Risk-averse stochastic bilevel programming: An application to natural gas markets

Abstract

This paper presents a risk-based integer bilevel programming model that represents two players (leader and follower) who interact strategically over an infrastructure network. Both players seek to maximize their individual profits in the face of demand uncertainty. Variations of the basic model are proposed that allow us to identify optimal strategies under risk-neutral and risk-averse decision-making. To solve the resulting stochastic bilevel problem, we develop an algorithm that relies on iteratively solving a restricted version of the problem to obtain feasible solutions to the original model. Extensive computational experiments are performed to evaluate algorithmic tractability and solution quality. The methodology is demonstrated using an example that addresses emerging strategic issues in natural gas markets. The model considers two players, a liquefied natural gas (LNG) operator as the leader and a natural gas (NG) producer as the follower. The LNG operator makes decisions on the locations of LNG facilities and the amount of gas to buy from the producer. The NG producer decides how much gas to produce and how to ship it through the existing pipeline network. Both players face demand uncertainty in their respective markets and have to make investment, production, and sales decisions. Our case study focuses on the Gulf-Southwest region of the United States and demonstrates the impact of risk-averse decision-making on investment and operational decisions.