Power and Energy Commons^™

Quantifying And Integrating Community Hardship Into Two-Stage Stochastic Grid Reliability Optimization With Battery Storage, Fredrica Arthur

LSU Master's Theses

Traditional reliability planning for conventional distribution systems is largely utility-oriented, with a focus on collective system performance metrics like Expected Energy Not Supplied (EENS), where implicitly all unserved energy is considered of equal weight in terms of post-outage economic hardship. Yet, it is well understood that extended outage durations cause an uneven level of hardship to socioeconomically disadvantaged communities. This thesis proposes a community-informed reliability planning framework where the hardship caused by outages is explicitly considered in the battery energy storage system (BESS) location and sizing problem. First, a hardship-weighted Energy Not Supplied (WENS) measure is proposed, where income, education, …

Participation Of Battery Energy Storage Systems In Load Frequency Control Of Power Systems, Zakaria Afsharbakeshloo

LSU Doctoral Dissertations

In this research, functionalities and roles of Battery Energy Storage Systems (BESSs) in power systems are extended beyond primary frequency control (PFC). The BESSs, while participating in PFC, are controlled through charge controllers to first maintain their state-of-charge (SOC) within an acceptable range through a primary charge controller, and to recharge the BESS to its maximum SOC through a secondary charge controller. This forms a hierarchical frequency and SOC control of power grids with BESSs. In this regard, multiple BESS case is considered as well, and by employing the SOC balancing principle, it is shown that desired power sharing among …

State-Of-Charge Estimation Using Deep Learning For Electric Vehicles, Samer Yahya Ribhe Tahboub

LSU Master's Theses

This thesis investigates the application of deep learning models for State of Charge (SOC) estimation in Battery Management Systems (BMS) for electric vehicles (EVs), focusing on optimizing EV range, lifespan, and performance while addressing challenges like range anxiety. The study explores three deep learning architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU)—each designed to capture complex temporal dependencies in battery data. The LSTM model is trained on EV battery data, including voltage, current, temperature, and SOC, providing a strong baseline for SOC estimation. The BiLSTM model enhances accuracy by processing data in both forward and backward …

Power Grid Resiliency Enhancement Against Flood-Induced Hazards, Mohadese Movahednia

LSU Doctoral Dissertations

Natural disasters, such as floods, may damage power system assets and lead to widespread and long outages. The impact of flood can be alleviated by preventive actions such as installing tiger dams around power substations before the flood. In this regard, it is imperative that critical substations are identified in terms of the connected load and imposed costs to the system. This study presents a resource allocation approach for protecting power substations against flood events a day ahead of the event. First, the required information for the model is extracted. Flood probability distribution functions are used to generate several flood …

Improving Cellphone-Based Bio-Imaging Technique For Fluorescence Detection, Erteza T. Efaz

LSU Master's Theses

The research presented in this thesis focuses on the design, development, and evaluation of a fluorescence detection system. The system is implemented primarily as an Android application, Auto Camera, which leverages smartphone cameras to capture and analyze fluorescent images. The application provides a user-friendly interface with some configurable features like exposure time, ISO speed, and storage limit; as well as defining detection thresholds and setting acquisition intervals. This study begins with the architectural framework of the Android application, which is written in Java using Android Studio. The API compatibility is set to version 33, and users are prompted to grant …

Learning–Assisted Constraint Filtering To Enhance Power System Optimization Performance, Fouad Hasan

LSU Doctoral Dissertations

Machine learning (ML) is a powerful tool that provides meaningful insights for operators to make fast and efficient decisions by analyzing data from power systems. ML techniques have great potential to assist in solving optimization problems within a shorter time frame and with less computational burden. AC optimal power flow (ACOPF), dynamic economic dispatch (D-ED), and security-constrained unit commitment (SCUC) are the three energy management optimization functions studied in this dissertation. ACOPF is solved every 5~15 minutes. Because of the nonconvex and complex nature of ACOPF, solving this problem for large systems is computationally expensive and time-consuming. Classification and regression …

Stabilizing Control Schemes For Grid-Connected Hybrid Pv-Energy Storage Systems, Indra Narayana Bhogaraju

