Spatial Analyses Of Electricity Supply And Consumption Turkey Economics Essay

Energy demand and supply on national and international bases have been analysed extensively during the last decennaries by both public and private establishments ( WEC-TNC 2004, Munasinghe and Meier 1993 ) , and the chief focal point of these analyses has chiefly been the security of energy supply, and the finding of income and monetary value snap of energy ingestion ( Zachariadis and Pashourtidou 2006 ) . In add-on, concerns about clime alteration were considered to be another motive of these analyses ( Kuik 2003 ) .

This sort of information is utile for doing an illation about energy policy deductions. Furthermore, energy dramas a critical function in the economic, societal and political development of any state ( Surrey 1996, Varian 2002, ECN 2006 ) . Therefore, no modern society can earnestly turn to issues of development if such consideration is non based on a foundation of adequate, sustainable, and low-cost energy supply ( Akinbami and Lawal 2009 ) .

However, an efficient, resilient energy substructure for dependable supply of sufficient energy to run into increasing demand with low-cost monetary value is important for accomplishing these ends. Like other states, Turkey besides faces an of all time increasing electricity demand. Between 1980 and 2000, the mean growing rate of entire electricity ingestion in Turkey was 8.1 % per annum, while the existent GDP grew, on norm, approximately 4.4 % yearly during the same period.

The electricity ingestion per capita besides steadily grew from 459 kWh in 1980 to 1457 kWh in 2000, which was still low compared to others in the Organisation of Economic Developing Countries ( OECD ) . Turkey faced some deficits of electricity supply during the early 1980s and the 1990s ; the same period was besides marked by economic crises.

The deficient public financess and hapless public presentation of the state-owned electricity monopolies led, following the economic crisis in 2001, to a reform in electricity sector, taking at a competitory energy market, ( A-zkA±vrak 2005, AltA±nay and Karagol 2005 ) .

For these grounds, Turkey must hold sufficient capacity and secure beginnings to be able to provide electricity invariably and at low-cost monetary values in order to vie on the universe markets. To accomplish this, effectual and mensurable decision-making tools and attacks for energy direction and policy devising should be in topographic point.

In this regard, this survey aims to look into the spacial distribution of electricity coevals and ingestion of Turkey utilizing Geographical Information System ( GIS ) and spacial informations analysis methods with a position of supplying information that will steer determination and policy-making on energy direction in Turkey.

The organisation of the chapter is as follows: The chapter starts with a treatment of the importance of energy direction and possible usage of spacial analysis in energy sector direction and policy-making in subdivision 14.1. After the brief debut, subdivision 14.2 describes the information aggregation and its processing. Section 14.3 screens visualization consequences of electricity coevals capacity and ingestion informations and subdivision 14.4 presents the geographic expedition consequences.

This subdivision besides involves a description of the methods employed and the concluding behind their use. The chapter ends with a decision and recommendation of farther surveies in Section 14.5.

14.2 Data Collection and Processing

Spatial analysis of electricity coevals capacity and ingestion foremost requires roll uping related informations based on certain geographical units. In this survey the Nomenclature of Territorial Units for Statistics degree 3 ( NUTS level 3 ) ; a criterion designed for European states, to help in referencing of administrative divisions for statistical intents, is adopted as the chief geographical representation of the informations.

The NUTS degree 3 divides Turkey into 81 states ( Figure 14.1 ) .

Fig.14.1. Map of Turkey Showing 81 States based on NUTS degree 3

The chief informations used in this survey are divided into three basic classs, viz. : electricity power workss, ingestion, and supply. The power workss informations which includes information on hydro, thermic, air current, geothermic, gas, fuel oil, hydro auto-producers which were from Turkish Electricity Generation Inc. ( EUAAz ) , Turkish Electricity Distribution Inc. ( TEDAAz ) , State Water Works ( DSA° ) , Energy Market Regulatory Authority ( EPDK ) , internet beginnings, diaries and other relevant literatures.

The information pertaining to power workss were obtained from the Turkish Electricity Generation Inc. ( EUAAz ) , the Turkish Electricity Distribution Inc. ( TEDAAz ) , the State Water Works ( DSA° ) , the Energy Market Regulatory Authority ( EPDK ) , internet beginnings, diaries and other relevant literature ; and covered hydro, thermic, air current, geothermic, gas and fuel oil workss. The informations were in assorted formats such as excel, form file, Jpeg etc.

