improved sustainable power supply for dagahabur and...

Bizuayehu Tesfaye

REYST report 05-2011

Bizuayehu Tesfaye Im

proved Sustainable Power Supply

RE

YS

T rep

ort 05-2011

Improved Sustainable Power Supplyfor Dagahabur and Kebridahar Town

of Somalia Region in Ethiopia

REYKJAVIK ENERGY GRADUATE SCHOOL OF SUSTAINABLE SYSTEMS

Reykjavík Energy Graduate School of Sustainable Systems (REYST) combines the expertise of its partners: Reykjavík Energy, Reykjavík University and the University of Iceland.

Objectives of REYST:Promote education and research in sustainable energy

earth sciences

REYST is an international graduate programme open for students holding BSc degrees in engineering, earth sciences or business.

REYST offers graduate level education with emphasis on practicality, innovation and interdisciplinary thinking.

REYST reports contain the master’s theses of REYST graduates who earn their degrees from the University of Iceland and Reykjavík University.

Main Report Final Master Thesis

ABSTRACT

The oil price volatility, growing concerns of global warming, and depleting oil/gas reserves have made it inevitable to seek energy from renewable energy resources. Many nations are embarking on introduction of clean/ renewable energy for displacement of oil-produced energy. Moreover, solar photovoltaic (PV)–Wind/batter hybrid power generation system technology is an emerging energy option since it promises great deal of challenges and opportunities for developed and developing countries. Ethiopia is developing country and as of 2009 the total population size estimated 84.9 million inhabitants. From the total population size, current figures indicated that only about 33% of the population is estimated to have access to electricity and the per capita energy consumption is 40.59kWh, which is the lowest in the world. Degehabur and Keberdahar towns are located Somalia region in Ethiopia and total population size is estimated 125,000. They could have access to electricity from conventional diesel generator and power supplied are only limited to six to eight hours per day. Somalia region of being enriched with higher level of solar radiation as well as a second class wind speeds are a prospective candidate for deployment of solar PV /wind hybrid systems. The aim of this study was to investigate alternative power supply options to replace the existing diesel-only power system for remotely located towns detached from the main electricity grid in Ethiopia with a hybrid PV–wind–battery power systems to meet energy consumption of commercial and residential building (with total annual electrical energy demand of 3,291,920 kWh) consumers. The monthly average daily solar global radiation for Kebridehar and Degehabur towns ranges from 5.5 to 7.03 kWh/m2/day and monthly average wind speed varies from 4.2 to 8.2 m/s. Two power supply options were identified. The first option was a hybrid (standalone Solar/wind/battery) system and the second option was to construct new transmission line from nearest substation to selected towns. The HOMER simulation program developed by the NREL has been used as the design tool for both options. From First option, the simulation results indicated that for a hybrid system composed of solar/wind/battery and battery storage of 48 h of autonomy has been selected. The cost of generating energy (COE, US$/kWh) from the above hybrid system was found 0.422 $/kWh and 0.441$/kWh for Kebri Dehar for Degehabur town respectively. But the diesel-only option in the existing arrangement, levelized cost of energy for Kebri Dehar and Degehabur are $0.564/kWh and $0.543/kWh respectively and if diesel remains at $1.0/liter. The costs of energy (COEs) of hybrid system would be lower than the COE of a diesel-only system. Though the optimum system configuration changes under different diesel price assumptions, the hybrid system remains most economically feasible solution than the existing arrangements (diesel-only), under all scenarios considered so the selected hybrid energy system with 100% renewable energy contribution eliminating the need for conventional diesel generator. The grid extension of energy cost for Kebri Dehar and Degehabur are 1.172 and 0.869 $/kWh for Kebri Dehar and Degehabur towns respectively. The grid connected option according to the given circumstances was found to be not economical feasible solution the power supplied for the two towns.


Acknowledgements

Primarily, I would like to give glory to God and the Virgin Mary without which the

completion of this thesis would have been unthinkable.

Next, I would like to thank Reykjavik Energy Graduate School of Sustainable Systems,

REYST for offering me the scholarship to do my MSc study at this prestigious University.

My deepest heartfelt gratitude goes to my supervisors Assistant professor Kristinn

Sigurjónsson for his generosity and kindness throughout the lifespan of my thesis work. The

thesis would not have been accomplished without his readiness to help; his willingness for

series of intensive discussions which brought about more valuable suggestions; and his

supports are highly appreciated in this regard.

I would like to extend my appreciation to The U.S. National Renewable Energy Laboratory

(NREL) to offer me free Homer optimization software to completion of this thesis.

I dedicate my thesis to my beloved parent; to my mother Etenesh Worku and to my wife;

Helina Tesfaye, my sources of inspiration and strength, who have dedicated their years

supporting my study, that make me feel loved, proud and fortunate.

Last but not least, I would like to thank some Ethiopian community live in Iceland, my

friends and my classmate who stood always by my side.

.

.


LIST OF NOMENCLATURE

The maximum power point efficiency under standard test conditions [%]

The temperature coefficient of power [%/°C]

The cell temperature under standard test conditions [25°C]

The rated capacity of the PV array, meaning its power output under

standard test conditions [kW]

The PV derating factor [%]

The solar radiation incident on the PV array in the current time step [kW/m2]

The incident radiation at standard test conditions [1 kW/m2]

The temperature coefficient of power [%/°C]

PV cell temperature in the current time step [°C]

The PV cell temperature under standard test conditions [25 °C]

The nominal operating cell temperature [°C]

The ambient temperature at which the NOCT is defined [20°C]

The solar radiation at which the NOCT is defined [0.8 kW/m2]

The solar transmittance of any cover over the PV array [%]

The solar absorptance of the PV array [%]

The solar radiation striking the PV array [kW/m2]

The electrical conversion efficiency of the PV array [%]

The coefficient of heat transfer to the surroundings [kW/m2°C]

The PV cell temperature [°C]

The ambient temperature [°C]

Number of batteries in the battery bank

Nominal voltage of a single battery [V]

Nominal capacity of a single battery [Ah]

Minimum state of charge of the battery bank [%]

Average primary load [kWh/d]

The battery bank autonomy

The zenith angle [°]

The angle of incidence [°]


The azimuth of the surface [°]

The latitude [°]

The solar declination [°]

The hour angle [°]

Gon The extraterrestrial normal radiation [kW/m2]

Gsc The solar constant [1.367 kW/m2]

The day of the year [a number between 1 and 365]

Go The extraterrestrial horizontal radiation [kW/m2]

Gon The extraterrestrial normal radiation [kW/m2]:

The extraterrestrial horizontal radiation averaged over the time step [kW/m2]

The hour angle at the beginning of the time step [°]

The hour angle at the end of the time step [°]

The global horizontal radiation on the earth's surface averaged

over the time step [kW/m2]

The extraterrestrial horizontal radiation averaged over the time step [kW/m2]

b The beam radiation [kW/m2]

d The diffuse radiation [kW/m2]

The slope of the surface [°]

The ground reflectance, which is also, called the albedo [%]

The efficiency of the PV array at its maximum power point [%]

B Lapse rate [0.00650 K/m]

z Altitude [m]

R Gas constant [287 J/kgK]

Standard temperature [288.16 K]

g gravitational acceleration [9.81 m/s2]

Standard pressure [101,325 Pa]

Hub height of the wind turbine [m]

The anemometer height [m]

The most frequent wind speed

The wind speed which carries the maximum amount of wind energy

The surface roughness length [m]

Wind speed at the hub height of the wind turbine [m/s]


ln (..) The natural logarithm

The air density in kg/m3and is given as = 1.225 kg/m3.

d Diurnal pattern strength (a number between 0 and 1)

f hour of peak wind speed (an integer between 1 and 24)

Mean wind speed of each month

The required battery bank capacity in (Ah)

The capacity of the selected battery in (Ah)

The number of batteries that needs to be in parallel.

The DC system voltage (Volt)

The battery voltage (Volt)

The number of battery that needs to be in series

The required battery bank capacity in (Ah)

The capacity of the selected battery in (Ah)

The number of batteries that needs to be in parallel.

The lifetime throughput of a single battery

The annual throughput (the total amount of energy that cycle through

The battery bank in one year)

The float life of the battery (the maximum life regardless of throughput).

Real interest rate [%] and

N Number of years.

Represent the maximum continues power load consumes.

The maximum power that can be supplied by the inverter.

Total annualized cost [$/year]

AC primary load served [kWh/year],

The deferrable load served [kWh/year].

