1 Electric Residential Load Growth in Kabul City-Afghanistan For

Electric Residential Load Growth in Kabul City-Afghanistan for Sustainable Situation

A thesis presented to

the faculty of

the Russ College of Engineering and Technology of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Mohammad S. Sharifi

November 2009

This thesis titled

Electric Residential Load Growth in Kabul City-Afghanistan for Sustainable Situation

MOHAMMAD S. SHARIFI

has been approved for

the Department of Electrical Engineering and Computer Science

and the Russ College of Engineering and Technology by

______

Jeffrey J. Giesey

Associate Professor of Electrical Engineering and Computer Science

______

Dennis Irwin

Dean, Russ College of Engineering and Technology

Abstract

SHARIFI, MOHAMMAD S., M.S., November 2009, Electrical Engineering

Electric Residential Load Growth in Kabul City-Afghanistan for Sustainable Situation

(104 pp.)

Director of Thesis: Jeffrey J. Giesey

Load growth/forecasting have become a significant importance research area to the operation of electricity organizations whether they are governmental or non- governmental entities. It enhances the reliability of the power system and energy efficiency. This thesis presents a study of residential load growth in Kabul City for a sustainable condition. The main stages are the analysis of data and information received from the semi-structured interview with authorities at the Ministry of Energy and Water, De Afghanistan Brishna Mossessa (DABM), power companies involved in distribution system, household drop-off survey, 30-min kWh demand data, and the historical data received from DABM. Eventually, it was found how much power does a customer consumes now and an appliance-based approach was initiated to investigate the kWh will be consumed by a residential customer on sustainable condition. Consequently, these results determine the number of customers to be connected to distribution transformers.

Approved: ______Jeffrey J. Giesey

Associate Professor of Electrical Engineering and Computer Science

Acknowledgements

As this thesis work goes essentially into its end, I am thrilled to extend my sincere and appreciation to my thesis advisor Dr. Jeffrey J. Giesey for his continued and generous support, help, and guidance through these years. I would also like to thank my thesis committee members, Dr. Savas Kaya, Dr. Wojciech M. Jadwisienczak, and Dr. Xiaoping Shen for their kind support. I have always believed that education is the foundation for a sustainable development in Afghanistan, and with that in mind I would like to thank all the outstanding professors of Ohio University from whom I have had the privileges to learn. That would be their great contribution for the rehabilitation of the war-torn Afghanistan, as cash injection proved to not be a sustainable solution. I also should thank my friends, collogues, former students, and authorities in Ministry of Energy and Water (MEW) and De Afghanistan Brishna Mossesa (DABM) for their endless support and provision of data. They have provided me with data and information I needed. It is also worth to thank Dr. Shad M. Sargand for his benevolent efforts to initiate my program and my sincere thanks to USAID for its financial support through the years of my study. Last but not least, my deepest appreciation to my family, my beloved mom and my dearest brothers. You are the ones who have taken care of my children and have helped me “stretch my dreams” to attain what I always thought to obtain. My three sons and two daughters are also greatly appreciated for their extra measure of patience, love, and support.

Table of Contents Page

Acknowledgements ...... 4

List of Tables ...... 8

List of Figures ...... 9

Chapter 1: Power Development in Afghanistan ...... 10

1.1 Introduction ...... 10

1.2 Overall Power System in Afghanistan ...... 11 1.2.1 Generation ...... 11 1.2.2 Transmission Line ...... 15 1.2.3 Distribution System ...... 16 1.2.4 Power Utility in Afghanistan ...... 17 1.2.5 Rural Electrification ...... 19

1.3 Power System in Kabul City ...... 21 1.3.1 Generation ...... 21 1.3.2 Transmission Line ...... 26 1.3.3 Distribution System in Kabul City ...... 27 1.3.4 Power Utility in Kabul City ...... 30

1.4 Demand for Electricity ...... 31

1.5 Objectives ...... 31

1.6 Outline of the Thesis ...... 32

Chapter 2: Power System Load and Load Forecasting ...... 34

2.1 Power System Load Models ...... 35 2.1.1 Static Load Models ...... 36 2.1.2 Dynamic Load Model ...... 38

2.2 Load Forecasting ...... 41 2.2.1 Short Term Load Forecasting (STLF) ...... 41 2.2.2 Medium and Long Term Load Forecasting (MTLF/LTLF) ...... 42

2.3 Factors Affecting Load ...... 43 2.3.1 Weather Factor ...... 43 2.3.2 Time Factor...... 43 2.3.3 Customer Class Factor ...... 44 2.3.4 Economical Factors ...... 46

2.4 Load Forecasting Method ...... 46

2.5 An Overview of Distribution Systems ...... 47

2.6 Aspects of the Loads ...... 51

2.7 Allocation of Load to Distribution Transformers ...... 53 2.7.1 Application of Diversity Factor ...... 53 2.7.2 Load Survey Method ...... 54 2.7.3 Transformer Load Management Method ...... 54 2.7.4 Metered Feeder Maximum Demand Method ...... 55

Chapter 3: Research Procedures ...... 56

3.1 Introduction ...... 56

3.2 Data Gathering ...... 56 3.2.1 Semi-structured Interviews with Relevant Parties...... 56 3.2.2 Household Drop-off Survey ...... 57 3.2.3 Meter-based Survey ...... 59 3.2.4 Data Received from DABM ...... 60

Chapter 4: Result ...... 61

4.1 Data Process ...... 61 4.1.1 Results from the Interview with MEW, DABM and Relevant Parties ...... 61 4.1.2 Household Drop-off Survey Result ...... 63 4.1.3 Meter-based Survey Findings ...... 67 4.1.4 Analysis of Data from DABM ...... 73 4.1.5 Appliance-based Approach ...... 76

4.2 Limitations of the Study...... 87 4.2.1 Availability of Data ...... 87

4.2.2 Blackouts ...... 87 4.2.3 Economics ...... 87 4.2.4 Short Period of Study ...... 88 4.2.5 Definition of Customer ...... 88

Chapter 5: Conclusion and Recommendations ...... 89

5.1 Summary of the Methodology ...... 89

5.2 Summary of the Results ...... 90

5.3 Limitations ...... 92

5.4 Conclusion ...... 92

5.5 Future Research ...... 95

Bibliography ...... 97

Appendix A: Daily Power Production [5] ...... 99

Appendix B: Household Drop-off Survey Sheet ...... 101

Appendix C: Tables ...... 104

List of Tables

Page Table 1. 1: Co-investment power plants list...... 12 Table 1. 2: Summary of power plants in Afghanistan [5]...... 13 Table 1. 3: Summary of Transmission Lines in Afghanistan (DABM)...... 16 Table 1. 4: Customers statistics for DABM [5]...... 18 Table 1. 5: List of standby emergency generators...... 24 Table 1. 6: Summary of Power plants Supplying Power to Kabul City...... 25 Table 1. 7: Kabul annual production since 2006 by power plant (in MWh)...... 26 Table 1. 8: Complete list of substation in Kabul City...... 29 Table 2. 1: Common values for α and β , for different load components...... 38

Table 2. 2: Static characteristics of load components...... 40 Table 2. 3: Sample characteristics of different load classes...... 40 Table 4. 1: Different customer class statistics from 2003 to 2008...... 62 Table 4. 2: 30 minute kW demand for 15 customers (July 24, 2009)...... 68 Table 4. 3: kWh usage categorized list...... 73 Table 4. 4: kWh usage for residential customers in different locations...... 75 Table 4. 5: Common standard household appliance energy usage...... 78 Table 4. 6: High standard household appliance energy usage...... 79 Table 4. 7: Monthly kWh consumed by each residential group...... 85

List of Figures Page

Figure 1. 1: The map of Kabul province with the power plants...... 21 Figure 1. 2: The location of power plants for Kabul City [5]...... 27 Figure 2. 1: One line diagram of a power system...... 35 Figure 2. 2: The bus load of a power system network with its components [8]. ...39 Figure 2. 3: Example of typical daily variation of electric load in Kabul...... 45 Figure 2. 4: Major power system components in Kabul city...... 48 Figure 2. 5: An improved substation scheme in Kabul...... 49 Figure 2. 6: Layout of feeders. (a) Radial layout. (b) Loop layout [21]...... 50 Figure 2. 7: A sample of single-phase lateral...... 54 Figure 4. 1: DTs and power allocation for different customer class in Kabul...... 62 Figure 4. 2: Lighting source for residential customers...... 64 Figure 4. 3: Heating source during the winter...... 64 Figure 4. 4: Energy sources for cooking...... 65 Figure 4. 5: Availability of power for residential customer...... 66 Figure 4. 6: Twenty four-hour demand curve customer # 4...... 69 Figure 4. 7: Twenty four-hour demand curve for customer # 7...... 70 Figure 4. 8: The diversified demand curve...... 72 Figure 4. 9: The load duration curve...... 72 Figure 4. 10: Customers versus range of kWh consumed...... 74 Figure 4. 11: kWh versus customer for 7578 customers in 22 residential areas. ....76 Figure 4. 12: The Average high and average low temperature for Kabul City...... 77 Figure 4. 13: Monthly consumption of both residential groups...... 86

Chapter 1: Power Development in Afghanistan

1.1 Introduction

Afghanistan is one of the least developed countries in the world with minimum access to electric power. Less than 10% of the entire population has access to modern energy services and electricity. The consumption of electricity is estimated to 67.3 kWh per capita for 2009, while it was less than 40 kWh in 2008 and the previous years (A. Waheed Qaium, De Afghanistan Brishna Mosesa, in discussion, August 10, 2008). From 1919, when Afghanistan emerged as an independent country, until 1978, when the legitimate government collapsed through a coup, the country had a slow and increasing development trend. Access to education, water and power, and social services such as, health and transportation improved at an acceptable level. Yet, during the last three decades (1978-2002), nearly two million were killed as a result of war and conflict, one million were disabled, at least two million were internally displaced, over six million forced to leave the country [1], and many more have been psychologically traumatized as a result of war.

In addition, this devastation contributed to the deaths of tens of thousands of children and mothers from hunger and disease. Recent drought, along with the UN sanctions imposed against Afghanistan in 1999, made the situation worse for Afghanistan in all aspects. The infrastructure had received little attention. The power system like many other entities received serious damage.

With the restoration of appropriate peace and security since 2002, the legitimate government of Afghanistan has begun to build up everything from the grass roots. Short and medium term development plans have been initiated to gradually improve life in Afghanistan.

Electricity available at affordable prices has always been considered one of the key elements for growth and development of Afghanistan [2]. Many activities have taken place to improve the system. Hundreds of kilometers of transmission line have been extended, tens of generator sets have been set up to solve the issue in short time, and most of the generation plants have been renovated [2]-[4].

In this chapter power development in Afghanistan is discussed. First, the status of the overall power system across the country is presented; in particular, the power system supply, distribution, and power authorities are highlighted.

1.2 Overall Power System in Afghanistan

1.2.1 Generation

In 1893, the first power generation operated in Afghanistan. The capacity of the generator was only 8 kW which set up in the king Amir Abdurahman Khan Palace. The generator was known as 40 bulb generator, because it was able to turn on 40 bulbs. Later, in 1911, a 20 kW steam power generator was set up in the king’s palace in Kabul. Five years later the third power generator with 19 kW was put into operation in Jalalabd province. Then in 1917 another small generator was operated in Paghman district of Kabul [2].

The construction of the first hydropower plant was started in 1915 and completed in 1920. The power plant is known as Jabel Saraj hydropower plant and has 3 turbines, each with a capacity of 500 kW. At that time, its voltage was stepped up to 44 kV and transmitted to Kabul city through 16 mm round solid copper conductors. Road accessibility to the project site was difficult and hence all the construction materials were carried by elephants to the project site. Almost contemporary to that, three other micro hydro generating turbines 20 kW, 60 kW, and 20 kW were installed in Paghman district of Kabul, Jalalabad, and in Kandahar respectively. Although, the installed projects were small ones in terms of size, they attracted great attention of the government and the industry.

It was 1935 that the government of Afghanistan developed a strategic plan and regulations for investment in industry. Those rules eased the way for those who were interested to invest in this area. Since for any type of industry the prime mover was electric power, many activities were placed and as a result, the following power plants as seen in Table 1.1 were built.

Table 1. 1: Co-investment power plants list.

Installed Name of Power Year No Type Location capacity in Plant built (kW)

1 Baba Wali Hydro Kandahar 330 1935

2 Chalwarcha Hydro Heart 80 1936

3 Dieseli Kabul Diesel Kabul 300 1937

4 Pashtoon Co Diesel Kandahar 150 1937

5 Genopress Diesel Kandahar 500 1938

6 Pahmina Bafi Diesel Kandahar 300 1938

7 Etehadia Punba Diesel Kundoz 500

8 Chak Wardak Hydro Wardak 3360 1940

9 Pulkhomry Hydro Baghlan 4800 1941

10 Qand Baghlan Steam Baghlan 1200 1943

The power plants listed in Table 1.1 were all joint projects. The government invested 51% and the private sector 49%. Yet, this practice did not work well in Afghanistan because the counterparts loitered in their performance and in late 1939, the government of Afghanistan decided to nationalize the power system. As a result, the following projects have been implemented.

Sorobi, the first major hydropower plant with 22 MW installed capacity was built in 1946. Along with load growth, there has been a gradual increase in the number of power plants also. In the years following, a number of other projects such as the Mahipar, Naghlo and Kajaki hydropower plants, Sheberghan, Badambagh and Pulcharkhi thermal stations were surveyed, designed and implemented. Today over 20 installed small and mini-hydropower plants, two thermal power plants, and over 30 diesel generators are functioning all over the country. Table 1.2 shows a complete list of these projects with their locations.

Table 1. 2: Summary of power plants in Afghanistan [5].

Region Power Plant Type Capacity

Jabelsaraj Hydro 2.2 MW

Pul-e-Khumri I Hydro 4.8 MW

Pul-e-Khumri I Hydro 9.0 MW

Andkhoy Imported 8.0 MW

Sar-e-Pul Imported 2.0 MW North Kondoz Imported 12.8 MW

Mazar Sharif Imported 25.6 MW

Aybak Thermal 1.76 MW

Faryab Imported 56 MW

Asadabad Hydro 0.7 MW

Sorobi Hydro 22.0 MW

Darunta Hydro 11.5 MW

Mahipar Hydro 66.0 MW

Naghlu Hydro 100.0 MW East Ghazni Thermal 1.6 MW

Khost Thermal 0.96 MW

NW Kabul 3 Thermal 22.0 MW

NW Kabul 4 Thermal 23.0 MW

Heart Imported 25.0 MW

West Zaranj Imported 4.0 MW

Table 1.2: (continued).

Region Power Plant Type Capacity

Grishk Hydro 2.4

Kajaki 1 Hydro 16.0 MW

Kajaki 3 Hydro 16.0 MW

Lashkargah Thermal 3.0 MW

Paktia Thermal 0.82 MW

Kandahar Thermal 10.2 MW

Qalat Thermal 3.4 MW

Trinkoot Thermal 1.0 MW

From the list shown in Table 1.2, the hydropower plants and NW Kabul 3 and NW Kabul 4 thermal power plants have been built 1946-1982. The thermal projects and imported electricity have been introduced since 2003. As it has been observed, most of the thermal plants are diesel generators ranging from several hundred kilo watts to several mega watts. Diesel generators are the easiest ways to address the problem of electrification in provinces. Yet, they are an expensive and air polluting technology.

In spite of the efforts of the government of Afghanistan and the international community, still the electric power does not have wide coverage. Only 10% [2] of the entire population has access to limited electric power. The consumption rate is as low as 67.3 kWh per year per capita (2009), while the consumption rate, for example in the USA, is 13,351 kWh, in China is 1585 kWh, in Pakistan is 425 kWh, and in Iran is 2035 kWh per capital per year [6].

To summarize this section, the power development in Afghanistan can be studied in three periods; before 1978, from 1978 to 2003, and after 2003. The capacity to generate electric power steadily increased from 59 MW in 1961 to 318 MW by

1978. Electric power generation also progressively improved from 127 GWh in 1961 to 840 GWh in 1978. From 1978-2003, only one thermal power plant was built in Kabul to serve during the peak loads. In spite of that, these were known as the decades of power degradation. Blackout were frequent on those years, less attention were paid to power plant. From 2003 to 2009, there has been a significant improvement. The daily report of the De Afghanistan Brishna Mossesa (DABM) shows a 5,381 MWh (2034 MWh from hydropower plants, 216 MWh from thermal stations, and 3131 MWh imported electricity) production. However, still Afghanistan has one of the lowest power consumption and generation all over the world and yet has many intact natural resources.

1.2.2 Transmission Line

With the completion of Kajaki 1 hydropower plant in 1975 until 2004, there was no significant progress on the transmission line, the line connecting the generation substations to distribution substations. Most of the thermal stations that have been installed do not need any transmission line because they are located on the spot. However, with the introduction of imported power from neighboring countries, transmission lines have been largely extended in the last couple of years. Currently, there are transmission lines carrying five different voltages; 20, 35, 110, 132, and 220 kV. A single circuit, 500 kV transmission line, has also been planned at Aqina substation on the border between Afghanistan and Turkmenistan with Andkoy substation to the north of Afghanistan. The length of this transmission line is estimated to be 40 Km. The existing and planned transmission lines are shown in Table 1.3.