The properties of the informations include: name of the power works, location ( state and town ) , installed capacity, one-year coevals capacity, operation beginning day of the month, and present operational position. On the other manus, the electricity ingestion and supply informations, covering the period 1997 to 2006, for all the 81 states was obtained from the Turkish Directorate of Statistics ( TUA°K ) in excel format.

ArcMap and MapInfo are the two major GIS package bundles utilized in this survey. Therefore, all the informations were transformed to a GIS information format, specifically shape file format ( ArcMap ) . Using the master/slave method of enrollment, the Jpeg files were registered and the necessary information digitized into different beds.

The excel informations files on the other manus, which contain attribute information, were linked to the spacial information ( i.e. power workss and states ) as a database. Conversion from form file to TAB file format ( MapInfo ) was done when the demand arose.

14.3 Visual image

Once the information is a prepared for spacial analysis, the following measure is to visualise it in order to find appropriate spacial informations analysis methods. Visualization is a simple but utile spacial information analysis tool for the informations incorporating geographical information. It gives the research workers and decision-makers an thought of the state of affairs in different locations with easy apprehensible colorss or graphs.

In short, visual image might be named as the ocular sum-up of informations sets incorporating geographical information. In this survey, the installed capacities of electricity coevals installations, ingestion, and supply for all the states are visualised utilizing thematic maps.

14.3.1 Installed Capacities of Electricity Generation Facilities

The installed electricity coevals capacity of Turkey in December 2007 was calculated as 40,392.63 MW based on the informations collected in this survey. Table 14.1 gives the distribution of this sum harmonizing to the different energy beginnings.

Table 14.1. Sum installed capacity in Turkey at the terminal of 2007 ( beginning: ain computations based on the informations obtained for this survey )

Type of Plant

Installed Cap. ( MW )

Installed Cap. ( % )

Hydro Autoproducers

559.70

1.39

Thermal Autoproducers

2,831.59

7.01

Fuel oil

197.90

0.49

Geothermal

27.90

0.07

Thermal

13,293.60

32.91

Wind

146.30

0.36

Natural Gas

10,518.79

26.04

Hydro

12,816.85

31.73

Sum

40,392.63

100.00

The hydro and thermic power workss are grouped into two: autoproducers and non-autoproducers because autoproducers are allowed to bring forth electricity for their ain usage in their installations and may sell up to 40 % to the chief grid ( EPDK, 2008 ) .

14.3.2 Existing electricity coevals capacities of states

The coevals capacity of each state is calculated by finding the geographic location of each available electricity coevals installation. As shown in Figure 14.2, hydro power workss are located where the hydro potency is available ; as one would anticipate, that is largely on the south-eastern and north-eastern seashores.

Sing autoproducers, thermic workss are located in the north-western states, particularly around Istanbul, because of the big concentration of industries, as shown in Figure 14.3.

Fig. 14.2. Existing Hydro Plants

Fig. 14.3. Thermal Autoproducer Plants

In add-on to the capacity and locations of different types mentioned above, the coevals installations from other beginnings are besides figured out and used to find the overall coevals capacity of each state. The entire electricity coevals capacity of each state is shown in Figure 14.4 as graduated colors.

Fig. 14.4. The electricity coevals capacities of each state in Turkey in 2007

There are seven states which have the highest coevals capacities. Three of them ( Istanbul, Kocaeli, and Bursa ) are in the north-west and extremely industrialised states, one ( A°zmir ) is in south-west, and the others ( DiyarbakA±r, AzanlA±urfa, and KahramamaraAY ) are in the south-east and less industrialised ( compared to those mentioned above ) states.

Based on the information shown in these maps, it can be concluded that installed capacities of electricity coevals workss are clustered harmonizing to two factors: industry, the instance of the north-western states and handiness of natural resources, the instance of the south-eastern states.

In fact, when the lower degrees are considered, such as 3rd degree ( xanthous coloring material ) , the finding factor becomes chiefly the handiness of natural resources. In Turkey this refers to the cardinal states and the 1s on the south seashore.

14.3.3 Electricity Consumption in Turkey

Visual image of electricity ingestion of all sectors is performed for the informations between 1997 and 2006. Although the survey comprises 10 old ages of information, merely those from 2006 ( Figure 14.5 ) are presented here. The difference between eastern and western states is clearly seen. The form shows an unreal separating line between north-west and south-east which diagonally crosses the cardinal Anatolia.

