Spatial mapping of potential zones for wind energy is crucial for sustainable regional planning. The Suez Canal Region, Egypt, is currently a focus for national government and international investments for developing the logistic area. The Suez Governorate region is known of its high wind speed along the Gulf of Suez coast. This paper aims at estimating and mapping the potential zones for harnessing wind energy in such region. The method utilizes satellite data and spatial multi-criteria evaluation. Landsat 8 OLI satellite image was used to derive the land-use/land-cover map. Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) was used in modeling the wind power density map using the region’s annual average wind speed data. Decision criteria including the climatic conditions, topography, infrastructure and land-cover maps were standardized, weighted and aggregated using weighted linear combination to identify the potential wind energy zones. The results reveal that the highest potential zones for wind energy reach a maximum value of 650 Watt/m 2 and a mean of 310 watt/m 2 and are located in the south-eastern part of the Suez Governorate Region along the Gulf of Suez. Findings indicate a high potential for harnessing wind energy in the region. The resultant maps can be used as guidelines for regional planning and zoning of renewable energy resources.
The twenty first century experienced attempts to improve urban planning process by taking into consideration energy efficiency. Egypt’s attempt to develop wind and solar energy was initiated in 1986 when the New and Renewable Energy Authority (NREA) was set up with the objective of assessing the country’s renewable energy resource and investigating the technology options through studies and demonstration projects. According to [
Estimation of wind power in a region has been a subject for several researchers all over the globe as well as energy planners, regional planners and urban development engineers. Intensive researches have been conducted. Methods include new technologies such as satellite data and remote sensing, Spatial Decision Support Systems (SDSS) and spatial cartographic models. Spatial Multlicriteria Evaluation (MCE) concepts are decision-making models frequently used to obtain continuous suitability maps [
The study area is bounded by the Mediterranean Sea on the north, by the Gulf of Suez to the south and encompasses the Suez Canal (
characterized by flat lands with contours ranging from half a meter to one meter above sea level as it is part of sandy coastline. It covers parts of the southern region of the Manzala Lake. Port Said sector is mainly a strip of sand bordered by the Mediterranean Sea from North and by the boundaries of Ismailia sector from South [
Shuttle Radar Topography Mission (SRTM) data acquired by space shuttle Endeavour mission in 2001 by C-band SAR interferometry instrument were used in this study. The data is processed by NASA and the USGS. Landsat 8 OLI data created by the U.S. Geological Survey and was obtained in geographic Tagged Image-File format (GeoTIFF) for August 2015. The topographic map published by the Egyptian General Survey Authority (1989) scale 1:50,000 was scanned, geometrically corrected, all data were projected to WGS-84 of the Universal Transverse Mercator System (UTM) of geographic coordinates.
A cloud-free Landsat OLI image acquired in August 2015 was used. Landsat 8 OLI satellite data level 1 consist of quantized and calibrated scaled Digital Numbers (DN) representing multispectral image data acquired by both the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). Atmospheric correction was conducted in ENVI 5.0 software. A supervised classification was conducted using the Maximum Likelihood Classification (MLC) algorithm to produce the land-use/land-cover maps. Seven classes were identified as urban, cultivated land, canal and water bodies, fish farms, sabkha, bare-land and desert zones. Field validation was carried out and an overall accuracy assessment of 84.79% was achieved.
Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) was processed using ESRI ArcGIS spatial to derive the slope and aspect angles. The DEM was used to estimate the air density as described in Section 3.1.1.
Spatial Multi-criteria evaluation and Analytic Hierarchy Process were used to determine the wind energy potential zones maps. The method involved specification of criteria, assignment of criteria weights, exclusion of constraint and aggregation by conducting a map overlay. A conceptual flow chart is depicted in
Wind energy resources can be mainly exploited in areas where wind speed
equals or exceeds 5.5 m/s [
According to [
W i n d p o w e r = 1 / 2 ρ V 3 (1) [
ρ = 1.225 − [ ( 1.194 * 10 − 4 ) * elevation ] (2) [
where :
V = average wind speed (m/s)
ρ = air density (kg/m3)
wpd = wind power density (watt/m2).
Distance from road network: To minimize construction costs, wind turbines should be located as closely as possible to the existing road network. [
Distance from electricity grid: In order to reduce costs associated with cabling and electricity losses over long transmission distances, wind farms should be located in the proximity of the electricity grid. [
Slope of terrain: [
Distance from Urban Areas: Similarly, the study area was classified into nine classes of equal distances, a setback buffer of 1,500 m. from urban areas to allow for future city expansion and green belt.
