Example 3: Cold Snap Scenario¶

This example demonstrates how the optimizer handles extreme cold weather when heating capacity is limited.

Scenario¶

Date: January 10, 2025 Location: Netherlands Weather: Cold snap with temperatures -5°C to 2°C Electricity pricing: Dynamic (Nord Pool)

Configuration¶

Building:
  Area: 150 m²
  Energy Label: C
  Windows: Standard (20 m² total)

Heat Pump:
  Base COP: 3.5
  K-factor: 0.03
  Max capacity: 12 kW thermal at -5°C outdoor

Challenge: Capacity Limits¶

During extreme cold, heat demand may approach or exceed heat pump capacity:

\[ Q_{max} = \text{COP} \times P_{electrical,max} \]

At -5°C outdoor, 50°C supply:

• Electrical capacity: 4 kW
• COP: ~2.5 (degraded in cold)
• Thermal capacity: 2.5 × 4 = 10 kW

But heat loss:

• U-value: 0.8 W/m²K
• Area: 150 m²
• ΔT: 20°C indoor - (-5°C) outdoor = 25°C
• Heat loss: 0.8 × 150 × 25 / 1000 = 3.0 kW (just base loss)
• With windows and infiltration: ~12 kW total

Problem: Demand ≈ Capacity, limited optimization flexibility!

Hourly Forecast¶

Optimization Strategy¶

Limited Flexibility¶

Unlike mild weather examples, cold snaps offer little room for optimization:

Optimal Offsets¶

Key insight: Offset stays high (0 to +4°C) throughout the day, with minimal variation.

Cost Analysis¶

Strategy A: Fixed Maximum Offset¶

Always use +4°C offset:

Daily total: 33.31 kWh electricity = €8.76

Strategy B: Optimized (Limited Flexibility)¶

Use variable offsets within capacity constraints:

Daily total: 32.67 kWh electricity = €8.54

Cost difference: €0.22 per day

Much lower than mild weather (where 10-30% is typical).

Why Limited Optimization?¶

1. Capacity Constraints¶

At peak demand, offset must be at maximum regardless of price:

Cannot reduce heating during expensive periods
because system already at minimum viable output

2. Low COP in Cold¶

COP is degraded at low outdoor temperatures:

• At -5°C: COP ≈ 2.4 - 2.7
• At +5°C: COP ≈ 3.3 - 3.8

Lower COP range means less benefit from offset optimization.

3. Minimal Solar Gain¶

Winter solar radiation is low (50-150 W/m² vs 600-800 W/m² in spring):

• Little solar gain to create buffer
• No buffering opportunities

Capacity Warning System¶

The integration detects when approaching capacity:

if heat_demand > 0.9 * heat_capacity:
    _LOGGER.warning(
        "Heat demand (%.1f kW) approaching capacity (%.1f kW). "
        "Optimization limited.",
        heat_demand, heat_capacity
    )

When this triggers:

• Offset forced to +3 or +4°C
• Buffer system disabled (no capacity to build buffer)
• Price optimization takes back seat to meeting demand

User Experience During Cold Snaps¶

What to Expect¶

1. High electricity costs: Inevitable, heat demand is high
2. Minimal offset variation: Stays at +3 to +4°C most of the time
3. Low buffer: Zero or near-zero throughout
4. Limited cost reduction: Much less than in mild weather

Warning Signs¶

Emergency Mode¶

If heat pump truly cannot meet demand:

1. Integration response:
2. Offset: +4°C (maximum)
3. Warning logged every hour

4. Diagnostics flag: capacity_exceeded

5. User actions:

6. Lower indoor setpoint (20°C → 18°C)
7. Close unused rooms
8. Use temporary electric heaters in critical areas
9. Check for air leaks

Optimization Still Helps¶

Even with limited flexibility, optimization provides benefits:

1. COP Efficiency¶

During warmer parts of day (12:00-15:00), lowering offset improves COP:

• Offset +4°C: COP 2.95, electricity 2.88 kWh
• Offset +1°C: COP 3.08, electricity 2.76 kWh
• Energy difference: 0.12 kWh

2. Avoiding Excess¶

Without optimization, system might over-heat during less cold periods:

• Fixed +4°C when only +2°C needed
• Wastes electricity maintaining too-high temperatures
• Integration prevents this

3. Predictive Capacity Planning¶

Integration forecasts capacity shortfalls:

attributes:
  capacity_shortfall_forecast: [0, 0, 1.5, 0.8, 0, 0]  # kW
  hours_at_capacity: 4

User can proactively adjust expectations or take action.

Recommendations for Cold Climates¶

1. Right-Size Heat Pump¶

Heat pump should handle:

\[ P_{thermal} \geq Q_{loss,design} \times 1.2 \]

Where design heat loss is at coldest expected outdoor temperature (e.g., -10°C for Netherlands).

2. Optimize Building First¶

Better insulation reduces heat loss:

• Upgrade to A or B energy label
• Better windows (U < 1.0 W/m²K)
• Seal air leaks

Impact: Heat loss drops 30-50%, increasing optimization headroom

3. Supplementary Heating¶

During extreme cold (<-5°C outdoor):

• Electric radiators in key rooms
• Reduces load on heat pump
• Allows optimization to resume

4. Realistic Expectations¶

During cold snaps:

• ✅ Expect high electricity costs (physics!)
• ✅ Expect minimal optimization benefit (2-5%)
• ✅ Focus on COP efficiency, not price timing
• ❌ Don't expect high cost reductions (limited opportunities at capacity)

Comparison: Cold vs Mild Weather¶

Next Example: Mixed Conditions - Varied weather with multiple optimization opportunities