Initial capital cost of the component

CRF() Capital recovery factor

Project lifetime

CNPC Total net present cost of the stand-alone power system [$]

Cost of power from the grid [$/kWh],

Capital cost of grid extension [$/km],

O&M cost of grid extension [$/yr/km]


Total annualized cost [$/year]

Total primary and deferrable load [kWh/yr]

Shape factor

Gamma function

List of Abbreviations and Acronyms

PV Photovoltaic

HOMER Hybrid Optimization Model for Electric Renewable

NREL National Renewable Energy Laboratory

COE Costs Of Energy

CO2 Carbon monoxide

KWh kilo Watt hour

GHG Greenhouse gas

RE Renewable energy

RAPS Remote area power supply

AC Alternating current

DC Direct current

TWh Terra watt hours

Tcal Terra calorie

ICS Inter connected system

SCS Self contain system

UEAP Universal Electricity Access Program

EEPCO Ethiopian Electric Power Corporation

SMSE Surface Meteorology and Solar Energy database

SWERA Solar and Wind Energy Resource Assessment

NMSA National Meteorological Service Agency

GIS Geographic Information Systems

PDF Probability density function

DOD Depth of discharge

MPPT Maximum Power Point Tracking

NPC Net present cost

O&M Operation and maintenances


Table of contents

ABSTRACT ............................................................................................................................................... iv

Acknowledgements ................................................................................................................................. v

LIST OF NOMENCLATURE ..................................................................................................................... vi

List of Abbreviations and Acronyms ...................................................................................................... ix

Table of contents .................................................................................................................................... x

LIST OF TABLES ......................................................................................................................................xiii

1.Introduction ......................................................................................................................................... 1

1.1 Problem Statement ....................................................................................................................... 1

1.1.1 Rural energy context ....................................................................................................... 3

1.1.2 Electricity provision in rural areas ................................................................................... 3

1.1.3 Off grid electricity from hybrid system ........................................................................... 5

1.2 National and Regional Overviews ........................................................................................... 5

1.2.1 National overviews ................................................................................................................ 5

1.2.2 Regional overviews ......................................................................................................... 7

1.3 EEPCo’s Background ...................................................................................................................... 7

1.3.1 Generation Facilities .............................................................................................................. 9

1.3.2 Transmission and Substation Facilities .................................................................................. 9

1.4 Objective of the study ................................................................................................................. 10

1.5 Scope of the study ...................................................................................................................... 11

1.6 Present Status of Electric Supply for Keberi Dehar and Degehabur towns ................................ 11

1.7 Methodology ......................................................................................................................... 12

1.7.1 Problem Identification ......................................................................................................... 12

1.7.2 Renewable Energy Resources Assessment ................................................................... 13

1.7.3 Power supply Options Identification ............................................................................. 13

1.7.4 Overall System Design and Analysis .............................................................................. 13

1.8 Structure of thesis ....................................................................................................................... 16

2. Wind power system ......................................................................................................................... 17

2.1 Introduction ................................................................................................................................ 17

2.2 History ......................................................................................................................................... 17

2.3 Location of Degehabuar and Kebri Dehar town ......................................................................... 18

2.4 Wind resource assessment for Degehabur and Keberedehar town ........................................... 19

2.4.1 Estimation of the frequency distribution and long term average Wind Speed of Degehabur and Kebedehar town .................................................................................................. 22


2.4.2 Wind Power density distributions and mean power density ........................................ 28

2.5 Wind Turbines ............................................................................................................................. 30

2.5.1 Different Types of Turbines ................................................................................................. 30

2.5.2 Wind Turbines Components ................................................................................................ 32

2.5.3 General Workings .......................................................................................................... 34

2.5.4 Wind system design ............................................................................................................. 34

2.5.3 Wind turbine in hybrid system ...................................................................................... 35

2.5.4 Wind Turbines Efficiency and Power Curve .................................................................. 35

2.6 Wind Speed Height Correction ................................................................................................... 38

2.7 Wind Power ................................................................................................................................. 40

2.7.1 Swept Area .................................................................................................................... 41

2.8 Annual wind energy production and capacity factor .................................................................. 42

3. PHOTOVOLTAIC POWER SYSTEMS .................................................................................................... 43

3.1 Introduction ................................................................................................................................ 43

3.2 History ......................................................................................................................................... 43

3.3 Photovoltaic ................................................................................................................................ 45

3.3.1 PV electricity ................................................................................................................. 45

3.3.2 General working principles Photovoltaic Cells .............................................................. 46

3.3.3 Solar Module Power Characteristics and Operating issue ............................................ 46

3.3.4 Photovoltaic Cells and Efficiencies ................................................................................ 47

3.3.5 PV installation ............................................................................................................... 49

3.3.6 Photovoltaic Modules ................................................................................................... 50

3.3.7 Photovoltaic Manufactures ........................................................................................... 50

3.4 Solar resource ............................................................................................................................. 51

3.4.1 Degehabur and Kebri Dehar Solar Resources ............................................................... 52

3.5 Synthesize Hourly solar data from monthly average radiation ...................................................... 54

3.6 Calculates the global radiation incident on the PV array ............................................................ 60

3.6.1 Calculates the PV Cell Temperature and PV array power output ................................................ 61

4. Batteries, PV controller, Inverters and Energy Consumption ........................................................... 64

4.1 Introduction ................................................................................................................................ 64

4.1.1 Batteries ................................................................................................................................... 64

4.1.2 Battery Electricity ................................................................................................................. 65

4.1.3 General working ................................................................................................................... 66

4.1.4 Storage capacity ................................................................................................................... 66


4.1.5 Battery modeling ................................................................................................................. 67

4.1.7 Battery Sizing ....................................................................................................................... 70

4.1.7 Batter life time ..................................................................................................................... 72

4.1.8 Battery Design ...................................................................................................................... 73

4.1.9 Battery in hybrid system ...................................................................................................... 73

4.2 PV Controllers ................................................................................................................................ 74

4.2.1 MPPT Charge Controllers ..................................................................................................... 74

4.2.2 General working princples ................................................................................................... 75

4.3 Inverters ...................................................................................................................................... 75

4.3.1 General working ................................................................................................................... 76

4.3.2 Inverter Sizing ...................................................................................................................... 76

4.4 Energy consumption for Kebridehar and Degehabur towns ...................................................... 77

4.4.1 Introduction ......................................................................................................................... 77

4.5 Load forecast for Degehabur and Kebri Dehar ........................................................................... 79

4.5.1 Methodology ........................................................................................................................ 79

-Residential Consumption ................................................................................................................. 79

4.5.2 Energy Requirement and Peak Power Demand ............................................................ 80

4.5.3 Forecast Results ............................................................................................................ 81

5. Hybrid energy systems ...................................................................................................................... 82

5.1 Introduction ................................................................................................................................ 82

5.2 Stand Alone Hybrid System ......................................................................................................... 82

5.2.1 Typical Stand Alone Hybrid Components and Efficiencies................................................... 83

5.2.2 Proposed Stand Alone Sizing Optimization Procedure ........................................................ 84

5.3 Economic Evaluation of the Hybrid System ................................................................................ 85

5.3.1 Annual real interest rate ...................................................................................................... 85

5.3.2 Levelized cost of energy ....................................................................................................... 86

5.3.3 Net present cost (NPC) ......................................................................................................... 86

5.4 Breakeven Grid Extension Distance ............................................................................................ 89

5.5 System architecture .................................................................................................................... 89

6.1 General ........................................................................................................................................ 93

6.2. Simulation results ...................................................................................................................... 94

6.2.1 Optimization results ............................................................................................................. 94

6.3 Comparison with “diesel only” for Kebri Dehar and Degehabur towns ..................................... 97

6.5 Sensitivity results ...................................................................................................................... 100


6.5.1 Cost of energy sensitivity to diesel price – Kebri Dehar and Degehabur towns ................ 100

6.6 Comparison of the Grid extension with standalone (Off Grid) and diesel-only system ........... 102

7. Conclusion and Recommendation .............................................................................................. 104

7.1 Conclusions ............................................................................................................................... 104

7.2 Recommendation and Further work .................................................................................. 106

Bibliography ........................................................................................................................................ 106

List of Appendixes ............................................................................................................................... 110

Appendix–A ......................................................................................................................................... 110

LIST OF TABLES

TABLE 1.1: ENERGY CONSUMPTION AND POPULATION SIZE OF KEBRI DEHAR AND

DEGEHABUR ............................................................................................................................................... 11

TABLE 2.1 PREVAILING WIND DIRECTIONS REGION ON EARTH ...................................................... 19

TABLE 2.2 TYPICAL SHAPE FACTOR VALUES ....................................................................................... 24

TABLE 2.3: WIND POWER DENSITY FOR DEGEHABUR AND KEBEREDEHAR TOWNS ............ 28

TABLE 2.4: WIND CLASS CATEGORY BY WIND SPEED AND POWER DENSITY ........................ 30

TABLE 2.5: REPRESENTATIVE SURFACE ROUGHNESS LENGTHS DIFFERENT TERRAIN

(SOURCE: HOMER) .................................................................................................................................... 39

