Matching Items (13)

157953-Thumbnail Image.png

Establishing the Value of Battery Energy Storage System in a dc Fast Charging Sta-tion

Description

The objectives of this research project were to develop a model of real power demand from a dc fast charging station both with and without an integrated battery energy storage

The objectives of this research project were to develop a model of real power demand from a dc fast charging station both with and without an integrated battery energy storage system (BESS). An optimal deterministic control strategy was devel-oped to perform load-shaping under various scenarios with various load-shaping goals in mind to establish the value for BESS’s with various power and energy capacities.

To achieve these objectives, first a statistical model of electric vehicle drivers’ charging behaviors (home charging and dc fast charging) was constructed and simu-lated according to empirical charging data and several key findings about people’s charging habits in the literature.

Data of private vehicles’ driving records was extracted from the National Household Travel Survey (NHTS), derived 42 statistical distributions that mathe-matically modeled people’s driving behaviors. From this start, two algorithms were developed to simulate driver behavior: one using a database sampling method (DSM) and another using probability distribution sampling method (PDSM) to simulate the electric vehicles’ driving cycles. Both methods used data and statistical distributions derived from NHTS. Next, a model of the EV drivers’ charging behavior was incor-porated into the simulation of the electric vehicles’ driving cycles, and then the ve-hicles’ charging behaviors were simulated. From these simulations, one can forecast the real-power demand of a typical dc fast charging station with six dc 50 kW fast chargers serving a population of 700 EVs. (The ratio of six dc fast chargers to 700 EVs was selected based on the current value of this ratio in the US.) Next, a BESS was integrated into the dc fast charging station demand model and the size and charging behavior was optimized to account for different criteria which were based on the goals of the different potential owners: SRP or a third-party owner. It was established when a BESS would become economically feasible using a simplified economic model.

It was observed that the real-power demand shape is a function of the size of the BESS and the owner’s objective, i.e., flattening the demand curve or minimizing the cost of electricity.

Contributors

Agent

Created

Date Created
  • 2019

157073-Thumbnail Image.png

Comparative Study of HVAC and HVDC Transmission Systems With Proposed Machine Learning Algorithms for Fault Location Detection

Description

High Voltage Direct Current (HVDC) Technology has several features that make it particularly attractive for specific transmission applications. Recent years have witnessed an unprecedented growth in the number of the

High Voltage Direct Current (HVDC) Technology has several features that make it particularly attractive for specific transmission applications. Recent years have witnessed an unprecedented growth in the number of the HVDC projects, which demonstrates a heightened interest in the HVDC technology. In parallel, the use of renewable energy sources has dramatically increased. For instance, Kuwait has recently announced a renewable project to be completed in 2035; this project aims to produce 15% of the countrys energy consumption from renewable sources. However, facilities that use renewable sources, such as solar and wind, to provide clean energy, are mostly placed in remote areas, as their installation requires a massive space of free land. Consequently, considerable challenges arise in terms of transmitting power generated from renewable sources of energy in remote areas to urban areas for further consumption.

The present thesis investigates different transmission line systems for transmitting bulk energy from renewable sources. Specifically, two systems will be focused on: the high-voltage alternating current (HVAC) system and the high-voltage direct current (HVDC) system. In order to determine the most efficient way of transmitting bulk energy from renewable sources, different aspects of the aforementioned two types of systems are analyzed. Limitations inherent in both HVAC and HVDC systems have been discussed.

At present, artificial intelligence plays an important role in power system control and monitoring. Consequently, in this thesis, the fault issue has been analyzed in transmission systems, with a specific consideration of machine learning tools that can help monitor transmission systems by detecting fault locations. These tools, called models, are used to analyze the collected data. In the present thesis, a focus on such models as linear regression (LR), K-nearest neighbors (KNN), linear support vector machine (LSVM) , and adaptive boost (AdaBoost). Finally, the accuracy of each model is evaluated and discussed. The machine learning concept introduced in the present thesis lays down the foundation for future research in this area so that to enable further research on the efficient ways to improve the performance of transmission line components and power systems.

Contributors

Agent

Created

Date Created
  • 2019

156913-Thumbnail Image.png

Electromagnetic Transient-Transient Stability Hybrid Simulation for Electric Power Systems with Converter Interfaced Generation

Description

With the increasing penetration of converter interfaced renewable generation into power systems, the structure and behavior of the power system is changing, catalyzing alterations and enhancements in modeling and simulation

With the increasing penetration of converter interfaced renewable generation into power systems, the structure and behavior of the power system is changing, catalyzing alterations and enhancements in modeling and simulation methods.

