Matching Items (41)
Description
This thesis addresses the issue of making an economic case for bulk energy storage in the Arizona bulk power system. Pumped hydro energy storage (PHES) is used in this study. Bulk energy storage has often been suggested for large scale electric power systems in order to levelize load (store energy when it is inexpensive [energy demand is low] and discharge energy when it is expensive [energy demand is high]). It also has the potential to provide opportunities to avoid transmission and generation expansion, and provide for generation reserve margins. As the level of renewable energy resources increases, the uncertainty and variability of wind and solar resources may be improved by bulk energy storage technologies.
For this study, the MATLab software platform is used, a mathematical based modeling language, optimization solvers (specifically Gurobi), and a power flow solver (PowerWorld) are used to simulate an economic dispatch problem that includes energy storage and transmission losses. A program is created which utilizes quadratic programming to analyze various cases using a 2010 summer peak load from the Arizona portion of the Western Electricity Coordinating Council (WECC) system. Actual data from industry are used in this test bed. In this thesis, the full capabilities of Gurobi are not utilized (e.g., integer variables, binary variables). However, the formulation shown here does create a platform such that future, more sophisticated modeling may readily be incorporated.
The developed software is used to assess the Arizona test bed with a low level of energy storage to study how the storage power limit effects several optimization outputs such as the system wide operating costs. Large levels of energy storage are then added to see how high level energy storage affects peak shaving, load factor, and other system applications. Finally, various constraint relaxations are made to analyze why the applications tested eventually approach a constant value. This research illustrates the use of energy storage which helps minimize the system wide generator operating cost by "shaving" energy off of the peak demand.
The thesis builds on the work of another recent researcher with the objectives of strengthening the assumptions used, checking the solutions obtained, utilizing higher level simulation languages to affirm results, and expanding the results and conclusions.
One important point not fully discussed in the present thesis is the impact of efficiency in the pumped hydro cycle. The efficiency of the cycle for modern units is estimated at higher than 90%. Inclusion of pumped hydro losses is relegated to future work.
For this study, the MATLab software platform is used, a mathematical based modeling language, optimization solvers (specifically Gurobi), and a power flow solver (PowerWorld) are used to simulate an economic dispatch problem that includes energy storage and transmission losses. A program is created which utilizes quadratic programming to analyze various cases using a 2010 summer peak load from the Arizona portion of the Western Electricity Coordinating Council (WECC) system. Actual data from industry are used in this test bed. In this thesis, the full capabilities of Gurobi are not utilized (e.g., integer variables, binary variables). However, the formulation shown here does create a platform such that future, more sophisticated modeling may readily be incorporated.
The developed software is used to assess the Arizona test bed with a low level of energy storage to study how the storage power limit effects several optimization outputs such as the system wide operating costs. Large levels of energy storage are then added to see how high level energy storage affects peak shaving, load factor, and other system applications. Finally, various constraint relaxations are made to analyze why the applications tested eventually approach a constant value. This research illustrates the use of energy storage which helps minimize the system wide generator operating cost by "shaving" energy off of the peak demand.
The thesis builds on the work of another recent researcher with the objectives of strengthening the assumptions used, checking the solutions obtained, utilizing higher level simulation languages to affirm results, and expanding the results and conclusions.
One important point not fully discussed in the present thesis is the impact of efficiency in the pumped hydro cycle. The efficiency of the cycle for modern units is estimated at higher than 90%. Inclusion of pumped hydro losses is relegated to future work.
ContributorsDixon, William Jesse J (Author) / Heydt, Gerald T (Thesis advisor) / Hedman, Kory W (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2014
Description
This research develops heuristics for scheduling electric power production amid uncertainty. Reliability is becoming more difficult to manage due to growing uncertainty from renewable resources. This challenge is compounded by the risk of resource outages, which can occur any time and without warning. Stochastic optimization is a promising tool but remains computationally intractable for large systems. The models used in industry instead schedule for the forecast and withhold generation reserve for scenario response, but they are blind to how this reserve may be constrained by network congestion. This dissertation investigates more effective heuristics to improve economics and reliability in power systems where congestion is a concern.