TABLE 2.6: TECHNICAL DATA OF EOLTEC CHINOOK 17-65 WIND TURBINE ............................... 42

TABLE 2.7: PREDICTED ANNUAL AND MONTHLY ENERGY PRODUCTION FROM A SINGLE

EOLTEC CHINOOK 17-65 WIND TURBINE FOR THE TOWNS ....................................................... 43

TABLE 3.1: SOLAR MODULE POWER AT STC RATING AND PRICE ................................................ 51

TABLE 3.2: PV MODULE CHARACTERISTICS FOR STANDARD TECHNOLOGIES ...................... 62

TABLE 4.1: MANUFACTURES DATA SHEET AND THE PRICES ........................................................ 65

TABLE 4.2: MPPT CHARGE CONTROLLERS MANUFACTURES ........................................................ 74

TABLE 4.3: INVERTERS MANUFACTURES .............................................................................................. 76

TABLE 4.4: TOTAL ANNUAL ENERGY CONSUMPTION FOR DEGEHABUR AND KEBRI DEHAR

TOWNS .......................................................................................................................................................... 79

TABLE 5.1: AVERAGE EFFICIENCY OF HYBRID SYSTEM COMPONENTS .................................... 84

TABLE 5.2: SYSTEM COST VALUES THAT USED IN SIMULATIONS ................................................ 87

TABLE 5.3: GRID EXTENSION COST FOR KEBRI DEHAR AND DEGEHABUR TOWNS ............. 89

TABLE 5.4: KEY MODEL INPUT ASSUMPTIONS FOR MODEL ........................................................... 91


TABLE 6.1: COMPARISON OF NET PRESENT COST, ENERGY COST AND GREEN HOUSE

GAS EMISSION DIESEL-ONLY OPTION WITH HYBRID SYSTEM ................................................ 97

TABLE 6.2: ECONOMIC PERFORMANCE OF THE HYBRID STAND ALONE SYSTEM FOR

DEGEHABUR TOWN .................................................................................................................................. 98

TABLE 6.3: ECONOMIC PERFORMANCE OF THE HYBRID STAND ALONE SYSTEM FOR

KEBRIDEHAR TOWN................................................................................................................................. 99

`TABLE 6.4: ENERGY COST COMPARISON FOR GRID EXTENSION WITH HYBRID AND

DIESEL-ONLY SYSTEM .......................................................................................................................... 103

LIST OF FIGURESFIGURE 1.1: PERCENTAGE OF ELECTRIFIED/NON-ELECTRIFIED RURAL POPULATION ......... 4

FIGURE 1.2: SYSTEM COMPONENT OF A CONCEPTUAL RENEWABLE HYBRID POWER

SYSTEM ........................................................................................................................................................... 5

FIGURE 1.3: MAP OF SOMALI REGION (MAPSOF, 2010) ................................................................. 7

FIGURE 2.1: WORLD WIND ENERGY - TOTAL INSTALLED CAPACITY (MW) (WORLD WIND

ENERGY 2009), 2009) ................................................................................................................................ 18

FIGURE 2.2: A GIS MAP SHOWING GEOGRAPHIC DISTRIBUTION OF WIND RESOURCES OF

ETHIOPIA (SOURCE: SWERA) ............................................................................................................... 21

FIGURE 2.3 MONTHLY AVERAGED WIND SPEED AT 50 M ABOVE THE SURFACE OF THE

EARTH (M/S) FOR DEGEHABUR AND KEBERDEHAR TOWN. (SOURCE: NASA) .................. 22

FIGURE 2.4: RAYLEIGH DENSITIES FUNCTION FOR VARIOUS MEAN WIND SPEED.

(SHENCK) ..................................................................................................................................................... 23

FIGURE 2.5 PROBABILITY DENSITY VS. WIND SPEED AT HUB HEIGHT FOR KEBEREHAR

TOWN ............................................................................................................................................................. 25

FIGURE 2 6: PROBABILITY DENSITY VS. WIND SPEED AT HUB HEIGHT IN DEGEHABUR

TOWN ............................................................................................................................................................. 25

FIGURE 2.7 WIND SPEED DAILY PROFILE FOR KEBEREDAR ........................................................... 27

FIGURE 2.8: WIND SPEED DAILY PROFILE FOR DEGEHABUR TOWN ........................................... 27

FIGURE 2.9: HORIZONTAL AXIS WIND TURBINES (HAWT) ARE EITHER UPWIND MACHINE

OR DOWN WIND MACHINES .................................................................................................................. 32

FIGURE 2.10: CUT-VIEW OF A WIND TURBINE. (SOURCE: DOE/NREL) .......................................... 32

FIGURE 2.11: DIFFERENT TYPE WIND TURBINES POWER CURVES BEING CONSIDERED FOR

THE SELECTED WIND FARM.................................................................................................................. 36

FIGURE 2.12: POWER OUTPUT OF EOLTEC CHINOOK 17-65 WIND TURBINES WITH STEADY

WIND SPEED CHARACTERISTICS. ...................................................................................................... 37

FIGURE 3.1: SOLAR MODULE RETAIL PRICE INDEX ........................................................................... 44

FIGURE 3.2: PV DIAGRAM .............................................................................................................................. 45

FIGURE 3.3: HOW PHOTOVOLTAIC CELLS WORK (CLEAN ENERGY ASSOCIATES) ............... 46

.FIGURE 3.4: I-V CURVES SHOWING THE EFFECT OF SOLAR ISOLATION AND

TEMPERATURES ON PV PANEL PERFORMANCE .......................................................................... 47


FIGURE 3.6: MONTHLY SOLAR RADIATION AND CLEARNESS INDEX FOR KEBRIDEHAR

AND DEGEHABUR TOWNS (SOURCE: NASA) .................................................................................. 53

FIGURE 3.7: GLOBAL DAILY SOLAR RADIATIONS ON HORIZONTAL SURFACES FOR KEBRI

DEHAR TOWN ............................................................................................................................................. 55

FIGURE 3.8: GLOBAL DAILY SOLAR RADIATIONS ON HORIZONTAL SURFACES FOR

DEGEHABUR TOWN .................................................................................................................................. 55

FIGURE 3.9: DIAGRAM OF THE SOLAR RADIATION CALCULATION ON PANEL SURFACE ... 56

FIGURE 3.10: CALCULATES THE GLOBAL RADIATION INCIDENT ON THE PV ARRAY WITH

TRACKING AND WITHOUT TRACKING SYSTEM ............................................................................. 61

FIGURE 4.1: KINETIC BATTERY MODEL CONCEPTS ........................................................................... 68

FIGURE 4.2: CAPACITY CURVE FOR DEEP-CYCLE BATTERY MODEL SURRETTE4KS25P .... 69

FIGURE 4.3: LIFETIME CURVE FOR DEEP-CYCLE BATTERY MODEL SURRETTE4KS25P ..... 70

FIGURE 4.4: HOURLY LOAD PROFILES FOR KEBRI DEHAR TOWN................................................ 78

FIGURE 4.5: HOURLY LOAD PROFILES FOR DEGEHABUR TOWN .................................................. 78

FIGURE 4.6: ENERGY AND POWER FORECAST FOR KEBERDEHAR TOWN ............................... 81

FIGURE 4.7: ENERGY AND POWER FORECAST FOR DEGEHABUR TOWN ................................. 81

FIGURE 5.1 PV/BATTERY/WIND STAND ALONE SYSTEM ..................................................................... 84

FIGURE 5.2: EQUIPMENT TO CONSIDER AND HYBRID SYSTEM CONFIGURATION FOR

DEGEHABUR TOWNS ............................................................................................................................... 90

FIGURE 5.3: EQUIPMENT TO CONSIDER AND HYBRID SYSTEM CONFIGURATION FOR

KEBRI DEHAR TOWN................................................................................................................................ 91

FIGURE 5.4: SIZES CONSIDERED FOR COMPONENTS FOR KEBRIDEHAR TOWN HOMER

MODEL RUN ................................................................................................................................................. 92

FIGURE 5.5: SIZES CONSIDERED FOR COMPONENTS FOR DEGEHABUR TOWN HOMER

MODEL RUN ................................................................................................................................................. 92

FIGURE 5.6: ARCHITECTURE OF HOMER SIMULATION AND OPTIMIZATION .............................. 93

FIGURE 6.1: OVERALL OPTIMIZATION RESULTS TABLE SHOWING SYSTEM

CONFIGURATIONS SORTED BY TOTAL NET PRESENT COST FOR KEBRIDEHAR TOWN.

