ENHANCING DISTRIBUTION PLANNING METHODS USING STATE OF THE ART MODELING TECHNIQUES TO FACILITATE HIGH GROWTH DISTRIBUTED ENERGY RESOURCES

1 online resource (193 pages) : PDF

2020

University of North Carolina at Charlotte

Climate change is one of the most pivotal issues for the world in which we live today. The power grid transformation to become, smarter sustainable and carbon-free, has been a primary emphasis in recent times. This includes the integration of Distributed Energy Sources (DERs). In this work, innovative and novel techniques are presented to facilitate and expedite the engineering, planning, and deployment of high penetration levels of renewable and distributed energy resources to aggressively attack climate change and move the industry to a new paradigm. Towards this end, both traditional and non-traditional techniques and methodologies are leveraged to enhance distribution planning methods such that more electric distribution feeders can be analyzed more dynamically. Tried and true iterative mathematical techniques and convergence algorithms are used to adhere to the Laws of Physics for the flow of electricity. Findings in the area of Control Theory and System Identification are used to develop dynamic and predictive models of the electric distribution system that analyze the impact of interconnecting high levels of renewable generation. These predictive models are represented by parametric models or transfer functions developed from the Laplace Transform technique, leveraging proven powerful tools of time domain and frequency domain analysis to evaluate system stability. Critical to this work is both the validation of realized models wherein these models can accurately predict system response at varying load levels, renewable energy penetration levels, all-around necessary sensitivities. Such a dynamical model development process can be used and applied to any electric distribution feeder to better optimize penetration levels and provide the planning engineer with smart models to optimize system planning.

doctoral dissertations

Electrical engineering
Power resources

Ph.D.

Distributed Energy Resources
Distribution Planning
Power System Modeling
Renewables
System Identification

Electrical Engineering

Kamalasadan, Sukumar

Kakad, Yogendra
Cecchi, Valentina
Noras, Maciej

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2020.

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Davis_uncc_0694D_12622

http://hdl.handle.net/20.500.13093/etd:2005