This work puts forth a Hybrid Electromagnetic Transient-Transient Stability simulation method implemented using MATLAB and Simulink, to study power electronic based power systems. Hybrid Simulation enables detailed, accurate modeling, along with fast, efficient simulation, on account of the Electromagnetic Transient (EMT) and Transient Stability (TS) simulations respectively. A critical component of hybrid simulation is the interaction between the EMT and TS simulators, established through a well-defined interface technique, which has been explored in detail.

This research focuses on the boundary conditions and interaction between the two simulation models for optimum accuracy and computational efficiency.

A case study has been carried out employing the proposed hybrid simulation method. The test case used is the IEEE 9-bus system, modified to integrate it with a solar PV plant. The validation of the hybrid model with the benchmark full EMT model, along with the analysis of the accuracy and efficiency, has been performed. The steady-state and transient analysis results demonstrate that the performance of the hybrid simulation method is competent. The hybrid simulation technique suitably captures accuracy of EMT simulation and efficiency of TS simulation, therefore adequately representing the behavior of power systems with high penetration of converter interfaced generation.

Contributors

Agent

Created

Date Created
  • 2018

158803-Thumbnail Image.png

Modeling, Control and Design of Modular Multilevel Converters for High Power Applications

Description

Modular multilevel converters (MMCs) have become an attractive technology for high power applications. One of the main challenges associated with control and operation of the MMC-based systems is to smoothly

Modular multilevel converters (MMCs) have become an attractive technology for high power applications. One of the main challenges associated with control and operation of the MMC-based systems is to smoothly precharge submodule (SM) capacitors to the nominal voltage during the startup process. The existing closed-loop methods require additional effort to analyze the small-signal model of MMC and tune control parameters. The existing open-loop methods require auxiliary voltage sources to charge SM capacitors, which add to the system complexity and cost. A generalized precharging strategy is proposed in this thesis.

For large-scale MMC-embedded power systems, it is required to investigate dynamic performance, fault characteristics, and stability. Modeling of the MMC is one of the challenges associated with the study of large-scale MMC-based power systems. The existing models of MMC did not consider the various configurations of SMs and different operating conditions. An improved equivalent circuit model is proposed in this thesis.

The solid state transformer (SST) has been investigated for the distribution systems to reduce the volume and weight of power transformer. Recently, the MMC is employed into the SST due to its salient features. For design and control of the MMC-based SST, its operational principles are comprehensively analyzed. Based on the analysis, its mathematical model is developed for evaluating steady-state performances. For optimal design of the MMC-based SST, the mathematical model is modified by considering circuit parameters.

One of the challenges of the MMC-based SST is the balancing of capacitor voltages. The performances of various voltage balancing algorithms and different modulation methods have not been comprehensively evaluated. In this thesis, the performances of different voltage-balancing algorithms and modulation methods are analyzed and evaluated. Based on the analysis, two improved voltage-balancing algorithms are proposed in this thesis.

For design of the MMC-based SST, existing references only focus on optimal design of medium-frequency transformer (MFT). In this thesis, an optimal design procedure is developed for the MMC under medium-frequency operation based on the mathematical model of the MMC-based SST. The design performance of MMC is comprehensively evaluated based on free system parameters.

Contributors

Agent

Created

Date Created
  • 2020

161584-Thumbnail Image.png

Coordinated Wide-Area Control of Multiple Controllers in a Modern Power System

Description

Low frequency oscillations (LFOs) are recognized as one of the most challenging problems in electric grids as they limit power transfer capability and can result in system instability. In recent

