Matching Items (15)
Description
This research identifies several barriers to large scale implementation of solar photovoltaics into the modern US electricity system, along with solutions to help mitigate these challenges. The need for new technologies and utility rate plans are identified as two of these key barriers. In place of expensive, developing technologies this research explores the use of thermal energy storage (TES), a widely used, inexpensive, mature technology as a potential solution for a portion of this problem. A real-life example from Arizona State University (ASU) is used to illustrate the potential of TES. In addition, shortcomings of modern electricity rate plans are identified using both cost and system characteristics of residential solar and battery systems. This rate and system modeling also gives insight into the value that solar can provide to residential customers in a variety of settings.
ContributorsRouthier, Alexander F (Author) / Honsberg, Christiana (Thesis advisor) / Hedman, Mojdeh (Committee member) / Kurtz, Sarah (Committee member) / Miller, Clark (Committee member) / Arizona State University (Publisher)
Created2022
Wide Bandgap Semiconductor Based Electric Vehicle Charging Systems: Modeling, Magnetics, and Control
Description
Adhering to an ever-increasing demand for innovation in the field of onboard electric vehicle (EV) charging, several technical aspects pertaining to the design and performance enhancement of integrated multi-port charger topologies are discussed in this study. This study also elucidates various research challenges pertaining to each module of the topology and elucidates technically validated solutions for each.Firstly, targeting the input side totempole power factor corrector (TPFC) circuit, a novel digital filter based Active Mitigation Scheme (AMS) is proposed to curb the third harmonic component, along with a novel discretized sampling-based robust control scheme. Experimental verification of these techniques yields an enhanced Total Harmonic Distortion (THD) of 1.68%, enhanced efficiency of 98.1% and resultant power factor of 0.998 (lag).
Further, focusing on the bidirectional CLLC based DC/DC converter topology, a general harmonic approximation (GHA) based secondary side turnoff current minimization technique is discussed. Numerous fabrication and design-based constraints and correlations for parametric modelling of high frequency planar transformer (HFPT) are explained with analytical and 3D Finite Element Analysis (FEA) findings. Further, characterization of the plant transfer function of all-inclusive CLLC model is described along with hybrid Sliding Mode Control (SMC) based control scheme. The steady state experimental results at 1kW rated load show a peak efficiency of 98.49%, while the quantification of dynamic response portray a settling time reduction of 46.4% and an over/undershoot reduction of 33%.
Further, comprehensive modeling of triple active bridge (TAB) DC/DC converter topology is presented with special focus on the control scheme and decoupling capabilities to independently regulate the output bridges. With an objective to reduce the overall losses and to add a dimension of controllability, a three-loop control scheme is proposed with power flow optimization. Inculcating the benefits of multiport and resonant topologies, a comprehensive multi-variable loss optimization study of a Triple Active C^3 L^3 (TAC^3L^3) converter is discussed. The performance of eight different hybrid modulation schemes is compared with respect to the developed global loss minimization objective function. Experimental validations for various loading conditions are presented for a wide-gain bidirectional operation (400V/500-600V/24-28V), portraying a peak converter efficiency of 97.42%.
ContributorsChandwani, Ashwin Vijay (Author) / Mallik, Ayan (Thesis advisor) / Ayyanar, Raja (Thesis advisor) / Kannan, Arunanchala Mada (Committee member) / Hedman, Mojdeh (Committee member) / Arizona State University (Publisher)
Created2022
Description
Power system robustness against high impact low probability events is becoming a major concern. About 90% of US power outages reported in the last three decades are due to Hurricanes and tropical storms. Various works of literature are focused on modelling the resilience framework against hurricanes. To depict distinct phases of a system response during these disturbances, an aggregated trapezoid model is derived from the conventional trapezoid model and proposed in this work. The model is analytically investigated for transmission system performance, based on which resiliency metrics are developed for the same.A probabilistic-based Monte Carlo Simulations (MCS) approach has been proposed in this work to incorporate the stochastic nature of the power system and hurricane uncertainty. Furthermore, the system's resilience to hurricanes is evaluated on the modified reliability test system (RTS), which is provided in this work, by performing steady-state and dynamic security assessment incorporating protection modelling and corrective action schemes using the Siemens Power System Simulator for Engineering (PSS®E) software. Based on the results of steady-state (both deterministic and stochastic approach) and dynamic (both deterministic and stochastic approach) analysis, resilience metrics are quantified. Finally, this work highlights the interdependency of operational and infrastructure resilience as they cannot be considered discrete characteristics of the system.
