Date of Award

August 2020

Document Type


Degree Name

Master of Science (MS)


Industrial Engineering

Committee Member

Yongjia Song

Committee Member

Scott J. Mason

Committee Member

Michael Carbajales-Dale


In this proposed thesis, we present a bilevel optimization model for setting the electricity prices according to Time-and-Level-of-Use (TLOU) policy. In TLOU, different price brackets are used for different periods during the day according to the actual load consumed by the customers. In our bilevel optimization model, we consider the retailer as the leader of the problem and customers as the followers. The retailer aims at maximizing the profit, and the customers seek to minimize their electricity costs. Using the bilevel optimization model, we compare the emissions model that considers the weighted sum of profits and emissions as the objective function to the one that considers only profits. In the bilevel models, the customers can either buy electricity from the retailer or from the competitor who uses the Time-of-Use (TOU) policy. Then we reformulated the bilevel models into a single-level problem using KKT conditions to generate the lower-level problem's optimality. Afterward, we apply a standard big-M method to linearize the non-linear constraints resulting from the KKT complementary slackness conditions into a mixed-integer linear programming (MILP) model. We solve this MILP using off-the-shelf solvers for an extensive set of instances using real-world data from the IAC database.

Our computational results indicate that the inclusion of emissions into the objective improves the retailer's weighted profits by using emission-efficient fuels. The retailer uses dynamic TLOU prices to reduce the demand peaks during on-peak hours. Additionally, customers also minimize their costs through demand shifts. Finally, we conclude that the retailer shall include the emission into the price-setting objective function to improve the weighted profits. We validate our results by conducting sensitivity to fuel supply, cost of emissions, and customers' flexibility for demand shift.



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