Abstract
When considering cost-optimal operation of gas transport networks, compressor stations play the most important role. Proper modeling of these stations leads to nonconvex mixed-integer nonlinear optimization problems. In this article, we give an isothermal and stationary description of compressor stations, state MINLP and GDP models for operating a single station, and discuss several continuous reformulations of the problem. The applicability and relevance of different model formulations, especially of those without discrete variables, is demonstrated by a computational study on both academic examples and real-world instances. In addition, we provide preliminary computational results for an entire network.
Highlights
Natural gas is one of the most important energy sources
One generic way to model the problem described in the previous section is presented in Sect. 3.1: a mixed-integer nonlinear program (MINLP) that incorporates binary variables for the discrete states of the compressor station
general disjunctive programming (GDP) models like this are generally built from separate sets of “local” constraints and objective terms that are combined in a logical disjunction
Summary
Natural gas is one of the most important energy sources. In 2013, it accounted for 25 % of the fossil energy used in Europe (Eurostat 2013). One main branch of research investigates the problems of minimum cost operation and feasibility testing for the stationary as well as the transient case These problems require models of complete gas networks comprising various types of elements like pipes, compressors, (control) valves, etc. By combining the MPEC techniques from the cited publications with the reformulation schemes discussed in this paper, it is possible to state purely continuous NLP type models of the genuinely discrete-continuous problems of minimum cost operation or feasibility testing. The outcome of this achievement is twofold.
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