This paper presents a comprehensive solution to the long-standing transient gas pipeline optimization problem. Consider a pipeline with time dependent delivery volumes at various load points. Such a system can be controlled by changing with time the pressure setpoints at stations to manipulate flow and linepack. We present an algorithm that computes an optimum set of time-varying setpoint values (out to a user-specified simulation horizon) simultaneously for all stations. These setpoint values are computed so that 1. From a current state of a pipeline, we achieve a desired (generally different) target state 2. We achieve this target state in a predetermined time interval, T, from the current state when time t= 0 3. Only controls that are available to the pipeline operator are exercised 4. We observe all pressure constraints and horsepower limitations 5. Pack is managed to meet all required time-varying deliveries within the interval T 6. Total compression power or fuel expended during interval T is minimized This algorithm can therefore be used both on pipelines that supply time-varying loads and seldom operate near steady state, and pipelines that need to transit periodically between differing steady states as load patterns change. It has been tested on gunbarrel and branched systems. The paper presents results from a 311-mile 7-station gunbarrel pipeline system patterned after an actual field system. The sample scenarios are not based on field data but are designed to illustrate functionality.

1. Purpose

The purpose of this paper is to present a new algorithm to assist pipeline operators in controlling linepack and fuel consumption so as to enable projected deliveries in a transient environment. The intent is to address the types of operating questions presented by contemporary pipeline operations during a required transition to meet new forecasted loads:

  1. How most efficiently to achieve in a specified time, T, a prescribed state designed to handle forecast loads

  2. How to deal with changing or unexpected loads during the transition to time T

  3. How to recognize unused capacity and quantitatively assess spot load capability during T

  4. How to minimize the total compression fuel (or power) expended to time T, observing all constraints.

2. Background and Motivation

The principal successes in optimal control of gas pipelines have historically resulted from establishing operating conditions to optimize the cost of transporting gas under steady state conditions. Given a forecast of loads and a detailed nonlinear model of the pipeline hydraulics and compressor characteristics, a steady state optimum provides the operator with a valuable set of controls that would meet the projected loads in an optimal and sustainable way, while observing all system limitations such as MAOP. Steady state optimization is also an invaluable tool for planning and design, in that it can quickly determine maximum sustainable throughput as well as most efficient operation for any proposed design and/or potential loading condition. However, steady state optimization is clearly not the complete solution to the problem of efficient pipeline operations.

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