Matching Items (26)
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 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.
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.
ContributorsNATH, ANUBHAV (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2019
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 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.
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.
ContributorsAthaide, Denise Maria Christine (Author) / Qin, Jiangchao (Thesis advisor) / Ayyanar, Raja (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2018
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 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.
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.
ContributorsAlbannai, Bassam Ahmad (Author) / Weng, Yang (Thesis advisor) / Wu, Meng (Committee member) / Dahal, Som (Committee member) / Arizona State University (Publisher)
Created2019
Description
As more electric vehicles (EVs) are adopted, users need a solution to meet their expectations when it comes to Level 2 EV Charging (EVC). Currently, Adaptive Charging (AC) Techniques are used in multi-unit, public, settings. In the future, AC should be utilized to provide an optimized charging experience for the EV user in a single-unit residential application. In this experiment, an Electric Vehicle simulation tool was created using Python. A training dataset was generated from Alternative Fuels and Data Center (EVI-Pro) using charging data from Phoenix, Arizona. Similarly, the utility price plan chosen for this exercise was SRP Electric Vehicle Price plan. This will be the cost-basis for the thesis. There were four cases that were evaluated by the simulation tool. (1) Utility Guided Scheduling (2) Automatic Scheduling (3) Off-Site Enablement (4) Bidirectional enablement. These use-cases are some of the critical problems facing EV users when it comes to charging at home. Each of these scenarios and algorithms were proven to save the user money in their daily bill. Overall, the user will need some sort of weighted scenario that considers all four cases to provide the best solution to the user. All four scenarios support the use of Adaptive Charging techniques in residential level 2 electric vehicle chargers. By applying these techniques, the user can save up to 90% on their energy bill while offsetting the energy grid during peak hours. The adaptive charging techniques applied in this thesis are critical to the adoption of the next generation electric vehicles. Users need to be enabled to use the latest and greatest technology. In the future, individuals can use this report as a baseline to use an Artificial Intelligence model to make an educated case-by-case decision to deal with the variability of the data.
ContributorsSnyder, Jack (Author) / Wu, Meng (Thesis director) / Walsh, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2023-05
ContributorsSnyder, Jack (Author) / Wu, Meng (Thesis director) / Walsh, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2023-05
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
As more electric vehicles (EVs) are adopted, users need a solution to meet their expectations when it comes to Level 2 EV Charging (EVC). Currently, Adaptive Charging (AC) Techniques are used in multi-unit, public, settings. In the future, AC should be utilized to provide an optimized charging experience for the EV user in a single-unit residential application.
In this experiment, an Electric Vehicle simulation tool was created using Python. A training dataset was generated from Alternative Fuels and Data Center (EVI-Pro) using charging data from Phoenix, Arizona. Similarly, the utility price plan chosen for this exercise was SRP Electric Vehicle Price plan. This will be the cost-basis for the thesis.
There were four cases that were evaluated by the simulation tool. (1) Utility Guided Scheduling (2) Automatic Scheduling (3) Off-Site Enablement (4) Bidirectional enablement. These use-cases are some of the critical problems facing EV users when it comes to charging at home. Each of these scenarios and algorithms were proven to save the user money in their daily bill. Overall, the user will need some sort of weighted scenario that considers all four cases to provide the best solution to the user.
All four scenarios support the use of Adaptive Charging techniques in residential level 2 electric vehicle chargers. By applying these techniques, the user can save up to 90% on their energy bill while offsetting the energy grid during peak hours.
The adaptive charging techniques applied in this thesis are critical to the adoption of the next generation electric vehicles. Users need to be enabled to use the latest and greatest technology. In the future, individuals can use this report as a baseline to use an Artificial Intelligence model to make an educated case-by-case decision to deal with the variability of the data.
ContributorsSnyder, Jack (Author) / Wu, Meng (Thesis director) / Walsh, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2023-05
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 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.
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.
ContributorsChen, Mengxi (Author) / Vittal, Vijay (Thesis advisor) / Hedman, Mojdeh Khorsand (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2019
Description
The high R/X ratio of typical distribution systems makes the system voltage vulnerable to active power injection from the distributed energy resources (DERs). Moreover, the intermittent and uncertain nature of the DER generation brings new challenges to voltage management. As guided by the previous IEEE standard 1547-2003, most of the existing photovoltaic (PV) systems in the real distribution networks are equipped with conventional inverters, which only allow the PV systems to operate at unity power factor to generate active power. To utilize the voltage control capability of the existing PV systems following the guideline of the revised IEEE standard 1547-2018, this dissertation proposes a two-stage stochastic optimization strategy aimed at optimally placing the PV smart inverters with Volt-VAr capability among the existing PV systems for distribution systems with high PV penetration to mitigate voltage violations. PV smart inverters are fast-response devices compared to conventional voltage control devices in the distribution system. Historically, distribution system planning and operation studies are mainly based on quasi-static simulation, which ignores system dynamic transitions between static solutions. However, as high-penetration PV systems are present in the distribution system, the fast transients of the PV smart inverters cannot be ignored. A detailed dynamic model of the PV smart inverter with Volt-VAr control capability is developed as a dynamic link library (DLL) in OpenDSS to validate the system voltage stability with autonomous control of the optimally placed PV smart inverters. Static and dynamic verification is conducted on an actual 12.47 kV, 9 km-long Arizona utility feeder that serves residential customers. To achieve fast simulation and accommodate more complex PV models with desired accuracy and efficiency, an integrative dynamic simulation framework for OpenDSS with adaptive step size control is proposed. Based on the original fixed-step size simulation framework in OpenDSS, the proposed framework adds a function in the OpenDSS main program to adjust its step size to meet the minimum step size requirement from all the PV inverters in the system. Simulations are conducted using both the original and the proposed framework to validate the proposed simulation framework.
ContributorsChen, Mengxi (Author) / Vittal, Vijay (Thesis advisor) / Ayyanar, Raja (Thesis advisor) / Hedman, Mojdeh (Committee member) / Wu, Meng (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