Table 1. 3: Summary of Transmission Lines in Afghanistan (DABM). Line Voltage Line Length (km) Type Size (mm2) (kV) Existing Planned 20 246 ACSR 120/300 35 142 ACSR 50/70/95/120/150/185 110 1062 545 ACSR 70/95/120/185/300 132 148 ACSR 288 220 443 1320 ACSR 300 500 40 ACSR 300 Total 2041 1905

Note that some of the lines are already designed to meet the expansion of the project in the future.

1.2.3 Distribution System The distribution system in Afghanistan was influenced by the ex-Soviet Union. This influence may be considered for two reasons: most of the power engineers had studied in Russia or have graduated from the Polytechnic University of Kabul which was supported by the ex-Soviet Union, and secondly they have funded most of the projects in the past. During the last three decades, the system has been seriously damaged either due to low maintenance, overloading or war. The system is a centralized system and the distribution voltage is mainly 15/20 kV. However, 6 kV system had also been operated. The distribution network includes stepped down substations, junction stations, primary feeders, distribution transformers (DT), secondary feeders, and meter boxes. The junction stations do not involve any transformers. They only manage and control the load of the feeders of the residential, commercial, and industrial areas. The primary feeders have been extended both as overhead lines and underground lines. Distribution transformers are spread across the coverage area. The size of DTs ranges from 400 KVA to 800 KVA. For some places more than one DT exist. The majority of DTs are indoor pad mounted transformers. However, pole mounted transformers also have been installed whenever an unplanned load growth has been reported. The

DTs step down the medium voltage to three-phase low voltage. The frequency across the power system is 50 Hz. Since 2003, the distribution system has received particular attention. The overall system has been revised and redesigned by the Ministry of Water and Energy (MEW) which is in charge of power and water in Afghanistan, power utility, and donor agencies. In the next section the power utility is discussed.

1.2.4 Power Utility in Afghanistan

At the beginning, in 1910, the precedent government of Afghanistan encouraged private sectors to join Sherkat Barq (Power Corporate Co) initiated by the government. Sherkat Barq was in charge overall of the power industry. From 1910 to 1956, Sherkat Barq experienced many leaps and bounds with its counterparts regarding their proportion of contributions. Eventually, in 1956, the partners’ contribution came to a minimum and most of the decisions were made and controlled by the government. In 1956, Sherkat Barq altered its name to De Afghan Brishna Mosessa (DABM). Since that, DABM is controlling all the three major components of the power system. DABM is a non-profit governmental entity directed by the MEW. Since the alteration, DABM has always been growing. In recent years, DABM has been expanded to provincial branches and continues to reach the district level too. DABM is controlled by a director who has four deputies: planning deputy, technical deputy, administration deputy, and accounting deputy. In addition to four deputy directors, there is an advisory board. The advisory board consists of two to three foreign expatriates, Afghan expatriates, and representatives of the international power companies involved in power system in Afghanistan. Currently, DABM has over 6,000 employees and an electricity presidency in almost every province. Each presidency is responsible for the management of distribution system in its province and reports to DABM. Most of the employees either are poorly educated or have not been trained well e.g. graduated from upper secondary technical school. Due to the lower salary scale ($ 50 per month), DABM has not been able to recruit power engineers and experts yet (A. W. Sherzai, deputy minister for MEW at in interview with Noor TV, Jun 5, 2009). On the other hand, the number of customers of DABM

18 has almost tripled since 2003 as shown in Table 1.4 with different categories of customers.

Table 1. 4: Customers statistics for DABM [5]. Customer No 2003 2004 2005 2006 2007 2008 Class 1 Residential 200,511 248,062 368,983 391,683 502,856 574,380 2 Commercial 23,574 25,809 34,030 30,888 39,811 47,139 3 Government 3,769 3,796 2,988 3,187 4,208 4,950 4 Holy Places - - 4,026 4,449 2,699 3,941 5 Industrial - - - 2,399 1,688 2,620

Total 227,854 277,667 410,027 432,606 551,262 633,030

Table 1.4 includes all customers of DABM in Kabul and all provinces. As we see, the number of customers in the residential category has increased significantly while this trend has not occurred for other customer classes. Almost one third of the customers are located in Kabul. DABM has been challenged with many technical and non technical problems. These challenges include provision of adequate training for its technicians and administrative staff, building the capacity of its power engineers with up to-date power knowledge, recruiting qualified engineers and experts, renovation of the entire system, increasing accessibility to grid power, improving the billing and tariff system, prevention of electricity theft, loss reduction, and customers’ satisfaction. Many, feasibility studies of the potential hydroelectric sources, and other technical issues exist there. The customer satisfaction involves several major elements such as power quality and reliability, customer service, billing and payment, and price. None of these has received much attention. At present, DABM cannot provide 24 hour service for its customers. It ranges from 6 to 18 hours daily. The voltage at the customer end is not stable and fluctuates from 160 to 220V. DABM is not able to read and record the meters on a regular basis, send tariffs to the customer on time, or collect service charges as soon as the tariffs are released.

Currently, electricity prices depend on the customer class and kWh consumed per meter reading period (two months is defined as one period). For example, for residential customers up to 300 kWh per period, the rate is AF-1.5 (AF is Afghan currency, Afghani and 1 AF is equal 2 US cents) per kWh. From 301-700 kWh per period is charged AF-4 per kWh. Above 700 kWh per period, the consumers are charged AF-6 per kWh they consume. For the commercial customers, governmental, nongovernmental, and foreign agencies the price is AF-10 per kWh for whatever kWh they consume in a period. Finally, for the registered industries, the price is AF-6 per kWh per period (Resolution No. 14, dated 07/06/2006, the cabinet, Afghanistan). DABM sells electricity on a subsidized policy. At the time of research for this thesis, negotiation and discussion was going on to alter DABM to a state owned entity, De Afghanistan Brishna Sherkat (DABS). The shareholders for DABS would include MEW, Ministry of Finance, and Ministry of Economy. DABS will operate in commercial affairs. It will stop the subsidized rate. The process is facilitated by the Inter-ministerial Commission for Energy (ICE), an Asian Development Bank (ADB) project. ICE consists of MEW, Ministry of Finance, Ministry of Economy, Ministry of Foreign Affairs, Ministry of Rural Rehabilitation and Development, and Ministry of Mine and Industry. ICE coordinates government policy in energy, leverages donor resources, and integrates sector planning (A. Waheed Qaium Director of DABM, in personal interview, August 2008).

1.2.5 Rural Electrification

Although nearly 65% of the population lives in rural area, they were totally ignored in the past because it was/is expensive to extend grid power lines to isolated remote rural areas. However, in the last six years, three initiatives have been launched to increase the rural dwellers to electricity; the governmental, non-governmental and individual efforts. In 2003, the Ministry of Rural Rehabilitation and Development of Afghanistan (MRRD) introduced a wide range project, National Solidarity Program (NSP). The aim of this program is to empower the communities to identify, plan, manage, and control their development projects. To increase the access of rural communities to

20 electric power is one of the objectives of the NSP program. NSP has developed two approaches to address this problem; distribution of solar panels and diesel generators. Generating power through solar panels is expensive and unaffordable for the vast Afghan communities. However, NSP with the financial support from the international communities has distributed almost a hundred thousand stand alone solar panels to the families in almost 1300 villages all over the country. The stand alone solar panels consisted of two packages 20 watts and 40 watts. Each package includes a solar panel, charger controller, and over current protection device. For the communities where the population is dense, NSP initiated the distribution of diesel generators program. The capacity of the generators varies from 10 KVA to 220 KVA. The allocation of generators is based on the number of families and the coverage area. This project has been implemented with the community participation. More information on these projects can be obtained from the MRRD website (www.mrrd.gof.af/nsp). In addition to solar panels and diesel generators, a number of other governmental organizations such as Ministry of Women’s Affairs distributed over twenty thousand solar lanterns to widow headed families and disabled people. In parallel with the government of Afghanistan, a number of international non- governmental organizations have also implemented rural electrification projects through installation of micro and pico hydro generating turbines in the range of 2 kW to 50 kW for the communities with access to sufficient supply of running water; and setting up small wind turbines (1-15 kW). The implementations of aforementioned projects have heated up interest increasing to electricity. The families have been encouraged to initiate their own efforts. As a result, most wealthy families in the rural areas have purchased 700 VA small Chinese generators. They are inexpensive and affordable. There exists no statistics to know how many families have their own generators. The quality of such generators is very poor. They are disposable ones and families need to apply for a new one every six to 10 months.

Since the concern of this thesis is the Kabul City distribution system, hence the remaining sections of this chapter are devoted to explain the condition of the power system in Kabul City.

1.3 Power System in Kabul City

Indisputably, we could say that Kabul is one of the few capitals whose population does not have 24 hour access to electric power. Originally, Kabul city was designed for a maximum of 500,000 people. All the facilities and services including the power system were considered for that population. Yet currently, it is estimated that over four million live in Kabul City. In this section, the generation of power, transmission of power, distribution, and involved parties are discussed.

1.3.1 Generation

To begin with generation, there are three hydropower plants, two thermal, and an imported power line supplying power for Kabul City. The three hydropower plants are: the Sorobi, Naghlo, and Mahipar. The two thermal stations are the North West Kabul and the Deh Sabz thermal power plants. Figure 1.1 is the map for Kabul province with the location of power plants. In this Figure H stands for hydropower plants and T stands for thermal power plants.

Figure 1. 1: The map of Kabul province with the power plants.

• Hydropower Plants Sorobi, the first larger hydropower plant was built on Kabul River 60 km to the east of Kabul in 1957. The voltage is stepped up on spot and transferred through a 110 KV transmission line to Kabul. It has two vertical Francis type turbines each with a capacity of 11 MW. The Sorobi plant has a small reservoir which is fed through the over flow and outflow of Naghlu dam. The water flows through a 700 m length penstock on the turbines. The expected life for this power plant was estimated to be 40 years. Since it was built rarely it has received regular maintenance. The efficiency of the plant has been reduced by 70%. Most of the essential parts are now old fashioned and will be expensive to replace. The plant has recently been rehabilitated by the Siemens Corporation financed by Kreditanstalt für Wiederaufbau, Credit Institute for Reconstruction (KFW) [5]. Mahipar power plant was constructed in 1967 on Kabul and Logar River to east part of Kabul. Its installed seasonal capacity is 66 MW. It has three vertical Francis turbines each having 22 MW capacity. The generated voltage is stepped up on the site and the power sent through 110 KV transmission line to Kabul [5]. There is no reservoir for the plant so it uses the current water. A 2200 m penstock connects the turbine with the dam. Its operation is seasonal and heavily dependent on the annual precipitation. The season is from October through May. However for five months of the year from December through April all three turbines can run. The plant has hardly received any service since it was built. In 2006, a German company rehabilitated one of the units and modernized a second one. Naghlu is the largest power plant in Afghanistan. It was commissioned in 1967 with ex-Soviet government support. The total installed capacity of the plant is 100 MW. The generated power from the plant is transmitted through a 110 kV a double circuit line to Kabul city. Naghlu has four Francis type vertical turbines. The plant has 45 m head and a large reservoir behind it. The reservoir was not protected well in the last two decades and is heavily affected by sedimentation. Due to deterioration, not regular maintenance, non-availability of the required spare parts, and limited service, the plant reliability has been questioned. However, some minor rehabilitation through aid from the World Bank took place. Yet, the plant needs an intensive renovation.

All three hydropower plants are extremely important for Kabul city. DABM is currently in negotiation with the donor agencies and World Bank for the replacement of the units which can no longer supply power. • Thermal Power Plants

After 1979, when the mass movement of people from the provinces to Kabul City due to security issues began, the three hydropower plants were not able to provide sufficient reliable electricity for the Kabul City. Addition to that, limited access to other energy forms also became a major concern. The government agreed with Asea Brown Boveri (ABB) of Switzerland for design, and set up two thermal stations; one in the North West (known as North West Thermal Power Plant) and the other one in the east of Kabul in Hootkhel area. Both projects were completed and put into operation in 1985. The east thermal power plant had two units (Unit one and Unit two) and was totally destroyed during the civil war. The North West Power plant has received minor damages. The plant consists of two gas turbines; Unit 3 and Unit 4, having a capacity of 21.8 MW and 23.2 MW respectively. The North West power plant is operating just during peak hours. The units were designed to operate with high speed diesel (HSD) and heavy fuel oil (HFO). At full load, each turbine consumes 160 liters per minute or 9,000 liters per hour HSD fuel.

Also, 100 MW diesel generator sets, provided through a fund by the United States Agency for the International Development (USAID) was built in north east of Kabul in Deh Sabz area. The plant consists of 15 units each with a capacity of 7 MW. Due to the high consumption of fuel this plant cannot operate 24 hours a day. Thus, it can only contribute to the peak hours if needed.

In addition to the aforementioned thermal power plants, in late 2006, the MEW decided to purchase a number of generators to keep up a number of important facilities (e.g., hospitals) and to run smoothly during a power blackout or any power failure. Eventually MEW purchased 25 standby small diesel generators from AKSA of Turkey. The generators are in different capacities and are scattered all over the MV and LV distribution networks. In Table 1.5 the list of emergency generators with their capacity are shown.

Table 1. 5: List of standby emergency generators.

Rating Quantity Total Output

1030 KVA 5 5,150 KVA

1130 KVA 11 12,430 KVA

1435 KVA 3 4,305 KVA

2200 KVA 6 13,200 KVA

Total 25 35,085 KVA

None of the backup generators is able to be operated for long periods due to poor quality, lack of fuel, poor safety features, and they cannot regulate the voltage properly. Furthermore, the generators are not synchronized with the distribution system. As a result most of them were sent to the provinces (A. Waheed Qaium, DABM, in discussion, August 10, 2008). • Import Power

The available hydropower plants, the thermal plants, and the standby generators were not able to supply sufficient power to at least be enough for the lighting system. However, a feasibility study on Kabul River to the east of Kabul shows that there is a potential for the construction of a fourth 100 MW power plant. For two reasons, this project has not been implemented yet; security and higher capital investment. Thus, the MEW and a consortium of donors, including USAID, the World Bank (WB), the ADB, the Government of India, and the Government of Germany launched an effort to import power from three northern neighboring countries; Tajikistan, Turkmenistan and Uzbekistan. The imported power is transmitted through the North East Power System (NEPS) program which is under implementation currently [6].

On January 20, 2009, 40 MW power on a 220 kV transmission line from Uzbekistan to Mazari Sharif and then to Kabul through Hindokush high mountains ranges was extended successfully.

The power flow from Tajikistan to Kabul through a 220 kV line has been planned in two stages. The first stage has already been launched and 100 MW is flowing everyday to Kabul. The second stage includes 80 MW and is supposed to be completed by the end of 2009.

According to another agreement, the Government of Turkmenistan is committed to export 300 MW power from Turkmenistan to Afghanistan by 2010/2011 through a 500 kV double circuit line. The project in terms of funding is a complex one. The estimated expected fund has not been fully secured yet.

This section is concluded with the Table 1.6 and Table 1.7. Table 1.6 shows the Summary of plants supplying Kabul City and Table 1.7 shows the annual production for the last four years. Note that the year 2009 covers up to the month of July.

Table 1. 6: Summary of Power plants Supplying Power to Kabul City.

Date of Rated Actual Station Type Construction Capacity Capacity

Mahipar Hydro Plant 1967 66 MW 44 MW

Naghlu Hydro Plant 1967 100 MW 100 MW

Surobi Hydro Plant 1957 22 MW 22 MW

North West Kabul Thermal Plant 1985 45 MW 45 MW

Deh Sabz Thermal 2009 100 MW 100 MW

Imported 140 MW

Total 451 MW

Table 1. 7: Kabul annual production since 2006 by power plant (in MWh).

Plant/Year 2006 2007 2008 2009

Naghlu 291,737 325,432 228,784 159514

Mahipar 42,081 71,680 76,426 98611

Sorobi 80,336 114,560 138,388 96733

North West 3 6,721 47,851 63,744 24848

North West 4 117,004 86,281 82,423 29755

DehaSabz 185

Imported 125058

Total 537,879 645,804 589,765 534,704

1.3.2 Transmission Line The transmission line for Kabul city can be studied in two categories; high voltage (HV) transmission line and medium voltage (MV) line. The HV line includes 110 kV line and 220 kV line. There are eight 110 kV lines: L-111, L-112, L-121, L- 141, L-142, L-143, L-144, and L-145. One 220 kV line exists. Lines L-111 and L-112 carry 110 KV through 185 mm2 conductors connecting Sorobi Power Plant and Breshnakot substation. The length of each line is 66 km. Line L-121 connects Naghlu and Sorobi through 185/120/95 mm2 conductors. Its length is 12 km. Lines L-141 and L-142 is extended from Naghlu power plant to the East substation. Its length is 55 km and the conductors are ACSR type with 185/120/95 mm2. L-143 connects the East and North substations through 27 km ACSR conductors having a 185/120 mm2 cross section. Line L-144 is between East and Breshnakot substations through 16 km conductor, having 185 mm2 cross section. Lines L-145a runs from East substation to Tower 27 with a length of 25 km. The conductor is ACSR and its cross section is 185/120 mm2. Line L-145b is between Tower 27—North 4 km length and the conductor is 120 mm2. Line L-145 links North with North West substation through 185 mm2 ACSR type conductor with a length of 9

27 km. Line L-142 hooks up Pulcharkhi substation with T-connection with a length of 5 km through ACSR 120-70 mm2 ACSR conductor [Source: DABM]. The 220 kV line, which is a recent practice in Afghanistan, connects Pulkhomri substation in the north with Chemtala substation. Its length is 202 km and the type of conductor is ACSR 300 mm2. 220 kV is applied to transmit the imported power. Figure 1.2 shows a single line diagram of the transmission lines for Kabul city, either connecting the power plants to the substations or connecting two substations together.

