  The ubiquity and energy needs of industrial refrigeration has prompted
several research studies investigating various control opportunities for
reducing energy demand. This work focuses on one such opportunity, termed
compressor sequencing, which entails intelligently selecting the operational
state of the compressors to service the required refrigeration load with the
least possible work. We first study the static compressor sequencing problem
and observe that deriving the optimal compressor operational state is
computationally challenging and can vary dramatically based on the
refrigeration load. Thus we introduce load shifting in conjunction with
compressor sequencing, which entails strategically precooling the facility to
allow for more efficient compressor operation. Interestingly, we show that load
shifting not only provides benefits in computing the optimal compressor
operational state, but also can lead to significant energy savings. Our results
are based on and compared to real-world sensor data from an operating
industrial refrigeration site of Butterball LLC located in Huntsville, AR,
which demonstrated that without load shifting, even optimal compressor
operation results in compressors often running at intermediate capacity levels,
which can lead to inefficiencies. Through collected data, we demonstrate that a
load shifting approach for compressor sequencing has the potential to reduce
energy use of the compressors up to 20% compared to optimal sequencing without
load shifting.