Two general approaches are developed. Both approximate the effects of recourse decisions without actually solving a stochastic model. The first approach procures more reserve whenever approximate recourse policies stress the transmission network. The second approach procures reserve at prime locations by generalizing the existing practice of reserve disqualification. The latter approach is applied for feasibility and is later extended to limit scenario costs. Testing demonstrates expected cost improvements around 0.5%-1.0% for the IEEE 73-bus test case, which can translate to millions of dollars per year even for modest systems. The heuristics developed in this dissertation perform somewhere between established deterministic and stochastic models: providing an economic benefit over current practices without substantially increasing computational times.
Two general approaches are developed. Both approximate the effects of recourse decisions without actually solving a stochastic model. The first approach procures more reserve whenever approximate recourse policies stress the transmission network. The second approach procures reserve at prime locations by generalizing the existing practice of reserve disqualification. The latter approach is applied for feasibility and is later extended to limit scenario costs. Testing demonstrates expected cost improvements around 0.5%-1.0% for the IEEE 73-bus test case, which can translate to millions of dollars per year even for modest systems. The heuristics developed in this dissertation perform somewhere between established deterministic and stochastic models: providing an economic benefit over current practices without substantially increasing computational times.
ContributorsLyon, Joshua Daniel (Author) / Zhang, Muhong (Thesis advisor) / Hedman, Kory W (Thesis advisor) / Askin, Ronald G. (Committee member) / Mirchandani, Pitu (Committee member) / Arizona State University (Publisher)
Created2015
Description
As global energy demand has dramatically increased and traditional fossil fuels will be depleted in the foreseeable future, clean and unlimited renewable energies are recognized as the future global energy challenge solution. Today, the power grid in U.S. is building more and more renewable energies like wind and solar, while the electric power system faces new challenges from rapid growing percentage of wind and solar. Unlike combustion generators, intermittency and uncertainty are the inherent features of wind and solar. These features bring a big challenge to the stability of modern electric power grid, especially for a small scale power grid with wind and solar. In order to deal with the intermittency and uncertainty of wind and solar, energy storage systems are considered as one solution to mitigate the fluctuation of wind and solar by smoothing their power outputs. For many different types of energy storage systems, this thesis studied the operation of battery energy storage systems (BESS) in power systems and analyzed the benefits of the BESS. Unlike many researchers assuming fixed utilization patterns for BESS and calculating the benefits, this thesis found the BESS utilization patterns and benefits through an investment planning model. Furthermore, a cost is given for utilizing BESS and to find the best way of operating BESS rather than set an upper bound and a lower bound for BESS energy levels. Two planning models are proposed in this thesis and preliminary conclusions are derived from simulation results. This work is organized as below: chapter 1 briefly introduces the background of this research; chapter 2 gives an overview of previous related work in this area; the main work of this thesis is put in chapter 3 and chapter 4 contains the generic BESS model and the investment planning model; the following chapter 5 includes the simulation and results analysis of this research and chapter 6 provides the conclusions from chapter 5.
ContributorsDai, Daihong (Author) / Hedman, Kory W (Thesis advisor) / Zhang, Muhong (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2014
Description
Corrective transmission topology control schemes are an essential part of grid operations and are used to improve the reliability of the grid as well as the operational efficiency. However, topology control schemes are frequently established based on the operator's past knowledge of the system as well as other ad-hoc methods. This research presents robust corrective topology control, which is a transmission switching methodology used for system reliability as well as to facilitate renewable integration.
This research presents three topology control (corrective transmission switching) methodologies along with the detailed formulation of robust corrective switching. The robust model can be solved off-line to suggest switching actions that can be used in a dynamic security assessment tool in real-time. The proposed robust topology control algorithm can also generate multiple corrective switching actions for a particular contingency. The solution obtained from the robust topology control algorithm is guaranteed to be feasible for the entire uncertainty set, i.e., a range of system operating states.
Furthermore, this research extends the benefits of robust corrective topology control to renewable resource integration. In recent years, the penetration of renewable resources in electrical power systems has increased. These renewable resources add more complexities to power system operations, due to their intermittent nature. This research presents robust corrective topology control as a congestion management tool to manage power flows and the associated renewable uncertainty. The proposed day-ahead method determines the maximum uncertainty in renewable resources in terms of do-not-exceed limits combined with corrective topology control. The results obtained from the topology control algorithm are tested for system stability and AC feasibility.
The scalability of do-not-exceed limits problem, from a smaller test case to a realistic test case, is also addressed in this research. The do-not-exceed limit problem is simplified by proposing a zonal do-not-exceed limit formulation over a detailed nodal do-not-exceed limit formulation. The simulation results show that the zonal approach is capable of addressing scalability of the do-not-exceed limit problem for a realistic test case.
