1.0 Introduction

In recent years, major structural changes have occurred in the gas industry and in its underlying economics. These changes demand responses from pipeline companies in order for them to ensure their profitability in the new environment. One such response is the major effort to reduce overall operating costs, and in particular, to reduce the cost of transporting natural gas. Figure 1 - Typical Transmission Systems Competitive Overview, illustrates the increased market competition in several geographic areas, where the company with the lowest costs and the least expensive gas will became the leader. Several companies have turned to transient simulation models and/or steady state optimization programs to assist their efforts. These tools, used separately and together, allow companies to realize significant fuel cost reductions. However, the separation of the transient analysis from the optimizer inherently limits the potential of the optimization procedure. A transient simulator requires manual manipulation to search for optimal operating strategies; a steady state optimizer will not necessarily give results which are in fact optimal for the actual transient operating conditions. Scientific Software-Intercomp has developed a transient gas optimization model which minimizes the system wide cost of transporting natural gas over time periods in which line pack and throughput are changing due to designated fluctuations in supply and demand. Given the optimal system wide set points, costs at individual stations are also minimized. The transient optimization is more difficult mathematically than the steady state, but can achieve higher savings. The major component of the optimization technique is an iterative method called the Generalized Reduced Gradient Method (GRG). As one might expect, the advantage of the transient optimizer over the steady state optimizer increases directly with the transient activity in the pipeline. Along with this result, we state the optimization problem in pipeline terms. We then describe the components of the computer model and outline the solution procedure. We follow with examples which compare a transient versus a steady state optimizer. Finally, we describe possible model enhancements and future applications.

1.1 Definition of the Problem

Gas dispatchers face many requirements for the pipeline system which must be met simultaneously. Pressures, flows, and temperatures throughout the system must remain within safe operational limits. Supply and delivery contracts must be fulfilled. A dispatcher's primary objective must be to operate the system in such a way that all these conditions are satisfied. However, many operating scenarios will qualify. A secondary objective, therefore, is to in some sense optimize the system, given the constraints of physical capacities and schedules. In mathematical terms, we define a function which quantifies the objectives of the optimization procedure. Any of the above-mentioned criteria can be used alone or in combination in an "objective function". The general optimization problem is to minimize (or maximize) the objective function subject to the required constraints. In pipeline terms: from all possible operating scenarios which satisfy physical and operational limits, find the scenario which minimizes (or maximizes) the objective function. Minimization is accomplished by choosing values for certain pipeline parameters.

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