1 Purpose

The purpose of this work was to develop and evaluate a mathematical programming approach to reducing fuel costs in long gas transmission lines with time-varying demands. A further purpose was to examine the feasibility of this approach by applying it to a line similar to one in the field and study the sensitivity of the results to inaccuracies in load forecasts. Gas transmission systems operate most fuel efficiently if they operate at a steady flow rate. Customers often use gas at rates which swing widely daily, and this decreases system fuel efficiency. It is standard practice to manage line pack to attempt to lessen fuel usage in such cases. The purpose of this work is to examine a typical situation to quantify what is achievable with and without linepack management and with or without the use of mathematical optimization techniques. The final 450 miles of a transmission system with 75-mile station spacing was studied. Daily the delivered load swings from about 73% to 125% of average. Pressure is controlled by power management or by throttling. The situation is similar to that in an actual pipeline. Several questions are studied:

  • What does the typical field strategy save over no linepack management?

  • Based on an accurate forecast of load changes how much can optimization techniques save over power used by the typical strategy?

  • What is the effect of load forecast inaccuracy on the ability to satisfy constraints?

  • What is the effect of load forecast inaccuracy on the expected cost savings?

A mathematical programming procedure was used to solve the optimization problem. Based upon the results, we concluded:

  • Linepack management with a typical field strategy in the situation studied yields a fuel savings of about 9% over running compressors continuously,

  • With an accurate forecast in the situation studied, additional fuel savings of about 8% can be achieved compared with the typical field strategy.


The purpose of this work was to develop and study the application of a mathematical programming approach for cutting fuel costs in the situation described. To evaluate the proposed method, a base case was established using a typical field linepack management strategy for time-varying loads.

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