To the left of this line, five high ingestion bunchs are observed which are located in the north-west, west, cardinal, southern seashore, and environments of the south-eastern seashore. The chief features of these states are that they are harboring energy intensive industries, such as Fe and steel, cement, and have high population densenesss. In fact, more than 70 % of the state ‘s entire population live in these constellating parts located on the left of the above mentioned line.

They besides produce about 86 % of Turkey ‘s GDP and accounted for 86 % of the entire electricity consumed in Turkey ( ain computations based on TUA°K 2008 and TEDAAz 2009 ) .

Fig. 14.5. Electricity Consumption ( kWh ) in 2006

On the right of this line, there are two constellating groups that belong to the low electricity consuming states, most of which have less population and less industrialisation. The chief industries are agribusiness and basic commercial activities in all the states of this group.

It should besides be highlighted that a few states in this group have a higher population than those with similar provincial economic features in the West ; nevertheless, their ingestion figure is well different. This consequence is observed largely due to the really high electricity escapes in these states e.g. DiyarbakA±r ( TUA°K, 2008 ) .

14.3.4 Sectoral Electricity Consumption in Turkey

Based on the available ingestion informations, the sectoral electricity ingestion have besides been visualized for 10 old ages from 1997 to 2006 but merely two of them i.e. 1997 and 2006 are presented here ( Figures 14.6 and 14.7, severally ) . The pie charts on these figures show merely the states that have ingestion more than 900,000 kWh for 1997 and 1,500,000 kWh for 2006.

Additionally, the antecedently mentioned line, which divides the state into two based on ingestion ( see subdivision 14.3.3 ) , is besides recognized in these figures. The larger the size of the pie chart the more electricity is consumed in the shown state.

These maps are utile because they provide information sing the alterations in sector size during the 10 old ages period. For case, the industrial sector electricity ingestion in Istanbul, which is the largest pie chart on north-west ( figure 14.6 ) , is about half of the entire ingestion in 1997, whereas in 2006 it represented merely 1/3 ( Figure 14.7 ) . This may be due to the switch in Istanbul ‘s economic activity focal point from energy intensive industry to the service sector.

When the ingestion in Ankara, the capital metropolis of Turkey, is considered it can be observed that neither industrial nor domestic sector has changed significantly during the same period. On the other manus, industrial activities can be considered as the chief factor set uping electricity ingestion in the central-southern portion of the state, as shown in Figure 14.6 and 14.7.

Fig.14.6. Sectoral electricity ingestion ( kWh ) in 1997

In contrast to the bunchs of high electricity devouring states in the West, the low electricity consuming states are located on the right of the line traversing from north-west to south-east that is discussed in subdivision 14.3.3. However, by and large the charts show that the fraction stand foring Industrial energy ingestion is similar to that of the larger consumers.

From this it might be concluded that the nature of the spacial form of electricity ingestion is a bunch which is straight related to industry type ( energy intensifier, high graduated table or services ) and population. In other words, the determiners of ascertained spacial form in Turkey are type of commercial activity and size of population ( see subdivision 14.3.3 ) .

Fig.14.7. Sectoral electricity ingestion ( kWh ) in 2006

14.4 Exploration

Visual image allows determination shapers to hold on the spacial nature of the phenomena under probe. However, researching spacial informations gives better penetration into the information for indentifying spacial forms and relationships. Exploratory spacial informations analysis methods are implemented in order to look into, in deepness, the spacial forms of, and correlativities between, electricity ingestion and supply.

These methods are utile to look into the cogency of the decisions derived from visualized informations given in subdivision 14.3. By utilizing the geographic expedition techniques, the analyses become more nonsubjective and, therefore, can by and large be used with higher assurance as a determination support tool during policy-making and direction procedures.

Exploration of spacial information has fundamentally two constituents: geographic expedition of first order effects and geographic expedition of 2nd order effects. The first order effects relate to planetary tendencies in the informations, while 2nd order effects refer to spacial correlativities. In this survey, Kernel Density Estimation, detailed in subdivision 14.4.1 is used for the first order geographic expedition.

As an option to Kernel Density Estimation, quadrat analysis can be used, where the survey country is divided into certain size quadrats and the figure of events ( sum of electricity ingestion, power workss etc. ) in each quadrat is mapped. However, in this attack it can be hard to specify an appropriate quadrat size.

As for the 2nd order analysis, the Local Moran ‘s I developed by Luc Anselin ( subdivision 14.4.2 ) and Getis-Ord Gi Hotspot Analysis statistics ( subdivision 14.4.3 ) are used as they give local graduated table correlativities. The 2nd order effects can besides be explored by the Moran ‘s I and Geary ‘s C correlativity steps.