The suitability of an area for locating wind turbines depends on the prevalent land cover type. In Egypt, the cultivated land is protected by law. Fish farms, sabkha, water bodies, urban areas are unsuitable for locating wind turbines. A constraint map was created for the developed, protected and vulnerable lands including the lakes, cultural values and nature protected sites, urban areas, cultivated lands and faults. The buffer zone of 500 meters was created around lakes to protect the gulls and the shoreline ecology. A buffer zone of 200 meters was created around the faults. Constraints maps were converted into binary maps giving a value of “zero” to the constraint zones and “one” to the developable lands.
Analytical Hierarchy Process [
Estimation of the consistency, involves the following operations: a) Determi- nation of the weighted sum vector by multiplying matrix of comparisons on the right by the vector of priorities to get a new column vector. Then divide first component of new column vector by the first component of priorities vector, the second component of new column vector by the second component of priorities vector, and so on. Finally, sum these values over the rows. b) Determination of
Extremely importance | 9 | Extremely less importance | 1/9 |
---|---|---|---|
Very to extremely strongly importance | 8 | Very to extremely strongly less importance | 1/8 |
Very strongly importance | 7 | Very strongly less importance | 1/7 |
Strongly to very strongly importance | 6 | Strongly to very strongly less importance | 1/6 |
Strongly importance | 5 | Strongly less importance | 1/5 |
Moderately to strongly importance | 4 | Moderately to strongly less importance | 1/4 |
Moderately importance | 3 | Moderately less importance | 1/3 |
Equally to moderately importance | 2 | Equally to moderately less importance | 1/2 |
Equally importance | 1 | 1 |
consistency vector by dividing the weighted sum vector by the criterion weights. Once the consistency vector is calculated, it is required to compute value for the consistency index (CI). The value for lambda is simply the average value of the consistency vector. This measure can be normalized as follows:
C I = ( λ − n ) / ( n − 1 ) (3)
The term CI, referred to as consistency index, provides a measure of departure from consistency. To determine the goodness of CI, AHP compares it by Random Index (RI), and the result is CR, which can be defined as:
C R = C I / R I (4)
Random Index is the CI of a randomly generated pairwise comparison matrix of order 1 to 10 obtained by approximating random indices using a sample size of 500 [
The consistency ratio (CR) is designed in such a way that if CR < 0.10, the ratio indicates a reasonable level of consistency in the pairwise comparisons; if, however, CR > 0.10, then the values of the ratio are indicative of inconsistent judgments. In such cases one should reconsider and revise the original values in the pairwise comparison matrix.
Aggregation of criteria was conducted according to [
Suitability = ∑ w i X i * ∏ C j (5)
where wi = weight assigned to factor i
Xi = criterion score of factor i
Cj = constraint j
Order matrix | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
R.I. | 0.00 | 0.00 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 |
The land slope map reveals flat lands in the northern parts while high slopes exist along the Red Sea coasts where most of the mountainous zones exist (
Results of standardization of factors attributes into a suitability scale and the buffer zones are presented in
The study area shows a land relief of wide range varying from flat lands to high mountains (
Results reveal that the potential wind energy zones are found south-east of the Suez Region (
potentiality for harvesting wind energy in such zone. A database was created for such zones. The zonal statistics as table tool in ESRI ArcGIS for the high potential zones and relevant wind power density. The mean, minimum and maximum values were calculated for each zone. This step gives the potential energy expected from each site knowing its land area and choosing a wind turbine system, the wind geographic and technical potentials can be calculated.
Model validation was conducted by a random selection and examination of 100 sites from the highest values of the suitability index to check their compliance with the decision rules. ESRI zonal statistics as table tool summarizes the values
Criteria | Factors | Attribute values (indicators) | Suitability rating ratint |
---|---|---|---|
Climate | Wind speed (m/sec) | 5.0 - 55.6 | |
