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Articles 1 - 5 of 5
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
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
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
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
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
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 …