Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Data-driven approaches for distribution grid modernization: exploring state estimaion, pseudo-measurement generation and false data detection(Montana State University - Bozeman, College of Engineering, 2023) Radhoush, Sepideh; Chairperson, Graduate Committee: Brad WhitakerDistribution networks must be regularly updated to enhance their performance and meet customer electricity requirements. Advanced technologies and infrastructure--including two- way communication, smart measuring devices, distributed generations in various forms, electric vehicles, variable loads, etc.--have been added to improve the overall efficiency of distribution networks. Corresponding to these new features and structures, the continuous control and monitoring of distribution networks should be intensified to keep track of any modifications to the distribution network performance. Distribution system state estimation has been introduced for real-time monitoring of distribution networks. State estimation calculations are highly dependent on measurement data which are collected from measurement devices in distribution networks. However, the installation of measurement devices is not possible at all buses to ensure the distribution network is fully observable. To address the lack of real measurements, pseudo- measurements are produced from historical load and generation data. Available measurements, along with physical distribution network topology, are fed into a state estimation algorithm to determine system state variables. Then, state estimation results are sent to a control center for further processing to enhance distribution network operation. However, the accuracy of state estimation results could be degraded by false data injection attacks on measurement data. If these attacks are not detected, distribution network operation could be significantly influenced. Different methods have been developed to enhance a distribution network operation and management. Machine learning approaches have also been identified to be beneficial in solving different types of problems in a power grid. In this dissertation, machine learning is applied to three areas of distribution systems: generating pseudo-measurements, performing distribution system state estimation calculations, and detecting false data injection attacks on measurement data. In addition to addressing these areas individually, machine learning is used to simultaneously perform distribution system state estimation calculation and false data injection attack detection. This is done by taking advantage of conventional and smart measurement data at different time scales. The results reveal that the operation and performance of a distribution network are improved using machine learning algorithms, leading to more effective power grid modernization.Item Resilience-aware management of active distribution networks(Montana State University - Bozeman, College of Engineering, 2021) Alali, Mohammad; Chairperson, Graduate Committee: Maryam Bahramipanah; Farshina Nazrul Shimim, Zagros Shahooei and Maryam Bahramipanah were co-authors of the article, 'Intelligent line congestion prognosis in active distribution system using artificial neural network' in the '2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)' which is contained within this thesis.; Zagros Shahooei and Maryam Bahramipanah were co-authors of the article, 'Resiliency-oriented optimization of critical parameters in multi inverter-fed distributed generation systems' submitted to the journal 'Sustainability journal', special Issue: 'Optimal dynamic control of active distribution power system' which is contained within this thesis.The electric power system is one of the greatest engineering achievements in the history of mankind. Electricity is integral to every part of our economy and society. Therefore, it is essential to make the electricity grid more resilient in facing different extreme events and black outs. In this work, four problems have been investigated to study and improve the resiliency of distribution networks. The first one focuses on the problem of power line congestion which can negatively harm the economy and different equipment in the grid. Two neural network models are used to predict where the congestion might happen and what would be the cause of it. Using these predictions, the problem can be alleviated in time and the resiliency of the grid will be improved. The second problem discusses power management of the distribution network under the occurrence of an extreme event. The problem is formulated as a Markov Decision Process using different agents and is solved using two Reinforcement Learning algorithms, namely, Q-Learning and Value Iteration. This approach is then tested on a benchmark system and the results show a remarkable improvement of the resiliency. The third problem studies the stability and power sharing of parallel inverters in a multi inverter-fed system. A small signal model of the power controller is studied. Further, the system's nonlinear dynamic equations are derived using accurate mathematical models. The system model is then trimmed and linearized around its operating point and the system's control parameters are optimized using Grey Wolf Optimization. Finally, improvement of stability and power sharing are verified by running time domain simulations. The last problem investigates the optimal siting and sizing of energy storage systems in a multi-microgrid system to improve the resiliency. A two-stage optimization method is used to solve the nonlinear and non-convex problem. The first stage involves an Optimal Power Flow and the second stage uses Genetic Algorithm. Further an investment cost based on the sensitivity analysis is introduced to improve the resiliency even