Power and Energy Commons^™

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 …

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 …

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 …

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 …

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 …