The objective of this work is to incorporate dynamic analysis and stochasticity in the resilience evaluation for a wind penetrated power system.
ContributorsVijay Iswaran, Giritharan (Author) / Hedman, Mojdeh (Thesis advisor) / Vittal, Vijay (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2022
Description
Environmental problems are more abundant because of the rapid increase in urbanization, climate change, and population growth leading to the depletion of natural resources and endangerment of some species. The availability of infrastructure as well as socio-economic factors facilitate the illicit trade of wildlife through supply chain networks, adding further threats to species. Ecosystem conservation and protection of wildlife from illegal trade and poaching is fundamental to guarantee the survival of endangered species. Conservation efforts require a landscape approach that incorporates spatial features for the effective functionality of the selected reserve. This dissertation studies combinatorial optimization problems with application to two classes of societal problems: landscape conservation and disruption of illicit supply chains. The first and second chapter propose a mixed-integer formulation to model the reserve design problem with budget and ecological constraints. The first uses the radius of the smallest circle enclosing the selected areas as a metric of compactness. An extension of the model is proposed to solve the multi reserve design problem and the reserve expansion problem. The solution approach includes warm start heuristic, separation problem and cuts to improve model performance. The enhanced model outperforms the linearized and the equivalent nonlinear model. The second chapter uses the Reock’s metric as a metric of compactness. The solution approach includes warm start heuristic, knapsack based separation problem to inject solutions, and cuts to improve model performance. The enhanced model outperforms the default model. The third chapter proposes an integer programming model to solve the wildlife corridor design problem with minimum width requirement and a budget constraint. A separation algorithm is proposed to identify boundary patches and violations in the corridor width. A branch-and-cut approach is proposed to induce the corridor width and is tested on real-life landscape. The fourth chapter proposes an integer programming formulation to model the disruption of illicit supply chain problem. The proposed model enforces that at least x paths must be disrupted for an Origin-Destination pair to be disrupted and at least y arcs must be disrupted for a path to be disrupted. The proposed model is tested on real-life road networks.
ContributorsRavishankar, Shreyas (Author) / Sefair, Jorge A (Thesis advisor) / Escobedo, Adolfo R (Committee member) / Grubesic, Anthony (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2023
Description
This report studies the optimal mechanisms for the vertically integrated utility to dispatch and incentivize the third-party demand response (DR) providers in its territory. A framework is proposed, with three-layer coupled Stackelberg and simultaneous games, to study the interactions and competitions among the pro t-seeking process of the utility, the third-party DR providers, and the individual end users (EUs) in the DR programs. Two coupled single-leader-multiple-followers Stackelberg games with a three-layer structure are proposed to capture the interactions among the utility (modeled in the upper layer), the third-party DR providers (modeled in the middle layer), and the EUs in each DR program (modeled in the lower layer). The competitions among the EUs in each DR program is captured through a non-cooperative simultaneous game. An inconvenience cost function is proposed to model the DR provision willingness and capacity of different EUs. The Stackelberg game between the middle-layer DR provider and the lower-layer EUs is solved by converting the original bi-level programming to a single level programming. This converted single level programming is embedded in an iterative algorithm toward solving the entire coupled games framework. Case studies are performed on IEEE 34-bus and IEEE69-bus test systems to illustrate the application of the proposed framework.
