Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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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.