GISdevelopment.net --> Application --> Utility
Sara Beheshtifar
K,N, Toosi university of Technology
Sara_beheshtifar@yahoo.com
Sadi Mesgari
K,N, Toosi university of Technology
smesgari@kntu.ac.ir
Abstract
In Iran, the electric power demand has grown rapidly over the past years. A demand study for the country shows the need for new capacity to meet the growing electric demand. Therefore, it’s necessary to increase the power supply by constructing new power plants.
One of the most important factors, which should be considered in new power plant construction process, is the location of the plant. The suitability of selected site for power plant affects the amount of generated energy and the cost of power generation. Suitable sites should be determined on the basis of technical, economical and socio-environmental issues. Therefore, multiple criteria should be considered in this process.
GIS along with appropriate models and spatial analysis method can be used to define the suitability of different locations for the construction of power plants.
This research focused on determining suitable locations for construction of a natural gas-fired power plants. At first, important parameters in power plant sitting were identified. Then, the maps of studied area were prepared and integrated. Boolean, index overlay, and fuzzy logic model were used for integrating of maps. Finally the suitable locations for the construction of power plant were selected using each model.
1. Introduction
In Iran, the electric power demand has grown rapidly over the past years. A demand study for the country shows the need for new capacity to meet the growing electric demand. Therefore, it’s necessary to increase the power supply by constructing new power plants.
One of the most important factors, which should be considered in new power plant construction process, is the location of the plant. The suitability of selected site for power plant affects the amount of generated energy and the cost of power generation and transmission.
Suitable sites should be determined on the basis of technical, economical and socio-environmental issues and meet multiple criteria.
GIS along with appropriate models and spatial analysis method can be used to define the suitability of different locations for the construction of power plants.
In this paper, the required conditions for the establishment of thermal power plants are comprehensively studied. Then, important parameters in power plant are identified. Later the factor maps of studied area was prepared and integrated.
There are several map combination processes, these are Boolean logic combination, algebraic combination, index overlay combination, fuzzy logic and vector fuzzy logic combinations, and so on. In this project, Boolean, index overlay, and fuzzy logic model were used for integrating of maps. Finally the suitable locations for the construction of power plant are selected.
2. Study area and data sets
Prediction of the electricity consumption for the coming years shows the necessity for a new power plant establishment in Fars province. Therefore, this province is selected as the study area. Fars province is in the southern part of Iran with an area of 122780 Km². Considering the available spatial data in the study area, the data layers of scale 1:250,000 were used in this project. Table 1 illustrates several data layers which have been used in this study.
Table1: data layers used in this study
3. Maps Generation and Data integration
Considering the mentioned layers, according to the characteristics of factors and their effect on power plant sitting, two different types of maps were generated: limitation maps and factor maps.
3.1. Limitation maps
Such a map defines the area that cannot be used for the power plant because of a limiting factor. As the purpose of creating such a map is to completely exclude the unsuitable areas, it can be a binary map, in which the areas with limiting condition (not suitable) are given the value of zero and the allowed (suitable) areas are given the value of one. These maps are overlaid using the Boolean Operation, where input maps can be integrated by using logical operators such as AND, OR, XOR and NOT (Bonham Carter and G.F., 1991). In this research logical 'AND' operator was used, which resulted in the selection of areas that have 'one' value in all binary maps(Fig.1). The list and criteria for generation of such maps is presented in table2.
Table 2: Limitation maps and their criteria
3.2. Factor maps
Some of the parameters do not affect the suitability of a location in an absolute manner (e.g. making it absolutely unsuitable), yet has a positive or negative effect on the suitability. In a factor map, areas can be given different weights according to their suitability for power plant locating. For example one of the most important criteria that should be considered in power plant sitting, is proximity to the roads. Therefore, in road factor map, values are decreasing when the distance from road is increased. For each of these parameters a factor map was created (See Table 3).
On the other hand, the effects of these parameters aren't the same in the power plant sitting. In this study, factor weights are defined to describe the significance of each parameter in the selection of proper location for combined cycle power plants. The impotencies of factors are listed in Table 4. Index Overlay and fuzzy logic model were used for factor maps combination.
Table 3: Factor maps and their classes
Table4: Factors and their relative importance
3.2.1. Index Overlay
The following Equation was used for integration of factor maps using index overlay method.
Where:
Wi= The weight of ith factor map
Sij= The ith spatial class weight of jth factor map
S= The spatial unit value in output map
It is resulted in a map with values for every location showing different suitability of locations for power plant construction.