ContributorsSajjadi, Sayyid Mohssen (Author) / Wu, Meng (Thesis advisor) / Hedman, Mojdeh (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2023
Description
Workforce planning in service systems is essential for customer satisfaction and profitability. Typical decisions include hiring levels, shift design, break scheduling, movement of workers around facilities, and matching worker skills to the requirements of tasks. The complexity of these decisions grows when realistic factors such as spatiotemporal demand dynamics, system performance assessment, or skill learning are incorporated into planning. As a result, optimal (or near-optimal) workforce plans should utilize resources efficiently and achieve satisfactory levels of service. This dissertation provides models and solution algorithms for three problems in service system optimization, which include realistic workforce management planning, nonlinear dynamics, and scheduling of tasks. The second chapter studies an airport security screening process and prescribes a daily operational plan, including service rate (i.e., number of servers), scheduling, and resource allocation decisions. The non-stationary arrivals are predicted and known in advance. A discrete-time queuing model that relies on a simple approximation and flow conservation equations embedded in a mixed-integer programming formulation, where consecutive time periods are connected. A multi-step solution method is proposed to optimize various metrics, such as maximum allowed queue lengths and wait times.
The third chapter extends the model from the second chapter. In this case, resources can move between different server locations, and their transit time is unproductive. Movement dynamics are captured via a multi-commodity flow model on a time-expanded network. A new discrete-time queue approximation scheme addresses the inaccuracies in existing methods stemming from server overload and underload fluctuations. Problem-specific valid inequalities are derived to improve the solution time, and a temporal decomposition algorithm is proposed to find initial feasible solutions.
The last chapter focuses on a medium to long-term scheduling problem with embedded workforce management decisions. The classic formulation for multi-mode multi-skill resource-constrained project scheduling problem is extended to incorporate worker task learning and training dynamics simultaneously. A discretization scheme to model the nonlinear learning process resulting from job assignments is developed. Training of workers is enabled to acquire new skills. A mixed-integer programming formulation is introduced, and a sequential solution scheme is proposed. The trade-offs between project duration and workforce skill composition objectives are investigated.
ContributorsArici, Berkin Tan (Author) / Sefair, Jorge A (Thesis advisor) / Askin, Ronald G (Committee member) / Escobedo, Adolfo R (Committee member) / Teksan, Zehra M (Committee member) / Arizona State University (Publisher)
Created2023
Description
The fast growth of the power system industry and the increase in the usage of computerized management systems introduces more complexities to power systems operations. Although these computerized management systems help system operators manage power systems reliably and efficiently, they introduce the threat of cyber-attacks. In this regard, this dissertation focuses on the load-redistribution (LR) attacks, which cause overflows in power systems. Previous researchers have shown the possibility of launching undetectable LR attacks against power systems, even when protection schemes exist. This fact pushes researchers to develop detection mechanisms. In this thesis, real-time detection mechanisms are developed based on the fundamental knowledge of power systems, operation research, and machine learning. First, power systems domain insight is used to identify an underlying exploitable structure for the core problem of LR attacks. Secondly, a greedy algorithm’s ability to solve the identified structure to optimality is proved, which helps operators quickly find the best attack vector and the most sensitive buses for each target transmission asset. Then, two quantitative security indices are proposed and leveraged to develop a measurement threat analysis (MTA) tool. Finally, a machine learning-based classifier is used to enhance the MTA tool’s functionality in flagging tiny LR attacks and distinguishing them from measurement/forecasting errors. On the other hand, after acknowledging that an adversarial LR attack interferes with the system, establishing a corrective action is imperative to mitigate or remove the potential consequences of the attack. This dissertation proposes two corrective actions; the first one is developed based on the worst-case attack scenario, considering the information provided by the MTA tool. After The MTA tool flags an LR attack in the system, it should determine the primary target and other affected transmission assets, using which the operator can estimate the actual loads in the post-attack stage. This estimation is essential since the corresponding security constraints in the first corrective action model are modeled based on these loads. The second one is a robust optimization that considers various load scenarios. The functionality of this robust model does not depend on the information provided by the MTA tool and is more reliable.
