In an isothermal compressed air energy storage (CAES) system, it is critical that the high pressure air compressor/expander is both efficient and power dense. The fundamental trade-off between efficiency and power density is due to limitation in heat transfer capacity during the compression/expansion process. In our previous works, optimization of the compression/expansion trajectory has been proposed as a means to mitigate this trade-off. Analysis and simulations have shown that the use of optimized trajectory can increase power density significantly (2–3 fold) over ad-hoc linear or sinusoidal trajectories without sacrificing efficiency especially for high pressure ratios. This paper presents the first experimental validation of this approach in high pressure (7bar to 200bar) compression. Experiments are performed on an instrumented liquid piston compressor. Correlations for the heat transfer coefficient were obtained empirically from a set of CFD simulations under different conditions. Dynamic programming approach is used to calculate the optimal compression trajectories by minimizing the compression time for a range of desired compression efficiencies. These compression profiles (as function of compression time) are then tracked in a liquid piston air compressor testbed using a combination of feed-forward and feedback control strategy. Compared to ad-hoc constant flow rate trajectories, the optimal trajectories double the power density at 80% efficiency or improve the thermal efficiency by 5% over a range of power densities.
- Dynamic Systems and Control Division
Air Compression Performance Improvement via Trajectory Optimization: Experimental Validation
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Saadat, M, Srivatsa, A, Li, PY, & Simon, T. "Air Compression Performance Improvement via Trajectory Optimization: Experimental Validation." Proceedings of the ASME 2016 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation. Minneapolis, Minnesota, USA. October 12–14, 2016. V001T04A005. ASME. https://doi.org/10.1115/DSCC2016-9825
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