Abstract
In this paper we describe numerical results for a market equilibrium model for the North American natural gas system. The model is based on the notion of maximizing total surplus less transportation costs (Takayama and Judge, 1971; Samuelson, 1952) resulting in a large-scale nonlinear program (Gabriel et al., 2000). The model relies on building up supply curves from the “bottom up” using a data base of some 17,000 natural gas reservoirs. This feature provides a good deal of realism in simulating the effects of technology, market forces, and policy considerations on the supply side of the market while making the computations challenging due to the lack of closed form supply curves. A successive linear programming strategy is employed to solve the overall nonlinear problem. We describe several mathematical algorithms that are employed in the successive LP approach to efficiently compute market equilibrium values. These algorithms are heuristic in nature with excellent convergence results. In the numerical results section of this paper, we describe several experiments regarding schemes to accelerate the overall convergence based on iterative smoothing (similar to a Gauss-Seidel strategy) as well as tests aimed at optimal spatial and temporal aggregation.
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Gabriel, S.A., Manik, J. & Vikas, S. Computational Experience with a Large-Scale, Multi-Period, Spatial Equilibrium Model of the North American Natural Gas System. Networks and Spatial Economics 3, 97–122 (2003). https://doi.org/10.1023/A:1023955701522
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DOI: https://doi.org/10.1023/A:1023955701522