Figure 1. 2: The location of power plants for Kabul City [5].

1.3.3 Distribution System in Kabul City

The distribution system in Kabul City, as many other places, consists of major facilities and equipment connecting a transmission line to the customer’s equipment. The facilities and equipment include step down substations, junction stations, distribution/primary feeders, switches, protective equipment, distribution transformers, secondaries, meter boxes, and services.

To begin with the substations, there are 7 substations in operation that changes AC voltages from HV to MV. The six of the substations change the voltage from the 110 kV high voltage to 15/20 kV medium voltage while the seventh one, Chemtala II, as seen in Table 1.8, steps down the voltage from 220 kV to 110 kV. Six more substations have already been proposed. Three out of six have received partial funding while the remainders are waiting for funds. In Table 1.8 the substations are listed. Partial construction works of the three substations have already started.

Table 1. 8: Complete list of substation in Kabul City. No Name Transformer Installed Primary Secondary Tertiary Status MVA voltage(KV) Voltage (KV) Voltage (KV) 1 Kabul East 1 x 40 & 1 x 25 65 110 20 15 Functioning

2 Kabul North West I 2 x 25 & 1 x 20 70 110 15 Functioning

3 Kabul North I 2 x 40 80 110 15 Functioning

4 Breshna Kot 2 x 25 50 110 20 15 Functioning

5 Pul-e-Charkhi 2 x 6.3 12.6 110 10 Functioning

6 Chemtala I 2 x 40 80 110 20 Functioning

7 Chemtala II 2 x 160 320 220 110 Functioning

8 Tarakhil 2 x 40 80 110 20 Proposed

9 Kabul North West II 2 x 40 80 110 20 Ongoing

10 Kabul North II 2 x 40 80 110 20 15 Ongoing

11 Botkhak 2 x 40 80 110 20 Ongoing

12 Chemtala III 1 x 160 320 220 110 Proposed

13 Kabul South 1 x 50 50 220 110 Proposed

Next, there exist 12 junction stations in Kabul City. The functions of the junction stations include switching the distribution feeders into and out of the system, measure the electric power quality flowing in the circuits, and management of power networks in the area under its control. Each junction station consists of 8-12 distribution/primary feeders. The majority of distribution feeders are extended over head. However, underground distribution feeders also still exist. Both feeders connect 15/20 kV circuits with distributions transformers (DT). The power is distributed to the customers through 852 DTs; 490 residential DTs, 98 commercial DTs (including industrial DTs), and 262 governmental DTs. The capacity of the DTs is different. The standard of DABM is 400 and 630 KVA transformers. However, 800 and 1000 KVA transformers also have been installed. In addition to that, a number of commercial private DTs varying from 50 KVA to 300 KVA have been registered with DABM. The transferred voltage is again stepped down through distribution transformers near the point of use to 400 volts (three phase) for commercial, light industrial and governmental units and 220 volts (single phase) for residential customers. The lengths of 15/20 KV feeders are normally up to 5.5 km owing to the density of customers. And finally, the secondary feeders or the low tension feeders are extended from DTs to meter boxes where the service lines are branched to serve the end use customers. The length of such feeders is generally between one to two kilometers. DABM is serving 217,525 residential, 13,877 commercial, 2,586 governmental, and 1,570 industrial customers. (A. Waheed Qaium, DABM, personal discussion, August 10, 2008).

1.3.4 Power Utility in Kabul City

As already mentioned in Section 1.2.4, the DABM has opened a branch in each province in order to control and manage the overall power distribution system locally. Thus, Kabul Electricity Department (KED) has the overall responsibility of distribution system in Kabul. KED reports to DABM on a daily basis.

1.4 Demand for Electricity

Despite of the extensive efforts of the international community and the government of Afghanistan and daily visible progress, still more than 60% of the population in Kabul does not have access to electricity. The question “how much power is required for Kabul city” for a more stable condition has remained unanswered. Any answer which has been given to this question is not more than a guess. DABM engineers believe one kWh per hour per residential consumer might be adequate. If that is a good presumption then why over 100 DTs have already been over loaded? In addition, how much power is required for the commercial and industrial sectors? The result of a sample survey conducted in the winter of 2008/09 and Jun 2009, the analysis of the data received from DABM, and appliance-based approach in this thesis will provide a clear answer of the residential customers.

1.5 Objectives

Electric power is a little bit like the air one breathes or the water he/she drinks. No one will think about it until it is missed. Yet, Afghanistan, for 30 years, that has been missing this important element of life. The people have approached different traditional means for the heating, cooling, cooking, refrigerator, light, sound, computation, entertainment…. Without electricity, life has become somewhat cumbersome. Since 2002, massive wok has been done to provide power and connect consumers into the power grid. However, consumer load is growing and MEW with the power companies has not been able to properly respond to the needs of the people with a plan, design, and management of a good reliable power system. Some of the families do not have access to electricity at all; one portion has 4 hours of electricity, another portion has 8 hours, and the other portion has 12 or 16 hours. If electricity is available around the clock, the load growth in Kabul City will not be “linear”. At this point the thesis aims at a determination of load model of normal operation of power. The main purpose of this work is the determination and forecasting of an appropriate representation of the residential load model in a sustainable and normalized condition.

1.6 Outline of the Thesis

Chapter 1 has presented a brief introduction to the power system in Afghanistan, particularly focusing on power status in Kabul City. It has also discussed the progress that has been achieved in recent years. Additionally, Chapter 1 summarizes the objectives and description of the facts that have motivated the realization of the work proposed in this thesis. Chapter 2 presents the power system load and load forecasting in the following sequence: • Power system load models o Static load models o Dynamic load models o Component-based approach • Load forecasting o Short term load forecasting o Medium and long term load forecasting • Factors affecting load forecasting o Weather o Time o Customer class o Economics • Load forecasting method • An overview of the distribution system • Aspects of the load • Allocation of load to distribution transformers o Application of the diversity factor o Load survey methods o Transformer load management method o Metered feeder maximum demand method

Chapter 3 presents the research procedures and methodologies used for collection of data. Chapter 4 explains the discussion, analysis, and the results

33 obtained. And finally Chapter 5 presents the conclusion of this thesis, and recommendations for future work. This is followed by the bibliography and the appendices A, B, and C.

Chapter 2: Power System Load and Load Forecasting

In power engineering, the stability of the system depends on the capability to continuously match the generators’ output to the load on the system. Consequently, the characteristics of the load also play an important role on system stability [8]. The load flow study in the power system reveals the electrical performance and flow of real and reactive power for specific conditions when the system is operating under steady state condition. Additionally the load flow study provides information about the line and transformer loads including the power losses. According to reference [9], a successful and stable power system under balanced three phase steady state conditions requires: • The generation units supply the customer demand (load) and the losses. • The magnitude of bus voltages and the generation units’ rated values should remain close. • The generators supply power within the specified limits. • Transformers and transmission lines must not be overloaded. Power flow study and load forecasting are very important in any power system. The former prevents the system from any potential damage [8] and the latter helps the utility planners to predict load growth for future expansion to meet new load demands and avoid the transformers from getting overloaded as well as in determining the best operation of the existing systems [10]. Both, due to their dynamic characteristics, are complex studies particularly in developing countries such as Afghanistan where it is insisted that the system should be as inexpensive as possible. In Section 2.1 we review the power system load and load modeling concepts, load composition and components. Then, in Section 2.2, load forecasting including short term load forecasting, medium, and long term load forecasting is discussed. Consequently, factors affecting the load, load forecasting methods, an overview of distribution system, and aspects of the load will be discussed. Finally, the chapter ends with allocation of the load.

2.1 Power System Load Models

In recent years, a number of large-scale electricity blackouts in major cities have happened, such as the one on August 2003, which hit Northeast United States and Ontario, Canada or the one on May 2005 in Moscow. Such outages have left a huge impact on individuals and business [11]. However, smaller scale blackouts (in terms of coverage area and duration) have not been reported widely, although they can also have a negative impact on customers connected. The performed simulations after the blackouts showed that the load models used do not reflect reality. Therefore the load modeling is recognized as an important element and has received intensive attention [12]. The definition of load depends on the type of desired analysis. For instance, the load for a steady state analysis of a transmission line has a different definition than that for the analysis of a secondary in a distribution feeder. The load on a power system is always changing, as one is closer to the customer, one can observe the ever changing load [13]. In a modern power system study, load modeling is the composite of load as seen from the bulk dispatcher point as illustrated in Figure 2.1 [8].

Figure 2. 1: One line diagram of a power system.

The load at the main bus, in addition to the connected loads (Individual and industrial customers), is composed of the transmission line load, the step down transformers load, the sub-transmission line load, the load of distribution

36 transformers, the effects of compensators and regulators, and the secondary transformer load. In general, load model is a mathematical representation of the relationship between a bus voltage and the power or current flowing into that bus load. The load models are basically classified into two wide categories: the static load models and the dynamic load models [8], [13], [14]. However, in [12] three types of load models are discussed: static load models, physical load models, and un-physical load models; however, there are no significant differences between the two.

2.1.1 Static Load Models

Static load models, which are also known as ZIP models, are typically classified into the following three categories [13], [14]: • Constant Impedance Load Models (Constant Z): In this type of model, the impedance remains constant. However, the active (P) and reactive (Q) components of powers vary with the square of the voltage magnitude. • Constant Current Load Models (Constant Current I): In this model the current is kept constant, instead, the active and reactive (P and Q) components of powers are directly proportional to the magnitude of the voltage. • Constant Power Load (Constant P): In this model, as its name implies, the active and reactive (P and Q) components of powers do not vary with the change in voltage magnitude. The static load models (ZIP model) can be represented such as (i) polynomial, or (ii) exponential functions as illustrated in the following subsections. 1. Polynomial Load Models: In this kind of representation, the active component and reactive components of load power varies as a polynomial equation of voltage magnitude. It consists of the three aforementioned constant models: (i) constant impedance (Z), (ii) constant current (I), and constant power (P). The real and reactive power component characteristics in the polynomial form is given by equations (2-1) and (2-2) [14]

2 ⎡ ⎛ V ⎞ ⎛ V ⎞ ⎤ P = Po ⎢aP ⎜ ⎟ + bP ⎜ ⎟ + CP ⎥ (2-1) ⎢ ⎜V ⎟ ⎜V ⎟ ⎥ ⎣ ⎝ o ⎠ ⎝ o ⎠ ⎦ and

2 ⎡ ⎛ V ⎞ ⎛ V ⎞ ⎤ Q = Qo ⎢aQ ⎜ ⎟ + bQ ⎜ ⎟ + CQ ⎥ (2-2) ⎢ ⎜V ⎟ ⎜V ⎟ ⎥ ⎣ ⎝ o ⎠ ⎝ o ⎠ ⎦ where:

Po and Qo are the real and reactive power consumed at the specific node at a reference voltage Vo; , and the coefficients: aP , aQ are the parameters for the constant impedance (constant Z) load component; bP ,bQ are the parameters for the constant current (constant I) load component;

CP , CQ are the parameters for the constant power (constant P and Q) load component.

The values for aP , aQ , bP , bQ , and CP , CQ are determined for different load types in the distribution system. They are usually experimental values. Also, the summation of coefficients of real power is equal to the summation of coefficients of reactive power and is always unit. That is aP + bP + CP = aQ + bQ + CQ =1. 2. Exponential Load Models: In this model the real and reactive powers as given in (2-3) and (2-4) are represented as an exponential of voltage.

α ⎛ V ⎞ P = Po ⎜ ⎟ (2-3) ⎝Vo ⎠ β ⎛ V ⎞ Q = Qo ⎜ ⎟ (2-4) ⎝Vo ⎠ where :

Po and Qo are the real and reactive powers dissipated at a node under a reference voltage Vo, respectively. The parameters α and β are associated with the type of the load connected. For a constant power load modelα = β = 0 ; for a constant current load modelα = β = 1; and for a constant impedance load model α = β = 2 . The values of α and β parameters for composite loads depend on the aggregate characteristics of load components [8]. The common values for the parameters of α and β for different load components are shown in Table 2.1 [15].

Table 2. 1: Common values for α and β , for different load components.

Load Component α exponent β exponent

Air Conditioner 0.50 2.50

Resistance Space Heater 2.00 0.00

Fluorescent Lighting 1.00 3.00

Pumps, fans other motors 0.08 1.60

Large industrial motors 0.05 0.50

Small industrial motors 0.10 0.60

In equations (2-3) and (2-4), at V=VO, the exponent values of α and β are almost equal to dP /dV and dQ /dV respectively. However, a typical range for α is between 0.5 and 1.8; while β usually ranges between 1.5 and 6. The exponent β varies as a non linear function of voltage due to the magnetic saturation in distribution transformers and motors [8].

2.1.2 Dynamic Load Model In cases where the static load models (ZIP model) cannot adequately represent the characteristic of the load, it is necessary to consider the alternative dynamic load model. Interarea oscillations studies, voltage stability, and long term stability are examples of such cases. The existence of motors in the power system network also requires a dynamic load model representation [8]. The parameters of dynamic load models can be determined by the following two approaches [8], [16]: • Measurement-Based Approach • Component-Based Approach The measurement-based approach is associated with three main issues: the generalization capability of a measurement-based load model, the number of sites to be selected to represent the whole system, and the influence of measurement-based load models on dynamic simulation is under studied [16].

Component-Based Approach: In this approach, the characteristics of each load are recognized individually and then they are aggregated into one single load. To reconsider Figure 2.1, if we look at any power system network, the bus load at the bulk power delivery point consists of industrial, commercial, and residential classes. Each class of load as shown in Figure 2.2 is represented in terms of load components. These components include appliances such as space heaters, water heaters, air conditioners, lighting, refrigerators, and freezers. Each of the load components is identified according to its components characteristics such as power factor (PF), real power and reactive power as a function of voltage and frequency, and motor parameters (if the load involves motors) [8].

Figure 2. 2: The bus load of a power system network with its components [8].

For simulation and analysis while designing a load class, the static characteristic of the load is required. Reference [8] provides typical voltage and frequency-dependent characteristics of a number of load components, which are listed in Table 2.2.

Table 2. 2: Static characteristics of load components. Component PF ∂P / ∂V ∂Q / ∂V ∂P / ∂f ∂Q / ∂f Air Conditioner 3-phase central 0.90 0.088 2.5 0.98 -1.3 1-phase central 0.96 0.202 2.3 0.90 -2.7 Window type 0.82 0.468 2.5 0.56 -2.8 Water heaters, Range 1.0 2.0 0 0 0 top, oven, Deep fryer Dishwasher 0.99 1.8 3.6 0 -1.4 Clothes washer 0.65 0.08 1.6 3.0 1.8 Cloth dryer 0.99 2.0 3.2 0.0 -2.5 Refrigerator 0.8 0.77 2.5 0.53 -1.5 Television 0.8 2.0 5.1 0 -4.5 Incandescent lights 1.0 1.55 0 0 0 Fluorescent lights 0.9 0.96 7.4 1.0 -2.8 Industrial motors 0.88 0.07 0.5 2.5 1.2 Fan motors 0.87 0.08 1.6 2.9 1.7 Agricultural pumps 0.85 1.4 1.4 5.0 4.0 Arc furnace 0.70 2.3 1.6 -1.0 -1.0 Transformer (unloaded) 0.64 3.4 11.5 0 -11.8

Furthermore, the characteristics of different load classes are summarized in Table 2.2. The summarized data is further broken down for the winter and summer. This is because the residential and commercial customers are using different appliances for each season of the year [8].

Table 2. 3: Sample characteristics of different load classes. Load Class PF ∂P / ∂V ∂Q / ∂V ∂P / ∂f ∂Q / ∂f

Residential Summer 0.9 1.2 2.9 0.8 -2.2 Winter 0.99 1.5 3.2 1.0 -1.5 Commercial Summer 0.85 0.99 3.5 1.2 -1.6 Winter 0.9 1.3 3.1 1.5 -1.1 Industrial 0.85 0.18 6.0 2.6 1.6 Power plant auxiliaries 0.8 0.1 1.6 2.9 1.8

The dynamic characteristics of induction motors are out of the scope of this thesis. However, detailed data can be found in references [8], [14], [15], [16].