3.2.1. Fuzzy logic model
In a fuzzy map, the associated value for each pixel (Fuzzy membership value), represent both the relative importance of the factors and the relative values corresponding to different locations on the map area.
Fuzzy membership values should be between zero and one. However, in this range, there is no limitation on the selection of the values. They are selected to represent the degree of membership in a set on the basis of subjective judgment
In fact, each membership value represents the suitability of the pixel area for the power station regarding to the related criteria.
For factor maps integration, some fuzzy operators, such as the fuzzy AND, the fuzzy OR, fuzzy algebraic product, fuzzy algebraic sum and fuzzy gamma operator can be used. These operators are as follows:
Usually one sole fuzzy operator can not be used to integrate all data layers. Different fuzzy operators provide a high level of flexibility in data integration. The selection of operator is on the basis of the characteristics of the data layers and their role in the application. Therefore, before the integration of data layers, they are classified on the basis of their role in power plant sitting (See Table 5).
Table5: Classification of data layers for integration
When using 'fuzzy AND' and 'fuzzy OR' operator, only one of the parameters (factor layers) is used to define the output value, which is contrary to our intention of using all factors. Except for water resources, in other cases the operator of 'SUM' and 'Υ' are used. In this study, assuming that just one water resource is enough for water supply, the maps of lake, river and water discharge were combined using OR operator.
Using of 'SUM' and 'Υ>0.7' has an increasing effect on the results such that the resulted value is larger or equal to maximum value of the input values. Here for integrating of elevation, slope, geology and land use maps fuzzy gamma operator was applied (Υ=0.88). Also the maps of pipeline and road were integrated using gamma operator (Υ=0.7). At last fuzzy Algebraic Sum operator was used for final combination of fuzzy data layers. The selection of 'SUM' and 'Υ' operators are such that a defined ratio is resulted among the factors of Consumption Center, Water Resources, Geophysical & land use and Infrastructure, on the basis of their characteristics and role in site selection.
4. Selection of suitable locations
The result of factor maps overlay is multiplied by the result of limitation maps overlay. The final integrated maps are presented in Fig2.
In general, in index overlay and fuzzy logic model, 0.12% and 0.17% of the study area was selected as suitable, respectively. The majority of selected area in both of the methods was the same.
In order to comparing the methods, 20 sites were selected in allowable area with high suitability in both of the methods. The result represents the relative suitability of a site between other sites in each of the methods. It also allows the user to compare the suitability of a particular site in different methods. After prioritizing the sites in each method, the sites with high suitability in both of the methods were determined as the suitable sites for power plant construction. As an example, some properties of the suitable sites are presented in table 6.
Fig 1. Limitation Maps Integration
Table 6. Suitable sites and their properties
5. Conclusion
The purpose of this study was to use several map combination methods in GIS for power plant sitting. Binary maps and Boolean operators were utilized to identify limited areas for power plant construction and 71% of the county fell in this limited area. Factor maps were integrated using index overlay method and fuzzy operators to determine suitable locations for power plant building in the remaining area.
In general, in index overlay and fuzzy logic model, 0.12% and 0.17% of the study area was selected as suitable, respectively. In both of the methods, the majority of suitable area was located in Fasa district, where electrical energy demand is more than other places. Flexibility of the fuzzy method allows the user to apply a variety of data integration methods based on the characteristics of the data parts and the way they effect (support or decline) each other regarding the application.
References
Comparison of Boolean, Index Overlay and Fuzzy Logic Methods for Data Integration in Power-Plant Sitting
Sara Beheshtifar
K,N, Toosi university of Technology
Sara_beheshtifar@yahoo.com
Sadi Mesgari
K,N, Toosi university of Technology
smesgari@kntu.ac.ir
Abstract
In Iran, the electric power demand has grown rapidly over the past years. A demand study for the country shows the need for new capacity to meet the growing electric demand. Therefore, it’s necessary to increase the power supply by constructing new power plants.
One of the most important factors, which should be considered in new power plant construction process, is the location of the plant. The suitability of selected site for power plant affects the amount of generated energy and the cost of power generation. Suitable sites should be determined on the basis of technical, economical and socio-environmental issues. Therefore, multiple criteria should be considered in this process.
GIS along with appropriate models and spatial analysis method can be used to define the suitability of different locations for the construction of power plants.
