(4fe) Optimization Models and Algorithms for Infrastructure Planning of Reliable and Resilient Power Systems

The goal of my Ph.D. research is to propose optimization models and algorithms for infrastructure planning of reliable and resilient power systems. Reliability is the ability of power systems to satisfy power demands, and resilience is the ability to recover to their normal operating conditions after a disruption quickly. While resilience is related to low probability and high impact (LPHI) events such as natural disasters and/or extreme weather conditions, reliability is concerned with high probability and low impact (HPLI) events such as equipment failures. Although it is important to consider reliability and resilience, there are not any studies that consider both in the infrastructure planning phase.

In the initial phase of my Ph.D. work, I focused on combining reliability and expansion planning models. First of all, I developed different reliability formulations and evaluated their impact on the optimal design and operation of power systems. Also, I successfully developed a Generalized Disjunctive Programming (GDP) optimization model and bilevel decomposition with tailored cuts for planning reliable power generation systems. The proposed model is applied to the San Diego County problem in collaboration with the California Energy Commission. In the second phase, I proposed a new expansion planning model that can integrate resilience and expansion planning. As the last piece of my Ph.D. work, I am currently working on combining all three aspects: expansion planning, reliability, and resilience.