2.2 Load Forecasting

In a modern power system, decision makers are interested in knowing what the electric load will be at future time interval of interest, i.e. load forecasting. In this regard, load forecasting has great value. It will help to prevent stability problems and also to plan the expansion of the electric network. In most cases, it has been observed that less attention to load forecasting has driven the power system network to fail, such as the one in Kabul-Afghanistan, where over 100 out of 852 DTs have already been overloaded, transformers fire and explosion also have increased (A. W. Sherzai, Deputy Minister, MEW-Afghanistan, personal communication, June 2009). In terms of time and duration, three distinct load forecasting methods exist such as; short term load forecasting (STLF), medium term load forecasting (MTLF), and long term load forecasting (LLFC). Different models and approaches have been developed for each category of the load forecasting to meet the desired objectives of the application [10].

2.2.1 Short Term Load Forecasting (STLF)

The short term load forecasting ranges from several hours to several days. As the range gets smaller the more complicated model arises. The ultra short term load forecasting which usually varies from several minutes to several hours is the most complicated one. The application of STLF includes the following areas [10], [17], [18], [19]:

• Generation Scheduling: the objective of scheduling depends on the type of plant. For hydropower plants, it predicts the amount of water flowing from the reservoirs. For thermal generating systems, forecasting anticipates unit commitment. It determines when a new generating unit should start up for a high forecasted load and vice versa. For hybrid systems such as hydrothermal systems, forecasting is requested to perform an economical operation. • Power System Security: Short term load forecasting secures the operation of power system before any contingencies arise. Contingencies could be an external or an internal event. For example, STLF can predict the effect of scheduled operation on power system security and allows the decision makers to apply prevention and correction actions.

• Power Generation Reserve of the System: Generation reserves play an important role against shortcomings when load increases suddenly or a plant fails. STLF can anticipate an appropriate amount of reserve. • Providing Information to Dispatchers: For the most economical operation of the system, a dispatcher would require the actual time information relevant to short term loads. STLF is a major interest of system operators, market operators, transmission owners, and other participants for scheduling adequate and economic energy transactions in developed countries [10].

2.2.2 Medium and Long Term Load Forecasting (MTLF/LTLF)

Frequently, the term medium term load forecasting is used for forecasts with a time horizon of one month to one year. The purpose of MTLF is to improve the capability of power companies in contract evaluation and evaluation of various sophisticated financial products on energy pricing offered by the market [13]. Also, MTLF allows the power companies to schedule major and minor services and maintenance of the system as well as scheduling fuel supply. Long term load forecasting refers to forecasts which are longer than a year. Its importance appears when the decision is made for a new power plant. At present, it is not possible to store large amounts of electric power. It takes years to build a new plant and requires a large amount of money (e.g. several million dollars per each MW) for investment. Therefore, it is necessary to perform a long term load demand in order to plan or operate power systems efficiently. Typical long term load forecasting is 10 to 20 years such as one studied by Kermanshahi for nine power companies in Japan [10]. However longer than that also has been seen in literature review [19]. Load forecast, regardless of its category, either short term, medium, or long term load forecasting, it is affected by a number of key factors which will influence the accuracy of the forecasted model [10]. In the following section, these factors as illustrated in [10] are briefly discussed and in the subsequent section, the methods used for load forecasting are illustrated.

2.3 Factors Affecting Load

Load forecasting is a difficult task to perform. The P-V relationship of the load which was discussed in Section 2.1 and the factors affecting the load patterns makes load forecasting an intricate mission. In order to develop a proper load forecast, it is essential to identify these factors, which should be taken into account to the greatest extent possible. Some of these factors include:

2.3.1 Weather Factor

Power electric load has a noticeable correlation to weather in general and in particular to temperature. It is the most influential factor in load forecasting. Its impact is significant when a change in temperature occurs. For example, during the winter when the temperature lowers, more power is required for the heating system, or during the summer when temperature rises, more power is need for the air conditioning and cooling systems [10]. In addition to temperature, there exist a number of others factors which could impact the amount of power needed such as: dry and wet temperature, dew point, humidity, wind, and precipitation.

2.3.2 Time Factor

In the load forecasting model, as reference [10] states, time factor also has a significant impact on electric load pattern. Time factors include the hour of the day, the day of the week, and holidays. For instance, the number of daylight hours in a season is a seasonal effect, or the consumption of power in weekdays is different from the weekend. For example, for monthly effect includes the month of Ramadan in Islamic countries such as in Afghanistan. In this month the amount of power and the peak time follows a different pattern than in the previous month and the following month. Holidays also have a significant effect on the load pattern. Load on holidays will mostly depend on culture and working regime of each country. For example, in Afghanistan, the industrial load will decrease while the residential load will increase in comparison to normal days.

2.3.3 Customer Class Factor

Power electric utilities usually serve different categories of customers such as residential, commercial, industrial, agricultural, mining, and governmental and holy places (like the ones in Afghanistan). The load shape for each customer is different. A load shape also depends on geographical conditions. For example, the load shape in urban areas is different from the one in rural areas. For example, in Figure 2.3 an example of different categories of daily load variation in one of the areas in Kabul is shown. Figure 2.3a shows a load behavior in a residential area. The graph is plotted from the data recorded from a 800 KVA distribution transformer. Figure 2.3b is the load behavior of a super market supplied through a 100 KVA transformer. And, Figure 2.3c is the load behavior of a weekday of a light industry in Kabul supplied through a 200 KVA transformer (source DABM).

(a)

Load behavior of a Commercial Class 90 80 70 60 h 50 40 kW 30 20 10 0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324 Midnight Hours of the Day

(b)

Load Behavior in a Light Industry 160 140 120 100 h 80 kW 60 40 20 0 123456789101112131415161718192021222324

Midnight Hours of the Day

2.3.4 Economical Factors

The economical condition of the area has its impact on the long term load forecasting patterns. These factors typically include type of customers (poor, middle- class, or rich), demographic conditions, industrial activities, and population growth [10] (and population movement such as in Kabul). The gross domestic product (GDP) rate is often a good indicator to be taken into consideration, since, a causal relationship exists between electricity consumption per capita and real GDP. For instance, reference [20] found a normalized cointegration coefficient of 0.88 between the electricity consumption per capita and the GDP in China. That is LELEC = 0.88 LGDP, where LELEC is the logarithmic electricity consumption and LGDP is the logarithmic gross domestic product. In addition to the factors discussed in Section 2.5.1 through 2.5.4, there exist a number of other factors that also could affect the load patterns. Such factors are random disturbances like a popular TV shows or a sudden shutdown of an industrial activity. Another function which is often discussed is the price factor in the electricity market.

2.4 Load Forecasting Method

The importance of load forecasting and the factors affecting it has already been discussed. Several models have been developed to predict an accurate load model. The type of model that the decision makers apply depends on the purpose for which they are trying to use the model, availability of input data (such as seasonal and weather, demographic and economic growth), and the time horizon. The models are mostly mathematical approaches such as regression model, similar day approach, statistical learning model and neural network. The methods that have been reviewed in [10], [17], [18], [19], [20], [21], [22] could be broken down as: • Expert Judgments • Linear Models o Linear Regression o Time Series Approach • Non-linear Model o Artificial Intelligence Artificial Neural Network (ANN)

Support Vector Machines o Non-linear Regression o Fuzzy Approach o Bayesian Approach The input data is the foremost important indicator for each of the methods mentioned above. Without an accurate data model and indicators affecting the forecast, the methods will not be reliable.

2.5 An Overview of Distribution Systems

As stated in reference [23], the aim of a modern power distribution system is to “satisfy the growing and changing system load demand during the planning period and within operational constraints, economically, reliably and safely, by making optimized decisions on the following: voltage levels of the distribution network; locations, sizes, servicing areas, load and building or expanding schedules of the substations; routes, conductor types, load and building schedules of the sub- transmission lines and feeders; other important issues such as the types and locations of switching devices, load voltage levels, and network and load reliability levels, etc.” The major components of a power system are generation, interconnected transmission system, bulk power substation network, distribution substation, and primary feeders. A typical power system with its components for Kabul city is shown in Figure 2.4. The overall function of each part was discussed briefly in Section 1.3. Such setups might be common in developing countries. For example, in India and Pakistan a similar system without significant differences has been seen. Nevertheless, they are slightly different from the one that exists in the United States (U.S.). One of the differences is the way that a small consumer is served. In Afghanistan, there is one larger distribution transformer (DT) serving 300 to 700 consumers depending on the size of transformer, while in the US, a small size (DT) serves two or a few more small consumers.

Figure 2. 4: Major power system components in Kabul city.

The level of voltage (220 V in Afghanistan) for the residential customers and the frequency (50 Hz) versus 120 V and 60 Hz in the USA are other differences to be mentioned. In recent decades, the distribution systems have improved significantly. The ultimate goal is to operate the distribution system at its maximum capacity. Therefore, the power engineers have been challenged with the new requirements. Examples of these challenges include; finding the maximum capacity (maximum capacity usually refers to the lowest cost), the methods to calculate the maximum capacity, finding the operating limits that must be satisfied, and improve the operating efficiency of the system [13], [21]. The configuration of substation differs from country to country and from utility to utility. The economic factor has an important effect on the configuration of the substation. The most complex system is the most expensive one. However, there exist following five basic functions in all distribution systems [13].

• High-side and low-side switching • Voltage transformation • Voltage regulation • Protection • Metering Figure 2.5 shows a comprehensive substation scheme of Kabul electric network which has been rehabilitated recently. In this substation two load tap changing transformers serves 6 feeders.

Figure 2. 5: An improved substation scheme in Kabul.

Normally, the circuit breakers C-3, C-7, C-12, and C-17 are open. They react when an outage occurs in one of the lines. Circuit breakers C-7, C-12, and C-17 respond when one of the transformers is out of service. The feeders’ layout is usually radial or loop scheme. The radial layout has one path for the power to flow from the distribution substation to the customers. It can be laid out in different ways such as “big trunk layout”, “multi-branch layout”, “Feathered”, and “mixed design” as seen in Figure 3.6a. Yet, it cannot be limited to

50 aforementioned layouts. Flexibility allows the distribution planners to design nearly one thousand workable feeder layouts [21].

(a) Examples of radial layout.

(b) Example of loop layout. Figure 2. 6: Layout of feeders. (a) Radial layout. (b) Loop layout [21].

The loop layout, which has been employed largely in European countries, is dual voltage distribution system (e.g. 33/11 KV) [21]. The Figure 2.6a shows different layout of feeders for an identical area, and Figure 2.6b is an example of loop layout. In Figure 2.6b there exist four 33/11 KV substations. Whenever any of the substations is out of service, the others will cover the shortage. Any of the layouts include the following major components [13]: • three-phase primary “main feeder”, • three-phase, two-phase, and single phase lateral • Step type voltage regulators • In-line transformers • Shunt capacitor bank

• Distribution transformers • Secondaries • Three-phase, two phase, and single-phase loads For design, analysis, and accurate modeling of a distribution system many relevant data needs to be acquired. Most of the data is related to the components mentioned above. Supplemental data such as standard pole configuration, conductors’ specification and characteristics, and the nature of loads are also important. Among all, the load is the main concern of this thesis which is discussed in the next section.

2.6 Aspects of the Loads

The consumer is the king in the distribution system and should be satisfied with the amount and type of power required. Quantity and quality are two expectations that can satisfy the consumer. The dynamic nature and characteristics of the load makes it complex to be analyzed, because, in every second there is a chance that a bulb will be turned on or off, or an electric appliance will be plugged in or removed. The integration of all these alterations will eventually change the load on the feeder. The following definitions and mathematical models are the core concepts for moving from complexity toward simplicity [13], [21].

1. Maximum Demand: Maximum demand or peak load is the greatest average value of the power (kW, kvar, A, or kVA) consumed by a customer in a specific interval of time (e.g. 15 minutes or 30 minutes) over a particular period such as a day or a week. 2. Off-Peak: Off-peak is the period of low demand as opposed to maximum or peak demand. 3. Average Demand (AD): AD is the estimated average load over a specific periods of time (15 min, 30 min, hour, day, week, month, etc) in kW, kvar, kVA, or A. Usually metering systems in modern and sophisticated substations record the demand in 15 minutes, 30 minutes and 1 hour:

n ∑ Pi Demand = i=1 (2-7) n

4. Diversified Demand: Diversified demand (DD) is the sum of all demands imposed by a group of loads over a specific period:

n DD = ∑ Di (2-8) i=1 where Di is the demand of individuals in a group of loads and n is the number of loads in the group. 5. Maximum Nocoincident Demand (MND): MND is the sum of the individual maximum demand for a class of loads without any restriction that they occur at the same time. 6. Diversity Factor (DF): DF is defined as the ratio of the maximum noncoincident demand to the maximum diversified demand. DF is usually greater than one. 7. 8. Maximum Diversified Demand (MDD): The greatest value of diversified demand over a particular period is nominated as maximum diversified demand. 9. Coincident Demand: The demand of a consumer at the time of power supplier’s peak system demand is called coincident demand. 10. Demand Factor (DF): Demand factor is the ratio of maximum demand to the total connected load on the system. It is usually presented as a percentage: D DF = max (2-9) ∑connected load 11. Utilization Factor (UF): The ratio of the maximum demand of a system or part of a system to rated capacity on the system is called utilization factor. UF gives an idea about how well the capacity of an electrical device is being utilized: D (in kVA) UF = max (2-10) Transformer rating (in kVA) 12. Load Diversity (LD): LD is the variation between the MND and the MDD. LD = MND − MDD 13. Load Curve (LC): Load curve is the graph of power supplied by an electric system versus time. In a LC plot, the variation of load is seen from hour to hour, from day to day, and from season to season. For the planners, the

annual peak load is a matter of interest because it is the greatest amount of power that must be delivered and based on this the capacity of requirements for equipments are taken into consideration such as feeders, transformers, and substations. It is more important for a growing power system. 14. Load Duration Curve (LDC): LDC is a chart that demonstrates load values in descending order of magnitude against the percent of time. 15. Load Factor (LF): Load factor is the ratio of the average demand of any individual customer or class of customers during a designated period to the maximum demand load occurring in that period (in kW). LF is a measure of efficiency which indicates the usage of the system’s equipment. Utilities are interested in boosting LF: Average demand (during a period) LF = (2-11) Maximum demand (during a period)

2.7 Allocation of Load to Distribution Transformers

According to the reference [13], the following four different methods have been applied for load allocation to distribution transformers or to a line segment. The selection of the method depends on the purpose of the analysis. For instance, for the determination of the possible maximum demand on a distribution transformer the transformer load management method or diversity factor would be applied. These methods are discussed briefly in the following sections.

2.7.1 Application of Diversity Factor

This method is a function of the sum of consumers located downstream and seen from transformer or the line segment. For example in Figure 2.7 a line is shown with n number of transformers and k, m, and n number of customers. If we assume the load are all constant power load (constant real power and constant reactive power load), and the voltage at N1, the impedance of the line segments, and the transformers specification are known then the voltage drop can be calculated easily.

Figure 2. 7: A sample of single-phase lateral.

2.7.2 Load Survey Method

Many utilities are interested to determine the relationship between the kWh consumed by the customer and the maximum kW demand. The maximum demand can be obtained through the installation of demand meter at customer’s location. Then, by applying the linear regression method, the equation of straight line that gives the kW demand as a function of kWh is acquired [13].

2.7.3 Transformer Load Management Method

Transformer load management method is a computer based program built on visual basic concepts. It determines the load on distribution transformers by knowing the kWh supplied by the transformers during a peak loading month. This program linearly relates the maximum diversified demand to the total kWh supplied by the transformer during a particular month. This program also requires several samples from distribution transformers as an input data in order to obtain relatively an accurate equation. Furthermore, this program requires that each utility must have a table of diversity factors [13]. For further details, one can be referred to reference [21] which discuses an improved application of the program which is based on an automated mapping/facilities management/geographic information system (AM/FM/GIS). This program presents information expectation, load forecasting and power flow analysis in distribution system. The program calculates the future load growth, estimates the saturation of new housing zone, and calculates the voltage drops at nodes, the currents at each branches and losses of the system. The foremost disadvantages of this program is its complexity with the database showing which customer is served by

55 which transformer and second formulation of the diversity factor which is usually not cost effective [13].

2.7.4 Metered Feeder Maximum Demand Method

The metered feeder maximum demand method is the foremost easiest method to apply and figure out the load for a distribution transformer. It requires less data than the first three methods previously discussed. The only requirement for this method is that the feeders at substations must be connect to a meter which should provide maximum diversified kW, kVA, or the maximum current during a month. The transformer demand can be computed by equation (2-12):

Transformer Demand = AF × kVAtransformer (2-12) where AF is an “allocation factor” and defined as the ratio of the metered demand to the sum of the kVA rating of all distribution transformers [13].

Chapter 3: Research Procedures

3.1 Introduction

Since 2003, a significant development occurred in the power distribution system in Kabul City. The number of distribution transformers increased from nearly 300 in 2002 to 852 DTs in 2008. Still over 60% of the citizens do not have access to reliable power. Furthermore, over 100 DTs have already been overloaded and many more are at risk to develop into overload too. One of the main reasons confronting the DTs with overload is the ignorance of load growth in design process. Thus, the main purpose of this work is to determine the residential load growth in Kabul City. In addition, the residential load growth is a focus for several other reasons. First, the need is high and prioritized by the MEW. Second, currently 63% DTs are residential ones and 58% of the power is consumed by the residential class. In this chapter, the methodology applied for data gathering is discussed. Initially, a semi-structured interview with relevant parties is discussed. Then, the household-drop-off survey is explained. Next, 30-min demand survey is discussed. Finally, the chapter ends with the collection of historical data from DABM.

