Floating Head Pressure — System Modeling & Temperature Bin Analysis

Abriliam Consulting — Industrial Energy Management

This notebook builds a compressor performance model for a grocery cold storage reciprocating rack system on R-448A and runs a temperature bin analysis using TMY weather data to estimate annual compressor energy savings from floating head pressure control under two retrofit tiers:

Tier 1: Controls-only float with TEV-constrained floor at 27°C
Tier 2: Full EEV retrofit enabling deeper float with floor at 18°C

Key outputs: Annual kWh savings, percentage reduction, and dollar savings for each tier.

1. System Parameters

The facility is a grocery chain cold storage warehouse in southern Ontario (~43.5°N latitude). The refrigerant is R-448A (Solstice N40). Two compressor circuits serve medium-temperature and low-temperature loads.

2. Compressor Performance Model

The COP is modeled using the Carnot-based approximation scaled by compressor isentropic efficiency:

$$\text{COP} \approx \frac{T_{\text{evap}}}{T_{\text{cond}} - T_{\text{evap}}} \times \eta_{\text{comp}}$$

Compressor power follows from $W_{\text{comp}} = Q_{\text{evap}} / \text{COP}$.

This is a simplified model — real compressor performance uses manufacturer polynomial maps. The model captures the dominant thermodynamic trend: as $T_{\text{cond}}$ decreases, COP improves and compressor power drops.

3. Compressor Power vs. Condensing Temperature

Plot showing how total rack compressor power varies with condensing temperature. This illustrates the thermodynamic basis for floating head pressure savings.

4. TMY Weather Data — Temperature Bin Construction

We generate a synthetic TMY-like annual temperature distribution for southern Ontario (~43.5°N). The distribution uses a sinusoidal seasonal model with daily variation, producing realistic temperature bins.

In practice, CWEC (Canadian Weather for Energy Calculations) data would be used for actual project work.

5. Temperature Bin Analysis — Energy Savings Calculation

For each temperature bin, we calculate:

Fixed scenario: $T_{\text{cond}} = \max(T_{\text{amb}} + \Delta T_{\text{approach}},; T_{\text{cond,setpoint}})$
Floating scenario: $T_{\text{cond}} = \max(T_{\text{amb}} + \Delta T_{\text{approach}},; T_{\text{cond,floor}})$
Power difference: $\Delta W = W_{\text{fixed}} - W_{\text{float}}$
Energy saved per bin: $\Delta E = \Delta W \times h_i$

6. Figure 1 — Compressor Power vs. Outdoor Temperature (Three Scenarios)

7. Figure 2 — Temperature Bin Histogram with Savings Per Bin

8. Condenser Fan Energy Tradeoff

At floating head pressure, condenser fans run harder to maintain the approach temperature. Fan power scales with the cube of speed (fan affinity laws). We estimate the net savings after accounting for increased fan energy.

10. Summary

The temperature bin analysis demonstrates that floating head pressure control yields significant compressor energy savings for this grocery cold storage facility in southern Ontario:

Metric	Tier 1 (TEV, 27°C floor)	Tier 2 (EEV, 18°C floor)
Annual savings	~15-20%	~25-35%
Dollar savings	~$10,000-$13,000/yr	~$17,000-$24,000/yr
Capital cost	~$12,000	~$55,000
Simple payback	<1.5 years	2.5-3 years

The majority of savings accrue during the 7,500+ hours per year when outdoor temperature is below 27°C — roughly 85-90% of all operating hours. The condenser fan energy penalty is 5-10% of gross compressor savings.

Next: Notebook 02 demonstrates the regression-based M&V framework for verifying actual savings post-implementation.