Low frequency oscillations (LFOs) are recognized as one of the most challenging problems in electric grids as they limit power transfer capability and can result in system instability. In recent years, the deployment of phasor measurement units (PMUs) has increased the accessibility to time-synchronized wide-area measurements, which has, in turn, enabledthe effective detection and control of the oscillatory modes of the power system. This work assesses the stability improvements that can be achieved through the coordinated wide-area control of power system stabilizers (PSSs), static VAr compensators (SVCs), and supplementary damping controllers (SDCs) of high voltage DC (HVDC) lines, for damping electromechanical oscillations in a modern power system. The improved damping is achieved by designing different types of coordinated wide-area damping controllers (CWADC) that employ partial state-feedback. The first design methodology uses a linear matrix inequality (LMI)-based mixed H2/Hinfty control that is robust for multiple operating scenarios. To counteract the negative impact of communication failure or missing PMU measurements on the designed control, a scheme to identify the alternate set of feedback signals is proposed. Additionally, the impact of delays on the performance of the control design is investigated.
The second approach is motivated by the increasing popularity of artificial intelligence (AI) in enhancing the performance of interconnected power systems. Two different wide-area coordinated control schemes are developed using deep neural networks (DNNs) and deep reinforcement learning (DRL), while accounting for the uncertainties present in the power system. The DNN-CWADC learns to make control decisions using supervised learning; the training dataset consisting of polytopic controllers designed with the help of LMI-based mixed H2/Hinfty optimization. The DRL-CWADC learns to adapt to the system uncertainties based on its continuous interaction with the power system environment by employing
an advanced version of the state-of-the-art deep deterministic policy gradient (DDPG) algorithm referred to as bounded exploratory control-based DDPG (BEC-DDPG). The studies performed on a 29 machine, 127 bus equivalent model of theWestern Electricity Coordinating Council (WECC) system-embedded with different types of damping controls have demonstrated the effectiveness and robustness of the proposed CWADCs.

Contributors

Agent

Created

Date Created
  • 2021

161588-Thumbnail Image.png

Power System Security Enhancement for Real-Time Operations During Multiple Outages using Network Science

Description

Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional methods of steady-state security assessment in

Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional methods of steady-state security assessment in power systems involve contingency analysis based on AC or DC power flows. However, power flow based contingency analysis is not fast enough to evaluate all contingencies for real-time operations. Therefore, real-time contingency analysis (RTCA) only evaluates a subset of the contingencies (called the contingency list), and hence might miss critical contingencies that lead to cascading failures.This dissertation proposes a new graph-theoretic approach, called the feasibility test (FT) algorithm, for analyzing whether a contingency will create a saturated or over-loaded cut-set in a meshed power network; a cut-set denotes a set of lines which if tripped separates the network into two disjoint islands. A novel feature of the proposed approach is that it lowers the solution time significantly making the approach viable for an exhaustive real-time evaluation of the system. Detecting saturated cut-sets in the power system is important because they represent the vulnerable bottlenecks in the network. The robustness of the FT algorithm is demonstrated on a 17,000+ bus model of the Western Interconnection (WI).
Following the detection of post-contingency cut-set saturation, a two-component methodology is proposed to enhance the reliability of large power systems during a series of outages. The first component combines the proposed FT algorithm with RTCA to create an integrated corrective action (iCA), whose goal is to secure the power system against post-contingency cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) that quickly secures the system against saturated cut-sets.
The first component is more comprehensive than the second, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. Analysis of different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology enhances the scope and speed of power system security assessment during multiple outages.

Contributors

Agent

Created

Date Created
  • 2021

157471-Thumbnail Image.png

Machine Learning Applications for Dynamic Security Assessment in presence of Renewable Generation and Load Induced Variability

Description

Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the

Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the grid. With the aid of advanced technology such as phasor measurement units (PMUs), considerable work has been done involving voltage stability analysis and power system dynamic behavior analysis to ensure security and reliability of the grid. Online dynamic security assessment (DSA) analysis has been developed and applied in several power system control centers. Existing applications of DSA are limited by the assumption of simplistic load profiles, which often considers a normative day to represent an entire year. To overcome these aforementioned challenges, this research developed a novel DSA scheme to provide security prediction in real-time for load profiles corresponding to different seasons. The major contributions of this research are to (1) develop a DSA scheme incorporated with PMU data, (2) consider a comprehensive seasonal load profile, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA. The ML algorithms that will be the focus of this study include decision trees (DTs), support vector machines (SVMs), random forests (RFs), and multilayer neural networks (MLNNs).

This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.

Contributors

Agent

Created

Date Created
  • 2019

157883-Thumbnail Image.png

An Examination of Transmission System Flexibility Metrics

Description

In recent years, with the increasing penetration of solar generation, the uncertainty and variability of the power system generation also have increased. Power systems always require a balance between generation

In recent years, with the increasing penetration of solar generation, the uncertainty and variability of the power system generation also have increased. Power systems always require a balance between generation and load. The generation of the conventional generators must be scheduled to meet the total net load of the system with the variability and uncertainty of the solar resources integrated. The ability to match generation to load requires certain flexibility of the conventional generation units as well as a flexible transmission network to deliver the power. In this work, given the generation flexibility primarily reflected in the ramping rates, as well as the minimum and maximum output of the generation units, the transmission network flexibility is assessed using the metric developed in this work.