LSU Doctoral Dissertations

A nonlinear stabilizing control scheme based on Lyapunov theory is proposed for a grid- connected hybrid photovoltaic (PV)/ battery/supercapacitor (SC) system. The system dynamics is developed in the stationary reference frame, and the state-space model of the system is derived and used to formulate the Lyapunov function (LF) candidate. The global asymptotic stability of the LF-based controller is discussed in detail. The real-time implementation feasibility of the proposed control scheme is validated through hardware-in-the-loop (HIL) studies of a grid- connected hybrid system under solar energy generation and grid load variations. To address the issue of digital computational time that leads …

Data-Driven Nonparametric Joint Chance-Constrained Programming For Power Systems Scheduling, Chutian Wu

LSU Doctoral Dissertations

This dissertation is dedicated to implementing data-driven nonparametric joint chance constraints (JCC) to power system optimization problems. Power generated by renewable sources, such as solar farms, is an uncertain parameter. Several approaches solve optimization under uncertainty, including stochastic programming, robust programming, and chance-constrained programming. Uncertain parameters may not belong to any parametric class of probability functions. Thus, methods that consider such uncertainty as a random variable that fits in a known probability density function (PDF) have limitations. This study focuses on chance-constrained programming under nonparametric or data-driven distributionally robust uncertainty settings.

Studies based on chance-constrained programming usually focus on individual …

Adaptive Balancing Compensation In Distribution Grid Of Ultra High-Power Manufacturing Plants, Motab Almousa

LSU Doctoral Dissertations

This dissertation presents a method of adaptive compensation, capable of handling power factor and the supply quality improvement under non-sinusoidal conditions. The focus in this dissertation is put on developing compensation for ultra-high power metallurgical plants, meaning on compensation in three-phase, four-wire ultra-high power dynamic, distribution systems. A separate attention in the dissertation is put on the adaptive compensation of ultrahigh power arc furnaces. The dissertation also presents an original method of adaptive compensation of DC current generated by such arc furnaces.

The proposed compensator is developed in the frame of the Currents’ Physical Components (CPC) – based power theory. …

Inductor Free Methods For Breaking Dc Currents, Sudipta Sen

LSU Doctoral Dissertations

The current interruption in dc circuits is more difficult to achieve compared to the ac circuits because, unlike ac systems, dc circuits do not have the natural current zero-crossing. Presently there are three categories of basic dc breaker models namely mechanical, static, and hybrid dc breakers. As current power systems is moving more towards microgrids, DERs, Electric Vehicles (EVs), and with recent advancements in dc circuits, wide verities of dc power sources, and loads, dc breakers are very important in future system protection.

New alternate methods to break dc currents for low-voltage applications are proposed in this work. The proposed …

An Improved Earned Value Management Method Integrating Quality And Safety, Brian Briggs

LSU Doctoral Dissertations

The construction industry invests significant time and money to improve quality and safety while reducing cost and schedule impacts. The industry has a sincere desire to improve construction project management methods to improve efficiency. Historically, quality and safety underperformances result from undermanaged quality control and safety activities. The cost and schedule impacts associated with poor quality work have always had an impact on construction operations. The unprecedented challenges and uncertainties of COVID-19 highlighted the need to improve the Earned Value Management (EVM) method within construction to reflect these quality and safety activities. The central goal of this dissertation is to …

Lyapunov Function-Based Stabilizing Control Scheme For Wireless Power Transfer Systems With Lcc Compensation Network, Abu Shahir Md Khalid Hasan

LSU Master's Theses

A stabilizing control scheme based on a Lyapunov function is proposed for wireless power transfer (or WPT) systems. A state-space model of the WPT system is developed and the Lyapunov function is formulated based on an energy equation of the system involving state variables. The internal resistance of a battery varies during charge and discharge. Therefore, if a WPT system is used to charge a battery, its output load will vary. Furthermore, the coupling coefficient between the transmitter (primary) and receiver (secondary) coils decreases when they are misaligned. Comparative case studies are conducted to verify the efficacy of the proposed …

Advanced Methods For Steady-State And Stability Analyses Of Hybrid Power Systems, Mohammad Mehdi Rezvani

LSU Doctoral Dissertations

The term hybrid power grids refer to the combination of two power systems with different intrinsic characteristic. For instance, ac-dc grids and transmission-distribution systems are kinds of hybrid power grids. Challenges in analyzing the hybrid power grids arise since two sets of equations should be solved either simultaneously or sequentially. In the simultaneous (unified) methods, the ac and dc system of equations are solved simultaneously, while, in the sequential approaches, these equations are solved in an error loop. In this dissertation, a unified method is proposed for steady-state and fault analyses of hybrid ac-dc power grids, while a sequential approach …

Intelligent Data-Driven Energy Flow Controllers For Renewable Energy And Electrified Transportation Systems, Juan Rafael Nunez Forestieri

LSU Doctoral Dissertations

In recent years, large scale deployments of electrical energy generation using renewable sources (RES) such as wind, solar and ocean wave power, along with more sustainable means of transformation have emerged in response to different initiatives oriented toward reducing greenhouse gas emissions. Strategies facilitating the integration of renewable generation into the grid and electric propulsion in transportation systems are proposed in this work.