These steps give the spacial correlativity throughout the whole survey country ; nevertheless, for a state graduated table application this is inappropriate as it does non reflect local spacial correlativities. For this ground local spacial correlativity steps were selected as the 2nd order geographic expedition methods.

14.4.1 Kernel Density Estimation

The Kernel Density Estimation ( KDE ) method has received considerable attending in the field of nonparametric appraisal of chance densenesss ( Wu and Mielniczuk 2002 ) and is besides a popular technique for analysing one and two dimensional informations ; see Scott ( 1992 ) , Simono ( 1996 ) for illustrations. There are two types of KDE maps: fixed and adaptative meats. The fixed meat map which is normally less computationally intensive utilizations an optimum spacial meat ( bandwidth ) over the infinite.

However, it can bring forth big local appraisal discrepancy in countries where informations are thin, and may dissemble elusive local fluctuations in countries where informations are heavy ( Fotheringham et al. , 2002 ; Paez et al. , 2002a, B ; Luo and DennisWei, 2009 ) . On the other manus, the adaptative meat map ensures a certain figure of close neighbors as local samples, and better represents the grade of spacial heterogeneousness ( Luo and DennisWei, 2009 ) . In this survey, the adaptative meat map was used. The general signifier of KDE is shown in equation ( 1 ) and is performed utilizing Spatial Analyst Tool in ArcMap 9.3.

( 1 )

where K ( ) is the meat, is the bandwidth, and the adaptative bandwidths is taken as:

( 2 )

where is known as the sensitiveness parametric quantity, and is the geometric mean of the pilot estimations at each.

KDE can rapidly and visually place hot spots from big datasets and hence supply a statistical and aesthetically satisfactory result ( Anderson, 2009 ) . It is used as the first geographic expedition attack for analyzing 2008 electricity coevals and 2006 electricity ingestion informations of the 81 states in this survey. Four different meats bandwidths are selected based on test and mistake attack. However, merely the result of a 225 kilometer bandwidth map ( see Figures 14.8 and 14.9 ) gives more realistic consequence.

Fig.14.8. Electricity coevals capacity in 2008 ( units in the fable are km, bandwidth: 225 kilometer )

Since KDE is used for happening the strength values of a given form, it does non affect any statistical illation and significance degree can non be assessed. As seen in Figure 14.8, four bunchs consisting the electricity coevals capacities of the Marmara Region can be clearly seen with Istanbul being at the Centre, the cardinal Aegean Region, the country around A°zmir, and the western portion of the South-east Anatolian Region, covering DiyarbakA±r, AzanlA±urfa, KahramanmaraAY , and Adana and Osmaniye.

Figure 14.9 shows the Kernel map consequence of electricity ingestion in 2006. There are four bunchs throughout Turkey. The first and the most outstanding among them is located in the north-western part. The 2nd bunch is seen on the Aegean seashore, in the West. The 3rd bunch is observed in the western portion of the South East Anatolia. The 4th bunch is seen in Central Anatolia around Ankara, the capital of the state.

Fig. 14.9. Electricity Consumption in 2006 ( units in the fable are km, bandwidth: 225 kilometer )

14.4.2 Anselin Local Moran ‘s I

Anselin Local Moran ‘s I is used for analyzing the autocorrelation of states based on electricity ingestion. A positive value for I indicates that the characteristic is surrounded by characteristics of similar values and vice-versa. In its most basic signifier, Anselin Local Moran ‘s I takes the undermentioned equation ( Bailey and Gatrell, 1995 ; Anselin, 1995 and Mitchell, 2005 ) .

( 3 )

where is an property for characteristic is the mean of the corresponding property, is the spacial weight between characteristic and ;

( 4 )

with comparing to the sum of characteristics.

The – mark for the statistics are computes as:

( 5 )

( 6 )

( 7 )

This analysis is performed utilizing the Spatial Statistics Tool of ArcMap 9.3. Figure 14.10 show the positive and negative autocorrelation of the states and their neighbors with regard to electricity consumed. The darker the coloring material the more positive the recorded autocorrelation is for a state and vice-versa. In this same figure, four positive autocorrelation bunchs are observed.

