🎓 Lesson 9
D5
Battery Dispatch Logic: Rule-Based vs. Model Predictive Control
Battery dispatch logic decides when to charge or discharge a battery to save money or support the grid—like choosing the best time to fill or empty a water tank based on electricity prices and demand.
🎯 Learning Objectives
- ✓ Explain the structural differences between rule-based and model predictive control strategies for battery dispatch
- ✓ Calculate battery state-of-charge (SoC) trajectories under a given rule-based policy using time-series load and price data
- ✓ Formulate and solve a simplified 3-step MPC optimization problem for cost-minimized dispatch using linear constraints
- ✓ Analyze trade-offs between computational complexity, forecast dependency, and economic performance in dispatch strategies
- ✓ Apply IEEE 1547-2018 requirements to evaluate grid-support functionality enabled by each dispatch method
📖 Why This Matters
In grid-interactive buildings, batteries are no longer just backup devices—they’re active participants in energy markets and grid stability. Choosing the wrong dispatch logic can waste 15–30% of potential savings or even violate interconnection standards. For mining operations with off-grid or hybrid microgrids (e.g., solar-diesel-battery systems at remote sites), intelligent dispatch directly impacts fuel consumption, equipment lifetime, and regulatory compliance. Understanding when—and why—to use simple rules versus predictive optimization is foundational to designing resilient, economical, and standards-compliant energy systems.
📘 Core Principles
Rule-Based (RB) dispatch relies on deterministic, human-defined logic: e.g., 'charge when price < $0.05/kWh and SoC < 80%; discharge when price > $0.12/kWh and SoC > 20%'. It’s transparent, low-latency, and robust to forecast errors—but blind to future opportunities. In contrast, Model Predictive Control (MPC) embeds a short-term forecast (e.g., 1–24 hours) into a mathematical optimization framework. At each time step, it solves: minimize total energy cost subject to SoC dynamics, power limits, ramp rates, and grid service constraints—then implements only the first control action before re-optimizing. MPC captures lookahead benefits (e.g., storing cheap solar for evening peak) but requires accurate forecasts, convex modeling, and secure real-time computation. Hybrid approaches—like RB fallback during forecast uncertainty—are increasingly common in industrial settings per UL 9540A and IEEE 1547-2018 Annex D.
📐 SoC Dynamics & MPC Objective
The core battery state evolution is governed by discrete-time SoC dynamics. In MPC, this becomes a constraint within a linear program (LP) minimizing total grid import cost over horizon N. The objective balances immediate and future costs while respecting battery physics.
💡 Worked Example
Problem: A 500 kWh lithium-ion battery (η_charge = 0.94, η_discharge = 0.96) starts at SoC₀ = 60%. Over 1 hour, it discharges 120 kW. What is SoC₁? Assume 1-hour timestep (Δt = 1 h).
1.
Step 1: Compute energy discharged = 120 kW × 1 h = 120 kWh
2.
Step 2: Account for discharge efficiency: usable energy withdrawn = 120 kWh / 0.96 = 125 kWh (i.e., battery expends 125 kWh to deliver 120 kWh)
3.
Step 3: Update SoC: SoC₁ = SoC₀ − (125 kWh / 500 kWh) = 0.60 − 0.25 = 0.35 → 35%
Answer:
The result is 35%, which falls within the safe operating range of 10–90% SoC recommended for cycle life preservation.
🏗️ Real-World Application
At Newmont’s Boddington Gold Mine (Western Australia), a 12 MW/34 MWh battery system uses hybrid dispatch: MPC for day-ahead market bidding and solar self-consumption optimization (using 4-hr PV and load forecasts), with rule-based ‘frequency response mode’ activated automatically during grid events detected via PMU data—ensuring compliance with AEMO’s FCAS requirements without waiting for forecast updates. This architecture reduced diesel runtime by 22% and increased renewable penetration from 28% to 41% year-over-year (2022–2023), as reported in the Australian Renewable Energy Agency (ARENA) project summary AR-0000287.
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🔧 Open Grid-Interactive Building Energy Systems Calculator📋 Case Connection
📋 New York Con Edison Brooklyn Microgrid Demonstration
Legacy grid infrastructure unable to absorb distributed solar exports; required bi-directional active power curtailment...