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2024INF003
Doctoral thesis

High-performance computational methods to improve the functioning of energy markets

  • 2024
High-performance computing
Energy markets
Parallel computing
Unit commitment scheduling
Economic dispatch
Renewable energy
Stochastic grid analysis
Edgeworth cycles
English This thesis delves into the enhancement of energy markets through the application of high-performance computational methods. The overarching objective is to bolster the efficiency, accuracy, and scalability of the computational methods that are used to analyze and operate these markets through both applied industrial and fundamental academic contributions. The work is presented through four interconnected chapters that underscore the vital role of high-performance computing in reshaping the energy landscape. Through: massively parallel deployment of power market optimization models on many-core HPC clusters; modeling techniques to improve performance and accuracy of power market optimization models; a data-driven refinement approach to power generation unit commitment; and computational techniques to scalably identify pricing cycles in retail gasoline markets, this work advances energy market analysis tools in terms of efficiency, scalability, and usefulness.
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Language
  • English
Classification
Computer science and technology
License
License undefined
Open access status
green
Identifiers
Persistent URL
https://n2t.net/ark:/12658/srd1328153
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