This research focused on determining suitable locations for construction of a natural gas-fired power plants. At first, important parameters in power plant sitting were identified. Then, the maps of studied area were prepared and integrated. Boolean, index overlay, and fuzzy logic model were used for integrating of maps. Finally the suitable locations for the construction of power plant were selected using each model.
1. Introduction
In Iran, the electric power demand has grown rapidly over the past years. A demand study for the country shows the need for new capacity to meet the growing electric demand. Therefore, it’s necessary to increase the power supply by constructing new power plants.
One of the most important factors, which should be considered in new power plant construction process, is the location of the plant. The suitability of selected site for power plant affects the amount of generated energy and the cost of power generation and transmission.
Suitable sites should be determined on the basis of technical, economical and socio-environmental issues and meet multiple criteria.
GIS along with appropriate models and spatial analysis method can be used to define the suitability of different locations for the construction of power plants.
In this paper, the required conditions for the establishment of thermal power plants are comprehensively studied. Then, important parameters in power plant are identified. Later the factor maps of studied area was prepared and integrated.
There are several map combination processes, these are Boolean logic combination, algebraic combination, index overlay combination, fuzzy logic and vector fuzzy logic combinations, and so on. In this project, Boolean, index overlay, and fuzzy logic model were used for integrating of maps. Finally the suitable locations for the construction of power plant are selected.
2. Study area and data sets
Prediction of the electricity consumption for the coming years shows the necessity for a new power plant establishment in Fars province. Therefore, this province is selected as the study area. Fars province is in the southern part of Iran with an area of 122780 Km². Considering the available spatial data in the study area, the data layers of scale 1:250,000 were used in this project. Table 1 illustrates several data layers which have been used in this study.
| Data layers | ||
| Elevation | cultivation | Lake |
| Slope | Protected areas | River |
| Fault | Orchard | City |
| Earthquake spots | Orchard | Village |
| Mine | Floodway | Road |
| Sandy land | Marsh | Gas pipe line |
| Geology | Marsh | Airport |
| Water Discharge | Dam | consumption center |
Considering the mentioned layers, according to the characteristics of factors and their effect on power plant sitting, two different types of maps were generated: limitation maps and factor maps.
3.1. Limitation maps
Such a map defines the area that cannot be used for the power plant because of a limiting factor. As the purpose of creating such a map is to completely exclude the unsuitable areas, it can be a binary map, in which the areas with limiting condition (not suitable) are given the value of zero and the allowed (suitable) areas are given the value of one. These maps are overlaid using the Boolean Operation, where input maps can be integrated by using logical operators such as AND, OR, XOR and NOT (Bonham Carter and G.F., 1991). In this research logical 'AND' operator was used, which resulted in the selection of areas that have 'one' value in all binary maps(Fig.1). The list and criteria for generation of such maps is presented in table2.
Some of the parameters do not affect the suitability of a location in an absolute manner (e.g. making it absolutely unsuitable), yet has a positive or negative effect on the suitability. In a factor map, areas can be given different weights according to their suitability for power plant locating. For example one of the most important criteria that should be considered in power plant sitting, is proximity to the roads. Therefore, in road factor map, values are decreasing when the distance from road is increased. For each of these parameters a factor map was created (See Table 3).
On the other hand, the effects of these parameters aren't the same in the power plant sitting. In this study, factor weights are defined to describe the significance of each parameter in the selection of proper location for combined cycle power plants. The impotencies of factors are listed in Table 4. Index Overlay and fuzzy logic model were used for factor maps combination.
| Factor | Score of factor |
| Electric demand | 20 |
| Gas pipe line | 15 |
| Road | 12 |
| Elevation | 12 |
| Slope | 8 |
| Geology | 8 |
| Land use | 6 |
| Water Discharge | 8 |
| River | 6 |
| Lake | 5 |
The following Equation was used for integration of factor maps using index overlay method.
Where:
Wi= The weight of ith factor map
Sij= The ith spatial class weight of jth factor map
S= The spatial unit value in output map
It is resulted in a map with values for every location showing different suitability of locations for power plant construction.
3.2.1. Fuzzy logic model
In a fuzzy map, the associated value for each pixel (Fuzzy membership value), represent both the relative importance of the factors and the relative values corresponding to different locations on the map area.
Fuzzy membership values should be between zero and one. However, in this range, there is no limitation on the selection of the values. They are selected to represent the degree of membership in a set on the basis of subjective judgment
In fact, each membership value represents the suitability of the pixel area for the power station regarding to the related criteria.
