Modeling and Simulation of Air Compressor Energy Use

This paper describes modeling and simulation of air compressor energy use to estimate energy savings in compressed air systems from air use reduction and other changes. The method uses measured power signatures and other compressor parameters as inputs to calculate various performance metrics and estimate energy savings. To enable recognition of control mode from measured power data, examples of power signatures for various control modes are shown. A generalized method for modeling the relationship between compressor power consumption and air output is developed. The use of this method to estimate energy savings from changing control modes and reducing air use is described and demonstrated with examples. Additional relationships for modeling compressor power as a function of system pressure, and for modeling the change in system pressure as function of compressed air storage volume are developed. The use of these relations in algorithms for modeling compressor control modes is described. The incorporation of these methods into new public-domain software for modeling air compressor energy use is described. Techniques for calibrating the software to measured energy use data, and estimating energy savings from retrofits are demonstrated with case study examples. Introduction Based on over 50 energy assessments of mid-sized industries, we found that the average unit energy cost of compressed air ranges from about $0.15 to $0.35 per thousand standard cubic feet and frequently comprises between 5% to 20% of a plant’s annual electric costs. The wide range in unit energy costs is a function of many factors, including cost of energy and number of compressors in operation. However, two of the most important factors influencing the cost of compressed air are the type of compressor control and proper compressor sizing. Oversized compressors, and compressors operating in inefficient control modes have the highest unit energy and annual operating costs. In this paper, we describe common types of compressor control and how to recognize control type based on their power consumption signatures. Next, we present a method for calculating compressed air output based on the control type and measured compressor power data. The use of this method for estimating energy savings from reducing compressed air demand, reducing system pressure or changing control mode is described. A case study example demonstrates the importance of using this method to determine savings from reducing compressed air demand; failure to do so can lead to overestimation of savings by 500%. In many cases, it is advantageous to incorporate these models into simulation software that can be quickly calibrated to measured energy use and system pressure. The paper describes the incorporation of these methods into a new public-domain software application for modeling air compressor energy use. An example of calibrating the simulation model to measured energy use and estimating savings from changing system parameters is demonstrated. The software can be used to estimate energy savings from reducing compressed air use, increasing system storage, changing the control mode, or modifying activation pressures.