🎓 Lesson 12
D5
Load Flexibility Quantification: Baseline Determination & Shiftable Load Profiling
Load flexibility quantification is how much and how quickly a mining operation’s electricity use—like crushers or conveyors—can be safely shifted in time to support grid stability or lower energy costs.
🎯 Learning Objectives
- ✓ Calculate baseline electrical load using 7-day rolling average and statistical outlier filtering
- ✓ Classify shiftable loads by controllability class (Type I–III) per IEEE 1547.4-2020
- ✓ Analyze load shift potential using time-of-use (TOU) arbitrage windows and equipment thermal recovery constraints
- ✓ Design a 15-minute resolution load flexibility profile for a primary crusher train under peak shaving scenarios
📖 Why This Matters
Modern mines are no longer passive electricity consumers—they’re active participants in grid resilience. With rising renewable penetration and volatile energy pricing, the ability to shift 5–15 MW of crusher, conveyor, or ventilation load—even by 30–120 minutes—can reduce annual energy costs by $200k–$1.2M and earn capacity payments from ISOs. This lesson equips you to quantify that value rigorously, avoiding costly overcommitments or underutilized DER assets.
📘 Core Principles
Load flexibility rests on three interdependent layers: (1) Baseline determination—the statistically robust reconstruction of 'what would have been consumed' without intervention; (2) Shiftable load segmentation—identifying loads with inherent process slack (e.g., ore stockpiling before crushing, buffer silos, batch charging); and (3) Constraint-aware profiling—mapping physical, operational, and safety limits (e.g., bearing thermal decay time, blast timing dependencies, ventilation air quality thresholds). Unlike commercial buildings, mining loads exhibit strong diurnal and event-driven patterns (e.g., post-blast surge), requiring event-synchronized baselines—not simple averages.
📐 Baseline Load Calculation (Robust Rolling Median)
The industry-standard baseline uses a 7-day rolling median filtered for outliers (±2σ from local mean) to exclude abnormal events (e.g., unplanned shutdowns, maintenance). This avoids bias from single-day anomalies and reflects true operational normalcy.
Robust Baseline Load (P_baseline)
P_baseline = median(P_i ∈ S | |P_i − μ_local| ≤ k·σ_local)Statistically filtered median power consumption over a representative period, excluding operational anomalies.
Variables:
| Symbol | Name | Unit | Description |
|---|---|---|---|
| P_i | Power measurement | MW | 15-minute averaged active power at interval i |
| S | Set of valid measurements | — | Measurements passing outlier filter |
| μ_local | Local mean | MW | Mean of P_i over sliding window (typically 7 days) |
| σ_local | Local standard deviation | MW | Standard deviation over same window |
| k | Outlier threshold multiplier | — | Typically 1.5–2.0 per ISO market rules |
Typical Ranges:
SAG mill (hard rock): 7.5 – 12.0 MW
Primary crusher train: 10.0 – 22.0 MW
💡 Worked Example
Problem: Given 7 days of 15-min interval power data (MW) for a SAG mill: [8.2, 8.4, 8.1, 15.3*, 8.3, 8.5, 8.2] — where * marks a maintenance event outlier. Calculate P_baseline.
1.
Step 1: Compute 7-day rolling median = median([8.2, 8.4, 8.1, 15.3, 8.3, 8.5, 8.2]) = 8.3 MW
2.
Step 2: Detect outlier: local mean = 9.14 MW, σ ≈ 2.4 → ±2σ = [4.3, 14.0]; 15.3 > 14.0 → flag and exclude
3.
Step 3: Recompute median on remaining 6 values: median([8.2, 8.4, 8.1, 8.3, 8.5, 8.2]) = 8.25 MW
4.
Step 4: Round to engineering precision: P_baseline = 8.25 MW
Answer:
The robust baseline is 8.25 MW, which falls within the typical range of 7.8–8.6 MW for this mill under normal operation.
🏗️ Real-World Application
At Newmont’s Boddington Mine (WA), a 2022 DER pilot used 15-min SCADA data to identify 12.4 MW of shiftable load in the primary crushing circuit. By shifting crusher start-up from 06:00 to 07:30 during summer peak (reducing coincident demand by 9.7 MW), the site earned $412,000/year in Western Australia’s Wholesale Demand Response Market—while maintaining throughput via optimized stockpile drawdown and buffer silo fill scheduling. Critical constraint: bearing cooling time (≥45 min post-shutdown) was embedded directly into the flexibility profile.
✏️ Student Exercise
Using the provided 1-hour SCADA dataset (MW) for a secondary cone crusher: [3.1, 3.0, 3.2, 3.1, 3.3, 3.2, 3.1, 3.0, 3.2, 3.1, 3.0, 3.1], calculate: (a) 12-point rolling median baseline; (b) Identify if any point exceeds ±1.5σ from local mean; (c) Determine maximum feasible 30-min load shift duration assuming thermal recovery limit = 25 min and minimum run-time = 40 min.
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