Fuel-Efficient Operation of Compressor Stations Using Simulation-Based Optimization

One of the primary concerns in the operation of a compressor station is minimization of fuel consumption while maintaining the desired throughput of natural gas. In practice, the station operator tries to achieve this by shutting down units or controlling individual unit speeds based on experience. This is generally a trial-and-error process without any guarantee of optimality. In this paper we present a robust structured solution process for tackling this problem using simulation-based optimization. The first step to develop this solution process is to devise an analysis scheme that provides the simulation support required by the optimization. This was achieved by developing a fully implicit finite difference formulation of the continuity, momentum and energy equations for flow under non-isothermal conditions. The performance of each compressor unit was modeled by fitting polynomials to the compressor map. These polynomial equations were appended to the flow equations to obtain a complete set of system governing equations. The nonlinear algebraic equations resulting from this formulation were then solved using a Newton-Raphson iteration to obtain system performance. The problem of optimizing the operation of a compressor station was then formulated as a nonlinear programming problem (NLP) in which the design variables are the compressor unit speeds and the objective function to be minimized is the fuel consumption. A constraint was also placed on the minimum mass flow rate through the station to ensure that adequate flow is maintained while minimizing fuel consumption. This NLP was then solved using a sequential unconstrained minimization technique (SUMT) with a derivative-free grid search for handling the unconstrained minimizations. The simulation algorithm mentioned earlier is invoked whenever the optimization needs to evaluate the system response at a candidate operating point. The results obtained show that the simulation works very well in terms of predicting system response, and the proposed simulation-based optimization approach is highly effective in minimizing fuel consumption in a systematic way. The approach is successfully applied to single stations as well as to a sequence of stations along a pipeline, thereby establishing its applicability to station-level and network-level optimization.