Multi Criteria Evaluation using Weighted Linear Combination for wind farm site selection in Dobrogea, Romania

Student: Simona Petrisor

Supervisor: Douglas Brown

Lecturer in GIS and Human Geography

School of Geography, Geology and Environment

Kingston University~2014~

O U T L I N E

Introduction

Justification of study

Aims and objective

Methodology and methods

Results and Discussions

Conclusions

Multi Criteria Evaluation using Weighted Linear Combination for wind farm site selection in Dobrogea, Romania

INTRODUCTION

IntroductionRenewable energy has become more popular throughout the world, particularly wind energy.

Advantages :- Clean energy source- Sustainable energy

source- Low environmental

impact- Wind resource widely

availableMulti Criteria Evaluation using Weighted Linear Combination for wind farm site selection in Dobrogea, Romania

INTRODUCTION

Justification of StudyWind energy has a fast growth rate in Romania but the essential issue is the location.This study evaluates the suitability for wind farm development in the particular region of Dobrogea.

AimsThe project aims to develop a suitability model which will help predict suitable areas across Dobrogea. Objectives- Suitability model- Identify the factors

influencing the suitability- Identify suitable areas- Critically evaluate the results

METHODOLOGY AND METHODS

Suitability Model

• WLC is a multi criteria evaluation Method common to raster data.

• WLC is used because it can incorporate

multiple criteria that influences the model.

• WLC works by considering all criteria-

factors of the analysis, standardized to a common numeric range, and then combines them using an weighting value scale.

COMPARISON OF RESULTS

FINAL SUITABILITY MAPS

ALTERNATIVE SCENARIOS

COMBINING ATRIBUTE MAPSWEIGHTED OVERLAY TECHNIQUE FUZZY OVERLAY TECHNIQUE

STANDARDISATION OF THE CRITERION MAPSGENERATING THE STANDARDISED

MAPS ASSIGIGNING WEIGHTS

WEIGHTED LINEAR COMBINATIONIDENTIFYING THE SET OF CRITERIA GENERATING CRITERION MAPS

Flow Chart

METHODOLOGY AND METHODS Identifying the Set of

Criteriao Wind resource : - for wind turbines efficient

functioning, wind speed in the area must be at least 5.1 m/s.

- the project uses two datasets: first one obtained from Romanian National Meteorological Agency and second one from MeteoBlue.o Land use

o Slopeo Urban o Roadso SCA and SPAo Hydrography

Generating Factor Maps

METHODOLOGY AND METHODS

Wind Speed Distribution Maps

Maps were created by interpolating the wind speed data for both datasets.

For dataset 1, wind speed data was obtained from 15 meteorological station, whereas for Dataset 2 from 25 points across the region.

The interpolation technique used was Ordinary Kiriging .

Ordinary Kriging works by assuming the constant mean unknown, and creates a surface of the phenomenon by predicting the values for each point location.

Wind Speed at 80 m height

Multi Criteria Evaluation using Weighted Linear Combination for wind farm site selection in Dobrogea, Romania

Standardization of the factorsAfter the processing step, all criteria were transformed in factor maps, standardised to a numeric range of 1to 5, where 1 represents the least suitable zones and 5 the highest suitability ones.

 