The main topic of this thesis is the examination of the transmission system flexibility using time series power flows (TSPFs). First, a TSPFs program is developed considering the economic dispatch of all the generating stations, as well as the available ramping rate of each generating unit. The time series power flow spans a period of 24 hours with 5-minute time interval and hence includes 288 power flow snapshots. Every power flow snapshot is created based on the power system topology and the previous system state. These power flow snapshots are referred to as the base case power flow below.

Sensitivity analysis is then conducted by using the TSPFs program as a primary tool, by fixing all but one of the system changes which include: solar penetration, wires to wires interconnection, expected retirements of coal units and expected participation in the energy

imbalance market. The impact of each individual change can be evaluated by the metric developed in the following chapters.

Contributors

Agent

Created

Date Created
  • 2019

158100-Thumbnail Image.png

Energy Management System in Naval Submarines

Description

An optimal energy scheduling procedure is essential in an isolated environment such as naval submarines. Conventional naval submarines include diesel-electric propulsion systems, which utilize diesel generators along with batteries and

An optimal energy scheduling procedure is essential in an isolated environment such as naval submarines. Conventional naval submarines include diesel-electric propulsion systems, which utilize diesel generators along with batteries and fuel cells. Submarines can charge the batteries by running diesel-electric generators only at the surface or at snorkeling depth. This is the most dangerous time for submarines to be detectable by acoustic and non-acoustic sensors of enemy assets. Optimizing the energy resources while reducing the need for snorkeling is the main factor to enhance underwater endurance. This thesis introduces an energy management system (EMS) as a supervisory tool for the officers onboard to plan energy schedules in order to complete various missions. The EMS for a 4,000-ton class conventional submarine is developed to minimize snorkeling and satisfy various conditions of practically designed missions by optimizing the energy resources, such as Lithium-ion batteries, Proton-exchange membrane fuel cells, and diesel-electric generators. Eventually, the optimized energy schedules with the minimum snorkeling hours are produced for five mission scenarios. More importantly, this EMS performs deterministic and stochastic operational scheduling processes to provide secured optimal schedules which contains outages in the power generation and storage systems.

Contributors

Agent

Created

Date Created
  • 2020

158388-Thumbnail Image.png

Coordinated Operation of the Electric Power System with Water Distribution Systems: Modeling, Control, Simulation, and Quantification of Resilience

Description

The electric power system (EPS) is an extremely complex system that has operational interdependencies with the water delivery and treatment system (WDTS). The term water-energy nexus is commonly used to

The electric power system (EPS) is an extremely complex system that has operational interdependencies with the water delivery and treatment system (WDTS). The term water-energy nexus is commonly used to describe the critical interdependencies that naturally exist between the EPS and water distribution systems (WDS). Presented in this work is a framework for simulating interactions between these two critical infrastructure systems in short-term and long-term time-scales. This includes appropriate mathematical models for system modeling and for optimizing control of power system operation with consideration of conditions in the WDS. Also presented is a complete methodology for quantifying the resilience of the two interdependent systems.

The key interdependencies between the two systems are the requirements of water for the cooling cycle of traditional thermal power plants as well as electricity for pumping and/or treatment in the WDS. While previous work has considered the dependency of thermoelectric generation on cooling water requirements at a high-level, this work considers the impact from limitations of cooling water into network simulations in both a short-term operational framework as well as in the long-term planning domain.

The work completed to set-up simulations in operational length time-scales was the development of a simulator that adequately models both systems. This simulation engine also facilitates the implementation of control schemes in both systems that take advantage of the knowledge of operating conditions in the other system. Initial steps for including the influence of anticipated water availability and water rights attainability within the combined generation and transmission expansion planning problem is also presented. Lastly, the framework for determining the infrastructural-operational resilience (IOR) of the interdependent systems is formulated.

Adequately modeling and studying the two systems and their interactions is becoming critically important. This importance is illustrated by the possibility of unforeseen natural or man-made events or by the likelihood of load increase in the systems, either of which has the risk of putting extreme stress on the systems beyond that experienced in normal operating conditions. Therefore, this work addresses these concerns with novel modeling and control/policy strategies designed to mitigate the severity of extreme conditions in either system.

Contributors

Agent

Created

Date Created
  • 2020