Optimizing Parallel Gas Compressor Operations Under Flow Disturbances

This paper investigates the operation of parallel compressors with variable speed drives to deliver gas at a desired flow rate while maintaining a target pressure at a common discharge header. We examine strategies to minimize energy consumption amid discharge flow fluctuations caused by changes in gas demand. Specifically, we model the energy consumption impact of varying operating points, accounting for efficiency sensitivity to flow. Our approach employs sample averaging to estimate expected energy usage under flow variations, which informs an offline surrogate objective function reflecting energy consumption under disturbances. This surrogate is subsequently used online in a deterministic nonlinear programming framework to approximate a stochastic optimization solution, determining optimal load distributions for the compressors. Additionally, we compare the proposed approach with an economic model predictive controller (eMPC). This approach first solves a tracking problem to stabilize header pressure, using compressor flows as manipulated variables, then redistributes the calculated control effort for the first step of the solution through an economic optimization. Both methods are implemented in a simulated pipeline compressor station, with a control hierarchy for station pressure, compressor flow, and anti-surge controllers. Simulation results, with and without flow disturbances, confirm that the stochastic load-sharing approach reduces energy consumption by 4.3% compared to a purely deterministic method, with the eMPC further improving efficiency by an additional 2.2%.