......................................................................................................................................................................... 94

FIGURE 6.2: OVERALL OPTIMIZATION RESULTS TABLE SHOWING SYSTEM

CONFIGURATIONS SORTED BY TOTAL NET PRESENT COST DEGEHABUR TOWN. ......... 95

FIGURE 6.3: MONTHLY AVERAGE ELECTRIC PRODUCTION FOR KEBRI DEHAR TOWN........ 96

FIGURE 6.4: MONTHLY AVERAGE ELECTRIC PRODUCTION FOR DEGEHABUR TOWN .......... 96

FIGURE 6.5: LIFECYCLE COSTS OF HYBRID SYSTEM BY COMPONENTS FOR DEGEHABUR

TOWN ............................................................................................................................................................. 99

FIGURE 6.6: LIFECYCLE COSTS OF HYBRID SYSTEM BY COMPONENTS FOR KEBRIDEHAR

TOWN ........................................................................................................................................................... 100


FIGURE 6.7: COST OF ENERGY (COE) OF OPTIMIZED HYBRID VS. DIESEL-ONLY UNDER

DIFFERENT DIESEL PRICE SCENARIOS. ......................................................................................... 101

FIGURE 6.8: NET PRESENT COST (NPC) OF OPTIMIZED HYBRID VS. DIESEL-ONLY UNDER

DIFFERENT DIESEL PRICE SCENARIOS .......................................................................................... 101

FIGURE 6.9: COMPARISON OF GRID EXTENSION WITH STANDALONE SYSTEM OF KEBRI

DEHAR TOWN ........................................................................................................................................... 103

FIGURE 6.10: COMPARISON OF GRID EXTENSION WITH STANDALONE SYSTEM OF

DEGEHABUR TOWN. ............................................................................................................................... 103


1.Introduction

The oil price volatility, growing concerns of global warming, and depleting oil/gas reserves

and due to the contradiction between gradual growth of the global energy demand,

renewable energy such as solar energy, wind energy, bio-energy, and hydropower might

become a new manner in which we produce energy for sustainable development.

Photovoltaic (PV) and wind energy systems are the most promising candidates of the future

energy technologies, and it has been widely noticed that stand alone and grid connected PV

and wind energy markets have grown rapidly. Energy generation system reliability has been

considered as one of the most important issues in any system design process. However,

natural energy resources are unpredictable, intermittent, and seasonally unbalanced.

Therefore, a combination of two renewable energy sources may satisfy bigger share of

electricity demand and offer reliable and consistent energy supply. The Hybrid PV and Wind

Electricity System is well suited to conditions where sun light and wind have seasonal shifts,

for example, in summer the sun light is abundant but windless, while in winter wind resource

increased that can complement the solar resource. The reliability of the stand-alone hybrid

PV-wind system in producing energy has been proven by earlier studies. In the last two

decades solar energy and wind energy has become an alternative to traditional energy

sources. These alternative energy sources are non-polluting, free in their availability and

renewable. But high capital cost, especially for photovoltaic, made its growth a slow one. The

best way to attempt to decrease the cost of these systems is by making use of hybrid designs

that uses both wind/photovoltaic.

The purpose of this study is to investigate alternative power supply options for Degehabur

and Kebidehar towns by replacing the conventional diesel powered electric supply which are

towns detached from main electricity grid system in Ethiopia.

1.1 Problem Statement

In spite of the huge hydroelectric potential of Ethiopia, severe power cuts in recent years have

a heavy impact on the country’s economy Ethiopia. It is known that the development of any

country depends on the amount of energy consumed. Energy consumption is proportionally

to the level of economic development. According to current figures only about 33% of the

population is estimated to have access to electricity and the per capita energy consumption is

40.59kWh, which is the lowest in the world and almost biomass. This had a direct impact on

deforestation. For lighting systems, in rural areas, kerosene is used which produces and

emission of pollutants. Though Ethiopia has a tremendous amount of hydro power potential,


because of the high initial cost, it is able to harness only 3 % of its potential so far. Moreover

the cyclic drought in the country was affected the hydrological situation and causing

“Electrical Energy Draught”.

In the Degehabur and Kebri Dehar towns in Ethiopia, access to electricity seems to be a

‘never ending’ problem. Urban communities of Ethiopia have yet to achieve reliable

electricity services, while people in rural areas like Keberi Dehar and Degehabur are still

dreaming of connecting to national grid limes. In 2010, these towns still not connected in the

national grid system. Despite its strong economy, the lack of electricity supply has

contributed to social issues such as poverty, poor health services, low education, and gender

inequity.

Furthermore, the environmental challenges with regard to greenhouse gas (GHG) emissions

from fossil fuel burning in diesel power stations, as the main power generators in Degehabur

and Kebri Dehar towns, are obvious. Finally, the lack of studies, expertise and experiences in

dealing with rural electrification programs, are some of the Ethiopia has potential to build a

mixed-power system strategy by introducing renewable energy (RE) to the existing (Diesel-

only)power generation systems. This dissertation explores the ways of harnessing the

promising sources of renewable energy in Degehabur and Kebridehar towns including wind

and solar photovoltaic (PV) energies as a contribution to replace the existing diesel only

electricity generation power plant the towns which located detached from the national grid

system. For the towns, they have being enriched with higher level of solar radiation as well as

a second class wind speeds are a prospective candidate for deployment of solar PV /wind

hybrid energy systems. Hybrid energy standalone (Off-Grid) system is an excellent solution

for electrification of remote rural areas where the grid extension is difficult and not

economical viable. Such system incorporates a combination of one or several renewable

energy sources such as solar photovoltaic and wind energy. So sun and wind energy can

bring an important contribution to a sustainable and decentralize energy mix for Ethiopia.

Hence the use of wind and photovoltaic systems are only worth to generate electricity for

island net systems which cannot join the main grid. As of 2009, the total transmission and

distribution losses is around 28%.The total system losses are comprise technical and non-

technical losses. Decentralized energy (Off-Grid) system also big contribution to reduce

energy losses comes to be associated with transmission and distribution line system.


1.1.1 Rural energy context

Energy, next to water, transport, education, training and other factor impacting the

development, forms of part a number of services often urgently needed in remote villages to

contribute to rural development and the creation of job opportunity.

The price of conventional energy sources in remote areas, such as candles, paraffin, gas, coal,

battery, is often more expensive than in urbanized areas due to the remoteness of the retailers

,rural people their goods from, and the corresponding overheads. Moreover, the cost per

energy service, for example lighting, is more expensive for rural in habitant than their urban

counter parts who often have access to grid electricity.

There are also other factors associated with conventional energy supply in remote areas, such

as the, often long, transport required to obtain these energy supplies and the dangers in their

use or storage. For example, women might have to walk for up to four hours each day to

collect sufficient wood to cook for their family or heat the house. Many health problems are

reported related to burns from the use of paraffin and respiratory conditions due to the

constant smoke exposure. Cutting of wood also aggravate deforestation.

1.1.2 Electricity provision in rural areas

The provision grid electricity in rural area is often associated with higher cost to the grid

supplier than off grid RAPS (remote area power supply) electricity technology option would

be..Grid electrification in rural areas in many case cases is financially inefficient particularly

due to low consumption take-up in the remote area.

As of 2009 figure indicate that, 85% of Ethiopians were living in rural areas, which 12

million households had no access to electricity. It estimate that in 2009 ,85% (see figure 1.1)

the rural population will still be unelectrified due to the high cost for grid extensions to very

remote extension to very remote towns and villages whereby average monthly households

electricity consumption can be as low as some 20 to 35 kWh.

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villages. Off-grid technology is option single source and hybrid, can in some case be an

economic alternative to remote grid extension

1.1.3 Off grid electricity from hybrid system

Off grid electricity can be generated by single-source system using solar photovoltaic panel,

wind turbine generators, micro-hydro power plant or fuel-powered combustion engine

generator sets, or by combining one or more types of these electricity generating sources in a

so-called hybrid system (see figure 1.2).hybrid system can supply power to AC or DC load or

both. It may require AC, DC or both types of buses power conversion devices are used to

transform power between DC and AC buses.

Figure 1.2: System component of a conceptual renewable hybrid power system

1.2 National and Regional Overviews

1.2.1 National overviews

Ethiopia is located in the eastern part of Africa between 3o to 15o north and 33o to 48o east

(approximately 820 km from north to south and 130 km east to west) with a surface area of

1.1 million square kilometers, it is the third largest country in Africa. It is the second most

populous country in Sub Saharan Africa with an estimated population of about 84.9 million,

which is mostly distributed in northern, central and southwestern highlands.

Ethiopia has a federal country composed of nine regional states. The country has a bicameral

parliamentary system, and government headed by a prime minister. Addis Ababa is the

capital of the country, and is the seat of many international and regional organizations, like

the African Union, and the UN ECA (Economic Commission for Africa). (Planning Power

system, 2008)

The country follows an agricultural led industrialization strategy, and is achieving

encouraging results. The economy has been growing at a rate of more than 10% for the last


six years consecutively, and large number of development projects is underway. The

agriculture sector is the leading source of foreign exchange for Ethiopia. Coffee distantly

followed by hides and skins, oil seeds and recently cut-flower are the major agricultural

export commodities. At present the per capita income in Ethiopia is less than USD 100.