3.2 Data Gathering

Kabul City was badly damaged during the various 1991-1997 civil wars. The author realized it must have been hard to obtain accurate and reliable data. Thus, a multi-method approach has been applied for the completion of this research and data collection. The methods include the following stages: • Semi-structured interviews with relevant parties • Household drop-off survey • Meter-based sample survey • and data collection from DABM

3.2.1 Semi-structured interviews with relevant parties

To begin with, in July and August 2008, the author organized several semi- structured interviews with key staff from DABM, the Planning Department of MEW,

57 and a number of companies involved in power construction in Afghanistan. These people include Eng. A. W. Qaium, the director of DABM, Eng. Kohistani, the Technical Deputy Director of DABM, Eng. M. H. Mojdha, the Deputy of Planning Department of MEW, Randall Nottingham, the Director of Afghan Energy Information Center (AEIC), Ahmad Omer, the Deputy Director for AEIC, Mark Tribble, from North East Power System (NEPS), and Eng. Fazel, the Director of Breshnakot substation. Groups of topics and questions were asked during the interviews. These questions covered the generation, transmission, and distribution system in Kabul City and major problems associated with them. Furthermore, topics such as electric energy services, access to electricity, reliability, stability, affordability, electric energy security, customers, tariff and billing system, load modeling, power losses, rehabilitation process, and power allocation were also discussed. In each step the author tried to narrow the focus to get to the point.

3.2.2 Household Drop-off Survey

In fact, findings over the course of one and half months interviews with authorities at MEW, DABM and other relevant involved parties, was enthusiastic and encouraged the author to focus on load growth as a key issue in distribution planning and design systems. Then, in winter 2008, a questionnaire with short closed-ended questions was developed for a base survey. It was a household drop-off survey conducted through the author’s former students and friends. The survey was purposely split into two parts; random and selected survey. In random survey, the customers were selected regardless of any criteria. However, the following algorithm was used to be identified as standard, mid-level, or high level from the survey sheet they fill out. • Standard Customer: the customer was identified as standard customer if the household: o had black and white TV set o consumed firewood for cooking and heating o consumes kerosene lantern for lighting o had 6 or more than 6 children o had one or two bedroom

o the head of household was a governmental/self-employee/sales person o use one electric fan for cooling o the customer shared the compound with at least other three families • Mid-level Customer: the customer was identified as mid-level if: o had a color TV o the household had a small Chinese (700 VA) generator or sharing a higher capacity generator with others. o consume gas for cooking and heating o consumes gas for lighting o the head of the household is a professional o the household possessed a dish receiver o the household had washing machine o the household had at least two fans for cooling o the household had refrigerator o the customer shared the compound with at least one other family o the customer had one CD/DVD player • High Level Customer : the customer was recognized as high level customer if o the household had at least one color Television with remote control o the household had two or more CD/DVD/VER o the household had a car o the household had refrigerator with freezer o the household possessed the compound without sharing with others o the household had cloth washer and dryer o the household had 2 kVA or higher high quality generator o using fuel stove for heating system o the head of the household/family had a private business

In selected customer survey, the households were chosen through Wakils1 based on the aforementioned algorithm. These two methods were used to minimize the error. However the questioner remained the same for both of methods. A copy of the questionnaire can be found in the Appendix B. The major components of the survey included: • To find out what sources are used for lighting, heating, and cooking during each season of the year • Availability of city power in regard to quality and quantity in each season. • Average consumption of electricity per month. • What other sources are utilized in absence of the grid power. • What electric appliances does a household have? • What changes will take place in electric appliances if reliable electric power is available. • Is electricity desired by families? The electric market of Kabul City was also surveyed to find what electric appliances is availability and what the sellers experience would be to introduce new appliances in the market in future.

3.2.3 Meter-based Survey

Due to the absence of accurate, reliable and timely information and data, a 30- min meter-based survey was initiated by the author and conducted by relatives and friends in July 2009. The meter-based survey included 15 residential customers from the same cluster. The customers were selected from the same cluster because of the same weather condition they have and if an outage happened will have the same outage at the same time. The survey was conducted for three main reasons. First, to find how many kWh a residential customer consumes as an average in 24 hours. Second, to compare the result with the data received from DABM. And finally, the last reason that encouraged the author to launch this survey was round the clock availability of power for certain residential places in Kabul.

1 Wakil is an influential member of the community who represents the community to local district offices and liaison back and forth between district office and community. He well knows the households and keep track of the movement of the families in and out of the community. There is one Wakil for 350- 500 house holds.

3.2.4 Data Received from DABM

In pursuit of goals, the author approached DABM for data exist there. Happily, the DABM generously provided him with two samples of data. The data includes 93 and 7578 residential customers in one and 22 different locations respectively. Mainly, we read the total kWh consumed by group of residential customers. The data is a two months (one period) kWh consumed by customers. However, attempt was made to receive data for at least a complete year. The data include different socio-economic levels of residential customers.

Chapter 4: Result

4.1 Data Process

The data and information gathered from the steps stated in Section 3.2 are analyzed in the following description of consequences.

4.1.1 Results from the Interview with MEW, DABM and Relevant Parties

To begin with, the interviews with the authorities attested to how the need is high in Kabul City in regard to generation, transmission, distribution, human resources, planning, designing, and implementation. They also discussed how they are managing with the challenges and how they are preparing to respond to meet the need. They clearly confessed that no load growth or a load forecast study has been conducted to this point. They stressed the need for such studies and acknowledged the importance of load growth in order to avoid the overloading of DTs. The power companies involved in power issues have a narrow task-oriented/proposal-based focus. They are responsible to MEW. Most of the information discussed in Chapter one came from the interview. The answers obtained resembled each other and that was “the distribution system is restoring from a war torn condition, the data base was mostly destroyed…” However, the interviews were useful and the following findings obtained. The author found that the total daily supplied power is ranged 4900-5300 MWh for the entire country (33% hydropower and 66% imported power), while particularly for Kabul City the allocated power varies from 2300 to 2500 MWh (67% imported power and 32% hydropower). The transmission lines also have a positive trend proportional to supply progress. It has increased from several hundred kilometers to 2041 km in 2008. In addition, another 1905 km has been planned. Accordingly, the number of customers has increased too. Currently, 633,000 customers (residential, commercial, governmental, and industrial) have been registered all over the country with DABM. In Kabul City, as the rehabilitation in distribution system progresses, the number of customers also grows fast. In Table 4.1 this trend is shown. For example, in 2004 a 6% increase in residential customers was observed while in 2005 there was a 56.4% increase. A positive trend eventually results in load growth.

Table 4. 1: Different customer class statistics from 2003 to 2008. Customer Class/Year 2003 2004 2005 2006 2007 2008

Residential 78697 83497 130588 136211 170290 217525 Commercial 4541 4754 5734 5892 7156 13877 Governmental 673 798 942 961 1308 2589 Total 83911 89049 137264 143064 178754 233991

Since the residential customer base is increasing, the DTs and power allocation also increases proportionally. As we see in Figure 4.1, column 1 indicates the percentage of DTs (based on count) for different customer classes and column 2 shows the percentage of power allocation (based on MVA) for different classes. Currently over 60% of the supplied power is allocated to residential customers. Note that at present 100 to 105 megawatts power is supplied to Kabul City. The absence of industrial customers in this figure is mainly because they have their private generators since the grid power is not reliable. However, light industries that consume 20- 50 kVA are already included in the commercial category.

DTs and Power Allocation for Customer Class 70% Residential Residential 60% 50% 40% Governmental Governmental 30% 20% Percentage Commercial Commercial 10% 0% 12 Customer Class

Figure 4. 1: DTs and power allocation for different customer class in Kabul.

From the interviews the author also found that the power losses are as high as 40% in Kabul distribution network. These losses include technical losses and non technical losses. The technical losses consist of sub-transmission losses, high voltage distribution segment losses, distribution substation segment losses, and low voltage distribution segment losses. The non technical losses integrate power theft, metering deficiencies, and errors in the meter reading and billing process. Meanwhile, the collection of electric revenue is as low as 75%. The authorities at the DABM were expecting to reduce gradually the losses from 40% to 20% by the end of 2010 where afterwards it will remain constant without a significant change. Their expectation on the revenue collection was also a bit optimistic to elevate it in three stages from 75% to 80% by the end of year 2009 and then to 90% by the end of 2010 where afterwards may not have a significant improvement. To conclude this section, the interviews with the authorities at the MEW, DABM, and non-governmental organizations involved in the power distribution sectors were useful and productive. The author gathered on what is going on at the MEW and DABM, what the strengths are and what the weakness are. And, eventually the outcomes from the interviews led the author for the base survey and meter-based data collection.

4.1.2 Household Drop-off Survey Result

The household drop-off survey was conducted in winter 2008. The sample size consisted of 510 residential customers. In the sample survey two approaches were taken into consideration: randomly chosen and selected residential customers in order to boost the accuracy of the results. In the latter, three categories of families, poor, mid-level and rich, were taken into consideration. Once the data was cleaned, the following findings were obtained. 1. Lighting: The survey showed that the residential customers could get the benefit of grid power as a lighting source for only 26% of the month. For the rest of time they were using other sources such as small generators, gas-based lanterns, fuel-based lanterns, solar panels, or a combination as shown in Figure 4.2.

Lighting Source

So lar, 3 % City Power, 26% Fuel, 18%

Gas, 2 8 % Gen erat o r, 25%

Figure 4. 2: Lighting source for residential customers.

2. Heating Source: It was found that the families approach several alternatives in the absence of grid power depending on their income and the economic condition of each. These alternatives include wood fire, liquid fuels such as diesel and kerosene, and gas. The result is shown in Figure 4.3. However, absolutely all families prefered electricity as a heating source, if available, because it is clean, safe, and does not require storage like other alternatives. “Others” in Figure 4.3 includes some minor alternatives such as sawdust and wood chips.

Source of Heating City Power, Others, 6% 5% Fire wood, Fuel, 34% 53%

Gas, 2%

Figure 4. 3: Heating source during the winter.

3. Cooking: The majority of families in Afghanistan like to cook at home and have their meals together. Cooking is dominantly the housewife’s job. Some women prefer to cook once a day and some others prefer to cook for each meal separately. Usually cooking time is between 10-12 in the morning and 4-6 in the evening. Generally,

Afghan families prefer to use a cheap and available source of energy. Based on the survey findings, as shown in Figure 3.4, 72% of the families consume gas for cooking purposes, while 11% use city power, 7% wood and 9% liquid fuels. However, grid power is the first choice of each family, if available.

Energy Sources for Cooking

Gas, 72%

Fuel, 9% Wood, 7% Electricity, 11% Others, 1%

Figure 4. 4: Energy sources for cooking.

4. Availability of electric power: From the survey result we found that availability of electric power is very limited. It varies from 6 to 24 hours a day due to the shortage of energy. A scheduled outage is applied to ensure every consumer is receiving some electricity. The result is summarized in the Figure 4.5. The availability of grid power is divided up into less than 6 hours, 6-12 hours, 12-18 hours, and 18-24 hours.

Figure 4.5 reflects the facts prior to 2009. However from the beginning of 2009, with the imported electric power from neighboring countries, things have gotten better.

Figure 4. 5: Availability of power for residential customer.

5. The answer to the question “does the interviewee have a private generator and what size” was surprising. The findings showed that 67% of the interviewees have a small 700 VA Chinese generator, 18% have a generator with the size of 1000 to 2000 VA, and 15% did not have generator. The 700 VA Chinese generators are inexpensive ($ 40 per unit) and affordable to many families compared with any other generators. The quality of such generators is significantly poor and they are known as “disposable generators” by the families. They use their generators mostly for the lighting and watching television in absence of grid power. The usage hour varied from one family to another one, depending on their economic condition. 61% of those have generator responded that they are using their generators less than 40 hour in a month, 22% responded 40-60 hours in a month and 17% more than 60 hours in a month.

4.1.3 Meter-based Survey Findings

The materials discussed in Sections 3.2.1 and 3.2.2, and their results explained in Sections 4.1 and 4.2, due to the shortage of power and scheduled outages, did not allow the development of a conclusion as to how much power would be required in the long term for residential customers in Kabul City. Thus, the author initiated two further approaches: first, a small sample survey whose result is discussed in this sections and a request for up-dated data from DABM whose result will be discussed in the next section. The small sample survey included 15 family customers. This survey was conducted through relatives and friends at an appropriate time that the customers have twenty four hour power. This survey was conducted on July 24, 2009. Two things were expected from this survey: first, to know how much power is consumed by a residential customer if the power is available around the clock and next, what would be the peak value and when will it occur. The 30-minute kW demand was obtained and shown in Table 4.2.

Table 4. 2: 30 minute kW demand for 15 customers (July 24, 2009). Time # 1 # 2 # 3 # 4 # 5 # 6 # 7 # 8 # 9 # # # # # # 10 11 12 13 14 15 0:00-0:30 0.1 0.3 0.2 0.2 0.4 0.4 0.5 0.8 0.9 0.7 0.4 0.7 0.3 0.4 0.5 0:30-1:00 0.1 0.3 0.2 0.2 0.4 0.4 0.5 0.8 0.9 0.7 0.4 0.7 0.3 0.4 0.5 1:00-1:30 0.1 0.3 0.2 0.2 0.4 0.4 0.5 0.8 0.9 0.7 0.4 0.7 0.3 0.4 0.5 1:30-2:00 0.1 0.3 0.2 0.2 0.4 0.4 0.5 0.8 0.9 0.7 0.4 0.7 0.3 0.4 0.5 2:00-2:30 0.1 0.3 0.2 0.2 0.4 0.4 0.5 0.8 0.9 0.7 0.4 0.7 0.3 0.4 0.5 2:30-3:00 0.1 0.3 0.2 0.2 0.4 0.4 0.5 0.8 0.9 0.7 0.4 0.7 0.3 0.4 0.5 3:00-3:30 0.1 0.2 0.2 0.2 0.3 0.2 0.5 0.8 0.9 0.7 0.3 0.7 0.3 0.4 0.5 3:30-4:00 0.1 0.2 0.2 0.2 0.3 0.3 0.2 0.6 0.8 0.6 0.3 0.4 0.3 0.3 0.4 4:00-4:30 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.6 0.8 0.6 0.3 0.4 0.3 0.3 0.4 4:30-5:00 0.1 0.2 0.2 0.2 0.3 0.3 0.2 0.5 0.8 0.6 0.3 0.4 0.2 0.3 0.4 5:00-5:30 0.1 0.2 0.1 0.2 0.3 0.3 0.2 0.5 0.8 0.6 0.3 0.4 0.2 0.3 0.4 5:30-6:00 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.6 1.1 0.7 0.4 0.4 0.3 0.4 0.5 6:00-6:30 0.4 0.6 0.5 0.6 0.3 0.7 0.9 1.5 1.4 1.4 0.8 0.8 0.8 0.5 0.7 6:30-7:00 0.4 0.6 0.5 0.6 0.6 0.4 0.6 1.0 0.7 0.9 0.5 1.1 0.4 0.9 1.2 7:00-7:30 0.2 0.3 0.2 0.3 0.5 0.4 0.3 0.5 0.7 0.7 0.5 0.4 0.3 0.5 0.7 7:30-8:00 0.2 0.3 0.2 0.3 0.3 0.4 0.4 0.5 0.7 0.7 0.4 0.4 0.3 0.4 0.5 8:00-8:30 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.8 0.7 0.4 0.4 0.3 0.4 0.5 8:30-9:00 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.6 0.8 0.6 0.3 1.0 0.3 0.4 0.5 9:00-9:30 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.6 0.7 0.8 0.3 1.0 0.3 0.4 0.4 9:30-10:00 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.5 0.6 0.7 0.3 1.0 0.3 0.3 0.4 10:00-10:30 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.5 0.9 0.7 0.4 0.6 0.3 0.5 0.5 10:30-11:00 0.2 0.2 0.4 0.3 0.2 0.2 0.3 0.5 0.9 0.6 0.5 0.6 0.3 0.5 0.9 11:00-11:30 0.4 0.8 0.4 0.6 0.4 0.3 0.5 0.7 1.2 0.6 0.5 0.5 0.6 0.7 1.2 11:30-12:00 0.4 0.7 0.5 0.6 0.4 0.4 0.5 0.9 0.9 1.0 0.6 0.5 0.6 0.7 1.2 12:00-12:30 0.4 0.5 0.5 0.3 0.5 0.5 0.5 0.9 0.8 1.0 0.6 1.1 0.3 0.5 0.7 12:30-13:00 0.3 0.4 0.4 0.4 0.3 0.7 0.6 1.1 0.8 0.7 0.5 0.5 0.3 0.5 0.7 13:00-13:30 0.3 0.4 0.3 0.3 0.3 0.7 0.6 1.0 0.8 0.7 0.5 0.7 0.4 0.5 0.7 13:30-14:00 0.2 0.3 0.3 0.3 0.3 0.7 0.6 0.9 0.8 0.9 0.5 0.7 0.4 0.5 0.6 14:00-14:30 0.2 0.3 0.3 0.3 0.3 0.3 0.6 0.6 0.8 0.7 0.4 1.3 0.4 0.4 0.5 14:30-15:00 0.2 0.3 0.2 0.2 0.2 0.5 0.5 0.7 0.8 0.7 0.4 1.1 0.3 0.4 0.5 15:00-15:30 0.2 0.3 0.3 0.3 0.2 0.3 0.6 0.5 0.8 0.8 0.5 0.5 0.5 0.4 0.6 15:30-16:00 0.2 0.3 0.2 0.2 0.6 0.3 0.5 0.9 0.8 0.6 0.5 0.5 0.5 0.4 0.6 16:00-16:30 0.2 0.2 0.2 0.2 0.6 0.4 0.6 1.0 0.8 0.6 0.4 0.5 0.4 0.4 0.6 16:30-17:00 0.2 0.2 0.2 0.2 0.3 0.4 0.5 0.7 0.8 0.6 0.4 0.5 0.4 0.4 0.6 17:00-17:30 0.4 0.3 0.3 0.3 0.3 0.5 0.6 0.8 0.8 0.6 0.5 0.5 0.5 0.6 0.6 17:30-18:00 0.4 0.4 0.4 0.4 0.3 0.5 0.6 0.8 0.9 0.6 0.6 0.5 0.5 0.7 0.7 18:00-18:30 0.4 0.9 0.6 0.8 0.3 0.5 0.6 0.8 0.9 1.3 0.7 0.5 0.9 0.8 0.8 18:30-19:00 0.5 0.9 0.7 0.8 0.7 0.9 1.1 1.6 1.4 1.6 0.9 1.2 0.7 1.0 1.1 19:00-19:30 0.5 0.5 0.5 0.5 0.8 0.6 0.7 1.3 1.1 1.1 0.6 1.1 0.6 0.7 0.9 19:30-20:00 0.5 0.4 0.4 0.4 0.5 0.5 0.6 1.0 1.0 1.0 0.6 1.1 0.5 0.6 0.8 20:00-20:30 0.4 0.4 0.4 0.4 0.5 0.5 0.6 1.0 1.0 1.0 0.6 1.0 0.5 0.6 0.8 20:30-21:00 0.4 0.4 0.4 0.4 0.5 0.5 0.6 1.0 0.9 1.0 0.5 0.9 0.5 0.6 0.8