5.0 - 5.6 | 5 | ||
5.6 - 6.4 | 6 | ||
6.4 - 7.5 | 7 | ||
7.5 - 8.6 | 8 | ||
>8.6 | 9 | ||
Topography | Slope | 0 - 5 | 9 9 |
5- 10 | 7 7 | ||
10 - 15 | 5 5 | ||
>15 | 0 0 | ||
Aspect | −1 (flat) | 9 | |
0 - 45 (Northeast) | 5 | ||
45 - 90 (Northeast-East) | 5 | ||
90 - 135 (East-Southeast) | 5 | ||
135 - 180 (Southeast-South) | 5 | ||
180 - 225 (South-Southwest) Soutwes | 5 | ||
225 - 270 (Southwest-West) | 5 | ||
270 - 315 (West-Northwest) | 9 | ||
315 - 360 (Northwest-North) | 9 |
Criteri | Factors | Attribute values (indicators) | Suitability rating ratint | Set back (meters) |
---|---|---|---|---|
Costs criteria (Location) | Distance to main roads (meters) | 0 - 8,846 8,847 - 17,693 | 9 | |
8,847 - 17,693 | 8 | 2000 m | ||
17,694 - 26,539 | 7 | |||
26,540 - 35,386 | 6 | |||
35,387 - 44,232 | 5 | |||
44,233 - 53,079 | 4 | |||
53,080 - 61,925 | 3 | |||
61,926 - 70,772 | 2 | |||
70,773 - 79,618 | 1 | |||
Distance to power grid (meters) | 0 - 7,921 | 9 | ||
7,922 - 15,843 15 | 8 | |||
15,844 - 23,764 23, | 7 | 2000 m | ||
23,764 - 31,685 | 6 | |||
31, 868 - 39,606 3 | 5 | |||
39,607 - 47,528 47 | 4 | |||
47,528 - 55,449 | 3 | |||
55,450 - 63,370 | 2 | |||
63,371 - 71,291 | 1 | |||
Distance to cities (meters) | 0 - 13,666 | 9 | ||
13,667 - 27,331 | 8 | |||
27,332 - 40,997 | 7 | |||
40,997 - 54,663 | 6 | |||
54,664 - 68,329 68, | 5 | 1500 m | ||
68,330 - 81,994 81, 81,995 - 95,660 | 4 | |||
81,995 - 95,660 95, | 3 | |||
95,661 - 109,326 1 | 2 | |||
109,326 -122,991 | 1 | |||
Environmental protection | Land use/land cover | Water bodies (lakes) | 0 | 500 m |
Bare land | 9 | |||
Cultivated land | 0 | |||
Desert, loose and shifting sand | 5 | |||
Fish farms | 0 | |||
Sabkha | 1 | |||
Urban areas | 0 | |||
Geology | Faults Streams | 0 | 200 m | |
Hi-order streams (100 m) | 0 | 100 m |
Climate | Topography | Location | Environment | Calculated weight | |
---|---|---|---|---|---|
Climate | 1 | 7 | 3 | 9 | 0.6436 |
Topography | 1/7 | 1 | 3/7 | 9/7 | 0.0693 |
Location (cost) | 1/3 | 7/3 | 1 | 9/3 | 0.2204 |
Environment | 1/9 | 7/9 | 3/9 | 1 | 0.0665 |
CI = 4.1442, RI = 0.90, Cr = 0.053.
Class | Class Area (sq∙km) | Percentage of total potential zones (%) | Wind Speed (m/sec) | Wind Power density (W/m2) |
---|---|---|---|---|
9 (most suitable) | 132.5 | 19.59 | 8.28 - 10.1 | 358 - 560 |
8 | 109.9 | 16.25 | 8.28 - 10.1 | 279 - 348 |
7 | 90.1 | 13.32 | 7.69 - 8.2 | 257 - 278 |
6 | 101.2 | 14.96 | 7.69 - 8.2 | 232 - 256 |
5 (suitable) | 242.6 | 35.87 | 7.2 - 7.6 | 174 - 236 |
of a raster within the zones of another datasets (in this case the zones are the 100 selected sites and the raster datasets are the slope, aspect and wind power density). It was used to examine the sampled pixels of wind power density values. First, sites were examined to verify their compliance with the topographic factors rules i.e. slopes less than 10 degrees and aspect zones are in prevailing wind directions (N-NW). Second, the selected sites are displayed with the constraints map to ensure they do not fall on a constraints zone.
The present study is an application that utilizes remote sensing data and a spatial decision support model for mapping potential wind energy zones in the Suez Canal Region, Egypt. Potential wind energy zones exist in the study area’s south- eastern zone along the Gulf of Suez coastal line. High potential energy zones fall in range of 279 to 650 Watt/m2 along the shoreline and on the elevated lands. Results reveal that maximum potential wind energy zones are found south-east of the Suez Region. High potential zones reached 538 sites. There are 39 zones of total area equivalent to 240.8 sq∙km having potential wind power energy greater or equal to 300 Watt/m2. Two zones of area 132 sq∙km have potential wind energy that is greater or equal to 400 Watt/m2. Two zones have potential energy greater or equal to 500 Watt/m2. Two zones of total area equivalent to 132 sq∙km having potential wind energy greater or equal to 600 Watt/m2. The inventory reveals a high to excellent potentiality for harvesting wind energy. Results of this work can be useful for energy planning, zoning and site selection of wind energy farms.
This paper is part of a Research Project funded by the National Authority for Remote Sensing and Space Sciences, NARSS, Egypt and conducted during the year 2015-2016.
Effat, H.A. (2017) Mapping Potential Wind Energy Zones in Suez Canal Region, Using Satellite Data and Spatial Multicriteria Decision Models. Journal of Geoscience and Environment Protection, 5, 46-61. https://doi.org/10.4236/gep.2017.510005