This research presents three topology control (corrective transmission switching) methodologies along with the detailed formulation of robust corrective switching. The robust model can be solved off-line to suggest switching actions that can be used in a dynamic security assessment tool in real-time. The proposed robust topology control algorithm can also generate multiple corrective switching actions for a particular contingency. The solution obtained from the robust topology control algorithm is guaranteed to be feasible for the entire uncertainty set, i.e., a range of system operating states.
Furthermore, this research extends the benefits of robust corrective topology control to renewable resource integration. In recent years, the penetration of renewable resources in electrical power systems has increased. These renewable resources add more complexities to power system operations, due to their intermittent nature. This research presents robust corrective topology control as a congestion management tool to manage power flows and the associated renewable uncertainty. The proposed day-ahead method determines the maximum uncertainty in renewable resources in terms of do-not-exceed limits combined with corrective topology control. The results obtained from the topology control algorithm are tested for system stability and AC feasibility.
The scalability of do-not-exceed limits problem, from a smaller test case to a realistic test case, is also addressed in this research. The do-not-exceed limit problem is simplified by proposing a zonal do-not-exceed limit formulation over a detailed nodal do-not-exceed limit formulation. The simulation results show that the zonal approach is capable of addressing scalability of the do-not-exceed limit problem for a realistic test case.
ContributorsKorad, Akshay Shashikumar (Author) / Hedman, Kory W (Thesis advisor) / Ayyanar, Raja (Committee member) / Vittal, Vijay (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2015
Description
There has been a considerable growth in distributed photovoltaic (PV) genera-tion and its integration in electric power distribution systems. This has led to a change in the distribution system infrastructure. Properly planned distributed gen-eration can offer a variety of benefits for system operations and enhance opera-tional performance of the distribution system. However, high penetration of PV resources can give rise to operating conditions which do not arise in traditional systems and one of the potential issues that needs to be addressed involves impact on power quality of the system with respect to the spectral distortion in voltages and currents.
The test bed feeder model representing a real operational distribution feeder is developed in OpenDSS and the feeder modeling takes into consideration the ob-jective of analysis and frequency of interest. Extensive metering infrastructure and measurements are utilized for validation of the model at harmonic frequencies. The harmonic study performed is divided into two sections: study of impact of non-linear loads on total harmonic voltage and current distortions and study of impact of PV resources on high frequency spectral distortion in voltages and cur-rents. The research work incorporates different harmonic study methodologies such as harmonic and high frequency power flow, and frequency scan study. The general conclusions are presented based on the simulation results and in addition, scope for future work is discussed.
The test bed feeder model representing a real operational distribution feeder is developed in OpenDSS and the feeder modeling takes into consideration the ob-jective of analysis and frequency of interest. Extensive metering infrastructure and measurements are utilized for validation of the model at harmonic frequencies. The harmonic study performed is divided into two sections: study of impact of non-linear loads on total harmonic voltage and current distortions and study of impact of PV resources on high frequency spectral distortion in voltages and cur-rents. The research work incorporates different harmonic study methodologies such as harmonic and high frequency power flow, and frequency scan study. The general conclusions are presented based on the simulation results and in addition, scope for future work is discussed.
ContributorsJoshi, Titiksha Vjay (Author) / Heydt, Gerald T (Thesis advisor) / Ayyanar, Raja (Committee member) / Vittal, Vijay (Committee member) / Arizona State University (Publisher)
Created2014
Description
The safety issue in an electrical power distribution system is of critical importance. In some circumstances, even the continuity of service has to be compromised for a situation that can cause a hazard to the public. A downed conductor that creates an electrical path between a current carrying conductor and ground pose a potential lethal hazard to anyone in the near proximity. Electric utilities have yet to find a fully accepted and reliable method for detecting downed conductors even with decades of research.
With the entry of more automation and a smarter grid in the different layers of distribution power system supply, new doors are being opened and new feasible solutions are waiting to be explored. The 'big data' and the infrastructures that are readily accessible through the smart metering system is the base of the work and analysis performed in this thesis. In effect, the new technologies and new solutions are an artifact of the Smart Grid effort which has now reached worldwide dimensions. A solution to problems of overhead distribution conductor failures / faults that use simple methods and that are easy to implement using existing and future distribution management systems is presented.