Table 4. 2: (continued). # # # # # # Time # 1 # 2 # 3 # 4 # 5 # 6 # 7 # 8 # 9 10 11 12 13 14 15 21:00-21:30 0.4 0.3 0.3 0.3 0.5 0.5 0.5 1.0 0.9 0.9 0.5 0.8 0.4 0.6 0.7 21:30-22:00 0.3 0.3 0.3 0.3 0.6 0.5 0.5 1.0 0.9 0.8 0.5 0.7 0.4 0.5 0.6 22:00-22:30 0.3 0.3 0.3 0.3 0.5 0.4 0.5 0.9 0.9 0.7 0.4 0.7 0.3 0.5 0.5 22:30-23:00 0.1 0.2 0.2 0.2 0.5 0.4 0.5 0.8 0.9 0.7 0.4 0.7 0.3 0.5 0.5 23:00-23:30 0.1 0.3 0.2 0.2 0.4 0.4 0.4 0.8 0.9 0.7 0.4 0.7 0.3 0.4 0.5 23:30-0:00 0.1 0.3 0.2 0.2 0.4 0.4 0.5 0.8 0.9 0.7 0.4 0.7 0.3 0.4 0.5

Findings: Taking the table 4.2 into consideration, the peak time was common among all customers and usually happened in the morning and in the evening. It either happened between 6:30 and 7:30 AM or between 6:00 to 7: 00 PM. The data analysis showed that over 66% of the customers consume less than 1 kW per hour as an average at this period of the year. In Figures 4.6 and 4.7 twenty-four hour demand curves for customers # 4 and # 7 have been plotted. Customer # 4 normally consumes 0.63 kW per hour as an average and 15.16 kW in twenty four hours while customer # 7 consumes 0.97 kW per hour as average and 23.21 kW in twenty hours.

Twenty - four hour Demand curve for Customer # 4 0.9 0.8 0.7 0.6 0.5 0.4 0.3

30 min kW Demand kW min 30 0.2 0.1 0.0 0:30 2:30 4:30 6:30 8:30 10:30 12:30 14:30 16:30 18:30 20:30 22:30 Time of the Day

Figure 4. 6: Twenty four-hour demand curve customer # 4.

Twenty-four hour Demand for Customer # 7

1.2

1.0

0.8

0.6

0.4 30 min kW Demand min kW 30 0.2

0.0 0:30 2:30 4:30 6:30 8:30 10:30 12:30 14:30 16:30 18:30 20:30 22:30 Hours of the Day

Figure 4. 7: Twenty four-hour demand curve for customer # 7.

The maximum 30 minute kW demand for each customer is

Customer kW Customer kW Customer kW #1 0.5 #6 0.9 #11 0.88 #2 0.89 #7 1.1 #12 1.30 #3 0.67 #8 1.64 #13 0.92 #4 0.78 #9 1.43 #14 0.99 #5 0.78 #10 1.58 #15 1.23

The total energy consumed during the day is the summation of all 30-min interval consumption. That is

Customer kWh Customer kWh Customer kWh #1 5.85 #6 10.15 #11 11.05 #2 8.40 #7 11.80 #12 16.85 #3 7.15 #8 19.32 #13 9.400 #4 7.65 #9 21.15 #14 11.75 #5 9.40 #10 18.70 #15 15.10

The 30-min average demand (kW) is determined by the total energy consumed during the day divided by 24 hours. That is

Customer kW Customer kW Customer kW #1 0.244 #6 0.423 #11 0.460 #2 0.350 #7 0.492 #12 0.702 #3 0.298 #8 0.805 #13 0.392 #4 0.319 #9 0.881 #14 0.490 #5 0.392 #10 0.779 #15 0.629

The load factor (LF) for each customer is the ratio of average 30-min kW demand and maximum 30-min kW demand.

Customer LF Customer LF Customer LF #1 0.488 #6 0.470 #11 0.512 #2 0.389 #7 0.447 #12 0.540 #3 0.426 #8 0.503 #13 0.435 #4 0.398 #9 0.629 #14 0.490 #5 0.490 #10 0.487 #15 0.524

The 30-min maximum noncoincident kW demand for the day for the group of customers is equal to

Max. noncoincident demand = 0.5 + 0.9 + 0.7 + 0.8 +0.8 + 0.9 + 1.1 + 1.60 + 1.4 + 1.6 + 0.9 + 0.1.3 + 0.9 + 1.0 + 1.2 = 15.6 kW

The 30-min maximum diversified kW occurs at 19: 00 and has a value of 15.07 kW. The diversity factors is calculated from the ratio of maximum noncoincident demand and maximum diversified demand which is 15.6/15.07= 1.035 The diversified demand curve for the group of 15 customers for the given period interval is plotted in Figure 4. 8

Figure 4. 8: The diversified demand curve.

The transformer load duration curve is plotted in Figure .4.9. It has been developed from the 15 customers. It shows the 30-min kW demand versus the percent of time the transformer activates at or above the specific kW demand.

Figure 4. 9: The load duration curve.

4.1.4 Analysis of Data from DABM

Upon request, the DABM provided the author with two samples of data, a small and a large one. The small size sample data included 93 customers from the same location with the kilowatts they consumed in two months time. These customers have many similarities from many points of view such as temperature, scheduled and unscheduled outages. Their differences may include the appliances they have. In terms of usage, for a good analysis they are grouped in the following categories as listed in Table 4.3.

Table 4. 3: kWh usage categorized list. S.No No of kWh usage Consumption Average kWh customers category (kWh) consumed 1 3 350-360 1068 356.0 2 9 361-370 3289 365.4 3 15 371-380 5625 375.0 4 13 381-390 5005 385.0 5 11 391-400 4345 395.0 6 4 401-410 1621 405.3 7 11 411-420 4566 415.1 8 6 421-430 2553 425.5 9 3 431-440 1305 435.0 10 4 441-450 1780 445.0 11 7 451-460 3183 454.7 12 6 461-470 2792 465.3 13 1 >471 502 502.0 Total 93 37,634

In Figure 4.10 the customers versus range of kWh consumed in a period of two months is plotted. The total kWh consumed by 93 customers is 37,634 in a period of two month, while each group consumed a different average kWh at that period. The average consumption of the group varies from 356 to 502 kWh. However, the overall average consumption of energy is calculated 404.7 kWh for the specified period of time. The last four categories of customers have a bit higher average rate. The high average rate of consumption for the last four groups reveals the presence of 20% higher energy consumers.

Customers vs range of kWh 16

14 15 12 13

10 11 11 8 9

Customers 6 7 6 6 4 4 4 2 3 3 1 0

0 0 0 0 0 0 0 0 0 0 0 0 1 6 7 8 9 0 1 2 3 4 5 6 7 7 -3 -3 -3 -3 -4 -4 -4 -4 -4 -4 -4 -4 4 0 1 1 1 1 1 1 1 1 1 1 1 > 5 6 7 8 9 0 1 2 3 4 5 6 3 3 3 3 3 4 4 4 4 4 4 4 Range of kWh

Figure 4. 10: Customers versus range of kWh consumed.

The large size sample data included residential customers living in several different locations which undergo unlike regime such as various scheduled outages, unexpected blackouts, and different economic classes of customers. The data are listed in Table 4.4. The list includes 7578 customers in 22 different residential areas. The average kWh consumed by each customer is calculated at the last column of the aforementioned table. The average kWh consumed by each customer varies from 305 to 510 kWh for a period of almost two months. While, the overall average of consumed power is 409.6 kWh.

Table 4. 4: kWh usage for residential customers in different locations. No. of customers Total usage kWh Average kWh consumed by each customer 1 217 87643 404 2 357 182070 510 3 400 178934 447 4 253 96478 381 5 175 71548 409 6 260 81558 314 7 342 157671 461 8 456 138872 305 9 556 204052 367 10 550 217654 396 11 245 94363 385 12 375 184715 493 13 501 221658 442 14 190 81226 428 15 210 82317 392 16 505 198700 393 17 356 177644 499 18 425 134470 316 19 309 117955 382 20 231 100450 435 21 550 246913 449 22 115 46723 406

In Figure 4.11 the average kWh consumed versus each group of customers has been plotted. The overall average kWh consumed by each customer is 409.6. Its average line is also seen in the figure. Scrutinizing Figure 4.11, one can see that the average kWh consumed by each customer varies from 305 kWh to 510 kWh for a period of two months. Several reasons predicate such variance from customer to customer. Difference in duration of outages, presence of higher and lower energy consumers, and occurrence of outages in different time of the day are examples to justify such differences. The time of the outage affects significantly the amount of energy consumed. For instance, if the outage is scheduled during the peak load time (say early in the morning between 6:00 to 8:00) or during the dip load time (say late midnight to 5:00 AM), the energy consumption differs. For the former, the customer has no choice unless to approach another energy source and for the latter, there is less need to consume energy and no need to apply any alternatives.

Average kWh consumed versus No. of customers 600

d 500 overall average 400 300 200

kWh consume kWh 100 0 12345678910111213141516171819202122 No. of Customers 404510447 381409 314461 305 367 396 385 493442 428 392 393 499 316 382435 449 406 Customers group

Figure 4. 11: kWh versus customer for 7578 customers in 22 residential areas.

4.1.5 Appliance-based Approach

The results obtained from Section 4.1.3 provided a rough estimation that how many kWh would be consumed by a residential customer according to the current limitations which are discussed in Section 4.2. However, for a long term and a sustainable condition absolutely more power are required than what is consumed currently. Results from the household drop-off survey from one side and the policies/guidelines of the National Environmental Protection Agency, an Afghan entity where insist is placed to reduce sources of air pollution and instead increase the usage of electric power, encouraged the author to employ an appliance based approach. In this approach, initially the average high and average low temperature for Kabul City as shown in Figure 4.12 was found. The solid line shows the monthly average high and the dashed line shows the monthly average low.

Figure 4. 12: The Average high and average low temperature for Kabul City.

Taking into account the average temperature in the course of a year shown in Figure 4.12, the observation from the surveys conducted, and a rough market survey, the residential customers in Kabul City can be classified into two broad categories: the standard class and high energy consumer class. The standard class includes customers who use minimum appliances with minimum usage hours in a month. While high energy consumed class comprises customers who operate large number of appliances with a high degree of usage in a month. The algorithm to differentiate these two categories of residential customers is basically on the appliances they use. Furthermore, the observations obtained from the household drop-off survey contributed to reduce the residential customer class from three to two categories. Taking into consideration the two classes of residential customers and their characteristics, two separate tables of appliances have been developed. Tables 4.5 and 4.6 indicate the appliances used by standard and high energy consumer class respectively. In both tables, the appliances with average usage for each month of the year are shown.

Table 4. 5: Common standard household appliance energy usage. Estimated average usage of appliances in each month of the year (hours)

Appliance Watts Apr May Jun July Aug Sep Oct Nov Dec Jan Feb Mar Portable Fan 115 75 300 600 600 500 200 0 0 0 0 0 0 Ceiling Fan 60 75 300 600 600 500 200 0 0 0 0 0 0 Electric Heater (2 Portable) 1200 0 0 0 0 0 0 90 300 1000 1000 1000 500 Clothes Washer 500 5 5 5 5 5 5 5 5 5 5 5 5 Iron (Hand) 1000 3 3 3 3 3 3 3 3 3 3 3 3 Lighting Single Lamp 60 30 30 30 30 30 30 30 30 30 30 30 30 Compact Fluorescent 18 450 450 450 450 450 450 450 450 450 450 450 450 Yard Light 100 328 311 287 304 328 348 392 409 437 429 365 372 Fluorescent (2 Tube 4 ft.) 100 60 60 60 60 60 60 60 60 60 60 60 60 Television 120 136 136 136 136 136 136 136 136 136 136 136 136 CD/DVD player 25 17 17 17 17 17 17 17 17 17 17 17 17 Refrigerator-Freezer Frost Free 300 300 300 350 350 350 300 150 150 150 150 150 150 Hair Dryer (Portable) 1000 2 2 2 2 2 2 2 2 2 2 2 2 Oven/cooker 2000 60 60 60 60 60 60 60 60 60 60 60 60 Water Heater 2000 30 30 30 30 30 30 30 30 30 30 30 30 Water Boiler 400 30 20 20 20 20 30 70 70 70 70 70 40

Table 4. 6: High standard household appliance energy usage.

Estimated average usage of appliances in each month of the year (hours) Appliance Watts Apr May Jun July Aug Sep Oct Nov Dec Jan Feb Mar Portable Fan 115 75 300 150 150 150 75 0 0 0 0 0 0 Ceiling Fan 60 75 300 150 150 150 75 0 0 0 0 0 0 Air Conditioner (Room) 750 0 100 150 270 270 20 0 0 0 0 0 0 Air Conditioner (Room) 1050 0 100 300 500 350 20 0 0 0 0 0 0 Electric Heater (3 Portables) 1200 0 0 0 0 0 20 90 800 1000 1300 1000 300 Clothes Washer, Automatic 500 5 5 5 5 5 5 5 5 5 5 5 5 Clothes Dryer 5000 5 5 5 5 5 5 5 5 5 5 5 5 Iron (Hand) 1000 3 3 3 3 3 3 3 3 3 3 3 3 Lighting Single Lamp (60W) 60 30 30 30 30 30 30 30 30 30 30 30 30 Compact Fluorescent 18 450 450 450 450 450 450 450 450 450 450 450 450 Ceiling Fixture (3 bulbs) 180 90 90 90 90 90 90 90 90 90 90 90 90 Yard Light 200 328 311 287 304 328 348 392 409 437 429 365 372 Chandelier (5 Lamp) 300 60 60 60 60 60 60 60 60 60 60 60 60 Fluorescent (2 Tube 4 ft.) 100 60 60 60 60 60 60 60 60 60 60 60 60 Television 180 136 136 136 136 136 136 136 136 136 136 136 136 CD/DVD player 25 17 17 17 17 17 17 17 17 17 17 17 17 Dish Receiver 40 30 30 30 30 30 30 30 30 30 30 30 30

Table 4.6: High standard household appliance energy usage (continued).

