Risk-sensitive optimization for power systems

In this thesis, we study methods of incorporating uncertainty, whether it be discrete component failures or continuous variability in renewable energy, explicitly in decision-making for power systems operations and planning. We model this uncertainty in a risk-sensitive fashion using the conditional value at risk measure, presenting formulations to capture these uncertainties, as well as market design around them. We then study algorithms to solve such problems, in a more general form, exploring decomposition-based approaches such as critical region exploration and Benders' decomposition to handle discrete uncertainty, and a stochastic approximation approach to handle continuous uncertainty.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Madavan, Avinash N.

Contributors dc:contributor

• Bose, Subhonmesh
• Basar, Tamer
• Srikant, Rayadurgam
• Dominguez-Garcia, Alejandro D
• Tong, Lang

• Risk-sensitive Optimization
• Conditional Value At Risk
• Power Systems
• Electricity Markets

Statement dc:rights

• Copyright 2022 Avinash Madavan

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/117802

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F117802