For factor maps integration, some fuzzy operators, such as the fuzzy AND, the fuzzy OR, fuzzy algebraic product, fuzzy algebraic sum and fuzzy gamma operator can be used. These operators are as follows:
Usually one sole fuzzy operator can not be used to integrate all data layers. Different fuzzy operators provide a high level of flexibility in data integration. The selection of operator is on the basis of the characteristics of the data layers and their role in the application. Therefore, before the integration of data layers, they are classified on the basis of their role in power plant sitting (See Table 5).
Using of 'SUM' and 'Υ>0.7' has an increasing effect on the results such that the resulted value is larger or equal to maximum value of the input values. Here for integrating of elevation, slope, geology and land use maps fuzzy gamma operator was applied (Υ=0.88). Also the maps of pipeline and road were integrated using gamma operator (Υ=0.7). At last fuzzy Algebraic Sum operator was used for final combination of fuzzy data layers. The selection of 'SUM' and 'Υ' operators are such that a defined ratio is resulted among the factors of Consumption Center, Water Resources, Geophysical & land use and Infrastructure, on the basis of their characteristics and role in site selection.
4. Selection of suitable locations
The result of factor maps overlay is multiplied by the result of limitation maps overlay. The final integrated maps are presented in Fig2.
In general, in index overlay and fuzzy logic model, 0.12% and 0.17% of the study area was selected as suitable, respectively. The majority of selected area in both of the methods was the same.
In order to comparing the methods, 20 sites were selected in allowable area with high suitability in both of the methods. The result represents the relative suitability of a site between other sites in each of the methods. It also allows the user to compare the suitability of a particular site in different methods. After prioritizing the sites in each method, the sites with high suitability in both of the methods were determined as the suitable sites for power plant construction. As an example, some properties of the suitable sites are presented in table 6.
Fig 1. Limitation Maps Integration
The purpose of this study was to use several map combination methods in GIS for power plant sitting. Binary maps and Boolean operators were utilized to identify limited areas for power plant construction and 71% of the county fell in this limited area. Factor maps were integrated using index overlay method and fuzzy operators to determine suitable locations for power plant building in the remaining area.
In general, in index overlay and fuzzy logic model, 0.12% and 0.17% of the study area was selected as suitable, respectively. In both of the methods, the majority of suitable area was located in Fasa district, where electrical energy demand is more than other places. Flexibility of the fuzzy method allows the user to apply a variety of data integration methods based on the characteristics of the data parts and the way they effect (support or decline) each other regarding the application.
References
- B. Carter and G.F., (Geographic Information System for Geoscientists: Modelling with GIS), Pergamon, Ontario, PP. 319-470, 1991.
- B.Mukhopadhyay, A.Saha, N. Hazra, Knowledge Driven GIS Modelling Techniques for Copper Prospectivity Mapping in Singhbhum Copper Belt–A Retrospection, ndia.
- Common Power Plant Siting Critria, Public Service Commission of Wisconsin, 1999, www.psc.wi.gov/consumerinfo/brochures/electric/6017b.pdf
- K. Delaney and A. Lachapelle ,A GIS Approach to Sitting a Coal-Fired Power Plant in Franklin County, Illinois, http://www.uoguelph.ca/geography/research/geog4480_w2003/group21/index.htm
- K. H. Chi, N. W. Park, Ch.J. Chung, Fuzzy Logic Integration for Landslide Hazard Mapping Using Spatial Data from Boeun, KOREA, http://www.isprs.org/commission4/proceedings02/pdfpapers/283.pdf
- K. Klassen and A. Marjerrison, Siting a Wind Turbine Farm in Pipestone County, Minnesota Using a GIS Framework,2002, http://www.uoguelph.ca/geography/filetran/geog4480_w2002/Group04/index.htm
- M. H. Tangestani, Landslide susceptibility mapping using the fuzzy gamma operation in a GIS, Kakan catchment area, Iran, http://www.gisdevelopment.net/application/natural_hazards/landslides/pdf/40.pdf
- M. J. Valadan Zoej , M. S. Mesgari, S. Beheshtifar1, M. Karimi1, Thermal Power Plant Site Selection Using GIS, Map Asia conference 2005.
- S. Beheshtifar, S. Mesgari, M.J. Valadan Zoej, M. Karimi, Data Integration Using Fuzzy Logic Model Application in: Power-Plant Sitting, Map India conference 2006.