Optimization of Gas Transmission Networks under Energetic and Environmental Considerations

Abstract

The transport of large quantities of natural gas (NG) is carried out by pipeline network systems across long distances. Pipeline network systems include one or several compressor stations which compensate for pressure drops. A typical network today might consist of thousands of pipes, dozens of stations, and many other devices, such as valves and regulators. Inside each station, there can be several groups of compressor units of various vintages that were installed as the capacity of the system expanded. The compressor stations typically consume about 3 to 5% of the transported gas. It is estimated that the global optimization of operations can save considerably the fuel consumed by the stations. Hence, the problem of minimizing fuel cost is of great importance. This study presents a mathematical formulation for NG transport through pipelines and compressors by considering the mass and energy balance equations on the basic elements of a didactic network from the literature. First, a deterministic optimization procedure is implemented. The objective of this formulation is the fuel minimization problem in the compressor stations for a fixed gas mass flow delivery. A second example is devoted to the simultaneous consideration of gas mass flow delivery maximization and fuel consumption minimization. In that case, two procedures are compared: a genetic algorithm coupled with a Newton-Raphson procedure and the scalarization method of ?-constraint. In both monobjective and biobjective cases, a study of carbon dioxide (CO2) emissions is carried out. The Pareto front deduced from the biobjective optimization can be used either for identifying the minimum and maximum network capacity in terms of CO2 emissions and mass flow delivery or for a given mass flow delivery for determining the minimal CO2 emissions from an appropriate operating of the compressor stations.

Operation Optimization of Natural Gas Transmission Pipelines Based on Stochastic Optimization Algorithms: A Review

“…This method assumes that the pipeline system is composed a number of nodes and elements [22,23]. The element contains all the pipes and devices, including compressors, regulators, valves, and other facilities, while gas sources, consumers, and connection points of elements are defined as the node.…”

“…Unlike the weighted method, the Pareto optimality provides a method to determine the optimal solutions from a set of candidate multiobjective solutions [35]. According to the concept of the Pareto optimality, the multiobjective function can be formulated as follows [23]:…”

“…Hence, an additional decision-making method is required to select the "best" one from a set of feasible solutions [36]. Related to this issue, Rodriguez et al [23] proposed a biobjective model to simultaneously optimize the minimum fuel consumption and the maximum gas delivery amount. Alinia Kashani and Molaei [18] established a unified model to optimize the maximum gas delivery amount, the maximum line pack, and the minimum fuel cost.…”

“…Rodriguez et al [23] used the NSGA-II to simultaneously optimize the maximum gas delivery amount, the minimum fuel consumption, and related CO 2 emissions amount of compressors. The Pareto front obtained from the multiobjective optimization algorithm can be served to determine the minimum and maximum line pack while considering the gas delivery and CO 2 emission amount.…”

“…Almost all related comparisons show that stochastic optimization algorithms not only are able to find similar solutions in comparison to the gradientbased methods but also have higher computation efficiency than traditional deterministic algorithms [20,23,27,49]. For example, Nguyen et al [72,73] simultaneously optimized the fuel cost, maintenance cost, startup cost, shut cost, and oversupply cost of all compressors by use of the mixedinteger linear programming (MILP), the expert system (ES), and the original GA provided in the MATLAB toolbox.…”