Wind Farm Layout Optimization Using CFD

Overview

A renewable energy developer was planning a large onshore wind farm with 40+ turbines. Optimal turbine placement was critical to maximize energy yield while minimizing destructive wake interactions that reduce output and increase structural fatigue.

The Challenge

Wind farm optimization involves complex trade-offs:

• •Complex terrain with varying elevation and surface roughness
• •Limited data from met mast measurements
• •Wake interactions reducing downstream output by 20-40%
• •Land constraints from regulatory and ownership limitations
• •Dual objectives balancing energy production with turbine loading

"Every percentage point of improved yield translates to crores of additional revenue over the project lifetime. Getting the layout right is critical."

Our Methodology

1. Wind Resource Assessment

Characterized site conditions:

• •Mesoscale modeling (WRF) for regional patterns
• •Microscale CFD for local terrain effects
• •Statistical analysis of wind rose data
• •Uncertainty quantification

2. Wake Modeling

Developed validated wake predictions:

3. Terrain-Aware CFD

High-fidelity simulations captured:

• •Speed-up over ridges
• •Flow separation in complex terrain
• •Atmospheric stability effects
• •Turbulence intensity mapping

4. Layout Optimization

Multi-objective optimization framework:

Objectives:
├── Maximize Annual Energy Production (AEP)
├── Minimize Wake Losses
├── Reduce Turbine Fatigue Loading
└── Respect Land Constraints

Algorithm: Genetic Algorithm + Gradient-Based Refinement
Evaluations: 10,000+ layout configurations

5. Fatigue Assessment

Evaluated structural loading:

• •Wake-induced turbulence effects
• •Fatigue damage equivalent loads
• •Blade and tower lifetime analysis

Technical Details

Mesoscale: WRF, 3km resolution
Microscale: OpenFOAM, 10m resolution
Wake Models: FLORIS, PyWake
Optimization: Python, DEAP
Validation: SCADA from existing farms

Results

The optimized layout delivered substantial improvements:

Financial Impact

Over 25-year project lifetime:

• •₹50 Crore improvement in project ROI
• •Reduced turbine maintenance costs
• •Lower insurance premiums due to reduced fatigue
• •Faster project financing approval

Deliverables

Provided comprehensive documentation:

• •Optimized turbine coordinates
• •Wind resource maps
• •Expected energy production curves
• •Uncertainty analysis
• •Sensitivity to key assumptions

Key Takeaways

1. •Wake effects can reduce production by 20-40%—optimization is essential
2. •Terrain-aware CFD captures effects that simplified models miss
3. •Multi-objective optimization balances energy with loading
4. •Validation against operational data builds confidence
5. •Methodology scales from single farms to portfolio optimization