According to EEPCO current data with only 8% of households connected and 33% of the

population having access to electricity from national grid, access to electricity in Ethiopia is

one of the lowest by any standards. Despite the fact that 80% of the population of Ethiopia

live in rural areas, electricity supply from the grid is almost entirely concentrated in urban

areas. Among other things, dispersed demand and very low consumption level of electricity

among rural consumers, limited grid electricity penetration to rural population to less than

1%. Based on the hitherto electricity expansion practices in the country, access to electricity

does not seem to be the reality of the near future for the greater percentage of the rural

population. However, the recent government’s strategy under Universal Electricity Access

Program (UEAP) ambitiously plans to increase access to electricity from the current 33% to

50% by the year 2015 by connecting 7500 new towns and villages to the grid. The UEAP

does not only aim to increase access, but also aims to raise the level of national per capita

consumption of electricity from the current 35.87 kWh to 128 kWh by the year 2015.

The Government of Ethiopia is aware of the fact that the national utility alone through

Continuous grid extension cannot accelerate rural access to electricity. In the struggle to

improve rural access to electricity, the government has recently streamlined its strategies and

embarked upon removal of barriers and constraints to accelerated off-grid rural

electrification.

The Rural Electrification Strategy provides opportunities for an increasing participation of

the private sector in the supply of electricity to un-electrified rural population. This has

included the design of institutional and financing framework for private sector-led rural

electrification, which is expected to remove barriers and facilitate private sector participation

in the provision of off-grid electricity supply (generation, transmission, distribution and

marketing).

The National energy policy of the country emphasizes the need for equitable development of

the energy sector in parallel with other social and economic developments. Specific policy

lines include the attainment of self-sufficiency through the development of indigenous

resources with minimum environmental impact and equitable distribution of electricity in all

regions. The policy envisages the development of hydro, geothermal, natural gas, coal, wind


and solar energy resources based on their techno-economic viability, social and

environmental acceptability.

1.2.2 Regional overviews

The Somali Region is one of nine regions of the Federal Republic of Ethiopia. Located in the

eastern Ethiopian lowlands bordering Djibouti, Somalia (including Somaliland) and Kenya,

the region is almost entirely inhabited by people of Somali ethnicity (95.6 per cent according

to Ethiopia’s Central Statistics Agency). They speak a common language, Somali, and share a

rich cultural heritage that spans Somalis living in Kenya, Ethiopia and Somalia. Ethiopia’s

Central Statistics Agency estimated the region’s population at just over 4.44 million in 2007,

which accounts about 6% of the country with a high sex ratio of 125 males to 100 females

[census, 2007], though some consider that an underestimate, as a proper census has not been

conducted for over a decade and population growth is rapid. Somalis are either the third or

fourth largest ethnic group in Ethiopia. (International, 2006)

Figure 1.3: Map of Somali region (Mapsof, 2010)

Somalia region is divided by nine zones which are Deghabur, Korahe (Keberdehar), Shinile,

Warder, Gode, Jigjiga, Afder, liben and Fik.

1.3 EEPCo’s Background

The Ethiopian Electric Power Corporation (EEPCO) is a statutory corporation owned by the

Government of Ethiopia, which was set up by regulation on 7th of July 1997 for the purpose

of generation, transmission and sale of electricity nationwide. EEPCO operates two power

supply systems, namely the main interconnected system (ICS) and the self-contained system

(SCS). (Planning Power system, 2008)


The main ICS, which serves the major towns and industrial centers, has a total installed

capacity of 1559.3 MW. This installed capacity is contributed by hydropower installations

having a total installed capacity of 1390.6 MW and thermal stations of about 168.7 MW. The

thermal stations are stand-by Diesel stations at different places in different parts of the

country (22.2 MW), Kaliti (11.2 MW), Awash Town (28 MW), Dire Dawa (40 MW), Adama

(30 MW) and Bishefetu (30 MW) which are required to mitigate the power shortage during

dry periods when the generations from hydro plants is at its minimum. The Aluto-Langano

geothermal station has an installed capacity of 7.3 MW, which is, at present, non-operational

due to low pressure of the thermal fuels (Planning Power system, 2008).

The SCS supplies isolated load centers, which are far from the ICS, mostly using Diesel as a

source of generation. Currently this system has an aggregate installed capacity of about 20.01

MW, of which 13.86 MW are being generated from Diesel stations. The rest 6.15 MW are

being generated from small hydro power plants located at Sor, Yadot and Dembi. (Planning

Power system, 2008)

EEPCO currently provides electricity to a total of about 1,400,923 customers in

approximately 1500 towns and communities in Ethiopia, which is only a small proportion of

the country from the total of about 74 million inhabitants. According to current figures only

about 33% of the population is estimated to have access to electricity and the per capita

energy consumption is 43.53 kWh, which is one of the lowest in the world. Out of the total

number of customers 95% are within the ICS and the remaining 5% within the SCS (Planning

Power system, 2008)

Access to electricity can make real difference to lives of people. Although electricity alone

will not reduce poverty, the lack of access to this modern energy is a severe constraint to

development. Improving access to electricity is thus essential for poverty reduction.

(Planning Power system, 2008)

To reduce the level of poverty one should enhance income generating means and bring

change in quality of life of people. The ultimate objective of electrification should thus be

provision of electricity to a large number of rural town and village dwellers on sustainable

basis, and to support income generating activities. To enhance income generating capacity

means provision of electric energy for driving electric motors necessary for small scale

manufacturing, pump sets for irrigation, motors for mills, etc. (Planning Power system, 2008)

Universal Electricity Access Program (UEAP) is launched by the Government of Federal

Democratic Republic of Ethiopia to meet the demand of agricultural sector (irrigation pumps,

prevention of farm products, etc.), industrial and commercial sector, rural water supply


installations, residential consumptions, education and health sectors. The program, while

broadening the national electrification coverage and increasing people’s access to electricity

from existing 33% to 50% in the coming five years, will underpin the ADLI strategy and

poverty reduction strategy of the government. In other words, the UEAP targets electricity

supply to towns, villages, social service giving centers and irrigation facilities in all Regional

states of the country. The concept of UEAP involves provision of power from least cost

source meeting required reliability standards to enhance capacity of wealth generation.

(Planning Power system, 2008)

The project is designed around the principles of least cost expansion through technical and

institutional innovations, and rapid growth of access to electricity supplies and to assist

customers in using electricity for income generating activities. (Planning Power system,

2008).

1.3.1 Generation Facilities

The existing ICS hydroelectric power plants are Koka, Awash II, Awash III, Finchaa, Melka

Wakena, Tis Abay I & II,Gilgel Gibe-I,Tekeze and Gilgel Gibe-II Besides the on-

rehabilitation geothermal plant at Aluto Langano there are standby diesel units at Alemaya,

Dire Dawa, Mekele, Adigrat, Axum, Lalibela, Sekota, Adwa, Korem, Nekemt, Ghimbi, Bizet

and Shire including the new emergency diesel units at Kaliti, Awash Town, Dire Dawa,

Adama and Bishofetu which are part of the ICS. Most of the diesel units serve on standby

basis. (planning, PTP, 2008)

The Generation facilities in the ICS are distributed throughout the country and are located far

away from the big load. The eleven hydro generating stations, six diesel stations and main

thermal stations with an aggregate installed capacity of 1559.3 MW

1.3.2 Transmission and Substation Facilities

Ethiopia’s widely distributed population has led to the development of an extensive

transmission network. The Ethiopian electric grid system consists of five principal levels of

transmission voltages: 400, 230, 132, 66 and 45 kV. The 400 and 230 kV high voltage (HV)

transmission lines are the backbone of the system connecting the generating stations of

Finchaa, Melka Wakena, Gilgel Gibe-I, Gilgel Gibe-II,Tekeze to the major load centers

(Addis Ababa) at Gefersa, Kaliti and Sebeta substations respectively. These substations are

also interconnected through double circuits of 132 kV and single circuits of 230 kV making a

complete ring at 132 kV and a partial ring at 230 kV around Addis Ababa. The 230-kV


system further extends from Addis Ababa about 400km eastward to Dire Dawa, south to

Melka Wakena and about 1000 km towards the west and north. (planning, PTP, 2008)

There are a number of 132 kV lines in the system either being the major distributors of

electricity from the 230 kV system or the major interconnecting lines of generating stations to

the system as that of Koka, Awash-II & III and Tis Abay-II. The 66 and 45 kV transmission

lines are also used to distribute bulk powers transmitted mainly by 132 and 230 kV

transmission lines. The 45 kV systems are being phased-out in favor of the 66 kV systems.

(planning, PTP, 2008)

The existing transmission system comprises a total of about 8,747 km of transmission lines,

2,053 km of which are at the 230-kV level, 3,983 km are at the 132-kV level, 2,235 km are at

the 66-kV and 476 km are at the 45-kV voltage level in the ICS system and the rest 245 km in

SCS (planning, PTP, 2008)

1.4 Objective of the study

The basic objective of the research described in this thesis to investigate alternative power

supply options for Degehabur and Keberdehar towns to improve the sustainable power supply

by replacing existing conventional diesel powered electric supply remotely located towns

detached from the main electricity grid in Ethiopia.

While trying to achieve this main objective, we will attempt to fulfill the following goals:

Develop a data base of published data on wind speed and solar radiation in the

Degehabur and Kebri Dehar towns.

