Testing the Effect of Different Optimization Parameters on ... · Software developed by AWS Truepower, LLC A tool for the design, optimization, and assessment of wind power projects

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Testing the Effect of Different Optimization Parameters on Layout

Design Using openWind®: A GIS Based Wind-Modeling Platform

Meagan Krawczyk, Wind Resource Analyst, Shell Wind

Nick Robinson, Director of openWind®, AWS Truepower

Sara Tyler, Wind Resource Manager, Shell Wind

ESRI Petroleum User Group Conference, Houston, TX

April 18th 2011

Date 9 May, 2011 ESRI PUG Presentation - openWind ® COE Optimizer 1


Overview


What is openWind®?

Optimizing wind farm layouts

Wind farm layout design basics

Test results / conclusions


openWind® and ArcGIS: Compatible, not competitive


openWind®:

Software developed by AWS Truepower, LLC

A tool for the design, optimization, and assessment of wind power projects

Open-source platform

Patterned after Geographical Information Systems (GIS)

Identical computations to those of other leading wind farm design programs

openWind® GIS functionality:

Create and display vectors and rasters

Clip vector layers

Edit attributes

Edit vectors

Label features

Export vectors/rasters to Google Earth

Add/Subtract rasters

Perform Vegetated and Non-Vegetated viewshed analysis (ZVI)


Approaches to wind farm layout design


Standard industry practice is to optimize turbines for maximum production

openWind® Enterprise has the capability to optimize layouts using the Cost of Energy Optimizer (COE)

Takes into account some project cost metrics

COE Testing: Hope to find a way to more automate the process of optimizing wind farm layouts for project valueTested whether or not the COE process had measurable impact on the resulting turbine layout

Plant Cost

Estimates

Financial

Assumptions

Site

ConditionsOptimization

Wind

Conditions

Turbine

Layout

Site

ConditionsOptimization

Wind

Conditions Turbine

Layout


Wind Farm Layout Design Basics: Create a wind map


Inputs to openWind®

Digital Elevation Model (DEM)

Roughness (RGH) – Values assigned to land cover for the site

Meteorology data (TAB file) – Historical, representative met data for the site in the form of wind speed and direction

(wind frequency)

Output (.WRG)

A grid containing probability (P) and wind speed (U) at each pixel in the grid

Defines wind conditions for the site.

Site Land cover (RGH) Elevation Data (DEM) Wind Rose (TAB file)

Wind Map (.WRG)


Wind Farm Layout Design Basics: Capture site constraints


Constraints:

Each site will have specific considerations to avoid, i.e. roads, rivers, steep slopes, etc.

Constraints are defined and mapped in order to create a buildable area in raster form

In Shell Wind, constraints are handled within ArcGIS


Wind Farm Layout Design Basics: Define Turbine Specifications

Date 9 May, 2011 ESRI PUG Presentation - openWind COE Optimizer 7

Turbine Specifications:

Hub height – The height of the turbine at the center of the hub

Rotor Diameter – The diameter of the circular path which the turbine blades rotate within

Cut in/out – The wind speed in which the turbine will start spinning (cut-in), and discontinue spinning

(cut-out)

Power Curve – The relationship between power production of the turbine and incoming wind speed

Thrust – Used to estimate impact of turbine on downstream wind flow

RPM – Rotational speed of the turbine rotor

* Photos courtesy of Vestas V100 Turbine Brochure

Side View of Turbine NacelleTurbine Hub Height Turbine Rotor Diameter


Wind Farm layout Design Basics: Choose a spacing


Spacing : Turbines need to be properly spaced from each other to lessen wake effects

Dominant Wind Direction

Max. Rotor SpacingMin. Rotor Spacing

Horns Rev Wind Farm : Photographed by

Christian Steiness


Wind Farm Layout Design Basics: Choose an optimizer


Optimize Layouts: There are 3 main ways to optimize layouts:

Gridded

Energy

Cost of Energy

1. Gridded Layout Optimizations – Turbines are packed into a linear array defined by angles, spacing and an area


Wind farm Layout Design Basics: Optimize for energy


2. Optimize for Energy (maximum capacity factor):

Inputs:

Wind Resource Grid

Turbine Specifications

Turbine Spacing

Number of Turbines

Constraints

Output:

Turbine Layout

Capacity Factor:

The ratio of estimated actual

output of energy over a

period of time and it’s output

if it had operated at full

capacity


Wind Farm Layout Design Basics: Optimize for cost of energy


Inputs: Same as energy optimizer AND:

Costs:

Roads

Cables

Turbine

Financial

Files:

Existing roads

Water bodies

Existing Cables

Cost Multipliers

Starting points (nodes) for cables

and roads must be defined

Should be as centrally located as

possible

Road start node should connect to

an existing main road

Outputs:

