A function-based approach to stock modelling applied to compressed air systems

Abstract

Improvements in energy efficiency across a broad range of industrial end-uses. While compressed air systems are important contributors to overall industrial electricity demand, substantial energy-efficiency potentials have been identified in the past. Various policy measures can be designed to exploit these saving potentials. Yet when efficiency improvements depend on replacing existing equipment, the impact of policy measures depends not only on their design, but also on the structure of the addressed end-uses. Stock models may serve to provide quantitative data regarding the impact of different policy measures on energy demand. But data availability is often a limiting factor for using stock models, as information about production, age, operating conditions and thus the structure of the energy demand of specific end-uses is often only available to a limited extent. In this paper, we therefore suggest a "function-based" bottom-up stock model approach. The basic idea of this modelling approach is to use both proxy functions to fit available data and to use expert estimations on functional parameters where little data is available. To a certain extent, this is common practice for some variables (e.g. for lifetimes), but we suggest to extend this idea to other energy-relevant characteristics. The advantage of this modelling approach is to provide detailed results with only a limited number of assumptions that can be stated in a very transparent manner. It thus can also improve the understanding of energy demand and help to evaluate the impact of policy measures. We apply this approach to a case study on compressed air systems in the EU in order to illustrate its benefits.