Select a set of photovoltaic modules and wind turbines suitable to generate electricity

using the wind and solar resource available in the selected towns

Propose an optimization procedure to determine the amount and type of PV modules,

storage battery and wind turbines needed, under stand alone conditions, to satisfy a

predetermined demand at minimum cost.

Perform an economic analysis to compute the net present value of the renewable

energy systems proposed

Conducted on Economic evaluation of the systems and compare different option.

To create strength of reliable power supply for Degehabur and Keberedehar towns

and to get electricity access from potential renewable energy resource through their

own alternative supply


Make conclusion on to replace existing diesel generator and reliable power supply at

Degehabur and Keberedehar town with solar (photovoltaic)/wind power/battery

hybrid technology.

1.5 Scope of the study

The scope of this study is to assess the technical and economical feasibility to replace the

existing diesel generator of detached towns from national grid in Ethiopia. The study will

investigate different renewable energy option to incorporate the existing diesel-only system.

This Study shall collect and analyze the data and information in the following fields among

others, examine and select the most suitable Power Generation and Supply Systems,

recommend necessary measures necessary measures that configure a system to accommodate

2011 electrical energy demand for the two towns. The study only focuses on solar energy and

wind energy resource assessment of among different renewable energy resource in the towns.

It compares estimates of the cost of electricity produced from renewable energy and the

present cost of fossil fuel (diesel) based electricity generated in Degehabur and Kebri Dehar

towns.

1.6 Present Status of Electric Supply for Keberi Dehar and Degehabur

towns

Presently, Degehabur and Kebridehar towns get diesel (SCS) supply from EEPCO. They

have diesel generators with an installed capacity of 400 and 375 kW each for Degehabur and

Kebridehar towns, respectively, which are located in eastern parts of Ethiopia. These towns

have not yet connected to a national grid as well as there is no transmission line passing

through it (See Appendix-A, Figure A.1).KebiDehar and Degehbur towns are located 160

and 210 kilometer, respectively from the nearest national grid In the table 1.2, indicated that

capita energy consumption per person of these towns still get low along with the working

hours of generators are limited to 6-10 hours per day. The power supply were insufficient the

normal daily blackout is experienced in the study area are around 11 hours, and in a worst

case situation could reach 13 hours a day .In addition towns, which are supplied from diesel,

do not have the freedom to get sufficient electric supply and as a result there is a suppressed

demand in study areas. This is due to the diesel generators are limited capacity with fairly

high fuel, operation and maintenance cost. Conventional diesel generator would be unreliable

energy supply system as well as environmental concerns in the towns. Table 1.1: Energy consumption and population size of Kebri Dehar and Degehabur


Name of Power station

Generator Capactiy

(KW)

Generated Energy (KWH)

Fuel Consumption

(kg) ServiceHours

Population Size

Capita energy consumption perperson(Kwh)

Degehabour 400 1,025,960 187,544 2,995 68,000 15

Kebri Dehar 375 514,930 161,678 4,716 57,000 9

Source: EEPCo

The average fuel efficiency of diesel generator is around 4 kWh/liter, to meet its electricity

requirements. Table 1.2 indicted that the total working hours of diesel generators 2995 hours

for Degehabur and 4716 hours for Kebridehar town per year while almost more than 50% the

rest of year, they didn’t get electricity at all and still have suffered in the darkness.

However, biomass energy is, and will remain, an important source of energy because most of

the people of the towns depend on traditional fuels or biomass energy, namely wood,

bamboo, twigs, wood shavings, agricultural residues such as straw, charcoal and cow dung

for their domestic consumption. For the vast majority of the population size of in these towns,

the main source of energy for cooking comes from such biomass fuel such as wood fuels,

crop residue and cow dung. Deforestation and burning of biomass significantly contribute to

greenhouse gas emissions. In addition to carbon dioxide emissions, wood burning also creates

the products of incomplete combustion (PICs) which have a global warming potential as

great as carbon dioxide itself.

At the local level, receding forests add to the hard work of women who have to travel longer

distances in search of fuel or, in extreme situations, are forced to switch to inferior fuels such

as roots, weeds, leaves, etc. An estimated 80% of rural women aged 10-59 years are affected

by fuel-wood scarcity in Ethiopia. Inefficient combustion of bio fuels in traditional cook

stoves produces smoke which can cause a variety of health problems such as conjunctivitis,

acute respiratory infections, upper respiratory irritation, etc

1.7 Methodology

Different methodologies have been applied to address each objective of this study. Each

methodology was selected to suit the seven phases used to undertake this research. The detail

methodology is presented in Figure 1.4

1.7.1 Problem Identification

The problem identification involved a literature survey in collecting general information

about Keberi Dehar and Degehabur towns such as geography, climate, population, current


electricity status of the towns and future demand, and cost of energy (COE) was collected.

The main focus of the survey was on the renewable energy sources available, electricity

supply crisis in the selected town come from conventional diesel generator power plant as

well as there are no transmission passing through these island.

1.7.2 Renewable Energy Resources Assessment

The potential solar resource, wind power resource for Kebei Dehar and Degehabur towns

were assessed. The wind speed, solar insolation and clearness index for a year long period

was obtained from the NASA Surface Meteorology and Solar Energy database (SMSE).wind

and solar map of Ethiopian was collected from SWERA.A detail information about diesel

generator obtained from Ethiopian Electric Power Corporation. Monthly mean annual

temperature value collected from Ethiopian Meteorological Agency.

1.7.3 Power supply Options Identification

Feasible options of generating electricity in the selected towns were proposed based on

available power generating technologies and local energy resource potentials. The paramount

feature of this task was power extraction from renewable resources. The available

technologies were identified through a literature survey based on best practices in off-grid

power systems applications

1.7.4 Overall System Design and Analysis

The following is the sequence of system design used in this dissertation.

Step 1: Wind power system design

Detail wind power energy generation design conducted on the part including wind resource

assessment, Estimation of the frequency distribution and long term average wind Speed as

well as synthesis of wind speed daily profiles. Computed wind Power density distributions

and predication monthly and annual mean power density, analyzed different power curves of

wind turbines and optimization annual wind energy output of single turbines for the selection

of appropriate wind turbines types of the two towns

Step 2: Photovoltaic power systems

Solar power design including detail solar resource assessment of the Keberi Dehar and

Degehabur towns, calculation, estimation of design of PV installation, Synthesize Hourly

solar data and calculates global radiation incident on the PV array from monthly average

radiation, Calculates the global radiation incident on the PV array with tracing system to

maximizing the power generation from solar PV modules, analyze the PV Cell Temperature

and PV array power output for each towns


Step 3: conducted on batteries design, selection maximum point tracker, Inverters design as

well as Energy Consumption and load forecasting of the two towns

In this step which includes Battery Design and modeling, better converter design, PV

controller design and analyzed daily energy profiles along with annual energy consumption.

Load forecast in planning horizon has been carried out with ACRS excel spread sheet model

of the rural electrification project employed for this purpose..

Step 4: Hybrid energy systems

Detail analysis and design of a combination of one or several renewable energy sources such

as solar photovoltaic, battery and wind energy as well as use diesel-only as for comprising for

hybrid system. A hybrid system uses a combination of energy producing components that

provide a constant flow of uninterrupted power, Stand Alone Hybrid System, Economic

Evaluation of the Hybrid System.

Step 5: Optimization of the model using HOMER software

Validation of the model .This is accomplished by comparing modeled results with data

collected in EEPCO for the existing energy system. For the hybrid energy systems, a

literature review highlighting the use of HOMER for hybrid system feasibility and sizing was

conducted. The input data are wind speed, solar radiation, clearness index, fuel price, wind

turbines cost, converter cost, PV panel cost, wind power curve, efficiency of solar panel, fuel

price generator cost, nominal operating cell temperature, primary and deferrable load (refer

detail input of the software in Appendix-E). In order to simulate and sense the behavior of

chosen power sources, all sources will be simulated in conjunction with each other. This will

be done using the simulation tool HOMER, provided by NREL, National Renewable Energy

Laboratory. A simulation tool can never reflect the true results of a project. It is however a

useful tool when comparing different system arrangements in terms of economical and

technical feasibility. A deeper presentation of the HOMER simulation tool will be provided

in connection to the simulation. The result of the simulations will then form a basis where

conclusions and proposals can be drawn concerning system performance and reliability.

Step 6: Conclusion & Recommendation

After detail analysis of the collected data a new alternative solution for the towns proposed.

The analysis of the simulation results was based on simulation output information and its

logical relation with inputs and underlying power system design. Refer figure 1.8 below, the

thesis methodology and sequence of step as well as design to achieve the general and specific

objective of the study.


Figure 1.4: Workflow and general outline of Research methodology


1.8 Structure of thesis

This thesis paper includes seven chapters and four appendices, which are organized as

follows:

Chapter 1: Introduction overviews the rationale for this study. It includes rural energy

context and Electricity provision in rural areas, EEPCO background, objectives of the study,

methodology adopted; scope of the study, Present Status of Electric Supply for selected

towns, and the structure of the thesis.

