Second-Order Effects in Modeling Resonant DC-DC Converters

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Description
The rising demand for energy and the depletion of fossil fuels has led to the rapid adoption of renewable energy sources. However, their variability poses challenges. Battery-based storage systems offer a solution, storing excess energy for peak demand or low…

The rising demand for energy and the depletion of fossil fuels has led to the rapid adoption of renewable energy sources. However, their variability poses challenges. Battery-based storage systems offer a solution, storing excess energy for peak demand or low generation periods. High-gain converters are key in efficiently integrating these systems with the grid by boosting battery voltage. Adding cell-integrated power electronics enhances reliability by providing localized control, reducing the impact of individual cell failures. The study investigates the efficiency of cell-level high-gain DC-DC two-stage converters for integrating renewable energy into the grid. To optimize performance across diverse DC link voltages, a comparison of different first-stage converters is performed based on factors such as component selection and switch losses. Following careful calculations and selection, the chosen converter is paired with an inverter for seamless grid integration. Operating at a power level of 200W, the converters transform low battery cell voltage from 2.5V to 40V into a grid-compatible 360V output. Results demonstrate the selected converter's superior efficiency and voltage regulation, displaying its suitability for grid integration applications. This research underscores the importance of such converters in facilitating reliable renewable energy integration, offering a pathway toward sustainable energy utilization. In the subsequent phase, the investigation delves deeper into assessing the performance of the LLC converter, particularly focusing on how it reacts to the secondary effects of the converter. This investigation gives special attention to a significant factor known as Intra-Winding capacitance, which refers to the unintended capacitance existing within the windings of the converter's transformer in close proximity. Furthermore, this analysis employs the General Harmonic Approximation (GHA) and compared against traditional Fundamental Harmonic Approximation (FHA).
Date Created
2024
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Electric Vehicle Charging Systems: From Converter Level Optimization To Impact Analysis on Power Systems

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Description
The burgeoning adoption of electric vehicles (EVs) necessitates a comprehensive exploration of the charging infrastructure, delving into both the optimization of EV charger converters and the pivotal role of EV chargers in the power grid. This dissertation comprises six technical…

The burgeoning adoption of electric vehicles (EVs) necessitates a comprehensive exploration of the charging infrastructure, delving into both the optimization of EV charger converters and the pivotal role of EV chargers in the power grid. This dissertation comprises six technical chapters, with a focused exploration of converters in Chapters 2 to 4 and an in-depth analysis of the role of EVs in power grids in Chapters 5 to 7.Chapters 2 to 4 showcase advancements in EV charger converters. Chapter 2 introduces a novel active harmonic reduction technique, mitigating the dominant third-order harmonic in the power factor corrector circuit’s input current. This innovation not only enhances grid power quality but also marks a critical step toward efficient and sustainable EV charging. In Chapter 3, a new gate signal modulation method in the dc-dc dual active converter minimizes conduction and switching losses, optimizing the charging process. Chapter 4 extends the converter optimization paradigm with a DC link voltage optimization method, enhancing the efficiency of the entire EV charger across ac-dc and dc-dc stages over the battery charging cycle. Chapters 5 to 7 transition seamlessly to the role of EV charging systems in the power grid. Chapter 5 explores the optimal utilization of bidirectional EVs for grid frequency support during critical events such as loss of generation and frequency drops. This chapter highlights the potential for EVs not merely as energy consumers but as dynamic contributors to grid stability. Chapter 6 presents a dynamic EV charging pricing strategy to distribute the EVs between charging stations (CSs) uniformly and thereby increase the revenue of the charging station operator (CSO) and enhance the charging satisfaction of EV users. Finally, in Chapter 7, a two-stage stochastic programming approach is developed for electric energy procurement in EV charging stations equipped with battery energy storage and photovoltaic generation. This innovative approach provides a roadmap for sustainable energy procurement, emphasizing the synergy between EV charging stations and renewable energy sources. In conclusion, this dissertation provides a holistic and pioneering exploration of EV charging systems, from converter optimization to grid integration. The research contributes significantly to the advancement of EV charging technology, offering solutions to enhance efficiency, power quality, and grid stability. The findings not only address current challenges in electric mobility but also lay a foundation for a sustainable and resilient energy future.
Date Created
2024
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Identifying cascading failures on synthetic power transmission systems

