Stochastic Programming with Economic and Operational Risk Management in Petroleum Refinery Planning under Uncertainty

• Main Author: Yam , Chee Wai
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi Petronas 2010
• Subjects: TP Chemical technology
• Online Access: http://utpedia.utp.edu.my/1361/1/FYP2_Dissertation_yam_chee_wai_10477.pdf; http://utpedia.utp.edu.my/1361/

Rising crude oil price and global energy concerns have revived great interests in the oil and gas industry, including the optimization of oil refinery operations. However, the economic environment of the refining industry is typically one of low margins with intense competition. This state of the industry calls for a continuous improvement in operating efficiency by reducing costs through business-driven engineering strategies. These strategies are derived based on an acute understanding of the world energy market and business processes, with the incorporation of advanced financial modeling and computational tools. With regards to this present situation, this work proposes the application of the two-stage stochastic programming approach with fixed recourse to effectively account for both economic and operational risk management in the planning of oil refineries under uncertainty. The scenario analysis approach is adopted to consider uncertainty in three parameters: prices of crude oil and commercial products, market demand for products, and production yields. However, a large number of scenarios are required to capture the probabilistic nature of the problem. Therefore, to circumvent the problem posed by the resulting large-scale model, a Monte Carlo simulation approach is implemented based on the sample average approximation (SAA) technique. The SAA technique enables the determination of the minimum number of scenarios required yet still able to compute the true optimal solution of the problem for a desired level of accuracy within the specified confidence intervals. We consider Conditional Value-at-Risk (CVaR) as the risk metric to hedge against the three parameters of uncertainty, which affords a framework that also involves the use of the Value-at-Risk (VaR) measure. We adopt two approaches in formulating appropriate two-stage stochastic programs with mean–CVaR objective function. The first approach is by using the conventional definition of CVaR that leads to a linear optimization model approximation coupled with a graphical-based solution strategy to determine the value of VaR using SAA in order to arrive at the optimal solution. The second approach is to utilize auxiliary variables to formulate a suite of stochastic linear programs with CVaR-based constraints. We conduct computational studies on a representative refinery planning problem to investigate the various model formulations using GAMS/CPLEX and offer some remarks about the merits of these formulations.