further. The effectiveness of this ESS placement is tested on a benchmark system and validated using a fault scenario.Item Market-based power management and control of resilient smart grids and microgrids using a game theoretic multi-agent system approach(Montana State University - Bozeman, College of Engineering, 2017) Dehghanpour, Kaveh; Chairperson, Graduate Committee: M. Hashem Nehrir; Hashem Nehrir, John Sheppard and Nathan Kelly were co-authors of the article, 'Agent-based modeling in electrical energy markets using dynamic bayesian networks' in the journal 'IEEE transaction on power systems' which is contained within this thesis.; Hashem Nehrir, John Sheppard and Nathan Kelly were co-authors of the article, 'Agent-based modeling of retail electrical energy markets with demand response' in the journal 'IEEE transaction on smart grid' which is contained within this thesis.; Hashem Nehrir were co-authors of the article, 'Intelligent microgrid power management using the concept of nash bargaining solution' in 'Intelligent System Applications to Power Systems (ISAP) Conference, 2017' which is contained within this thesis.; Hashem Nehrir was a co-author of the article, 'Real-time multiobjective microgrid power management using distributed optimization in an agent-based bargaining framework' in the journal 'IEEE transaction on smart grid' which is contained within this thesis.; Hashem Nehrir was a co-author of the article, 'An agent-based hierarchical bargaining framework for power management of multiple cooperative microgrids' in the journal 'IEEE transaction on smart grid' which is contained within this thesis.; Hashem Nehrir was a co-author of the article, 'A market-based resilient power management technique for distribution systems with multiple microgrids using a multi-agent system approach' submitted to the journal 'Electric power components and systems' which is contained within this thesis.In this dissertation, we address the problem of optimal resource allocation through distributed market-based techniques in future power systems. Several connected problems are considered in different stages of this research project: 1) optimal decision making of generation companies in wholesale markets, 2) demand response and energy pricing in retail markets, 3) single and multiple microgrid power management within the retail sector, and 4) introducing a resiliency-aware power management for microgrid-based distribution systems. Hence, this work addresses the challenges that are connected to the economics of energy. All of these problems are concerned with optimizing the behavior of the participants in electricity markets (in wholesale and retail sectors) to achieve certain objectives in power system operation (e.g., profit maximization, peak shaving, resiliency improvement, extreme event awareness and preparedness, etc.). Solution concepts from the fields of machine learning and game theory have been employed to address these problems. Moreover, distributed agent-based frameworks are designed for modeling the interactive decision making processes and implementing control laws in electricity markets. The goal is to introduce automated 'intelligent' decision making capabilities into power systems to improve the efficiency of grid operation. This is closely related to the concept of 'smart grids'. The proposed solution strategies are verified through numerical simulations in MATLAB environment. The results demonstrate that using the proposed distributed decision tools and machine-learning-based techniques we can improve the performance of power systems to achieve higher levels of controllability and efficiency, while enhancing system resiliency.Item Scheduling for optimized network resource utilization #smartgrid #cloud(Montana State University - Bozeman, College of Engineering, 2017) Yaw, Sean; Chairperson, Graduate Committee: Brendan MumeyThe performance of distributed applications is heavily dependent on the interplay between the applications and the underlying network. Disparity between the requirements of the applications and the capabilities of the network leads to degraded application performance, which in turn results in a drop in application usage or revenue. For example, many real-time interactive applications require lower latency than the public Internet provides, resulting in a poor experience for application users. At other times though, applications fail to effectively utilize all network capabilities. For example, conventional electrical appliances are currently unable to leverage the increased communication capabilities provided by the future smart power grid to decrease costs or modify consumption. Scheduling is an optimization technique to temporally and spatially allocate resources in such a way as to achieve some desired parameter optimization, such as minimized cost. In this dissertation, I study the use of scheduling techniques to counteract application performance degradation present due to the disparity between application requirements and network capabilities. I explore this disparity in both the smart grid and cloud networks, and propose novel algorithms that rely on numerous algorithmic techniques to realize application performance increases.Item Power management and frequency regulation for microgrid and smart grid : a real-time demand response approach(Montana State University - Bozeman, College of Engineering, 2014) Pourmousavi Kani, Seyyed Ali; Chairperson, Graduate Committee: M. Hashem Nehrir; Andrew S. Cifala and M. Hashem Nehrir were co-authors of the article, 'Impact of high penetration of PV generation on frequency and voltage in a distribution feeder' in the journal 'IEEE North American power symposium' which is contained within this thesis.; M. Hashem Nehrir was a co-author of the article, 'Real-time central