AN EXAMINATION OF MULTIPLE OPTIMIZATION APPROACHES TO THE SCHEDULING OF MULTI-PERIOD MIXED-BTU NATURAL GAS PRODUCTS

As worldwide production and consumption of natural gas increase, so does the importance of maximizing profit when trading this commodity in a highly competitive market. Decisions regarding the buying, storing and selling of natural gas are difficult in the face of high volatility of prices and uncertain demand. With the introduction of alternative sources of fuels with lower levels of methane, the primary component of natural gas, these decisions become more complicated. This is an issue faced by investors as well as operational planners of industrial and commercial consumers of natural gas where incorrect planning decisions can be costly.A great deal of research in the academic and commercial arenas has been accomplished regarding the problem of optimizing the scheduling of injection and withdrawal of this commodity. While various commercial products have been in use for years and research on new approaches continues, one aspect of the problem that has received less attention is that of combining gases of different heat contents. This study examines multiple approaches to maximizing profits by optimally scheduling the purchase and storage of two gas products of different energy densities and the sales of the same in combination with a product that is a blend of the two. The result provides an initial basis for planners to improve decision making and minimize the cost of natural gas consumed.This multi-product multi-period finite (twelve-month) horizon product-mix problem is NP-Hard. The first approach developed is a Branch and Bound (B&B) technique combined with a linear program (LP) solver. Heuristics are applied to limit the expansion the trinomial tree generated. In the second approach, a stochastic search algorithm-linear programming hybrid (SS-LP) is developed. The third approach implemented is a pure random search (PRS). To make each technique computationally tractable, constraints on the units of product moved in each transaction are implemented.Then, using numerical data, the three approaches are tested, analyzed and compared statistically and graphically along with computer performance information. The best approach provides a tool for optimizing profits and offers planners an advantage over approaches that are solely history-based