Large-Area Identification of Wind Projects and Optimization of Farm Layout

1. Large-Area Identification of Wind Projects and Optimization of Farm Layout A. Singh, S. Giannoulakis, N. Chokani, R. S. Abhari Laboratory for Energy Conversion, ETH Zürich February 6, 2013 EWEA 2013, Vienna

2. Antriksh Singh Overview Introduction Motivation Objectives Approach Results Summary

3. Antriksh Singh Introduction – Growth of Wind Industry • In EU, 15.6% annual growth rate of installed wind power capacity (1996-2011) • EU member states adopted binding renewable energygoals for 2020 • Wind power capacity to reach 230GW, from 94GW in 2011 • On-shore wind power capacity to double * Data: EWEA, 2011

4. Antriksh Singh Challenges – Identification of Viable Wind Projects • At 3MW/km2, over 35000km2 of land identification for wind farm development • Large-area (country/state) prospecting for wind farm development is time and resource intensive • Wind project development dependent on local factors- environmental, geographical, anthropological, policy and finance • Site assessment and feasibility study takes 1-2 years • Simultaneous assessment of large number of sites - Difficult • Performance/assessment of wind farms is susceptible to systemic uncertainties and exogenous risks • Relative risk assessment of spatially distributed potential projects - Necessary

5. Antriksh Singh Research Objectives • Develop Geographical Information Systems (GIS) based integrated econometric assessment tool for planning of wind power development; capable of • Large-area assessment of land for site identification • Long-term performance and financial assessment • Model performance risk for optimum portfolio development • Model auxiliary systems – Transmission grid, hydro storage etc.

6. Antriksh Singh Site Identification • Identification of wind farm development constraints based on anthropological, geographical, environmental factors – 16 in case of Poland • Map each constraint at spatial resolution of 30m x 30m (more than 350 million pixels/map for Poland) • Test exclusivity of each pixel from development constraints and corresponding regulatory buffer areas for identification of ‘eligible areas’ 0 300km Case: Poland • 38% of land area eligible for siting • More than 30,000 sites

7. Antriksh Singh Performance Assessment • Life cycle assessment of energy yield and performance uncertainty Wind Distribution (WRF) – Weibull Maps (1-5 years; 10x10 sq km) Monte Carlo Uncertainty Propagation Vertical Turbine Selection – Lowest cost of energy turbine for local wind regime

8. Antriksh Singh Financial Assessment

9. Antriksh Singh Mapping – Risk & Returns IRR on Equity (Debt/Equity:70/30) Standard Deviation in IRR • Mapping expected returns and performance risk facilitates creation of portfolios according to investors’ preferences

10. Antriksh Singh Optimum Portfolio for Poland’s 2020 RE targets • Portfolio constraints • Higher IRR for a fixed value of risk (3%) • Proximityto transmission grid and load centers • Time for analysis of Poland – 72 hours • Analysis provides a ‘crude’ portfolio based on meso-scale assessment • Further refinements using micro-scale wind resource assessment

11. Antriksh Singh Meso-scale to Micro-scale – Refining Predictions Mesoscale wind simulation using WRF Grid Resolution – 10x10km Micro-scale wind farm simulations – in-house RANS CFD solver Grid resolution – of the order of rotor diameter

12. Antriksh Singh Micro-scale Refinement – Farm Layout Optimization • Evolutionary optimization technique for placement of turbines • Invasive Weed Optimization • Behavior of weeds – fast and greedy search for resources • In resource rich regions - High rate of growth and reproduction (and vice versa) • Objective: • minimize(Cost of energy production for each wind turbine) • Explore wind rich regions • Decrease wake losses

13. Antriksh Singh Farm Layout Optimization – Rules for Turbine Placement Probability space for turbines’ relative placement

14. Antriksh Singh Farm Layout Optimization – An Example Real wind farm (80MW) and micro-scale wind resource map (100x100 sq m) Optimized layout for the wind farm Normalized Mean Velocity Profile N NW Dominant wind directions (80 percentile) • Turbines relocated from poorer to richer wind regions (from (2) to (1)) • Optimized layout avoid wake interactions

15. Antriksh Singh Layout Optimization – Improved Energy Extraction Cost of Energy Production (cents €/kWh) Iterations • The cost of energy production improved by €0.2cents/kWh • Increasing revenue by €400,000/annum

16. Antriksh Singh Other Capabilities Grid infrastructure modeling– Optimal power flow modeling Logistics of farm development – Turbine transportation modeling Hydro-storage modeling – Improved wind power penetration and risk hedging View-shed development – Reducing ‘NIMBY’ effect …

17. Antriksh Singh Summary • Integrated approach to identify economically viable sites over large areas is demonstrated • Analysis of Poland is presented, identifying 38% of area eligible for development • A portfolio of wind projects to meet Poland’s 2020 targets is developed • Micro-site layout optimization technique - based on Invasive Weed Optimization - is demonstrated to reduce the Cost of Energy Production • Other capabilities developed within the framework are introduced

18. Antriksh Singh Thank you.