demand response for primary frequency regulation in microgrids' in the journal 'IEEE transactions on smart grid' which is contained within this thesis.; M. Hashem Nehrir was a co-author of the article, 'Real-time optimal demand response for frequency regulation in smart microgrid environment' in the journal 'International conference on power and energy system' which is contained within this thesis.; M. Hashem Nehrir was a co-author of the article, 'Introducing dynamic demand response in the LFC model' in the journal 'IEEE transactions on power systems' which is contained within this thesis.; M. Hashem Nehrir was a co-author of the article, 'LFC-DR model expansion to multi-area power systems' submitted to the journal 'IEEE transactions on power systems' which is contained within this thesis.; Stasha N. Patrick and M. Hashem Nehrir were co-authors of the article, 'Real-time demand response through aggregate electric water heaters for load shifting and balancing intermittent wind generation' in the journal 'IEEE transactions on smart grid' which is contained within this thesis.; M. Hashem Nehrir and Ratnesh K. Sharma were co-authors of the article, 'Ownership cost calculation for distributed energy resources using uncertainty and risk analyses' submitted to the journal 'IEEE Transactions on power systems' which is contained within this thesis.; Ratnesh K. Sharma and Babak Asghari were co-authors of the article, 'A framework for real-time power management of a grid-tied microgrid to extend battery lifetime and reduce cost of energy' in the journal 'IEEE innovative smart grid technologies' which is contained within this thesis.; M. Hashem Nehrir, Christopher M. Colson and Caisheng Wang were co-authors of the article, 'Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization' in the journal 'IEEE transactions on sustainable energy' which is contained within this thesis.; M. Hashem Nehrir and Ratnesh K. Sharma were co-authors of the article, 'Multi-timescale power management for islanded microgrids including storage and demand response' submitted to the journal 'IEEE transactions on smart grid' which is contained within this thesis.Future power systems (known as smart grid) will experience a high penetration level of variable distributed energy resources to bring abundant, affordable, clean, efficient, and reliable electric power to all consumers. However, it might suffer from the uncertain and variable nature of these generations in terms of reliability and especially providing required balancing reserves. In the current power system structure, balancing reserves (provided by spinning and non-spinning power generation units) usually are provided by conventional fossil-fueled power plants. However, such power plants are not the favorite option for the smart grid because of their low efficiency, high amount of emissions, and expensive capital investments on transmission and distribution facilities, to name a few. Providing regulation services in the presence of variable distributed energy resources would be even more difficult for islanded microgrids. The impact and effectiveness of demand response are still not clear at the distribution and transmission levels. In other words, there is no solid research reported in the literature on the evaluation of the impact of DR on power system dynamic performance. In order to address these issues, a real-time demand response approach along with real-time power management (specifically for microgrids) is proposed in this research. The real-time demand response solution is utilized at the transmission (through load-frequency control model) and distribution level (both in the islanded and grid-tied modes) to provide effective and fast regulation services for the stable operation of the power system. Then, multiple real-time power management algorithms for grid-tied and islanded microgrids are proposed to economically and effectively operate microgrids. Extensive dynamic modeling of generation, storage, and load as well as different controller design are considered and developed throughout this research to provide appropriate models and simulation environment to evaluate the effectiveness of the proposed methodologies. Simulation results revealed the effectiveness of the proposed methods in providing balancing reserves and microgrids' economic and stable operation. The proposed tools and approaches can significantly enhance the application of microgrids and demand response in the smart grid era. They will also help to increase the penetration level of variable distributed generation resources in the smart grid.Item Real-time energy management of an islanded microgrid using multi-objective particle swarm optimization(Montana State University - Bozeman, College of Engineering, 2013) Litchy, Aric James; Chairperson, Graduate Committee: M. Hashem NehrirThe purpose of this thesis is to design an optimal combined heat and power islanded microgrid, through technology selection and unit sizing software, and perform optimal real-time energy management simulations using intelligent optimization techniques. Two software packages, HOMER® and WebOpt®, originally developed at the National Renewable Energy Laboratory (NREL) and Lawrence Berkley Laboratory (LBL), respectively, were utilized. Using these programs, different cases were created and compared to justify the selected technologies and their respective prices. The final microgrid design contains renewable and alternative energy generation, hydrogen as an energy carrier, and electric storage. Two intelligent optimization techniques, a modified Multi-objective Particle Swarm Optimization algorithm and a Multi-objective Genetic Algorithm in the Matlab optimization toolbox were used for energy management of the designed microgrid and their performance were compared. Simulation