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Full-Text Articles in Power and Energy

Models And Machine Learning Techniques For Improving The Planning And Operation Of Electricity Systems In Developing Regions, Santiago Correa Cardona Jun 2022

Models And Machine Learning Techniques For Improving The Planning And Operation Of Electricity Systems In Developing Regions, Santiago Correa Cardona

Doctoral Dissertations

The enormous innovation in computational intelligence has disrupted the traditional ways we solve the main problems of our society and allowed us to make more data-informed decisions. Energy systems and the ways we deliver electricity are not exceptions to this trend: cheap and pervasive sensing systems and new communication technologies have enabled the collection of large amounts of data that are being used to monitor and predict in real-time the behavior of this infrastructure. Bringing intelligence to the power grid creates many opportunities to integrate new renewable energy sources more efficiently, facilitate grid planning and expansion, improve reliability, optimize electricity …


Improving The Programmability Of Networked Energy Systems, Noman Bashir Jun 2022

Improving The Programmability Of Networked Energy Systems, Noman Bashir

Doctoral Dissertations

Global warming and climate change have underscored the need for designing sustainable energy systems. Sustainable energy systems, e.g., smart grids, green data centers, differ from the traditional systems in significant ways and present unique challenges to system designers and operators. First, intermittent renewable energy resources power these systems, which break the notion of infinite, reliable, and controllable power supply. Second, these systems come in varying sizes, spanning over large geographical regions. The control of these dispersed and diverse systems raises scalability challenges. Third, the performance modeling and fault detection in sustainable energy systems is still an active research area. Finally, …


Data-Driven Control, Modeling, And Forecasting For Residential Solar Power, Akansha Singh Bansal Mar 2022

Data-Driven Control, Modeling, And Forecasting For Residential Solar Power, Akansha Singh Bansal

Doctoral Dissertations

Distributed solar generation is rising rapidly due to a continuing decline in the cost of solar modules. Most residential solar deployments today are grid-tied, enabling them to draw power from the grid when their local demand exceeds solar generation and feed power into the grid when their local solar generation exceeds demand. The electric grid was not designed to support such decentralized and intermittent energy generation by millions of individual users. This dramatic increase in solar power is placing increasing stress on the grid, which must continue to balance its supply and demand despite the potential for large solar fluctuations. …


Data-Driven Decarbonization Of Residential Heating Systems: An Equity Perspective., John Wamburu, Emma Grazier, David Irwin, Christine Crago, Prashant Shenoy Jan 2022

Data-Driven Decarbonization Of Residential Heating Systems: An Equity Perspective., John Wamburu, Emma Grazier, David Irwin, Christine Crago, Prashant Shenoy

Publications

Since heating buildings using natural gas, propane and oil makes up a significant proportion of the aggregate carbon emissions every year, there is a strong interest in decarbonizing residential heating systems using new technologies such as electric heat pumps. In this poster, we conduct a data-driven optimization study to analyze the potential of replacing gas heating with electric heat pumps to reduce carbon emissions in a city-wide distribution grid. We seek to not only reduce the carbon footprint of residential heating, but also show how to do so equitably. Our results show that lower income homes have an energy usage …


A Moment In The Sun: Solar Nowcasting From Multispectral Satellite Data Using Self-Supervised Learning, Akansha Singh Bansal, Trapit Bansal, David Irwin Jan 2022

A Moment In The Sun: Solar Nowcasting From Multispectral Satellite Data Using Self-Supervised Learning, Akansha Singh Bansal, Trapit Bansal, David Irwin

Publications

ABSTRACT

Solar energy is now the cheapest form of electricity in history. Unfortunately,

signi.cantly increasing the electric grid’s fraction of

solar energy remains challenging due to its variability, which makes

balancing electricity’s supply and demand more di.cult. While

thermal generators’ ramp rate—the maximum rate at which they

can change their energy generation—is .nite, solar energy’s ramp

rate is essentially in.nite. Thus, accurate near-term solar forecasting,

or nowcasting, is important to provide advance warnings to

adjust thermal generator output in response to variations in solar

generation to ensure a balanced supply and demand. To address the

problem, this paper develops a …