The most obvious among them is the 1 in the north-western portion of the state, including Istanbul and so followed by the 1 on the western seashore. These states are characterised by a developed industrial sector and high population densenesss. Therefore, they have higher electricity ingestion compared to the staying two bunchs located in the northern and north-eastern portion of the state.

On the other manus, two bunchs of negative autocorrelation ( lighter coloured group ) are observed, one of which around the cardinal portion, including Ankara the capital of Turkey and the south-eastern portion of the state.

Fig. 14.10. Anselin Local Moran ‘s I analysis for electricity ingestion in 2006

14.4.3 Hot Spot Analysis ( Getis-Ord Gi* )

Gi ( vitamin D ) and Gi* ( vitamin D ) statistic is described by Ord and Getis ( 1995 ) for the survey of local forms in spacial informations. Ord and Getis indicate the extent to which a location is surrounded by a bunch of high or low values. In other words, this statistics shows countries where higher-than-average values tend to be found near each other or vice-versa.

Positive values indicate bunch of high property value locations and negative value indicates constellating of low property value locations. The more positive or negative the value, the more important the consequences are. Equation 8 gives the usual signifier of Getis-Ord Gi* ( Bailey and Gatrell, 1995 ; Mitchell, 2005 and Scott and Warmerdam, 2005 ) . The analysis is carried out utilizing Spatial Statistics Tool of ArcMap 9.3.

( 8 )

where is the attribute value for characteristic is the spacial weight between characteristic is equal to the entire figure of characteristics and:

( 9 )

( 10 )

Gi* is utile in placing the spacial footmark of bunchs ( electricity ingestion ) since it examines forms of co-location, or bunchs, across areal unit boundaries within a specified vicinity. In contrast, other steps, such as location quotients, examine merely the value for a individual areal unit without giving mention to values in neighboring countries ( Smith et al. 2007 ) .

Since the most recent informations should be plenty to find the most recent hot topographic point locations of high electricity ingestion, informations of 2006 is used for this analysis ( Figure 14.11 ) . The map shows that Istanbul, in the north-western corner of the state, is the most important hot topographic point location of electricity ingestion in 2006 with more than 99 % assurance.

This this shows is that there is a high opportunity that the country can be considered a “ hot-spot ” . Four states with the 2nd biggest circles, two of which are north-west neighbors of Istanbul, one of which is in the West and the 4th is the capital in the Centre of the state, create the 2nd important hot topographic point group with a assurance degree of more than 95 % .

The common characteristics of these states related to the electricity ingestion, are a high grade of industrial activity, particularly high energy intensive and high capacity industries, and high population densenesss as discussed in visual image subdivision ( subdivision 14.3 ) .

Last but non the least, the unreal separating line between north-west and south-east as discussed in subdivision 14.3.3 is besides observed in this analysis clearly.

Fig.14.11. Getis-Ord Gi* Analysis of Electricity Consumption in 2006

14.5 Decisions and Outlook for Further Study

The survey revealed that thermal, hydro and natural gas power workss are the three chief types of installed electricity coevals capacity in Turkey. In comparing, geothermic and air current beginnings have the least part and as such they are yet to be to the full exploited.

Sing the nature of ingestion form, the chief determiners impacting the ascertained form are seen as industrialisation degree of states and type of the industries, such as high energy intensive and high capacity mills owned by multi-national or national retentions or little and average sized endeavors with low efficiency and competitory substructures.

In add-on, the considerable impact of population on electricity ingestion has besides been observed as a consequence of analyzing entire sectoral ingestion sum of the available 10 old ages informations.

The deductions of a continuance of the present development in energy usage forms might be concluded as a well big difference in electricity ingestion form between eastern and western parts. Hence, high demand bunchs locate chiefly in the north-western part, Marmara Region, and the other large metropoliss in the western and cardinal parts of the state, while low demand bunchs are seen in East Anatolian and north-eastern states, on the Black Sea seashore.

In fact, even the drawn-out survey, of which merely a really brief summery is given in this paper, on electricity coevals and ingestion informations between 1997 and 2008 is still non plenty for good developed, effectual energy direction and decision- devising. The ground for this is that the results of this survey do non give any information sing possible alterations in approaching old ages.

Therefore, projection of future coevals and ingestion figures should be done utilizing a theoretical account that should likely utilize ingestion and coevals informations every bit good as population and GDP of states, lifetime of current installations and consideration of the installations planned and under-construction. If such a projection could be developed so the consequence of this survey and besides consequences of projection would be really effectual tool for energy direction and decision-making on energy policies in Turkey.