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Utilizing Load Shifting for Optimal Compressor Sequencing in Industrial Refrigeration

Rohit Konda, Vikas Chandan, Jesse Crossno, Blake Pollard, Dan Walsh, Rick Bohonek, Jason R. Marden

TL;DR

The paper addresses energy-efficient compressor sequencing in industrial refrigeration, highlighting that static sequencing is computationally challenging and often results in inefficient intermediate-capacity operation. It introduces load shifting (precooling) over a horizon and shows that the dynamic, optimal solution can be realized by a fixed-order water-filling policy under a horizon-dependent order, yielding substantial energy savings. A relative savings bound is derived, and a Butterball facility case study confirms up to about $20\%$ energy reductions when incorporating load shifting, with a simple online algorithm achieving comparable gains. The work demonstrates the practical viability of precooling-assisted compressor scheduling and points to future work on more realistic heat-transfer models and pricing-informed strategies.

Abstract

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.

Utilizing Load Shifting for Optimal Compressor Sequencing in Industrial Refrigeration

TL;DR

The paper addresses energy-efficient compressor sequencing in industrial refrigeration, highlighting that static sequencing is computationally challenging and often results in inefficient intermediate-capacity operation. It introduces load shifting (precooling) over a horizon and shows that the dynamic, optimal solution can be realized by a fixed-order water-filling policy under a horizon-dependent order, yielding substantial energy savings. A relative savings bound is derived, and a Butterball facility case study confirms up to about energy reductions when incorporating load shifting, with a simple online algorithm achieving comparable gains. The work demonstrates the practical viability of precooling-assisted compressor scheduling and points to future work on more realistic heat-transfer models and pricing-informed strategies.

Abstract

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.
Paper Structure (8 sections, 3 theorems, 7 equations, 7 figures, 2 tables, 2 algorithms)

This paper contains 8 sections, 3 theorems, 7 equations, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Optimal Compressor Sequencing
  3. Preliminaries
  4. Fixed Order Compressor Sequencing
  5. Optimal Compressor Sequencing
  6. Optimal Compressor Sequencing with Load Shifting
  7. The Butterball Facility: A Case Study on Opportunities in Compressor Sequencing
  8. Conclusion

Key Result

Proposition 1

Let $q^{\rm{in}}$ be the incoming refrigeration load. The optimal compressor state, as given by the solution of Eq. opt-cs, can be realized by the water filling algorithm given in Algorithm alg:water with a specific order $\mathcal{O}$ that depends on $q^{\rm{in}}$.

Figures (7)

  • Figure 1: A simplified diagram of the refrigeration components are depicted showing the flow of ammonia through the vapor compression process.
  • Figure 2: This figure highlights the cumulative distribution functions for the slide valve position for four compressors operating at the Butterball facility during the month of June, 2023. Here, the slide valve position is associated with compressor capacity, where $100\%$ means that the compressor is running at full capacity. We also highlight the percentages in which each compressor is operating at full capacity (where the slide valve sensor is measured above $99\%$) or trim as well as the percentage of time the compressor is turned on and off. Note that the compressors are often operating in trim, suggesting that there are potential opportunities to save energy by operating the compressors at full capacity more often.
  • Figure 3: Approximate power breakdown for a Butterball facility at Huntsville.
  • Figure 4: For each of the compressors, the estimated power and heat capacity for each minute in the month of June was recorded for a Butterball facility. We depict the resulting spread in the given figure and notice a fairly affine relationship, which we denote in red. This is also supported from manufacturing simulation software for the compressors.
  • Figure 5: In this figure, for each possible refrigeration load, we calculate the power usage between water filling using the best and worst order of the four compressors in operation at Butterball. Notice that there can be potentially a large gap between power usage between the different orders.
  • ...and 2 more figures

Theorems & Definitions (7)

  • Example 1
  • Proposition 1
  • proof
  • Proposition 2
  • proof
  • Proposition 3
  • proof