results show the modified Multi-objective Particle Swarm Optimization performs better. It is used to perform 24 hour energy management simulations. The simulation results show the benefits of the real-time optimization and the freedom of choice users have to meet their energy demands.Item Towards real-time power management of microgrids for power system integration : a decentralized multi-agent based approach(Montana State University - Bozeman, College of Engineering, 2012) Colson, Christopher Michael; Chairperson, Graduate Committee: M. Hashem NehrirThe steadily increasing need for electrical power, rising costs of energy, market forces and industry deregulation, an aging infrastructure, tight constraints on new long distance transmission lines, global environmental concerns, and a public demand for greater electrical reliability and security are overwhelming our existing power system. One technology that offers solutions to many of these challenges and addresses smart grid objectives directly is: microgrids. A microgrid is a small (typically several MW or less in scale) power system incorporating distributed generators, load centers, potentially storage, and the ability to operate with or apart from the larger utility grid. Properly managed, assets connected within a microgrid can provide value to the utility power network, improve energy delivery to local customers, and facilitate a more stable electrical infrastructure, benefitting environmental emissions, energy utilization, and operational cost. While microgrids can achieve significant improvements for customers and utilities alike, microgrid research is in its infancy and, to date, a comprehensive means of managing microgrid operations has not been realized. In this work, two primary efforts are undertaken. First, given the lack of a comprehensive software test bed for microgrids, a simulation environment capable of incorporating microgrid operational concepts, electrical modeling, asset dynamics, and control conditions is developed. Second, using the simulation environment, an enhanced decentralized multi-agent power management and control system is designed and evaluated for the purpose of supervising multiobjective microgrid operations under normal and emergency conditions. Results presented demonstrate effective multi-agent methods that yield improved microgrid performance, as well as facilitate coordinated system decision-making without reliance on a centralized controller. These advancements represent innovation towards the autonomous operation of microgrids, as well as provide important insight into new tradeoff considerations associated with multi-objective design for power management. Microgrids are infrastructure elements that bridge the gap between emerging energy technologies and the existing power system. Simply put, smart grid objectives including higher penetration of renewables, integration of storage, delivery efficiency improvements, more responsive system elements, stronger resiliency, and improved flexibility will be difficult to achieve without microgrids. The simulation environment developed and the power management methodology presented are important steps towards enabling microgrids and realizing their benefits.Item Control of aggregate electric water heaters for load shifting and balancing intermittent renewable energy generation in a smart grid environment(Montana State University - Bozeman, College of Engineering, 2011) Patrick, Stasha Noelle; Chairperson, Graduate Committee: M. Hashem NehrirThe majority of electrical energy in the United States is produced by fossil fuels, which release harmful greenhouse gas emissions and are non-renewable resources. The U.S. Department of Energy has established goals for a smart electric power grid, which facilitates the incorporation of clean, renewable generation sources, such as wind. A major challenge in incorporating renewable energy sources onto the power grid is balancing their intermittent and often unpredictable nature. In addition, wind generation is typically higher at night, when consumer demand is low. Residential electric water heaters (EWHs), which currently account for 20% of the U.S. residential daily energy demand, are the largest contributors to the morning and evening peaks in residential power demand. The simulations in this thesis tested the hypothesis that controlling the thermostat setpoints of EWHs can shift EWH electrical energy demand from hours of higher demand to hours of lower demand, provide a large percentage of the balancing reserves necessary to integrate wind energy generation onto the electric power grid, and economically benefit the customer, while maintaining safe water temperatures and without significantly increasing average daily power demand or maximum power demand of the EWHs. In the experimental simulation, during on-peak hours for demand, when electricity prices are high, the thermostat setpoints of EWHs were set to the minimum, in order to consume minimal energy. The result was that the vast majority of EWH demand occurred during off-peak hours, a significant improvement over the base case (normal operation in which no setpoint control was implemented). During off-peak hours, the thermostat setpoints of EWHs were controlled by the utility in order to provide balancing reserves necessary to maintain power system stability when wind generation is included in the system. The EWHs were able to provide the balancing reserves desired by the utility a majority of the time. In this combined control method, the customer benefitted financially by saving in electrical energy costs when compared to the base case, the EWH water temperatures always remained within safe limits. There was only a small increase in the total energy consumption, but the peak power demand did not change.