Enhancing Decision-making in the Electric Power Sector with Machine Learning and Optimization

• Main Author: Eikeland, Odin Foldvik
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: UiT Norges arktiske universitet 2023
• Subjects: VDP::Mathematics and natural science: 400::Information and communication science: 420::Mathematical modeling and numerical methods: 427; VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Matematisk modellering og numeriske metoder: 427; Arctic
• Online Access: https://hdl.handle.net/10037/31514

The electric power system infrastructure is essential for modern economies and societies, as it provides the electricity needed to power homes, businesses, and industries. It is of critical importance that the operation of the electric power system is optimized to serve the electricity demand reliably and sustainably. Advances in machine learning and optimization have enabled the potential to enhance decision-making in the electric power sector by gaining insight into the vast amount of data stored digitally. The operation of electric power systems poses many challenges, such as the rising integration of renewable energy sources, energy storage, and the aging transmission infrastructure. This thesis explores machine learning and optimization techniques to enhance decision-making concerning decarbonization targets, integration of renewable energy sources, cost savings, and reliable power supply. The first work presents a framework for predicting electricity demand. Comparing statistical and machine learning models for short- and medium-term forecasting revealed that machine learning methods provide higher accuracy and demonstrate good transferability. This highlights the importance of choosing the appropriate model to accurately predict the electricity demand, especially where historical data may be scarce. Next, we examined the electricity transmission grid using machine learning classification techniques to identify causes of power distribution network disturbances. Besides indicating variables that explain fault occurrences on average, identifying specific variables for each fault is essential. To address this challenge, we used a technique called Integrated Gradients for interpreting the decision process of a deep learning model, emphasizing the value of detailed insights into specific fault occurrences. In the third work, we adopted probabilistic forecasting to account for the the uncertainty when predicting electricity generation from wind power. As point forecasts don't account for uncertainties in the ...