Estimated average usage of appliances in each month of the year (hours)

Appliance Watts Apr May Jun July Aug Sep Oct Nov Dec Jan Feb Mar Coffee Maker 900 6 6 6 6 6 6 6 6 6 6 6 6 Dishwasher 1300 12 12 12 12 12 12 12 12 12 12 12 12 Frying Pan 1150 3 3 3 3 3 3 3 3 3 3 3 3 Microwave Oven 1300 4 4 4 4 4 4 4 4 4 4 4 4 Oven 4000 90 90 90 90 90 90 90 90 90 90 90 90 Refrigerator 500 300 300 350 350 350 300 150 150 150 150 150 150 Toaster 1150 2 2 2 2 2 2 2 2 2 2 2 2 Hair Dryer (Portable) 1000 2 2 2 2 2 2 2 2 2 2 2 2 Computer (Monitor & Printer) 200 60 60 60 60 60 60 60 60 60 60 60 60 Stereo 30 5 5 5 5 5 5 5 5 5 5 5 5 Water Pump 750 15 15 15 15 15 15 10 10 10 10 10 10 Vacuum Cleaner (Portable) 800 10 10 10 10 10 10 10 10 10 10 10 10 Clock 5 720 720 720 720 720 720 720 720 720 720 720 720 Water Boiler 400 15 15 15 15 15 15 15 20 20 20 20 20 Water Heater 3800 30 30 30 30 30 30 30 30 30 30 30 30

The algorithm used to develop the Table 4.5 and Table 4.6 includes several factors. The most important factors were the typical necessary appliances a household from each group need, length of the day, and the average day and night temperature. Hence, the appliances were grouped in the following three sequences. 1. Fixed Rate Dependent Appliances (FRDA): This type of appliances includes all those appliances that regardless of any factor they will be operated either in daily- bases or weekly-bases without any changes. Laundry, ironing, compact fluorescent lamp, fluorescent lamp, television, CD/DVD player, dish receiver, coffee maker, and dishwasher are examples of (FRDA). Here are, how the estimated usage hour of each calculated. • Cloth Washer/Dryer: Generally, a family has laundry 8 to 10 times in a month. Its time depends to the quantity and quantity of the cloths. The estimated average usage of washer/dryer is calculated to be 5 hrs/month. • Iron (3 hrs/month): Normally, a family (average 6 people) need to iron the cloth such as suits, and beds sheets once a week and each time it takes 45 minutes as an average. • Light single lamp (30 hrs/month): This light is used in the bathroom. It is estimated that a family generally uses the bathroom 10 times a day (6 min each). • Compact florescent lamp (450 hrs/month): It is a package of five bulbs for kitchen, bedroom, corridor, and outdoor lighting. The outdoor lighting is used for the length of the night, while the rest vary from 1 to 2 hours. The package as an average is estimated to be in use for 15 hrs per day. • Fluorescent lamp (60 hrs/mon): This is only for the standard residential class and is used for the living room 2 hrs per day. • Television (136 hrs/mon): Usually a family watches television (broad casting channels) four hours every day and addition to that four hours on weekends (weekend is one day in Afghanistan). • CD/DVD Player (17 hrs/mon): It is taken half hour per day plus half hour additional on weekends. • Hair dryer (2hrs/mon): Four minutes as an average per day. 82

• Oven/cooker (60 hrs/mon): Two hours per day (one hour for lunch and one hour for dinner). The oven usage time both standard and high standard residential customer classes are the same. The only difference is the type of oven and the capacity of the oven of each. • Ceiling Fixture (90 hr/mon): It is considered 3 hours per day. • Chandelier (60 hr/mon): It is measured 2 hours per day but could be used alternatively with ceiling fixture. • Dish Receiver (30 hrs/mon): It is taken one hour into account per day. • Coffee Maker (6 hrs/mon): Or twice a day for 6 minutes each. • Dish Washer (12 hrs/mon): Twice a day for 12 minutes each. • Frying Pan (3 hrs/mon): Six minutes per day or 12 minutes every other day. • Microwave oven (4 hrs/mon): It is considered to be used twice a day, each for 4 minutes. • Toaster (2 hrs/mon): Four times per day. It is only considered for the morning. • Computer (60 hrs/mon): It is measured 2 hours per day. The two hours computer usage is for learning purpose as well as business work. • Stereo (5 hrs/mon): 75 minutes (1 hr and 15 min) per week. • Vacuum Cleaner (10 hr/mon): 20 minutes per day including vacuuming the car. • Clock (720 hr/mon): 24 hours per day. • Water heater (30 hrs/mon): One hour per day for taking shower. 2. Day Length Dependent Appliances: Yard lights and outdoor lights which need to be on at nights are counted in this category. To calculate the estimated average usage per month, first the length of each night for the course of each month was found and summed up. It continued for each month of the year as seen in tables 4.5 and 4.6. For standard and high energy consumer class. 3. Temperature and seasonal Dependent Appliances: Several series of appliances can be studied under this group. Cooling and heating appliances are examples of very direct temperature affect appliances while refrigerator, water boiler, and water pump are counted as seasonal appliances which are affected by temperature in a bit longer span. The algorithm used to calculate their estimated average usage for each month of the year is described below.

• Refrigerator: The usage of refrigerator is split into three periods. In April and May when the temperature is moderate, the average usage of refrigerator is estimated 10 hours per day. In Jun, July, and August when the temperature is very high, the usage of refrigerator also increases. Thus, its usage is estimated to be 11.6 hrs on daily basis. In contrast to this, September to March is considered the cold months of the year where the usage of refrigerator also significantly reduces. So, for the aforementioned months its usage is estimated to be 150 hrs per month. However, the refrigerators will remain plugged in all the time and this is the thermostats to maintain the temperature of the refrigerator at a desired level. • Water Boiler: Water boiler usage is split into two spans of time. The first span is from April to October (15 hrs per month or 30 minutes per day). The second span is from November to March when the weather is cold. In the latter span, its average usage is estimated 20 hrs per month (40 minutes per day). In these months it takes a bit longer for the water reach to boiling point. • Water Pump: Water pump is used for feeding the reservoir, watering the lawn, washing the yard and washing the automobile. Its usage is split into two periods; from April to September and from October to March. In the former, it is estimated to be 15 hrs per month (30 minutes each day) and in the latter 10 hrs per month (20 minutes per day). • Cooling/heating Devices: Normally, the temperature in Kabul City fluctuates from -7 Co in the winter to 34 Co in the summer. However, Kabul experiences the highest 40 Co and the lowest -15 Co also, but does not last longer. For this thesis work 20-25 Co were considered as room temperature where one generally no needs to operate the cooling/heating devices. Usually, from mid-April, the weather daily high temperature progresses from 25 Co to 33 Co by the beginning of July. For the months of July and August, it is almost stable and fluctuates one or two Celsius degrees. From the middle of August, it begins to drop from 33 Co to 25 Co by the end of September. From the first week of October, the average daily high temperature steadily starts to drop from 25 Co to 4 Co. Accordingly, the daily low temperature also gradually drops from 5 Co to -7 Co by mid-January. From mid-January until mid-February the daily average high and daily average low will remain constant to 5 Co (average high) and -7 Co (average low) without a

significant change. From mid-February till to the middle of May, again it begins to get warm. During this period, the daily high average increases from 5 Co to 25 Co by mid-April. Similarly, the daily average low temperature develops from -7 Co to 5 Co. Based on the above facts and taking into account the daily temperature, the estimated usage of cooling and heating appliances have been calculated as seen in Table 4.5 and Table 4.6 for both classes of residential customers. Our observation shows that 20% of the residential customers represent the high energy consumers, while the rest falls into the standard category. Yet, these percentages may be affected by the economic growth. As stated in [20], a unidirectional linear causality running from real GDP to electricity consumption. Thus, as much as economic growth improves the percentages of standard class decreases and instead the percentage of high energy consumer class increases. According to this, a general formula as illustrated in Equation 4.2 can be developed.

PTotal = n()a ∑∑PSt + b PH (1+ δ ) (4.1) where: n represents the total number of residential customers, a and b indicates the proportion of standard and rich category,

PSt and PH are the total power consumed by standard and high energy consumer class respectively in a particular month, and δ is the factor by which electricity consumptions increases versus economic growth.

The sum of a and b is always one that is a + b = 1. PSt and PH can be calculated from the multiplication of appliance capacity and the monthly hours of operation matrices of :

P = []Watts of apppliances . [monthly hour of operation] St St St (4.2) PH = [][]Watts of apppliances H . monthly hour of operation H

The average consumption of each class of residential customer is calculated and listed in Table 4.7. The average consumption per month would be 819 and 1550 kWh for standard and high energy consumer classes respectively.

Table 4. 7: Monthly kWh consumed by each residential group.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Standard 1145 1414 892 368 468 785 1160 1053 650 414 445 736 (kWh) High energy consumer 3171 3109 1566 554 853 1248 1722 1684 1038 790 916 1954 (kWh)

Following result also can be obtained from the analysis of Table 4.7. For standard customer group:

Customer Annual Monthly Daily Hourly group consumption average average average kWh kWh kWh kWh Standard 9530 794.1667 26.109 1.0879 High energy consumer 18605 1550.417 50.973 2.124

To take into account the proportion of each customer, for a mixed customer for the course of a year, the total consumption in kWh is equal to

Customer Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mixed 1150 1753 1027 405 545 878 1272 1179 728 489 539 980

The daily average consumption for each month is

Customer Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mixed 50.01 62.61 33.12 13.51 17.58 29.25 41.05 38.04 24.25 15.78 17.97 31.6

Similarly the hourly average consumption for each month of the year is

Customer Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mixed 2.084 2.609 1.380 0.568 0.733 1.219 1.710 1.585 1.011 0.658 0.749 1.317

The overall consumption for a mixed customer would be

Customer Annual Monthly Daily Hourly average group consumption average average kWh kWh kWh kWh Mixed 11345 945.42 31.08 1.295

Figure 4.13 describes the smooth graph for high energy consumer, standard group, and a mixture of standard and high energy consumer.

Figure 4. 13: Monthly consumption of both residential groups.

Note that from 30-min kW demand survey we found that a customer consumed 23.21 kWh in 24 hours in the month of July. From appliance-based survey it was calculated that a customer may consume 41.05 kWh in a day for the month of July. If we look at these two figures, it is almost doubled. Several reasons contribute to verify the differences. At present, most of the customers do not have electric appliances which they desire to have in future. The availability of power is not reliable and customers are not willing to invest on appliances such as washer, dryer,

87 and oven. Finally, the DABM limits the current of services through a fuse that is out of reach of the customer. Once the fuse blows up, it takes couple of days to be fixed. In this case the customers do not want to be involved in a headache like this.

4.2 Limitations of the Study

There were some limitations with respect to the data and its analysis that may affect the accuracy of the results.

4.2.1 Availability of Data

As already mentioned in Chapter one, the distribution system in Kabul City was seriously damaged during the civil war from 1992 to 1995. The data for the previous years were all burned or destroyed. The data for the recent years since the system undergoes rehabilitation either has not been recorded systematically or may not be a complete data.

4.2.2 Blackouts

The blackouts, scheduled outages, mixed of customer classes within the same DTs, and sharing several families a single meter were hard to obtain information. The duration and time of outages for each cluster of residential customers varied. For example, a cluster of customer suffered several days outage, or another group suffered an outage in the morning, another group in the afternoon, and another group in the evening. Such outages and their time have significant effect on the usage of kWh.

4.2.3 Economics

It was hard and still is hard to distinguish the exact number of residential customers that locates in higher, meddle or lower level of economic condition. It was difficult to spot them from the kWh they consumed since the provision of power is not around the clock currently. The economic condition and the appliances each category employ differs.

4.2.4 Short Period of Study

As already mentioned due to the lack of data for a full year, this project focused on a two months study. However, the average consumption due to the change in temperature in each season of the year varies. For example, in the summer and winter it may increase while fall and spring are the lowest consumption seasons.

4.2.5 Definition of Customer

Who should be called a customer was/is a confusing word. DABM register a beneficiary as a residential customer who lives in a compound. Or in other words, DABM assigns a meter per compound regardless how many families are living in the compound. The number of families living in a compound varies from compound to compound and from one residential area to another one. It also depends on the size of the compound. In many places have been observed that more than five families have shared the same meter. However the least number of families that have shared one meter is two. This affects any forecast of power for a residential area.

Chapter 5: Conclusion and Recommendations

The main purpose of this thesis was to find out, at a first glance, how many kilowatt hours does a residential customer consume currently in Kabul City, and how many kWh will be required for residential class in Kabul City in a sustainable condition? However, since 2002 many efforts and much support by international communities have taken place to enable the MEW and DABM to supply electric power for the people in Kabul City and other cities. The power distribution network of Kabul City was seriously taken into consideration. For example, in the last five years over 500 distribution transformers have been installed. Yet, without a qualified assessment and well designed plan any progress may fail. The overloading of slightly over 100 transformers is an example of such failure. In spite of many limitations, the author developed a model for load growth in Kabul City for normal conditions. In order to help the MEW and DABM/KED, while they are planning and designing the distribution system to not only prevent the DTs for being overloaded, but also to provide adequate and reliable power for the residential customers, I recommend the result of this thesis will be taken into consideration. In the next sections, the methodology and the obtained results are summarized. Then, the conclusion and limitations of the research are discussed in brief. And finally, this chapter ends with highlights for possible future research.

5.1 Summary of the Methodology

In order to achieve the goals and objectives of this thesis, the following methods have been applied. • Semi-structured interviews occurred with authorities at the Ministry of Energy and Water, De Afghanistan Brishna Mossessa, Afghan Energy Information Center, and a number of other non-governmental organizations involved in the rehabilitation of power network in Kabul City. Meanwhile, observations were also collected from field trips to the substations, junction stations and transformer houses.

• A household drop-off survey was conducted in winter 2008. The sample survey included 510 residential customers living in several areas of Kabul City. It was conducted under direct management of the author through his former students, friends, Wakils, and local authorities. The survey was split into two parts; a random sample and selected customers. Finding the lighting sources, heating sources, cooking sources, consumption of energy, and socio- economic level of customers were the objectives of the survey. • Meter-based Survey: A 30-minute meter based survey was conducted. The sample contained 15 customers. The main purpose of this survey was to find the current demand of a residential customer on a daily basis. Also to find what the peak load is and when it occurs. • Data collection from DABM: Two sets of data were received from DABM. The first one included 93 residential customers from one area and the second included 7578 customers from 22 different living areas. Both examples of data explain the kWh consumed by customers in a two months period.

5.2 Summary of the Results

Based on the analysis discussed in Chapter four, the following summarized results are extracted. From Section 4.1.1 we learned that 217,525 residential, 13,877 commercial, and 2589 governmental customers have been registered with DABM/KED in Kabul City and 353,859 MWh were consumed only by residential customers in 2008. Or in other words, each residential customer on average consumed 1626 kWh in 2008. Currently, Kabul City is supplied with 100-105 MW/hr which are delivered through 852 distribution transformers. The majority of the DTs are either 400 KVA or 630 KVA. However, 800 and 1000 KVA DTs also have been installed. Due to the poor network design, over 100 of the DTs have already been overloaded. From Section 4.1.2 we found that there is not adequate power. Customers use several alternative resources, and the customers suffer frequent scheduled outages. Their lighting has been maintained 26% by grid power, 25% through private generators, 28% by gas-based lanterns, 18% through fuel-based lanterns, and 3% by

91 solar PV or solar lanterns. Similarly, their heating system energy is obtained 55% from firewood, 34% kerosene stoves, 2% gas stoves, 5% grid power, and the rest from saw dust and wood chips or charcoal. Also, for cooking, 72% of the customers use gas, 9% fuel, 11% electricity, and 7% wood. The availability of grid power differs from season to season. Up to the time of research for this thesis, 95% of the customers had less than 6 hours access to grid power, in the autumn it was 75%, in the summer 68%, while, in the spring, there was a different scenario. Over 60% of the customers had 6-12 hours access to grid power and 28% had fewer than 6 hours. In Section 4.1.3, in a 30 min kWh demand survey, it was observed that 67% of the customers consumed less than 1 kWh/hr and the remaining from 1- 1.8 kWh/hr. Or in other words, the customers consumed 15.16 kWh/day on average. We also found that the peak load varies from 1 kWh to 3.2 kW. The peak time is 6:30 to 7:30 in the morning and from 6-7 in the evening. In addition, a bit higher consumption has been observed at noon. From the first part of Section 4.1.4, data analysis from DABM, it was found that the average consumption of energy varied from 356 to 502 kWh in two months time. Or, the average consumption within the group of 93 customers was 404.7 kWh. Similarly, 7578 residential customers in 22 different areas consumed 305-510 kWh as an average in a period of two months. Or, the average for this group of customers was calculated to be 409.6 kWh. The 30-min kWh demand survey showed that for one day consumption under current conditions no outages occurred, while the two months data included several outages in a week. The analysis of Sections 4.1.1-4.1.4 inspired the author to approach the appliance based analysis. Based on the results obtained from the household drop-off survey and the 30- min kWh demand, originally the residential customers were divided up into two groups; a standard group and higher energy consumer customers. The standard group covers relatively low and mid-level of socio-economic categories of the society. They are the ones who would like to minimize energy consumption to the greatest extent possible. The higher energy consumer, which includes the high level socio-economic group of the society, as its name implies, will consume much more energy than the former group.

For each group, a separate list of appliances was developed. Taking into account several factors such as temperature, season of the year, day and night, an average usage of appliances for each month for the course of one year was estimated. As a result, it was projected that a standard class customer will consume 819 kWh and a higher energy customer will consume 1550 kWh as average in a month. In other words, regardless of classification of customers, one customer will consume 965 kWh on average per month.

5.3 Limitations

There were several limitations that need to be acknowledged and addressed regarding the present research. Initially, it was hard to get data, because DABM lost its data bank for the years prior to the occurrence of civil war in Kabul city. The recent rehabilitated data bank has not been digitalized yet. Second, the outages’ (scheduled and non-scheduled) report and their time and duration were not available or even have not been recorded. Another limitation was related to the economics. The socio-economic status of the people has not been studied yet for Kabul City. It will help to know the categories of the people and their class definition. Furthermore, no study was found describing how the economic growth and electric energy consumption is related in Afghanistan. Finally, the definition of customer: customer is not yet well defined. According to DABM, a customer is a household living within a compound. During the study, it was recognized that the number of customers living in a compound varied from one to 5 families.

