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

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Overview1. Introduction

2. Motivation

3. Objectives

4. Approach

5. Results

6. Summary

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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 energy goals for 2020

• Wind power capacity to reach 230GW, from 94GW in 2011

• On-shore wind power capacity to double

* Data: EWEA, 2011

2011 20200

50

100

150

200

250

Inst

alle

d W

ind

Pow

er C

apac

ity (

GW

) –

Euro

pean

Uni

on *

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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

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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.

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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

• 38% of land area eligible for siting

• More than 30,000 sites

Case: Poland

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Performance Assessment• Life cycle assessment of energy yield and performance uncertainty

Wind Distribution (WRF) – Weibull Maps (1-5 years; 10x10 sq km)

Turbine Selection – Lowest cost of energy turbine for local wind regime

Monte Carlo Uncertainty PropagationVertical

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Financial Assessment

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Mapping – Risk & ReturnsIRR on Equity

(Debt/Equity:70/30)Standard Deviation in IRR

• Mapping expected returns and performance risk facilitates creation of portfolios according to investors’ preferences

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Optimum Portfolio for Poland’s 2020 RE targets

• Portfolio constraints

• Higher IRR for a fixed value of risk (3%)

• Proximity to 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 assessmentPortfolio – Poland 2020

Number of Projects 60

Capacity 6600 MW

Avg. Performance Uncertainty 10.1%

Average IRR 15.6%

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Meso-scale to Micro-scale – Refining PredictionsMesoscale 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

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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

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Farm Layout Optimization – Rules for Turbine Placement

Probability space for turbines’ relative placement

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Farm Layout Optimization – An Example

• Turbines relocated from poorer to richer wind regions (from (2) to (1))• Optimized layout avoid wake interactions

Norm

alized Mean Velocity Profile

Real wind farm (80MW) and micro-scale wind resource map (100x100 sq m)

Optimized layout for the wind farm

Dominant wind directions (80 percentile)

NWN

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Layout Optimization – Improved Energy Extraction

• The cost of energy production improved by €0.2cents/kWh • Increasing revenue by €400,000/annum

Iterations

Cost

of E

nerg

y Pr

oduc

tion

(ce

nts

€/kW

h)

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Other Capabilities1. Grid infrastructure modeling –

Optimal power flow modeling2. Logistics of farm development –

Turbine transportation modeling

3. Hydro-storage modeling – Improved wind power penetration and risk hedging

4. View-shed development – Reducing ‘NIMBY’ effect

5. …

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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

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Thank you.