ContributorsKaviani, Ramin (Author) / Hedman, Kory (Thesis advisor) / Vittal, Vijay (Committee member) / Hedman, Mojdeh (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2022
Description
The stable and efficient operation of the transmission network is fundamental to the power system’s ability to deliver electricity reliably and cheaply. As average temperatures continue to rise, the ability of the transmission network to meet demand is diminished. Higher temperatures lead to congestion by reducing thermal limits of lines while simultaneously reducing generation potential. Furthermore, they contribute to the growing frequency and ferocity of devasting weather events. Due to prohibitive costs and limited real estate for building new lines, it is necessary to consider flexible investment options (e.g., transmission switching, capacity expansion, etc.) to improve the functionality and efficiency of the grid. Increased flexibility, however, requires many discrete choices, rendering fully accurate models intractable. This dissertation derives several classes of structural valid inequalities and employs them to accelerate the solution process for each of the proposed expansion planning problems. The valid inequalities leverage the variability of the cumulative capacity-reactance products of parallel simple paths in networks with flexible topology, such as those found in transmission expansion planning problems. Ongoing changes to the climate and weather will have vastly differing impacts a regional and local scale, yet these effects are difficult to predict. This dissertation models the long-term and short-term uncertainty of rising temperatures and severe weather events on transmission network components in both stochastic and robust mixed-integer linear programming frameworks. It develops a novel test case constructed from publicly available data on the Arizona transmission network. The models and test case are used to test the impacts of climate and weather on regional expansion decisions.
ContributorsSkolfield, Joshua Kyle (Author) / Escobedo, Adolfo R (Thesis advisor) / Sefair, Jorge (Committee member) / Mirchandani, Pitu (Committee member) / Hedman, Mojdeh (Committee member) / Arizona State University (Publisher)
Created2022
Description
This study aims to combine the wisdom of crowds with ML to make more accurate stock price predictions for a select set of stocks. Different from prior works, this study uses different input elicitation techniques to improve crowd performance. In addition, machine learning is used to support the crowd. The influence of ML on the crowd is tested by priming participants with suggestions from an ML model. Lastly, the market conditions and stock popularity is observed to better understand crowd behavior.
ContributorsBhogaraju, Harika (Author) / Escobedo, Adolfo R (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description
Computational social choice theory is an emerging research area that studies the computational aspects of decision-making. It continues to be relevant in modern society because many people often work as a group and make decisions in a group setting. Among multiple research topics, rank aggregation is a central problem in computational social choice theory. Oftentimes, rankings may involve a large number of alternatives, contain ties, and/or be incomplete, all of which complicate the use of robust aggregation methods. To address these challenges, firstly, this work introduces a correlation coefficient that is designed to deal with a variety of ranking formats including those containing non-strict (i.e., with-ties) and incomplete (i.e., unknown) preferences. The new measure, which can be regarded as a generalization of the seminal Kendall tau correlation coefficient, is proven to satisfy a set of metric-like axioms and to be equivalent to a recently developed ranking distance function associated with Kemeny aggregation. Secondly, this work derives an exact binary programming formulation for the generalized Kemeny rank aggregation problem---whose ranking inputs may be complete and incomplete, with and without ties. It leverages the equivalence of minimizing the Kemeny-Snell distance and maximizing the Kendall-tau correlation, to compare the newly introduced binary programming formulation to a modified version of an existing integer programming formulation associated with the Kendall-tau distance. Thirdly, this work introduces a new social choice property for decomposing large-size problems into smaller subproblems, which allows solving the problem in a distributed fashion. The new property is adequate for handling complete rankings with ties. The property is leveraged to develop a structural decomposition algorithm, through which certain large instances of the NP-hard Kemeny rank aggregation problem can be solved exactly in a practical amount of time. Lastly, this work applies these rank aggregation mechanisms to novel contexts for extracting collective wisdom in crowdsourcing tasks. Through this crowdsourcing experiment, we assess the capability of aggregation frameworks to recover underlying ground truth and the usefulness of multimodal information in overcoming anchoring effects, which shows its ability to enhance the wisdom of crowds and its practicability to the real-world problem.
ContributorsYoo, Yeawon (Author) / Escobedo, Adolfo R (Thesis advisor) / Mirchandani, Pitu B (Committee member) / Pavlic, Ted P (Committee member) / Chiou, Erin K (Committee member) / Arizona State University (Publisher)
Created2021