Doctor of Philosophy (Ph.D.)
Degree Granting Department
Industrial and Management Systems Engineering
Bo Zeng, Ph.D.
Tapas K. Das, Ph.D.
Mark S. Daskin, Ph.D.
Lingling Fan, Ph.D.
Michael Fountain, Ph.D.
Shuai Huang, Ph.D.
In this dissertation, we elaborate on the inherent risks and uncertainties in power systems and associated industries, and develop practical solution methods to eliminate their adverse effects.
our research agenda consists of practice-driven problems in different stages of power generation as follows. (1) Affordable fuel procurement through developing a comprehensive fuel supply chain design and operations planning system for electricity generation companies, (2) reliable electricity generation through incorporating dynamic asset rating concept in the unit commitment problem, and (3) efficient demand management through proposing a job scheduling model for effective local generation consumption.
Since reliability cannot be compromised in energy sector, robust optimization has been adopted as a powerful method to model multiple sources of uncertainty, and to protect the performance of the systems against worst situations. Exact and heuristic methods are then developed and customized to solve these computationally challenging problems. In particular, inspired by the challenges in solving two-stage robust optimization problems, we developed a multi-scenario cutting plane generation algorithm, that considers all the realizations of the uncertainty set at once, and thus, alleviates the computational challenge.
Scholar Commons Citation
Danandeh, Anna, "Achieving Reliable Generation \& Delivery of Energy Through Robust Optimization" (2015). Graduate Theses and Dissertations.