A European type distribution system using three phase supply is utilized as the test bed for the concepts presented. Fault analysis is performed on the primary and the secondary distribution system using the free downloadable software OpenDSS. The outcome is a set of rules that can be implemented either locally or central using a voltage based method. Utilized in the distribution management systems the operators will be given a powerful tool to make the correct action when a situation occurs. The test bed itself is taken from an actual system in Norway.
With the entry of more automation and a smarter grid in the different layers of distribution power system supply, new doors are being opened and new feasible solutions are waiting to be explored. The 'big data' and the infrastructures that are readily accessible through the smart metering system is the base of the work and analysis performed in this thesis. In effect, the new technologies and new solutions are an artifact of the Smart Grid effort which has now reached worldwide dimensions. A solution to problems of overhead distribution conductor failures / faults that use simple methods and that are easy to implement using existing and future distribution management systems is presented.
A European type distribution system using three phase supply is utilized as the test bed for the concepts presented. Fault analysis is performed on the primary and the secondary distribution system using the free downloadable software OpenDSS. The outcome is a set of rules that can be implemented either locally or central using a voltage based method. Utilized in the distribution management systems the operators will be given a powerful tool to make the correct action when a situation occurs. The test bed itself is taken from an actual system in Norway.
ContributorsAbusdal, Geir Magne (Author) / Heydt, Gerald T (Thesis advisor) / Ayyanar, Raja (Committee member) / Heydt, George (Committee member) / Arizona State University (Publisher)
Created2014
Description
The objective of this thesis is to detect certain cyber attacks in a power distribution ener-gy management system in a Smart Grid infrastructure. In the Smart Grid, signals are sent be-tween the distribution operator and the customer on a real-time basis. Signals are used for auto-mated energy management, protection and energy metering. This thesis aims at making use of various signals in the system to detect cyber attacks. The focus of the thesis is on a cyber attack that changes the parameters of the energy management system. The attacks considered change the set points, thresholds for energy management decisions, signal multipliers, and other digitally stored parameters that ultimately determine the transfer functions of the components. Since the distribution energy management system is assumed to be in a Smart Grid infrastructure, customer demand is elastic to the price of energy. The energy pricing is represented by a distribution loca-tional marginal price. A closed loop control system is utilized as representative of the energy management system. Each element of the system is represented by a linear transfer function. Studies are done via simulations and these simulations are performed in Matlab SimuLink. The analytical calculations are done using Matlab.
Signals from the system are used to obtain the frequency response of the component transfer functions. The magnitude and phase angle of the transfer functions are obtained using the fast Fourier transform. The transfer function phase angles of base cases (no attack) are stored and are compared with the phase angles calculated at regular time intervals. If the difference in the phase characteristics is greater than a set threshold, an alarm is issued indicating the detection of a cyber attack.
The developed algorithm is designed for use in the envisioned Future Renewable Electric Energy Delivery and Management (FREEDM) system. Examples are shown for the noise free and noisy cases.
Signals from the system are used to obtain the frequency response of the component transfer functions. The magnitude and phase angle of the transfer functions are obtained using the fast Fourier transform. The transfer function phase angles of base cases (no attack) are stored and are compared with the phase angles calculated at regular time intervals. If the difference in the phase characteristics is greater than a set threshold, an alarm is issued indicating the detection of a cyber attack.
The developed algorithm is designed for use in the envisioned Future Renewable Electric Energy Delivery and Management (FREEDM) system. Examples are shown for the noise free and noisy cases.
ContributorsRavi, Vaithinathan (Author) / Heydt, Gerald T (Thesis advisor) / Karady, George G. (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation carries out an inter-disciplinary research of operations research, statistics, power system engineering, and economics. Specifically, this dissertation focuses on a special power system scheduling problem, a unit commitment problem with uncertainty. This scheduling problem is a two-stage decision problem. In the first stage, system operator determines the binary commitment status (on or off) of generators in advance. In the second stage, after the realization of uncertainty, the system operator determines generation levels of the generators. The goal of this dissertation is to develop computationally-tractable methodologies and algorithms to solve large-scale unit commitment problems with uncertainty.
In the first part of this dissertation, two-stage models are studied to solve the problem. Two solution methods are studied and improved: stochastic programming and robust optimization. A scenario-based progressive hedging decomposition algorithm is applied. Several new hedging mechanisms and parameter selections rules are proposed and tested. A data-driven uncertainty set is proposed to improve the performance of robust optimization.