Turbine Layout

Road Design

Collection System


Wind Farm Layout Design Basics: Assess your design


Generate layout-specific producible energy estimates based on:

Input wind map (WRG)

Wind speed distribution (.tab file)

Turbine model specifications

Site specific air density

Site reference height

Estimates Include:

Gross Energy : Energy before wake effects

Net Energy : Gross Energy – site specific losses (i.e. wake effects)

Capacity Factor : Net Energy / (#MW *8760)

Example: 10 Turbine – 2 MW Machine:

Cost of energy


Testing Methodology: COE vs. Energy


Two test sites were selected to run both the energy optimizer and the COE optimizer

Inputs were kept the same for both optimizers, other than inputs specific to COE

COE inputs were defined by engineers within Shell Wind

Each optimization was run for the same number of iterations

Reports were run for each scenario and results compared

Reported conclusions are based on averaged results from 2 sites (4 scenarios)

Each of the 4 scenarios had a different total number of turbines/MW : 100, 250, 400 and 380 MW scenarios

This methodology was chosen to see if an increase in available land (by decreasing the number of turbines)

would increase the difference between costs


Results: COE vs. Energy


Designs were compared on basis of the following parameters:

Across the board, COE optimized layouts were lower in each category than energy optimized layouts

Scenarios with a higher ratio of land to turbines had a higher difference between energy and cost between the COE and

energy optimized layouts

Gross

Energy Net Energy

Capacity

Factor

Array

Efficiency Cost/MWh Total Cable Cost Total Road Cost

Total Cable

Length

Total Road

Length

COE

~0.08%

lower

COE

~1.41%

lower

COE

~1.40%

lower

COE

~0.61%

lower

COE ~1.90%

lower

COE ~17.7%

lower

COE ~30.9%

lower

COE

~12.0%

lower

COE ~17.7%

lower


Results: 100 MW Vestas V112 Scenario:

Energy Optimization Cost of Energy Optimization


Road NodeTurbine

Cable Node





Road Node TurbineCable Node





Road NodeTurbineCable Node


Results: 380 MW SWT 101 Scenario:



Road NodeCable Node Turbine


Testing Methodology: Sensitivity to DEM resolution and start nodes


50 meter and 10 meter resolution DEMs were tested

Start nodes for cables and roads were shifted

The start nodes were shifted from the 1st location to different locations that were still feasible for the project

*Red circle represents Cable Start Node (Substation), Yellow circle represents Road Start Node

50 m DEM, 1st set start nodes 10 m DEM, 2ND set start nodes


Results: Sensitivity to DEM resolution and start nodes


Comparison 1: Variable is DEM

Site 1: 50 m DEM, 1st set start nodes (base case)

Site 2: 10 m DEM, 1st set start nodes (comparator)

Comparison 2: Variables are DEM and start nodes


Site 3: 10m DEM, 2nd set start nodes (comparator)

Comparison 3: Variable is start nodes


Site 3: 10m DEM, 2nd set start nodes (comparator)

ComparisonGross

Energy Net Energy

Capacity

Factor

Array

Efficiency Cost/MWh

Total

Cable Cost

Total Road

Cost

Total Cable

Length

Total Road

Length

1 + 0.16% - 0.14% Negligible -0.34% + 0.32% -1.52% + 4.38% -2.02% + 1.02%

2 + 0.11% - 0.17% - 0.28% - 0.34% - 0.04% - 4.83% - 0.26% - 3.32% - 2.18%

3 - 0.05% - 0.03% Negligible Negligible - 0.35% - 3.26% - 4.85% -1.28% - 3.24%


Results: Sensitivity to DEM resolution and start nodes

250 MW Vestas V100 sensitivities:


10 m DEM, 2ND set start nodes50 m DEM, 1st set start nodes 10 m DEM, 1st set start nodes


Conclusions:


The COE optimizer produces layouts that are more linear and less like the energy optimized layouts for sites that are less

constrained (or have more available land)

Based on the cost calculator within openWind®, COE optimized layouts cost less to build than energy optimized layouts per MW

installed capacity

COE optimized layouts produce less energy per MW installed than energy optimized layouts

Energy optimized layouts are optimized for energy, and therefore tend to be more spread out to minimize wake effects

COE optimized layouts minimize the cost of energy rather than maximizing the energy capture and tend to be more tightly packed

COE optimized layouts are generally more linear in their design, which could make construction and maintenance easier

Using a higher resolution DEM impacts COE optimized designs, indicating that the highest resolution of DEM available to the user

should be used

Shifting the start nodes for cables and roads has shown to affect the outcome of the COE optimized layout. The user should be

aware of this relationship and test various scenarios to see what fits best for the site

Documents

Testing the Effect of Different Optimization Parameters on ... · Software developed by AWS Truepower, LLC A tool for the design, optimization, and assessment of wind power projects