard C57.91-2011 is used to quantify transformer life under different scenarios and to validate the efficacy of the proposed overloading mitigation strategies. It is observed that EV charging can have a significant impact on the life of distribution transformers. To mitigate the impact of EV charging on the distribution transformer, first, a practical solution based on reactive power compensation is proposed. The method is based on reducing the over- all transformer losses by providing a component of the residential reactive power demand through non-unity power factor operation of the EV charger. A centralized recursive control structure is proposed to compute and communicate the required reactive power values to the individual EVs. It is shown that the proposed technique increases the distribution transformer’s life by an average of nearly 47% in all four scenarios considered. Moreover, the proposed controller’s structure makes it effective even on low-bandwidth, high-latency communication networks. To verify this, the proposed controller’s stability under communication delays and its robustness against potential communication failures is also validated. This research also studies potential concerns about the charger’s reliability by non-unity power factor operation. Accordingly, an alternative overloading mitigation strategy is also proposed based on fixed charging current magnitude. This second method is shown to be more effective in reducing transformer overloading at the cost of a marginal decrease in the charging rate. Lastly, a high-level overview of the existing vehicle-to-grid communication standards is presented to provide a better context for practical implementation and identify potential challenges.

In this dissertation, three major contributions are presented in a photovoltaic (PV) energy system. Firstly, a three-port grid-forming (GFM) microinverter and a lithium-ion battery pack are integrated at the back of PV panel. As a result, they form an AC-PV energy system module that produces an AC output voltage. The technoeconomic analysis, battery capacity optimization, PV panel size optimization, electrical and thermal model of batteries, battery heat generation model, battery management system and thermal management system are discussed in the AC-PV module by using stochastic analysis and battery test results. Secondly, a three-phase 540 KVA bidirectional inverter and a 1.86 MWh lithium-ion battery energy storage system (BESS) were integrated at the Florida Solar Energy Center (FSEC). A case study is performed for this system by acquiring the energy consumption of the building, the reduced energy consumption, the battery testing, the load shifting, and the peak shaving. The total harmonic distortion (THD) values are also provided. Among eight power management scenarios, the scenarios that include PV panels are satisfied via simulation. However, the scenarios that do not include PV panels are analyzed and presented based on the real-world setting measurements. Thirdly, a modified droop control method is designed for grid-tied and off-grid scenarios. The simulation results are obtained based on three scenarios. The first one is that the voltage and frequency regulation control algorithm is discussed when GFM inverters have the equal power ratings. Then, the load sharing control algorithm is determined based on different GFM inverters' power ratings. The last scenario includes Grid connection. Loads are added and removed from the system to ensure that the frequency and voltage stability is the range of continuous operation. The coupling reactance effect on power sharing is investigated.

Yes / Polymer extrusion is an energy intensive production process and process energy e ciency has become a key concern in the current industry with the pressure of reducing the global carbon footprint. Here, knowledge of the pattern of energy usage and losses of each component in the plant is highly useful in the process energy optimization. Moreover, it is essential to maintain the melt quality while improving the energy e ciency in polymer processing. In this work, an investigation was made on the total energy consumption, drive motor energy consumption, power factor and the melt temperature profile across the die melt flow (as an indication of the melt thermal quality) of an industrial scale extruder with three di erent screw geometries, three polymer types and wide range of processing conditions (altogether 135 di erent processing situations were observed). This aims to widen the knowledge on process energy and thermal behaviors while exploring possible correlation/s between energy demand and melt quality (in terms of melt temperature fluctuations across the melt flow). The results showed that the level and fluctuations of the extruder’s power factor is particularly dependent upon the material being processed. Moreover, it seems that there is a relation between the level of energy demand of the heaters and the level of melt temperature fluctuations. While the extruder specific energy consumption decreases with increasing screw speed, specific energy consumption of the drive motor may have either increasing or decreasing behavior. Overall, this study provides new insights in a wide range on process energy demand and melt thermal quality in polymer extrusion. Moreover, further research is recommended to establish strong correlation/s between process energy consumption and melt thermal quality which should help to enhance process control and hence the product quality in single screw polymer extrusion.

