Author ORCID Identifier

https://orcid.org/0000-0002-2839-9098

Semester

Summer

Date of Graduation

2023

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Ali Feliachi

Committee Member

Muhammad Choudhry

Committee Member

Natalia Schmid

Committee Member

Hong Jian Lai

Committee Member

Vinod Kulathumani

Committee Member

Ali Feliachi

Abstract

Security Constrained Unit Commitment (SC-UC) is a complex large scale mix integer constrained optimization problem solved by Independent System Operators (ISOs) in the daily planning of the electricity markets. After receiving offers and bids, ISOs have only few hours to clear the day-ahead electricity market. It requires a lot of computational effort and a reasonable time to solve a large-scale SC-UC problem. However, exploiting the fact that a UC problem is solved several times a day with only minor changes in the system data, the computational effort can be reduced by learning from the historical data and identifying the patterns in the historical data using data mining techniques.

In this research study, two data driven approaches based on predictive modeling techniques are proposed to solve a SC-UC problem in a day ahead electricity market which can be used as alternative backup methods for solving a SC-UC problem. In the first approach, the SC-UC is partially modeled using predictive modeling techniques to enhance the computational speed of the problem, while in the second approach, the optimization problem is completely replaced by data driven predictive models to further enhance the computational efficiency, however, at the cost of some optimality loss. The proposed approaches are validated through numerical simulations on different IEEE case studies to demonstrate and study the effectiveness of the developed approaches. The results obtained from the proposed approaches are compared with those obtained from commercial optimization solvers e.g., IBM CPLEX MIQP and GUROBI MIQP solvers.

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