Chapter 2: Wind power system comprises Introduction, Wind resource assessment,

Estimation of the frequency distribution and long term average Wind Speed, Wind Power

density distributions and mean power density, different Types of Turbines, general working

principles, power curve and turbines efficiency, Wind Speed Height Correction Annual wind

energy production and capacity factor

Chapter 3:Photovoltaic Power System comprises Introduction, PV electricity , General

working principles Photovoltaic Cells , Solar Module Power Characteristics and Operating

issue Photovoltaic Cells and Efficiencies , PV installation , Solar resource assessment of the

selected towns ,i Calculates the global radiation incident on the PV array , Calculates the PV

Cell Temperature and PV array power output.

Chapter 4: Batteries, PV controller, Inverters and Energy Consumption comprises

Introduction, Batteries Electricity, general working principles, storage capacity , battery

modeling , battery sizing ,Batter life time, Battery Design , Battery in hybrid system , PV

Controllers , Inverters , Inverter Sizing , Energy consumption for Kebridehar and Degehabur

towns and load forecasting

Chapter 5: Hybrid Energy Systems comprises Introduction, Stand Alone Hybrid System

Typical Stand Alone Hybrid Components and Efficiencies, Proposed Stand Alone Sizing

Optimization Procedure, Economic Evaluation of the Hybrid System, Breakeven Grid

Extension Distance, and System architecture.

Chapter 6: Results and Discussion comprises General, Simulation results Comparison with

“diesel only” system with hybrid system of the selected towns, Sensitivity results with

different diesel price scenarios, Comparison of the Grid extension with standalone system

(Off Grid).

Chapter 7: Conclusion and Recommendation presents conclusions that have been derived

from this study, followed by recommendations for further study and for practical


implementation of a proposed option. This chapter concludes with a few brief final. Different

data tables and graphs are presented in the appendices

2. Wind power system

2.1 Introduction

Winds are produced by uneven solar heating of the earth’s land and sea surfaces. Thus, they

are a form of “solar” energy. On average, the ratio of total wind power to incident solar

power is on the order of two present, reflecting a balance between input and dissipation by

turbulence and drag on the surface.

Wind is the movement of air caused by the irregular heating of the Earth's surface. It happens

at all scales, from local breezes created by heating of land surfaces that lasts some minutes, to

global winds caused from solar heating of the Earth. Wind power is the transformation of

wind energy into more utile forms, typically electricity using wind turbines (Gipe, 2004).

2.2 History

Wind has always been an energy source used by several civilizations many years ago. The

first use of wind power was to make possible the sailing of ships in the Nile River some 5000

years ago. Many civilizations used wind power for transportation and other applications. The

Europeans used it to crush grains and pump water in the 1700s and 1800s. The first wind mill

to generated electricity in the rural U.S. was installed in 1890 (Patel, 2006). However, for

much of the twentieth century there was small interest in using wind energy other than for

battery charging for distant dwellings. These low-power systems were quickly replaced once

the electricity grid became available. The sudden increases in the price of oil in 1973

stimulated a number of substantial Government-funded programs for research, development

and demonstrations of wind turbines and other alternative energy technologies. In the United

States this led to the construction of a series of prototype turbines starting with the 38

diameter 100kW Mod-0 in 1975 and culminating in the 97.5m diameter 2.5MW Mod-5B in

1987. Similar programs were pursued in the UK, Germany and Sweden (T. Burton, 2001).

Today, even larger wind turbines are being constructed such as 5MW units. Wind generated

electricity is the fastest renewable growing energy business sector (Gipe, 2004).Growth in the

use of larger wind turbines, as made small wind turbines increasingly be attractive for small

applications such as, powering homes and farms. Wind power has become a very attractive

renewable energy source because it is cheaper than other technologies and is also compatible

with environmental preservation. Wind power showed a growth rate of 31.7 %, the highest


rate since 2001. The trend continued that wind capacity doubles every three years. All wind

turbines installed by the end of 2009 worldwide are generating 340 TWh per annum,

equivalent to the total electricity demand of Italy, the seventh largest economy of the world,

and equaling 2 % of global electricity consumption (World Wind Energy 200).

Worldwide capacity reached 159,213 MW, out of which 38,312 MW were added in 2009 is

approximately 73,904MW. Figure 2.1[World Wind Energy 2009] shows the total installed in

the last few years and provide a prediction for 2010.

Figure 2.1: World Wind Energy - Total Installed Capacity (MW) (World Wind Energy 2009),

2009)

2.3 Location of Degehabuar and Kebri Dehar town

Degehabur and Keberedahr towns located within Ethiopia Coordinates and Somalia region:

8° 13 N, 43° 34 E and 6° 44 N, 44° 16 E respectively. The prevailing wind is the wind that

blows most frequently across a particularly region. Different regions on Earth have different

prevailing wind directions which are dependent upon the nature of the general circulation of

the atmosphere and the latitudinal


Table 2.1 Prevailing Wind Directions region on Earth

Latitude Direction Common Name

90-60°N NE Polar Easterlies60-30°N SW Southwest Antitrades 30-0°N NE Northeast Trades

0-30°S SE Southeast Trades30-60°S NW Roaring Forties90-60°S SE Polar Easterlies

Source:(Climate)

The prevailing wind directions are important when sitting wind turbines, since we obviously

want to place them in the areas with least obstacles from the prevailing wind directions.

The prevailing wind of these towns comes from the northeast trade winds. We therefore need

not be very concerned about obstacles to the west or Southwest of wind turbines, since

practically no wind energy would come from those directions.

The monthly average wind direction for a given month of the two towns, averaged for that

month over the 10-year period (July 1983 - June 1993) and Wind direction values are for 50

meters above the surface of the earth. These value are presented in Appendix-B, table B.1.

2.4 Wind resource assessment for Degehabur and Keberedehar town

Wind resource is the most important element in projecting turbine performance at a given

place. The energy that can be extracted from a wind stream is proportional to the cube of its

velocity, meaning that doubling the wind velocity increases the available energy by a factor

of eight. Also, the wind resource itself rarely is a constant or has a steady flow. It varies with

year, season, time of day, elevation above ground, and form of terrain. Proper location in

windy sites, away from large obstructions, improves wind turbines performance.

Wind speed generally decreases as one move from higher latitudes towards the equator. The

energy transported to a higher altitude gets stronger as the latitude increases (i.e. as the area

decreases flow of energy density increases). However, the local effects might be quite

important - presence of geographic structures such mountains, valleys and coastal areas may

enhance wind speed. Ethiopia being located near the equator, its wind resource potential is

very much limited. There are few promising windy areas in Ethiopia located alongside the

main east African Rift Valley, the North Eastern escarpment of the country near Tigray

regional state and the eastern part of the country (near North east of the Somali regional

state).


Wind data has been collected and documented by National Meteorological Service Agency

(NMSA) primarily for a purpose of aviation. This data is not of much use for estimation of

the resource as most of the met-stations do not qualify the required standard for wind speed

measurements. Most of the met station measurements for wind speed were taken at heights

lower than the accepted standard of 10 m and over half were taken at just 2 m above ground

level. The energy output from wind is very much dependent on wind speed. Estimation of the

resource is however not a precise art. Identification of locations for wind energy generation

depends on several factors other than the speed of the wind. Physical accessibility of

locations, proximity to electricity grid, exclusion of designated areas such as national parks

and visual impacts on areas of outstanding beauty are some of the factors that need to be

taken into consideration while estimating the potential of the resource (Development, 2007).

The estimation considers the whole land area of the country that practically fall under various

wind resource categories without excluding land areas that could possibly be eliminated for

reasons of accessibility, economics or environmental. This estimation provides the bigger

picture of the country in terms of locating windy areas. The practicable potential is certainly

lower than the first estimation as more land will be eliminated with further screening

(Development, 2007).

Areas estimated to have Moderate and higher (Class 3 and above) wind resource are

primarily located in the highlands featuring a sudden change in altitude from the neighboring

land masses. These areas are basically the escarpments along the Great Rift Valley extending

to the Southern, Eastern, North Eastern parts of the country, and the Central highlands. The

strongest wind resource with energy density per annual above 800 W/m2 is located on the

ridge of the highlands in the central part of the rift valley (Development, 2007).

See figure 3 wind map of Ethiopia with including projects area of at a height of 50m above

the ground.


Figure 2.2: A GIS map showing geographic distribution of wind resources of Ethiopia (Source: SWERA)

The wind resource classifications, Class 1 to Class 7, are indicated by color-codes as

indicated in the GIS map above– Class 7 indicating the strongest wind regions. Each color

code has an assigned range of values to represent annual wind power density in W/m2.

In addition to wind map from SWERA, there is another wind speed data obtained from

NASA is presented in figure 2.3 below. From these wind resources data, we can have an

initial idea of about wind speed resource and their energy potential of the selected sites. Both

wind map and wind speed data seem to have correlated. Wind resources data are significant

for the long term wind power forecasting to establish a reliable hybrid energy system design

for the two towns. The data is presented in the Figure 2.1 was ten years monthly average

wind speed of the two towns.