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>

A successful power system in military applications (warship, aircraft, armored vehicle etc.) must operate acceptably under a wide range of conditions involving different loading configurations; it must maintain war fighting ability and recover quickly and stably after being damaged. The introduction of energy storage for the power system of an electric warship integrated engineering plant (IEP) may increase the availability and survivability of the electrical power under these conditions. Herein, the problem of energy storage control is addressed in terms of maximizing the average performance. A notional medium-voltage dc system is used as the system model in the study. A linear programming model is used to simulate the power system, and two sets of states, mission states and damage states, are formulated to simulate the stochastic scenarios with which the IEP may be confronted. A genetic algorithm is applied to the design of IEP to find optimized energy storage control parameters. By using this algorithm, the maximum average performance of power system is found.

Energy consumption has increased drastically during the last century. Currently, the worldwide energy consumption is about 17.4 TW and is predicted to reach 25 TW by 2035. Solar energy has emerged as one of the potential renewable energy sources. Since its first physical recognition in 1887 by Adams and Day till nowadays, research in solar energy is continuously developing. This has lead to many achievements and milestones that introduced it as one of the most reliable and sustainable energy sources. Recently, the International Energy Agency declared that solar energy is predicted to be one of the major electricity production energy sources by 2035. Enhancing the efficiency and lifecycle of photovoltaic (PV) modules leads to significant cost reduction. Reducing the temperature of the PV module improves its efficiency and enhances its lifecycle. To better understand the PV module performance, it is important to study the interaction between the output power and the temperature. A model that is capable of predicting the PV module temperature and its effects on the output power considering the individual contribution of the solar spectrum wavelengths significantly advances the PV module designs toward higher efficiency. In this work, a thermoelectrical model is developed to predict the effects of the solar spectrum wavelengths on the PV module performance. The model is characterized and validated under real meteorological conditions where experimental temperature and output power of the PV module measurements are shown to agree with the predicted results. The model is used to validate the concept of active optical filtering. Since this model is wavelength-based, it is used to design an active optical filter for PV applications. Applying this filter to the PV module is expected to increase the output power of the module by filtering the spectrum wavelengths. The active filter performance is optimized, where different cutoff wavelengths are used to maximize the module output power. It is predicted that if the optimized active optical filter is applied to the PV module, the module efficiency is predicted to increase by about 1%. Different technologies are considered for physical implementation of the active optical filter.

Heterojunctions of CuInSe2 (CIS) nanowires with cadmium sulfide (CdS) were fabricated demonstrating for the first time, vertically aligned nanowires of CIS in the conventional Mo/CIS/CdS stack. These devices were studied for their material and electrical characteristics to provide a better understanding of the transport phenomena governing the operation of heterojunctions involving CIS nanowires. Removal of several key bottlenecks was crucial in achieving this. For example, it was found that to fabricate alumina membranes on molybdenum substrates, a thin interlayer of tungsten had to be inserted. A qualitative model was proposed to explain the difficulty in fabricating anodized aluminum oxide (AAO) membranes directly on Mo. Experimental results were used to corroborate this model. Subsequently, a general procedure to use any material that can be deposited using sputtering or evaporation as a back contact for nanowires grown using AAO templates was developed. Experimental work to demonstrate this by transferring thin AAO templates onto flexible Polyimide (PI) substrates was performed. This pattern transfer approach opens doors for a wide variety of applications on almost any substrate. Any material that can be deposited by physical means can then be used as a back contact. Electron-beam induced deposition using a liquid precursor (LP-EBID) was used to selectively grow preconceived patterns of compound semiconductor (CdS) nanoparticles. Stoichiometric CdS nanoparticle patterns were grown successfully using this method. They were structurally and optically characterized indicating high purity deposits. This approach is promising because it marries the precision of e-beam lithography with the versatility of solution based deposition methods.