An Accurate Bilinear Cavern Model for Compressed Air Energy Storage

TL;DR

This paper introduces a new bilinear cavern model for compressed air energy storage that balances accuracy and computational efficiency, enabling better integration into power system optimization.

Contribution

The paper presents an accurate bilinear cavern model derived from thermodynamics, simplifying nonlinear models for improved computational performance in energy storage optimization.

Findings

The bilinear model closely matches nonlinear and field data results.

The model enables efficient optimization in energy storage scheduling.

Field data validation confirms model accuracy.

Abstract

Compressed air energy storage is suitable for large-scale electrical energy storage, which is important for integrating renewable energy sources into electric power systems. A typical compressed air energy storage plant consists of compressors, expanders, caverns, and a motor/generator set. Current cavern models used for compressed air energy storage are either accurate but highly nonlinear or linear but inaccurate. The application of highly nonlinear cavern models in power system optimization problems renders them computationally challenging to solve. In this regard, an accurate bilinear cavern model for compressed air energy storage is proposed in this paper. The charging and discharging processes in a cavern are divided into several real/virtual states. The first law of thermodynamics and ideal gas law are then utilized to derive a cavern model, i.e., a model for the variation of temperature and pressure in these processes. Thereafter, the heat transfer between the air in the cavern and the cavern wall is considered and integrated into the cavern model. By subsequently eliminating several negligible terms, the cavern model reduces to a bilinear model. The accuracy of the bilinear cavern model is verified via comparison with both an accurate nonlinear model and two sets of field-measured data. The bilinear cavern model can be easily linearized and is then suitable for integration into optimization problems considering compressed air energy storage. This is verified via comparatively solving a self-scheduling problem of compressed air energy storage using different cavern models.