Wednesday, 15:45 - 16:10 h, Room: MA 549


Xiang Li
Stochastic nonconvex MINLP models and global optimization for natural gas production network design under uncertainty

Coauthors: Paul I. Barton, Asgeir Tomasgard


Scenario-based stochastic nonconvex MINLP models are developed to facilitate the design of natural gas production networks under uncertainty. Here the nonconvexity comes from bilinear, quadratic and power functions involved in the equations for tracking the gas qualities and pressures. As a gas network involves large investments, a small performance gain made in the design can translate into significant increase in profits, it is desirable to solve the nonconvex MINLPs to global optimality. An extension of generalized Benders decomposition (GBD), called nonconvex generalized Benders decomposition (NGBD), is developed for the global optimization of the stochastic MINLPs. As it takes advantage of the decomposable structure of the problem, NGBD has significant computational advantage over state-of-the-art global optimization solvers (such as BARON). The advantages of the proposed stochastic nonconvex MINLP models and NGBD are demonstrated through case studies of an industrial gas production system.


Talk 2 of the invited session Wed.3.MA 549
"Stochastic programming in energy" [...]
Cluster 18
"Optimization in energy systems" [...]


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