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Description

Abstract:

Cascading failures across a network propagate localized issues to more broad and potentially unexpected failures in the network. In power networks, where load must be delivered in real-time by a generation source, network layout is an important part of cascading…

Abstract:

Cascading failures across a network propagate localized issues to more broad and potentially unexpected failures in the network. In power networks, where load must be delivered in real-time by a generation source, network layout is an important part of cascading failure analysis. In lieu of real power network data protected for security reasons, we can use synthetic networks for academic purposes in developing a validating methodology. A contingency analysis technique is used to identify cascading failures, and this involves randomly selecting initial failure points in the network and observing how current violations propagate across the network. This process is repeated many times to understand the breadth of potential failures that may occur, and the observed trends in failure propagation are analyzed and compared to generate recommendations to prevent and adapt to failure. Emphasis is placed on power transmission networks where failures can be more catastrophic.

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Identifying cascading failures on synthetic power transmission systems

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Description

Abstract:

Cascading failures across a network propagate localized issues to more broad and potentially unexpected failures in the network. In power networks, where load must be delivered in real-time by a generation source, network layout is an important part of cascading…

Abstract:

Cascading failures across a network propagate localized issues to more broad and potentially unexpected failures in the network. In power networks, where load must be delivered in real-time by a generation source, network layout is an important part of cascading failure analysis. In lieu of real power network data protected for security reasons, we can use synthetic networks for academic purposes in developing a validating methodology. A contingency analysis technique is used to identify cascading failures, and this involves randomly selecting initial failure points in the network and observing how current violations propagate across the network. This process is repeated many times to understand the breadth of potential failures that may occur, and the observed trends in failure propagation are analyzed and compared to generate recommendations to prevent and adapt to failure. Emphasis is placed on power transmission networks where failures can be more catastrophic.

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A Proposal for Infrastructure Adaptation and Cascading Failures for Black Swans

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Description
Infrastructure managers are continually challenged to reorient their organizations to mitigate disturbances. Disturbances to infrastructure constantly intensify, and the world and its intricate systems are becoming more connected and complex. This complexity often leads to disturbances and cascading failures. Some…

Infrastructure managers are continually challenged to reorient their organizations to mitigate disturbances. Disturbances to infrastructure constantly intensify, and the world and its intricate systems are becoming more connected and complex. This complexity often leads to disturbances and cascading failures. Some of these events unfold in extreme ways previously unimagined (i.e., Black Swan events). Infrastructure managers currently seek pathways through this complexity. To this end, reimagined – multifaceted – definitions of resilience must inform future decisions. Moreover, the hazardous environment of the Anthropocene demands flexibility and dynamic reprioritization of infrastructure and resources during disturbances. In this dissertation, the introduction will briefly explain foundational concepts, frameworks, and models that will inform the rest of this work. Chapter 2 investigates the concept of dynamic criticality: the skill to reprioritize amidst disturbances, repeating this process with each new disturbance. There is a dearth of insight requisite skillsets for infrastructure organizations to attain dynamic criticality. Therefore, this dissertation searches other industries and finds goals, structures, sensemaking, and strategic best practices to propose a contextualized framework for infrastructure. Chapters 3 and 4 seek insight into modeling infrastructure interdependencies and cascading failure to elucidate extreme outcomes such as Black Swans. Chapter 3 explores this concept through a theoretical analysis considering the use of realistic but fictional (i.e., synthetic) models to simulate interdependent behavior and cascading failures. This chapter also discusses potential uses of synthetic networks for infrastructure resilience research and barriers to future success. Chapter 4 tests the preceding theoretical analysis with an empirical study. Chapter 4 builds realistic networks with dependency between power and water models and simulates cascading failure. The discussion considers the future application of similar modeling efforts and how these techniques can help infrastructure managers scan the horizon for Black Swans. Finally, Chapter 5 concludes the dissertation with a synthesis of the findings from the previous chapters, discusses the boundaries and limitations, and proposes inspirations for future work.
Date Created
2023
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Data Sourcing and Mini-grid Design for Electricity-Poor Communities Across Fiji