Chapter 2 investigates the grid-connected operation of a wave energy converter (WEC) along with a hybrid supercapacitor/undersea energy storage system (HESS). A combined sizing and energy management strategy (EMS) based on reinforcement learning (RL) is proposed. Comparisons …

Parallel And Asynchronous Distributed Optimization For Power Systems Operation, Ali Mohammadi

LSU Doctoral Dissertations

Distributed optimization approaches are gaining more attention for solving power systems energy management functions, such as optimal power flow (OPF). Preserving information privacy of autonomous control entities and being more scalable than centralized approaches are two primary reasons for developing distributed algorithms. Moreover, distributed/ decentralized algorithms potentially increase power systems reliability against failures of components or communication links.

In this dissertation, we propose multiple distributed optimization algorithms and convergence performance enhancement techniques to solve the OPF problem. We present a multi-level optimization algorithm, based on analytical target cascading, to formulate and solve a collaborative transmission and distribution OPF problem. This …

Temporal Decomposition For Multi-Interval Optimization In Power Systems, Farnaz Safdarian

LSU Doctoral Dissertations

Large optimization problems are frequently solved for power systems operation and analysis of electricity markets. Many of these problems are multi-interval optimization with intertemporal constraints. The size of optimization problems depends on the size of the system and the length of the considered scheduling horizon. Growing the length of the scheduling horizon increases the computational burden significantly and might make solving the problem in a required time span impossible. Many simplifications and approximation techniques are applied to reduce the computational complexity of multi-interval scheduling problems and make them solvable in a reasonable time span. Geographical decomposition is presented in the …

Smart Charging Of Future Electric Vehicles Using Roadway Infrastructure, Sara Ahmed, Ethan Ahn, Mahmoud Reda Taha, Samer Dessouky, Moneeb Genedy, Daniel Fernandez, Ann Sebestian, Patience Raby

Data

Corresponding data set for Tran-SET Project No. 18ITSTSA03. Abstract of the final report is stated below for reference:

"Inspired by the fact that there is an immense amount of renewable energy sources available on the roadways such as mechanical pressure and frictional heat, this study presented the development and implementation of an innovative charging technique for future electric vehicles (EVs) by fully utilizing the existing roadways and the state-of-the-art nanotechnology and power electronics. The project introduced a novel wireless charging system, SIC (Smart Illuminative Charging), that uses LEDs powered by piezoelectric nanomaterials as the energy transmitter source and thin film …

Smart Charging Of Future Electric Vehicles Using Roadway Infrastructure, Sara Ahmed, Ethan Ahn, Mahmoud Reda Taha, Samer Dessouky, Moneeb Genedy, Daniel Fernandez, Ann Sebestian, Patience Raby

Publications

Inspired by the fact that there is an immense amount of renewable energy sources available on the roadways such as mechanical pressure and frictional heat, this study presented the development and implementation of an innovative charging technique for future electric vehicles (EVs) by fully utilizing the existing roadways and the state-of-the-art nanotechnology and power electronics. The project introduced a novel wireless charging system, SIC (Smart Illuminative Charging), that uses LEDs powered by piezoelectric nanomaterials as the energy transmitter source and thin film solar panels placed at the bottom of the EVs as the receiver, which is then poised to deliver …

Improving Power Network Resilience Against Threats, Hamzeh Davarikia

LSU Doctoral Dissertations

The work reported in this dissertation aims at developing a practical tool for utility transmission planners to improve the power network resilience against high-impact, low-frequency events (HILF). Such events rarely happen, but when occurred are catastrophic. In this dissertation, we studied the impact of HILF from the static and dynamic point of view and proposed the planning strategy to lower the devastating effect of the HILF.

