UC Berkeley

This dissertation describes tools and techniques for applying data to inform decisions made in electric power systems. We explore how limitations inherent to data sets can contribute to model uncertainty, and formulate quantitative methods for issuing recommendations that are robust to uncertainty. The work provides a basis for justifying advancements in data collection and monitoring to increase confidence in decision-making outcomes.

Data about the past can offer valuable insights about the present and future. Data can improve the fidelity of simulations for characterizing performance, and can shed light on emerging risks. Data offers a basis to validate performance, to inform targeted maintenance, and to predict the outcome of candidate decisions.

Though data can be a powerful tool, our interpretations of data and even the data themselves are subject to error. Data analysts must judge whether the data are sufficient to answer the question at hand, whether the questions themselves are well-posed, or whether more suitable data should be sought after. The current work describes methods to inform these judgements by exploring how they could alter interpretations of risk and performance, contextualizing these different interpretations in terms of recommendations issued to decision-makers.

The work centers around three case studies that apply imperfect data to inform such recommendations. We consider data sets where relevant information is lost, where observational history is sparse, and where spatial coverage is limited. We synthesize data using exploratory data analysis, hierarchical modeling, and Bayesian optimization. These methods internalize what is known about the limitations of underlying data sets to examine uncertainty in decision-making outcomes. Each case study formulates a quantitative decision-support model, and offers a quantitative basis to inform the collection of new information that could help decision-makers to act with greater confidence.