Description

There are approximately 300 unelectrified villages in Fiji. These villages are scattered across the many islands of Fiji and lack a connection to the main electrical grid, which is only available on the islands of Viti Levu and Vanua Levu.…

There are approximately 300 unelectrified villages in Fiji. These villages are scattered across the many islands of Fiji and lack a connection to the main electrical grid, which is only available on the islands of Viti Levu and Vanua Levu. Mini-grids and solar home systems are effective options for rural electrification that cannot be reached through grid extension. This work completes data acquisition, modeling, and technical and financial analysis to design mini-grid systems for remote communities. These designs are compared and tested against generation outages, storm simulations and carbon emission reduction. Having backup diesel generators provides an easy solution to the issue of resiliency during storms or expected maintenance though creates more emissions than solar-only or hybrid counterparts. Systems with net zero carbon emissions are also considered to be realistic options if these align closer to project goals and are seen to be reliable for up to a week with limited solar irradiance. An assessment was also completed of components locally available to build the systems.

Date Created
2023-05
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Understanding Social Media Influence, Semantic Network Analysis, and Thematic Campaign Campaign Classification Using Machine Learning.

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Description
Individuals and organizations have greater access to the world's population than ever before. The effects of Social Media Influence have already impacted the behaviour and actions of the world's population. This research employed mixed methods to investigate the mechanisms to…

Individuals and organizations have greater access to the world's population than ever before. The effects of Social Media Influence have already impacted the behaviour and actions of the world's population. This research employed mixed methods to investigate the mechanisms to further the understand of how Social Media Influence Campaigns (SMIC) impact the global community as well as develop tools and frameworks to conduct analysis. The research has qualitatively examined the perceptions of Social Media, specifically how leadership believe it will change and it's role within future conflict. This research has developed and tested semantic ontological modelling to provide insights into the nature of network related behaviour of SMICs. This research also developed exemplar data sets of SMICs. The insights gained from initial research were used to train Machine Learning classifiers to identify thematically related campaigns. This work has been conducted in close collaboration with Alliance Plus Network partner, University of New South Wales and the Australian Defence Force.
Date Created
2022
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Alternative Energy Integration and Policy Workforce Development with Laboratory of Energy
and Power Solutions

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Description

One answer to the lack of general knowledge for alternative energy and integration topics is seen in the workforce development content Laboratory of Energy and Power Solutions has generated for the past 6 years. LEAPS is a world-changing organization that…

One answer to the lack of general knowledge for alternative energy and integration topics is seen in the workforce development content Laboratory of Energy and Power Solutions has generated for the past 6 years. LEAPS is a world-changing organization that provides both technical and business solutions in areas of grid modernization, workforce development, and global energy access that facilitates the global transition to a resilient, low-carbon economy. This paper will aim to explain the contributions of David Hobgood, an Arizona State University senior, to LEAPS workforce development content through the course of the Spring 2022 semester. This paper goes into detail on the process of completing this educational content, amplifies key aspect, and presents the results of a two week pilot that presented the generated content.