In the static analysis, we consider the steady-state situation of the power grids and plan to protect the grid’s components, i.e., generators, buses, and transmission lines, to maximize preserving demand in a disastrous situation. …

Optimal And Efficient Decision-Making For Power System Expansion Planning, Mahdi Mehrtash

LSU Doctoral Dissertations

A typical power system consists of three major sectors: generation, transmission, and distribution. Due to ever increasing electricity consumption and aging of the existing components, generation, transmission, and distribution systems and equipment must be analyzed frequently and if needed be replaced and/or expanded timely. By definition, the process of power system expansion planning aims to decide on new as well as upgrading existing system components in order to adequately satisfy the load for a foreseen future.

In this dissertation, multiple economically optimal and computationally efficient methods are proposed for expanding power generation, transmission, and distribution systems. First, a computationally efficient …

Design, Simulation, And Construction Of An Ieee 14-Bus Power System, John Albert Boudreaux

LSU Master's Theses

Today’s bulk power system is massive, complex, and very dynamic. The U.S. power grid spans from coast to coast and even as far reaching as Canada. With the addition of new technologies such as renewable energies and power electronics to aid in power conversion and control, the power system grows more complex by the day. The most common approach of analyzing power system stability is through computer modeling and simulation. Due to the vast size and inaccessibility of transmission systems, real time testing can prove difficult. The motivation of this project was to design, simulate, and construct an IEEE 14 …

A Study On Reduction Of Harmonic Distortion Caused By Ac Arc Furnaces, Venkata Mani Gadiraju

LSU Master's Theses

In AC arc furnaces, arc ignition is stabilized by inductors which are connected across its supply lines. They cause power factor degradation in the system. When furnaces are modelled as linear RL load, the arc nonlinearity reduces the impact of this degradation to a significant extent. Furnace Power factor is improved by the DC voltage on the arc. In this research study, power factor of AC furnaces is estimated on the assumption that DC voltage on the arc is constant and furnace operates in steady state. Primary objective of this research was to determine the power factor of the furnace …

Decentralized Optimal Control With Application In Power System, Boyu Wang

LSU Doctoral Dissertations

An output-feedback decentralized optimal controller is proposed for power systems with renewable energy penetration. Renewable energy source is modeled similar to the classical generator model and is equipped with the unified power flow controller (UPFC). The transient performance of power system is considered and stability of the dynamical states are investigated. An offline decentralized optimal controller is designed that utilizes only the local states. The network comprises conventional synchronous generators as well as renewable sources with inverter equipped with UPFC. Subsequently, the optimal decentralized controller is compared to the initial stabilizing controller used to obtain the optimal controller. An online …

Advanced Modeling, Design, And Control Of Ac-Dc Microgrids, Hossein Saberi Khorzoughi

LSU Doctoral Dissertations

An interconnected dc grid that comprises resistive and constant-power loads (CPLs) that is fed by Photovoltaic (PV) units is studied first. All the sources and CPLs are connected to the grid via dc-dc buck converters. Nonlinear behavior of PV units in addition to the effect of the negative-resistance CPLs can destabilize the dc grid. A decentralized nonlinear model and control are proposed where an adaptive output-feedback controller is employed to stabilize the dc grid with assured stability through Lyapunov stability method while each converter employs only local measurements. Adaptive Neural Networks (NNs) are utilized to overcome the unknown dynamics of …

Considerations On Direct Balancing Of Ultra-High Power Ac Arc Furnaces In Uneasy State, Ikenna Louis Ezeonwumelu

LSU Master's Theses

During the operation of ultra-high-power ac arc furnace the negative effects of unbalance mostly occur in the secondary terminals of the transformer connected to the load. Hence balancing and compensating the furnace at this terminal will not only improve the transformer efficiency but also reduce energy losses that do occur. In this thesis a computer modeling of a reference ac arc furnace in both balanced and unbalanced states were simulated, and the effects of a reactive balancing compensator installed on the secondary side of the furnace transformer was evaluated to see how much delivered energy improvement can be obtained. The …

Microgrid Energy Management With Flexibility Constraints: A Data-Driven Solution Method, Okan Ciftci

LSU Master's Theses

Microgrid energy management is a challenging and important problem in modern power systems. Several deterministic and stochastic models have been proposed in the literature for the microgrid energy management problem. However, more accurate models are required to enhance flexibility of the microgrids when accounting for renewable energy and load uncertainties. This thesis proposes key contributions to solve the energy management problem for smart building (or small-scale microgrid). In Chapter 3, a deterministic energy management model is presented taking into account system flexibility requirements. Energy storage systems are deployed to enhance the grid flexibility and ramping capability. The objective function of …