In the second part of this dissertation, a framework to reduce the two-stage stochastic program to a single-stage deterministic formulation is proposed. Most computation of the proposed approach can be done by offline studies. With the assistance of offline analysis, simulation, and data mining, the unit commitment problems with uncertainty can be solved efficiently.
Finally, the impacts of uncertainty on energy market prices are studied. A new component of locational marginal price, a marginal security component, which is the weighted shadow prices of the proposed security constraints, is proposed to better represent energy prices.
In the first part of this dissertation, two-stage models are studied to solve the problem. Two solution methods are studied and improved: stochastic programming and robust optimization. A scenario-based progressive hedging decomposition algorithm is applied. Several new hedging mechanisms and parameter selections rules are proposed and tested. A data-driven uncertainty set is proposed to improve the performance of robust optimization.
In the second part of this dissertation, a framework to reduce the two-stage stochastic program to a single-stage deterministic formulation is proposed. Most computation of the proposed approach can be done by offline studies. With the assistance of offline analysis, simulation, and data mining, the unit commitment problems with uncertainty can be solved efficiently.
Finally, the impacts of uncertainty on energy market prices are studied. A new component of locational marginal price, a marginal security component, which is the weighted shadow prices of the proposed security constraints, is proposed to better represent energy prices.
ContributorsLi, Chao (Author) / Hedman, Kory W (Thesis advisor) / Zhang, Muhong (Thesis advisor) / Mirchandani, Pitu B. (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2016
Description
The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost.
Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.
This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.
Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.
This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.
ContributorsSinghal, Nikita Ghanshyam (Author) / Hedman, Kory W (Thesis advisor) / Vittal, Vijay (Committee member) / Sankar, Lalitha (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the penetration of distributed renewable energy and the development of
semiconductor technology, power electronic devices could be utilized to interface re-
newable energy generation and the distribution power grid. However, when directly
connected to the power grid, the semiconductors inside the power electronic devices
could be vulnerable to the power system transient, especially to lightning strikes.
The work of this research focuses on the insulation coordination of power elec-
tronic devices connected directly to the power distribution system. The Solid State
Transformer (SST) in Future Renewable Electric Energy Delivery and Management
(FREEDM) system could be a good example for grid connected power electronic
devices. Simulations were conducted in Power Systems Computer Aided Design
(PSCAD) software. A simulation done to the FREEDM SST showed primary re-
sults which were then compare to simulation done to the grid-connected operating
Voltage Source Converter (VSC) to get more objective results.
Based on the simulation results, voltage surges caused by lightning strikes could
result in damage on the grid-connected electronic devices. Placing Metal Oxide Surge
Arresers (MOSA, also known as Metal Oxide Surge Varistor, MOV) at the front lter
could provide eective protection for those devices from power transient. Part of this
research work was published as a conference paper and was presented at CIGRE US
National Conference: Grid of the Future Symposium [1] and North American Power
Symposium [2].
semiconductor technology, power electronic devices could be utilized to interface re-
newable energy generation and the distribution power grid. However, when directly
connected to the power grid, the semiconductors inside the power electronic devices
could be vulnerable to the power system transient, especially to lightning strikes.
The work of this research focuses on the insulation coordination of power elec-
tronic devices connected directly to the power distribution system. The Solid State
Transformer (SST) in Future Renewable Electric Energy Delivery and Management
(FREEDM) system could be a good example for grid connected power electronic
devices. Simulations were conducted in Power Systems Computer Aided Design
(PSCAD) software. A simulation done to the FREEDM SST showed primary re-
sults which were then compare to simulation done to the grid-connected operating
Voltage Source Converter (VSC) to get more objective results.
Based on the simulation results, voltage surges caused by lightning strikes could
result in damage on the grid-connected electronic devices. Placing Metal Oxide Surge
Arresers (MOSA, also known as Metal Oxide Surge Varistor, MOV) at the front lter
could provide eective protection for those devices from power transient. Part of this
research work was published as a conference paper and was presented at CIGRE US
National Conference: Grid of the Future Symposium [1] and North American Power
Symposium [2].
ContributorsRong, Xuening (Author) / Karady, George G. (Thesis advisor) / Heydt, Gerald T (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2017