Date Created
2022-05
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Stability and Security of Distribution Networks with High-Penetration Renewables

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Description
Rapid increases in the installed amounts of Distributed Energy Resources are forcing a paradigm shift to guarantee stability, security, and economics of power distribution systems. This dissertation explores these challenges and proposes solutions to enable higher penetrations of grid-edge devices.…

Rapid increases in the installed amounts of Distributed Energy Resources are forcing a paradigm shift to guarantee stability, security, and economics of power distribution systems. This dissertation explores these challenges and proposes solutions to enable higher penetrations of grid-edge devices. The thesis shows that integrating Graph Signal Processing with State Estimation formulation allows accurate estimation of voltage phasors for radial feeders under low-observability conditions using traditional measurements. Furthermore, the Optimal Power Flow formulation presented in this work can reduce the solution time of a bus injection-based convex relaxation formulation, as shown through numerical results. The enhanced real-time knowledge of the system state is leveraged to develop new approaches to cyber-security of a transactive energy market by introducing a blockchain-based Electron Volt Exchange framework that includes a distributed protocol for pricing and scheduling prosumers' production/consumption while keeping constraints and bids private. The distributed algorithm prevents power theft and false data injection by comparing prosumers' reported power exchanges to models of expected power exchanges using measurements from grid sensors to estimate system state. Necessary hardware security is described and integrated into underlying grid-edge devices to verify the provenance of messages to and from these devices. These preventive measures for securing energy transactions are accompanied by additional mitigation measures to maintain voltage stability in inverter-dominated networks by expressing local control actions through Lyapunov analysis to mitigate cyber-attack and generation intermittency effects. The proposed formulation is applicable as long as the Volt-Var and Volt-Watt curves of the inverters can be represented as Lipschitz constants. Simulation results demonstrate how smart inverters can mitigate voltage oscillations throughout the distribution network. Approaches are rigorously explored and validated using a combination of real distribution networks and synthetic test cases. Finally, to overcome the scarcity of real data to test distribution systems algorithms a framework is introduced to generate synthetic distribution feeders mapped to real geospatial topologies using available OpenStreetMap data. The methods illustrate how to create synthetic feeders across the entire ZIP Code, with minimal input data for any location. These stackable scientific findings conclude with a brief discussion of physical deployment opportunities to accelerate grid modernization efforts.
Date Created
2021
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Model predictive control for resilient operation of hybrid microgrids

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Description
This dissertation develops advanced controls for distributed energy systems and evaluates performance on technical and economic benefits. Microgrids and thermal systems are of primary focus with applications shown for residential, commercial, and military applications that have differing equipment, rate structures,…

This dissertation develops advanced controls for distributed energy systems and evaluates performance on technical and economic benefits. Microgrids and thermal systems are of primary focus with applications shown for residential, commercial, and military applications that have differing equipment, rate structures, and objectives. Controls development for residential energy heating and cooling systems implement adaptive precooling strategies and thermal energy storage, with comparisons made of each approach separately and then together with precooling and thermal energy storage. Case studies show on-peak demand and annual energy related expenses can be reduced by up to 75.6% and 23.5%, respectively, for a Building America B10 Benchmark home in Phoenix Arizona, Los Angeles California, and Kona Hawaii. Microgrids for commercial applications follow after with increased complexity. Three control methods are developed and compared including a baseline logic-based control, model predictive control, and model predictive control with ancillary service control algorithms. Case studies show that a microgrid consisting of 326 kW solar PV, 634 kW/ 634 kWh battery, and a 350 kW diesel generator can reduce on-peak demand and annual energy related expenses by 82.2% and 44.1%, respectively. Findings also show that employing a model predictive control algorithm with ancillary services can reduce operating expenses by 23.5% when compared to a logic-based algorithm. Microgrid evaluation continues with an investigation of off-grid operation and resilience for military applications. A statistical model is developed to evaluate the survivability (i.e. probability to meet critical load during an islanding event) to serve critical load out to 7 days of grid outage. Case studies compare the resilience of a generator-only microgrid consisting of 5,250 kW in generators and hybrid microgrid consisting of 2,250 kW generators, 3,450 kW / 13,800 kWh storage, and 16,479 kW solar photovoltaics. Findings show that the hybrid microgrid improves survivability by 10.0% and decreases fuel consumption by 47.8% over a 168-hour islanding event when compared to a generator-only microgrid under nominal conditions. Findings in this dissertation can increase the adoption of reliable, low cost, and low carbon distributed energy systems by improving the operational capabilities and economic benefits to a variety of customers and utilities.
Date Created
2019
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