1. Mathematical Optimization Models
Linear/Nonlinear Programming frameworks maximize revenue by solving for optimal charge/discharge decisions under market price forecasts and physical constraints.
- Variables: Charge/discharge rates (
charge_t,discharge_t), state of charge (SoC_t), and market participation. - Constraints: Round-trip efficiency (
SoC_{t+1} = SoC_t + \eta \cdot charge_t - discharge_t), charge/discharge limits, and cycle life degradation. - Multi-market participation: Co-optimize for energy arbitrage (day-ahead/real-time markets), frequency regulation, and ancillary services.
2. Grid Support Value Streams
Batteries can address local grid needs alongside wholesale markets:
- Peak shaving: Reduce demand charges by discharging during high-load periods (e.g., summer afternoons).
- Voltage regulation: Inject/absorb reactive power to stabilize distribution grids.
- Capacity firming: Mitigate intermittency of renewable generation.
3. Multi-Use Stacking
Operators increasingly combine roles to capture layered revenue:
- Frequency regulation remains the most common use due to fast response times.
- Arbitrage: Charge during low-price periods (e.g., midday solar oversupply) and discharge during high-price peaks.
- Ramping/load following: Smooth renewable generation variability.
4. Advanced Forecasting
Price and load forecasting accuracy directly impacts optimization:
- Day-ahead markets: Perfect-foresight models (using actual prices) provide theoretical revenue ceilings.
- Hybrid models: Combine probabilistic price forecasts with battery degradation costs to balance risk-reward.
5. Technology-Specific Modeling
For non-lithium technologies (e.g., vanadium redox flow batteries):
- Auxiliary losses: Account for pump power and standby consumption in dispatch algorithms.
- Deeper cycling: Leverage extended duration (4+ hours) for prolonged demand shaving.
Implementation Tools
- REopt®: Used by NREL to co-optimize wholesale market bids and grid services.
- Custom LP solvers: Open-source tools (e.g., Python’s PuLP) for day-ahead scheduling.
By integrating these strategies, operators can achieve 20–30% higher net present value compared to rule-based dispatch.
Original article by NenPower, If reposted, please credit the source: https://nenpower.com/blog/what-strategies-can-be-used-to-optimize-dispatch-for-utility-scale-batteries/
