Grant, FloydWollega, Ebisa2015-05-072015-05-072015-04-23http://hdl.handle.net/11244/14589Natural gas storage valuation is an optimal scheduling of natural gas storage facilities. It is a complex predictive decision making research problem since it involves the financial decisions and the physical storage facility characteristics. The challenge arises from large scale stochastic input data sets and complex mathematical models. Research in the literature has been heavily focused on the financial facet of the valuation with little emphasis on the physical storage facility characteristics. The mathematical models and the solution approaches provided in the literature so far are also either overly simplified or are only relevant for very small scale problems. The contribution of this research is on the physical storage facility characteristics in combination with the financial aspect of the natural gas storage valuation. A large scale stochastic non-linear natural gas storage valuation problem that includes underground and aboveground storage facilities is formulated and solved efficiently. A new heuristic simulation and optimization natural gas storage valuation algorithm that handles a very complex and large size problems is proposed. The algorithm (i) decreases significantly the computation time from hundreds of days to fractions of a second, (ii) provides a reasonable solution quality, and (iii) incorporates all the possible underground and aboveground physical gas storage facility complexities. The research has both practical applications and mathematical significance. Practically, natural gas storage facility managers can use the models developed in this research as decision support tools to make a predictive storage decision under uncertainty within a reasonable time. Mathematically, a novel perspective to solving a non-linear natural gas storage facilities valuation problem is provided. Such approach can be used in a variety of applications; for instance, the algorithm can be applied to a high penetration of renewables to electric power grid and fluid flow network optimization among others.Engineering, Industrial.Engineering, Petroleum.