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Center for Nonlinear Studies |
Nowadays, the world is facing several major challenges that include air and water pollution, global warming and the rising market prices of the primary energy resources, among others. Natural gas, as an energy source, offers several advantages in comparison with other non-renewable energy sources to overcome these problems. For example, natural gas is a cleaner fossil fuel than oil or coal, i.e., it emits a lower percentage of carbon dioxide than gasoline, diesel or coal. Natural gas is also more economically attractive than gasoline despite that its listing on the financial sector has been increasing in recent years (which represents better profits for the industrial sector). Since natural gas has become a good candidate for being one of the preferential supplies of primary energy, the natural gas industry has had to quickly expand its transmission networks in order to satisfy the increasing demand of the gas consumption. Hence, this presentation aims at integrating mathematical models and solution approaches for tackling various optimization problems in natural gas transport via pipeline systems. Mainly, three challenging problems and their underlying optimization methods are addressed: (1) The fuel cost minimization problem -formulated as a non-linear programming (NLP) model- for which three different solution methodologies are proposed, namely, (a) a heuristic method that includes a non-sequential dynamic programming technique, (b) a tree decomposition technique and a dynamic programming algorithm, which is proposed to overcome dense network instances, and (c) an adaptive discretization (multi-local search) heuristic to enhance the application of the dynamic programming. (2) Natural gas transport problems with variable specific gravity and compressibility factor. Here, an enhanced mathematical model is proposed to account for more accurate estimates in maximum flows on steady-state transmission network systems. This problem arises since traditional approaches in steady-state flow problems assume the gas specific gravity and compressibility factor as universal constants, thus leading to misleading results. Due to the non-convexity of the suggested model, a heuristic algorithm based on an iterative scheme is proposed in which a simpler NLP model is solved. (3) The line-packing problem. Here, a mathematical model is proposed to optimize the short-term storage and transport of natural gas in pipelines for a given planning horizon. The proposed model adopts all characteristics of a mixed-integer non-linear programming (MINLP) model. A thorough computational evaluation based on a global optimizer is conducted to assess the computability of the model. Empirical evidence over a wide set of problem instances illustrate the usefulness and positive impact of the proposed strategies resulting in cconsiderably high-quality solutions when compared to existing approaches and commercial methods.
Host: Michael Chertkov