A MILP approach for the operational optimization of gas networks

Ghilardi, Lavinia Marina Paola; Pascali, Matteo Luigi De; Casella, Francesco; Barbati, Daniele; Palazzo, Roberto; Martelli, Emanuele

doi:10.1016/j.ifacol.2022.07.056

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…12,41 Notably, other researchers have also adopted the concept of fixing flow directions, but via other simplified optimization forms. 9,11,17,18,31 Furthermore, we extend this concept to fix flow directions in compressors and regulating valves.…”

Section: Fixing Flow Directionsmentioning

confidence: 99%

“…Indeed, fixing the integer variables predetermines flow directions in dynamic flow optimization; however, it does not mandate unidirectional flows. Indeed, the flow directions may change from one time step to another to ensure the economic gains of bidirectional flows. , Notably, other researchers have also adopted the concept of fixing flow directions, but via other simplified optimization forms. ,,,, Furthermore, we extend this concept to fix flow directions in compressors and regulating valves.…”

Section: Solution Procedures For Network Optimizationmentioning

confidence: 99%

“…Notably, the proposed linear model improves the steady-state accuracy of the short pipe model in the literature by including pressure-induced flow bounds. Fixing flow directions: The solution procedure models bidirectional pipe flows using integer variables in a static flow optimization and fixes flow directions accordingly to avoid the associated computational complexity in dynamic flow optimization. While fixing flow directions has previously been applied in daily operation optimizations with dynamic flows , and planning optimizations without temporal dynamics, ,, our approach combines the scalability of static flow models with the accuracy of dynamic ones. This combination enables integrating linepack flexibility into hourly resolved optimizations with an annual time horizon. …”

Section: Introductionmentioning

confidence: 99%

“…A major challenge in these optimization problems is the nonconvex nature of the equations governing the gas flow dynamics in network pipes. Thus, techniques such as semidefinite relaxation, , second-order cone (SOC) relaxation, ,, and linear approximation − have been deployed to obtain computationally tractable dynamic flow models for operations optimization on daily horizons.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gas Flow Models and Computationally Efficient Methods for Energy Network Optimization

Akbari,

Gabrielli,

Sansavini

2024

Ind. Eng. Chem. Res.

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The equations governing gas flow dynamics are computationally challenging for energy network optimization. This paper proposes an efficient solution procedure to enable tractability for an hourly resolved yearly decision horizon. The solution procedure deploys linear and second-order cone gas flow models alternatively based on the length−diameter ratio of pipes, achieving maximum efficiency within accuracy limits. Moreover, it addresses the computational complexity of bidirectional pipe flows by fixing the associated integer variables according to a preceding optimization with a static flow approximation. The procedure also precisely aggregates parallel and serial pipes for increased efficiency. Mathematical derivations and single-pipe analyses substantiate the model selection criterion. Network optimizations validate the accuracy, success rate, and scalability of the procedure, achieving up to 3.1% cost savings compared to static models, enhancing the success rate by a minimum of 96%, and boosting computational efficiency up to 3 orders of magnitude over full dynamic models.

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