Criteria

Least

Suitable

1

2 3 4

Highly

Suitable

5

Wind

Dataset1

4.4-

4.9m/s

5-5.4 m/s 5.5-6.1 m/s 6.2-6.7 m/s 6.8-7.5

m/s

Wind

Dataset2

3.7-4 m/s 4.1-4.4

m/s

4.5-4.9 m/s 5-5.4 m/s 5.5-6 m/s

Land Use Wetlands Water

bodies

Artificial

surfaces

Forest and

semi natural

areas

Agricultur

al areas

Slope 20-41% 15-20% 10-15% 5-10% 0-5 %

Urban 0-300m 300-

500m

500-750m 750-1000m >1000m

Roads 0-300m 300-

500m

500-750m 750-1000m 1000-

5000m

SCI 0-300m 300-

500m

500-750m 750-1000m >1000m

SPA 0-300m 300-

500m

500-750m 750-1000m >1000m

Rivers 0-300m 300-

500m

500-750m 750-1000m >1000m

Lakes 0-300m 300-

500m

500-750m 750-1000m >1000m

METHODOLOGY AND METHODS

o In order to standardize

the factor maps, they were reclassifiedaccording to theirperceived importance, as resulted from theresearch literature,as follows:

Reclassification of Slope and Land Use Maps according to the common value scale

- Euclidian Distance from Roads and Urban - Reclassified to the standardised numeric range

- Euclidian Distance from SPA and SCI- Reclassified to the standardised numeric range

- Euclidian Distance from Rivers and Lakes - Reclassified to the standardised numeric range

METHODOLOGY AND METHODS Assigning weights to factor mapso Weighting of factor maps was done using a pairwise comparison between factors. Comparing them in sets of two, the highest importance between them was identified and the final ranking of the criteria determined.o Using the pairwise comparison, uncertainties regarding the overall

importance of each factor were eliminated.     Weight

Wind A 16

Land

Cover

B

13

Slope C 11

Urban D 13

SPA E 11

SCI F 11

Roads G 11

Rivers H 8

Lakes I 8

SUM 100

  Wind Land Slope Urban SPA SCI Roads Rivers Lakes

  A B C D E F G H I

Wind A - B A D A A A A A

Land B - B B B B G H I

Slope C - C C C C H I

Urban D - D D G D D

SPA E - EF E E E

SCI F - F F F

Roads G - G G

Rivers H - HI

Lakes I                 -

o Weighted overlayIt works by applying the set of weights determined earlier, adding the weights values across the image to create a composite map, representing a continuous surface of suitability.

METHODOLOGY AND METHODS

o Fuzzy overlayFuzzy Sum used within the project adds the fuzzy values of standardised individual sets to the cell location it belongs to, the output map representing an increasing linear combination function influenced by the number of factors used. Alternative Scenarios The model developed uses two sets of wind data. Therefore two alternative scenarios were generated.

Combining factor maps

RESULTS AND DISCUSSIONS

In scenario 1(Ro wind data), the percent of the area that falls under ‘ideal’ suitability is 0.36% representing 56.75 sq km, whereas the ‘unsuitable’ areas represent 66.01 % (10,270.00 sq km).

In scenario 2 (MeteoBlue data), the percent of ‘ideal’ suitability areas has increased to 1.36% representing 212.05 sq km, whereas the ‘unsuitable’ areas represent 61.81 % (9,615.00 sq km).

Using Fuzzy Overlay, the results are similar with the ones from Weighted Overlay, the highest suitability areas (bright red) being identified in the same regions of Dobrogea as in Weighted Overlay.

DISCUSSIONS

Similar ‘ideal’ location for both scenarios, and for both overlay techniques.

Suitability model is very sensitive to the wind resource criteria.

Few suitable areas, despite that Dobrogea has the highest wind potential of all country regions.

Wind turbine with 80m hub height, therefore for different types of turbines, the suitability areas could change.

The analysis performed could go further by evaluating each individual site location indicated as ‘ideal’ ,

CONCLUSIONS

The project presented GIS based multi-criteria evaluation approach for identifying potential site locations for wind farms in Dobrogea, using WLC method.

WLC allowed to develop a suitability model which can be used as a planning tool and help in decision making process.

The model could substantially benefit from a centralised wind data. However, this study tries to make predictions, provide insights and understandings, rather than to prescribe a ‘correct’ solution to the issue.

The paper described how the use of WLC together with different aggregation techniques provides a prediction of land suitability for wind farm development.

THANK YOU!

Simona Petrisor

k1255387

~2014~