5.4 Conclusion

Load growth is an important complement to any power distribution network. It helps the power utilities not only to secure and plan adequate power but also helps them to prevent and avoid any potential risks and damages to the equipment and devices (such as transformers, switches, protection devices and feeders). In this thesis the load growth for Kabul City for a sustainable situation was studied. According to

93 the identified results from the 30-min demand survey and the appliance-based approach in Chapter 4 the following has been achieved. In general, a customer experiences two peak times, morning and evening. The peak load is temperature dependent and varies from month to month. From Table 4.7 it has been observed that the peak demand month is January for the high energy consumer class and February is the peak demand month for standard customers. Standard customers consume 1,414 kWh and a high energy consumer customer dissipates 3,171 kWh. And the lowest consuming month is April where standard and higher energy groups consume 368 and 544 kWh respectively. On an annual basis, the total power dissipated by a customer is equal to the 80% annual consumption of standard class plus 20% annual consumption of higher energy consumer. That is ∑ annual (kWh) = 80%(9530)+ 20%(18605) = 11,345 kWh 11,345 kWh is the amount of energy that may be consumed by a customer in a year. In this regard, a customer is a family consisting of 5 to 8 (7 as an average) individuals. For the four million estimated population of Kabul City, the total required power is equal to the sum of energy consumed by the families plus the 20% reduced network losses. That is:

6 ⎡4×10 3 ⎤ 9 ∑ Annual required power (GWh) = ⎢ ×11345×10 ()1+ 0.2 ⎥ /10 = 9076 GWh ⎣ 6 ⎦

In other words, for the residential customers in Kabul City as average 1036.07 MW power is required to be fully adequate. If the load factor (LF) is similar to 0.482 that was found in the 15 customers’ survey, then the capacity needs to be 2.2 GW. Although, currently the total supplied energy is only 100 to 105 MW. For expansion rehabilitation and safety purposes of any power distribution network, it is recommended to consider the size of distribution transformers based on the following calculations: Customer load: As discussed in Section 4.1.5, the customer load consists of 80% standard class and 20% higher energy consumers. To determine the transformer size, the average of peak month is taken into consideration which occurs in January and February. Thus,

Standard customer load = 0.8(1,414)/30/24 = 1.57 kWh Higher energy consumer load = 0.2(3,109)/30/24 = 0.86 kWh Total customer load = 2.43 kWh Transformer losses: Any transformer, even the most efficient one, has an amount of loss associated with the current flowing through it. The loss of a transformer includes core loss or also termed no load loss, and the copper loss which is also called load loss at rated power. The loss of a transformer varies as the kVA of the transformer varies. To take this fact into account, according to [24] the typical loss of 400 and 600 kVA transformers are: 400 kVA transformer: No load loss = 930 W Load loss = 4600 W 600 kVA transformer No load loss = 1300 W Load loss = 6500 W The 400 kVA and 600 KVA transformers are the standard of DABM and used vastly across the distribution network in Kabul City. However, 800 and 1000 kVA transformers also have been installed. Thus, the total loss is 5.53 kW and 7.8 kW for 400 kVA and 600 kVA transformers respectively. The typical losses of the other sizes of transformer are found in Table A at appendix C. Secondaries losses: The losses of secondaries also must be added as a load of the distribution transformers. These losses are simply the I2R, where R is the resistance of conductor and I is the current flowing through the line. The length of the secondaries varies from 1000 meter to 1800 m. The loss of secondaries is a function of length as well as the current flowing through it at each instant. If we assume such losses to be 2% of the transformer rating and PF equal to 95%, then the number of customers connected to transformer (TF) should not exceed:

TF × PF − TF losses − secondaries losses Customer for 400 kVA TF = rating []0.8()1414 + 0.2 ()3109 / 30 / 24 =151

Similarly, the number of customers for 600 kVA transformers is determined 226. The total numbers of transformers needed depends on the size of the transformer. For example, if we assume all transformers to be 400 kVA, then (4,000,000)/7/151 = 3784 transformers are required. For 600 kVA transformers, or 1000 kVA, or any combinations of them a similar approach should be applied. Currently, 852 DTs exist in Kabul City and DABM estimates at least another 1000 DTs will be required. The author believes that DABM is capable to procure and install this many thousand DTs once the power supply is secured. In 2004, DABM proposed an estimated $2,785 million to increase the urban access to 90% by 2015. At least 50% of the proposed budget has already been secured by donor agencies and the remaining has been committed.

5.5 Future Research

The power system in general and Kabul power distribution network in particular has become a fast growing area for funding in recent years. In order to minimize the waste of resources and waste of time it is required to well plan, design and implement the projects. With no doubt, without prior feasible research all the efforts will eventually fail. The load growth for residential customers in Kabul City has been identified and studied in this thesis through an appliance-based approach. The first objective in the continuing work is the extension of this study into a larger sample size, more customers in multiple districts and at least for the course one year. The second aim is identification and analysis of commercial load, governmental load, and industrial load. How much energy do they need and how should their load be managed? How to distinguish commercial, governmental and industrial load from residential load? A third objective is to study the electricity consumption and economic growth in Kabul City. The literature review showed that a unidirectional linear causality is running from real GDP to electricity consumption. Since, the real GDP is growing fast, it will affect proportionally the consumption of energy.

A fourth objective for future work is to study the power losses in the Kabul distribution network. The recommendations and out-comes of such study will propose what should be done to prevent non-technical losses and what should be done to minimize the technical power losses to a possible and acceptable level. Currently both the technical and non-technical power losses are as high as 40 to 45%. Finally, the last, but not the least essential objective for future work is the usage of renewable energy resources, in particular the usage of solar power. Solar power has become an even more important form of renewable energy. Solar power generating projects could contribute in supplying power to Kabul City and especially for the rural areas. Afghanistan, with at least 300 sunny days, has a great potential for generating power by solar technology. For Kabul City, I recommend the ministries should invest in solar power and obtain at least a significant portion of their power from solar technology. Two thirds of the Afghan population, more than sixteen million people, are living in rural areas without electricity. In such areas, population density is low and this means that grid connection becomes very expensive. Yet, solar power is the only solution for providing power to the rural communities. The applications of solar power supply may include community centers such as schools and health clinics, and domestic solar power supplies. Even though the investment and capital cost for solar projects apparently seems high, it needs a careful study. I am sure the study will suggest solar power because the population density is as low as 40-44 people per square kilometer. Wide-spread population from one side, vast area of rocks, and difficult accessibility makes the grid connection cost-prohibitive. The high losses of the transmission and sub-transmission lines will contribute to make it more challengeable. The load growth study described throughout the thesis will hopefully be a valuable source for the continuation of this work and for authorities at the Ministry of Energy and Water as well as De Afghanistan Brishna Mossessa and its provincial departments.

Bibliography

[1] L. P. Goodson, Afghanistan’s Endless War: State Failure, Regional Politics, and the Rise f Taliban. Seattle, WA: University of Washington Press, 2003. [2] M. M. Sediq, “Power sector strategy for the Afghanistan national development strategy,” Kabul, 2007. [Online]. Available: http://www.ands.gov.af/ands/final_ands/src/final/Afghanistan%20National%2 0Development%20Strategy_eng.pdf [3] Afghanistan National Development Strategy 208-2013. [Online]. Available: http://www.ands.gov.af/ands/final_ands/src/final/Afghanistan%20National%2 0Development%20Strategy_eng.pdf [Accessed: Jul. 3, 2009]. [4] Advanced Engineering Associates International (AEAI), “Kabul City medium voltage (MV) and low voltage (LV) distribution system assessment study, Close-out report,” Sep. 2007. [5] Afghan Energy Information Center. [Online]. Available: http://www.afghaneic.org. [Accessed: Jul. 18, 2009] [6] Nation Master. [Online]. Available: http.nationmaster.com/graph/ene_ele_pow_con_kwh_percap-power- consumption-kwh-per-capita. [Accessed: Jul 20, 2009] [7] P. D. Arizon and J.C. Ledezma, “Afghanistan transmission system power analysis,” Siemens Power Transmission and Distribution Inc., Final Report, P/21-113141, Sep. 2007. [8] P. Kundur, Power System Stability and Control. New York: McGraw-Hill, 1994. [9] J. D. Glover and M. S. Sarma, Power System Analysis and Design, 3ed. Pacific Grove, CA: Brooks/Cole, 2007. [10] M. Shahidehpour and H. Yamin, Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, New York: John Wiley & Sons Inc., 2002. [11] U.S.-Canada, Power System Outage Task Force. Final Report on the August 14, 2003 Blackout in the United States and Canada: Causes and Recommendations, 2003. [12] P. Ju and Y. Tang, “Load modeling in China-Research, application & tendencies,” In Proc. 3rd International Conference on Electric Utility Deregulation and Restructuring and Power Technology, 2008, pp. 42-45. [13] W. H. Kersting, Distribution System Modeling and Analysis. New York: CRC Press, 2007. [14] C. A. Canizares, “Effect of static load models on Hopf bifurcation point and critical modes of power systems,” Thammasat Int. J. Sc. Tech. vol. 9, no. 4, pp. 69-76, Oct.-Dec., 2004. [15] G. L. Dous, Voltage Stability in Power Systems: Load Modeling Based on 130kV Field Measurements, Technical Report No. 324L. Department of Electrical Power Engineering. Chalmers University of Technology. Göteborg, Sweden 1999.

[16] H. Dong, H. Renmu, X. Yanhui, M. Jin, and H. Mei, “Meaurement-based load modeling validation by artificial three-phase short circuit tests in North East power grid,” 2007, IEEE, Power Engineering Society General Meeting, June 24-28, Tampa, FL. [17] S. H. Ling, H. F. Leung, H. K. Lam, and P. K. Tam, “Short-term electrical load forecasting based on neural fuzzy network,” IEEE Transactions on Industrial Electronics, vol. 50, no. 6, pp. 1305-1316, Dec. 2003 [18] B. Kermanshahi and H. Iwamiya, “Up to year 2020 load forecasting using neural nets,” Electric Power and Energy Systems, vol. 24, pp. 789-797, 2002. [19] H. Hahn, S. M. Nieberg, and S. Pickl, “Electric load forecasting methods: Tools for decision making,” European Journal of Operational Research, vol. 199, pp. 902-907, 2009. [20] J. Yuan, C. Zhao, S. Yu, and Z. Hu, “Electricity consumption and economic growth in China: Cointegeration and co-feature analysis,” Energy Economics, v. 9, pp. 1179-1191. [21] H. L. Willis, Power Distribution Planning Reference Book, 2nd edition. New York: CRC Press, 2004. [22] A. S. H. Hamza, A. Hegazy, and S. El-Debeiky, “Electric load forecast for developing countries,” IEEE Electrotechnical Conference, Melecon pp. 429- 441, 2002. [23] Y. Tang, “Power distribution system planning with reliability modeling and optimization,” IEEE Transactions on Power Systems, vol. 11, no. 1, pp. 181- 189, 1996. [24] G. Petrecca, Industrial Energy Management: Principles and Applications, New York: Springer, 1992.

Appendix A: Daily Power Production [5]

100

101

Appendix B: Household Drop-off Survey Sheet

Household Drop-off Survey for Electricity Name: Location: Occupation: Family size

A – Lighting: What sources are used for lighting? Circle anyone applies to you. 1- City power 2- Generator 3 – Solar 4 - Gas Lanterns 5 – Fuel-based Lantern 6 – mixed B - Heating System- What sources do you use to heat up the house? Circle anyone applies to you. 1 – City Power 2 – Fuel 3 – Wood 4 – Sandali 5 – Others C - Cooking - What sources are you using for cooking 1 – City Power 2 – Fuel 3 - Gas 4 – Wood 5 – Others (specify) Resources : 1- City Power 1.1- How often the city power is available? 1.1-1. During the spring? 1.1-1.1. How many hours per day, the city power is available? a – 6 hrs b – 12 hrs c – 18 hrs d – 24 hrs 1.1-1.2. What are you using this for? a - Lighting b- Cooking c - Heating d – Entertainment d - Others (specify) 1.1-1.3. What would be the billing in terms of KW as an average per month? a - <100 KW b - <200 kW c - < 300 kW d- < 400 kW e - < 500 KW 1.1-2. During the summer 1.1-2.1. How many hours per day, the city power is available? a – 6 hrs b – 12 hrs c – 18 hrs d – 24 hrs 1.1-2.2. What are you using this for? a - Lighting b- Cooking c - Heating d - Entertainment d - Others (specify) 1.1-2.3. What would be the billing in terms of KW as an average per month? a - <100 KW b - <200 kW c - < 300 kW d- < 400 kW e - < 500 KW (Specify) 1.1-3. During the fall 1.1-3.1. How many hours per day, the city power is available? a – 6 hrs b – 12 hrs c – 18 hrs d – 24 hrs 1.1-3.2. What are you using this for? a - Lighting b- Cooking c - Heating d - Entertainment d - Others (specify) 1.1-3.3. What would be the billing in terms of KW as an average per month? a - <100 KW b - <200 kW c - < 300 kW d- < 400 kW e - < 500 KW 1.1-3.4. What are you using this for? a - Lighting b- Cooking c - Heating d - Entertainment d - Others (specify) 1.1-4. During the winter 1.1-4.1. How many hours per day, the city power is available? a – 6 hrs b – 12 hrs c – 18 hrs d – 24 hrs 1.1-4.2. What are you using this for? a - Lighting b- Cooking c - Heating d - Entertainment d - Others (specify) 1.1-4.3. What would be the billing in terms of KW as an average per month? a - < 100 KW b - < 200 kW c - < 300 kW d- < 400 kW e - < 500 KW 1.2 Reliability 1.2.1 Outages per year 102

a- One 9 b – two 9 c – Three 9 d- Four 9 1.2.1 Voltage? 1.2.2 Frequency 1.2.3 Adequacy of supply 1.2.4 Security of supply 2- Private generator Yes No 2.1 Do you have generator? If yes, what size it is? a - < 1 kW b - < 1.5 kW c - < 2 kW d – < 3 kW 2.2 How much fuel is spent as an average per month? a - < 30 lit b - < 50 lit c - < 100 lit d - < 200 lit 2.3 When do you turn on the generator? a – during the day b – during the night c – all day d- on weekends 2.4 How many hrs will the generator be running in a month? a – <40 hrs b – 40 - 60 hrs c – 60 – 90 hrs d - > 90 hrs 2.5 What are you using it for? a – lighting b – entertainment c – cooking d – others (specify) 2.6 How much does it cost you per month (Fuel and maintenance)? a - < $ 50 b - < $ 75 c - < $ 100 d - < $ 200 e - > $ 200 3- Community Generator 3.1 When the electric power is available from community generator? a – during the day b – during the night c – all day 3.2 What are you using it for? a – lighting b – entertainment c – others (specify) 3.3 How many hrs does it run per day? a – < 3 hrs b – < 6 hrs c - < 10 hrs d - >10 hrs 3.4 How much do you pay for community generator? a - < $ 10 b - < $ 30 b - < $ 60 d - < $ 100 4- Solar Panels 4.1 If you have solar module, what is the size of solar panel? a – 20 W b – 40 W c – 60 W d – 80 W e – 120 W 4.2 What are you using it for ? a – lighting b – entertainment c – others (specify) 4.3 What was the capital cost for it? (a) <$ 200 (b) < $ 400 (c) < $ 500 (d) < $ 600 (e) > $ 600

Household Electric Appliances

Category Devices Wattage Hrs used /day Entertainment TV/VCR and Equipment combinations

Digital versatile disc (DVD) Video disc player Digital satellite system (DSS) Video game player Kitchen Oven Microwave oven Bread cooker Rice cooker toasters Freezer Refrigerator Water boiler Personal Care Hair dryer

103

Home Care & Vacuum cleaner Maintenance Fan Iron Home Office Desktop computer Equipment Laptop computer Copier Printer Miscellaneous Water pumps Lighting Bulbs Bathroom Water warmer

List any electric appliances you would like to have in future.

If grid power is available 24 hours, would you like to switch every thing to grid power? Yes No

If “No” what alternatives would like to keep? 1- For lighting (a) Generator (b) Solar (c) Gas Lanterns (d) Fuel-based Lanterns

2- For Cooking (a) City Power (b) Fuel (c) Gas (d) Wood (e) Others (specifiy)

3- For Heating (a) Fuel (b) Wood (c) Sandali (d) Others (specifiy)

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Appendix C: Tables Table A: Standard parameters for oil-insulated transformer MT/BT (normal and reduced losses) [24]. Rated No-load Load losses r= Pcn/Po No-load Vcc % Power Loss at rated current (KVA) Po(Watt) power Pcn(Watts) 50 190 1100 5.8 2.9 4 100 320 1750 5.5 2.5 4 160 460 2350 5.1 2.3 4 250 650 3250 5.0 2.1 4 400 930 4600 4.9 1.9 4 600 1300 6500 5.0 1.8 4 1000 1700 10500 6.2 1.5 4 1600 2600 17000 6.5 1.3 4 2000 3200 22000 6.9 1.2 6 2500 3800 26500 7 1.1 6