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Author ORCID Identifier
Open Access Dissertation
Doctor of Philosophy (PhD)
Electrical and Computer Engineering
Year Degree Awarded
Month Degree Awarded
Computer Engineering | Data Science | Power and Energy
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 consumption, and enhance sustainability. While this innovation is mainly occurring in industrialized countries due to the availability of specialized sensing systems, low- and middle-income regions are often constrained in technical and budget capacities.
Even though building an intelligent electricity ecosystem in emerging economies remains a serious challenge, they also present opportunities to develop new technologies that have not been built in industrialized regions -- for example, we can leverage side-channel sensing methods to obtain the data required to enable a smart grid. My research aims to develop intelligent applications in the context of emerging markets that create similar features to smart grids with less fixed and sophisticated sensing infrastructure but heavier use of data and algorithms. In this dissertation, I propose a set of mechanisms for improving the planning and operation of electricity systems: enabling sustainable access to electricity in rural off-grid areas, modeling deployment strategies of crowd-sourced sensors to measure power reliability, improving the scheduling of flexible demand in electric mobility, and develop learning models for sensing infrastructure using multi-temporal remote sensing data. These components represent some efforts to reach the United Nations Sustainable Development Goal of ensuring access to affordable, reliable, sustainable, and modern energy by 2030.
Correa Cardona, Santiago, "Models and Machine Learning Techniques for Improving the Planning and Operation of Electricity Systems in Developing Regions" (2022). Doctoral Dissertations. 2509.
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Available for download on Saturday, May 13, 2023