Operational Optimization of a Cyclic Gas Pipeline Network with Consideration of Thermal Hydraulics

Abstract

Following the rapidly increasing global demand for natural gas, many countries are launching projects to expand gas pipeline networks (GPNs). As a result, more cyclic GPNs are under construction with more rigorous physical constraints required, bringing new challenges to GPN optimization. This paper proposes a novel nonconvex mixed-integer nonlinear programming (MINLP) formulation for operational optimization of the cyclic GPN with simultaneous consideration of thermal hydraulics and flow direction reversibility, which has not been explored in the literature. To solve the proposed MINLP model, a three-level decomposition algorithm is proposed to generate an approximate solution, from which the flow direction is extracted and used to fix all discrete variables in the original MINLP model to construct two-stage NLP models. The NLP models are then solved to improve solution feasibility and quality. The computational results show that the proposed approach outweighs several state-of-the-art commercial MINLP solvers with better solutions and shorter computational time.

Gas Flow Models and Computationally Efficient Methods for Energy Network Optimization

“…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.…”

“…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.…”

“…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. …”