Figure 2.3 Monthly Averaged Wind Speed at 50 m above the Surface of the Earth (m/s) for Degehabur and Keberdehar town. (Source: NASA)

Referred Figure 2.3 above, the windiest month of years was recorded in July and the lowest

wind speed was recorded on the month of April and average wind speed of Kebredehar a bit

higher than Degehabur.

2.4.1 Estimation of the frequency distribution and long term average Wind Speed

of Degehabur and Kebedehar town

In probability theory and statistics, the Weibull distribution is a continuous probability

distribution. It is named after Waloddi Weibull who described it in detail in 1951, although it

was first identified by Fréchet (1927) and first applied by Rosin & Rammler (1933) to

describe the size distribution of particles. In probability theory, a probability density function

(abbreviated as PDF, or just density) of a continuous that describes the relative likelihood for

this random variable to occur at a given point in the observation space (Wikipedia, 2010).

The probability of a random variable falling within a given set is given by the integral of its

density over the set. In most locations worldwide, the distribution of wind speeds keeps fairly

close to a Weibull or (simplified) Rayleigh distribution of wind speeds, shown below figure

2.4. There are non-Rayleigh locations where the curve takes on other shapes, but these are

relatively rare. The distribution shown here is relatively common.

Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec

Degehabur 5.75 5.39 4.51 3.43 4.59 7.27 7.64 7.11 5.64 3.88 4.71 5.49

Keberedehar 5.98 5.72 4.65 3.68 5.88 8.19 8.4 7.96 6.51 4.26 4.12 5.49

0

1

2

3

4

5

6

7

8

9w

ind

spee

d in

(m/s

) Degehabur

Keberedehar


Figure 2.4: Rayleigh Densities Function for Various Mean Wind speed. (Shenck)

Notes: the k=2 form of the Weibull PDF, commonly known as the Rayleigh density function.

If the probability density is known, alternatively, the mean wind speed can be determined

from

2-1

The wind speed probability distributions and the functions representing them mathematically

are the main tools used in the wind-related literature. Their use includes a wide range of

applications, from the techniques used to identify the parameters of the distribution functions

to the use of such functions for analyzing the wind speed data and wind energy economics.

Two of the commonly used functions for fitting a measured wind speed probability

distribution in a given location over a certain period of time are the Weibull and Rayleigh.

The probability density function of the Weibull distribution is given by (Celik, 2003),

2-2

Where: -

Thus the cumulative distribution is the integral of the probability density function. The

cumulative probability function is(sustainable energy among option):

) 2-3

is the wind speed

Where k > 0 is the shape parameter and c >0 is the scale parameter of the distribution.


The shape factor will normally range from 1 to 3. These typical values are known from

experience and multiple observations of sites where wind speed measurements have been

taken. These wind types are categorized as inland, coastal, and trade wind (off-shore) sites.

Table 2.2 shows typical values for the shape factor (RETSCREEN).

Table 2.2 Typical Shape Factor Values

Types of wind Shape factor (k) Inland Winds 1.5 to 2.5 Coastal Winds 2.5 to 3.5 Trade Winds 3 to 4

If Weibull k is not known, use k = 2 for inland sites, use 3 for coastal sites, and use 4 for

island sites and trade wind regimes

If Eq. (3) is solved together with Eq. (4) making the substitution of =(v/c)k for v, the

following is obtained for the mean wind speed,

2-4

2-5

For k=2, the following is obtained from Eq.2-1

for Degehabur

town where as average wind speed is 5.91m/s and scale factor 6.68 m/s for Keberedehar

town.

For this thesis A Weibull factor of 2 (k=2) since the selected towns for inland sites were used

to develop probability density function (PDF) hourly profile of the wind speeds for a

hypothetical year. Figure 2.8 and 2.9 shows the probability distribution function of the

baseline wind resources for Kebri Dehar and Dehehabur with the best-fit line smoothing

function overlaid. The detail probability density function of wind speed of throughout the

years for the two towns are presented in Appendix-B, table B.2 and table B.3.


Figure 2.5 Probability density vs. wind speed at hub height for Keberehar town

Note: Mean Annual Average = 5.45 m/s

Figure 2 6: Probability density vs. wind speed at hub height in Degehabur Town

Note: Mean Annual Average = 5.45 m/s

One of the most distinct advantages of the Rayleigh distribution is that the probability density and the cumulative distribution functions could be obtained from the mean value of the wind speed.


Moreover, the diurnal pattern strength is a measure of how strongly the wind speed tends to

depend on the time of day. Because the wind is typically affected by solar radiation, most

locations show some diurnal (or daily) pattern in wind speed.

In order to measure the strength of the diurnal pattern, it can calculate the average diurnal

profile. Each of the 24 values of the average diurnal profile represents the annual average

wind speed for that hour. It then fits a cosine function to this average diurnal profile The

cosine function fitted to the average diurnal pattern is of the form (NREL, 2008):

2-6

The anemometer height at which data was collected is 50 m according to the data source,

NMSA. Typical values for diurnal pattern strength range from 0 to 0.4 (NREL, 2008); by

varying the values within the range, repeatedly running the software and checking the results

against the measured data, a value of 0.25 has been selected. The autocorrelation function is a

measure of the tendency of what a wind speed is likely to be, given what it was earlier

(NREL, 2008). For complex topography the autocorrelation factor is (0.70 - 0.80) while for a

uniform topography the range is higher, (0.90 - 0.97). A typical range for the autocorrelation

factor is 0.8 – 0.95 (NREL, 2008). An average value of 0.85 is used here because the selected

towns are of averagely uniform topography. The typical range for the time of peak wind

speed, which is the time of day that tends, on average, to be the windiest throughout the year,

is 14:00-16:00 (NREL, 2008). This has also been observed in the available raw data for some

of the months. In addition to this, the software has been run for different times between 14:00

and 18:00, the results have been checked against the measured data and the time of 15:00 has

been chosen for the calculations.

The figure below indicate that the wind speed variation with 24 hours. As you observed in the

figure the highest wind speed occur during around 15:00 each consecutive month and it is

just the sun directly overhead at solar noon. Figures 2.1 and 2.3 show daily wind speed

profiles of Kebri Dehar and Degehabur respectively. The detail value of daily wind speed

variation of pattern is presented in Appendix-B, table B.3 and table B.4.


Figure 2.7 Wind speed daily profile for Keberedar

Figure 2.8: Wind speed daily profile for Degehabur town


2.4.2 Wind Power density distributions and mean power density

The monthly average wind speed using Weibull distributions is determined as in Eq.2-4:

The power of the wind per unit area is given as:

2-7

The average power density for each month is calculated using actual probability density

distribution for the specified month, which is calculated using Eq.2-8, and is given as:

2-8

Where, the subscript m stands for the month and n is the number of records for the specified

month.

The average power density using Weibull probability distribution is calculated as follows:

2-9

From the above equation for k=2, the following is obtained

is the gamma function and given as:

For k=2, the following is obtained from Eq.2-5

From Eq. 2-5 for k=2 and

Finally power density each month is calculated as follow:

2-10

From the equation 2-10, we get the wind power density values each month for two towns.

This figure is obtained in table 2.3 below. Table 2.3: wind Power density for Degehabur and Keberedehar towns


For Degehabur town For Keberdehar town

Month

Monthly average

windspeed

Scale factor

Power density(W/m2)

Monthly average

windspeed

Scalefactor

Power density(W/m2)

Jan 5.75 6.49 222.50 5.98 6.75 250.28 Feb 5.39 6.08 183.27 5.72 6.45 219.03 Mar 4.51 5.09 107.36 4.65 5.25 117.67 Apr 3.43 3.87 47.23 3.68 4.15 58.33 May 4.59 5.18 113.18 5.88 6.63 237.93 Jun 7.27 8.20 449.70 8.19 9.24 642.95 Jul 7.64 8.62 521.92 8.4 9.48 693.68

Aug 7.11 8.02 420.66 7.96 8.98 590.29 Sep 5.64 6.36 209.97 6.51 7.35 322.90 Oct 3.88 4.38 68.36 4.26 4.81 90.48 Nov 4.71 5.31 122.29 4.12 4.65 81.85 Dec 5.49 6.19 193.66 5.49 6.19 193.66

Monthly annual

average 5.45 6.15 189.46 5.91 6.67 241.59

The power densities values consider in the table 2.4 is calculated by using Eq 2.4 and Eq

2.10.It is clearly indicated that a figure inside the table 2.4, the power density for Dehehabur

is not fairly constant and shows a large month to month variation. The minimum power

densities occur in April and October, with 47.23 and 68.36 W/m2, respectively. It is

interesting to note that the highest power density values occur in the summer months of June,

July and August, with the maximum value of 521.92 W/m2 in July. The power densities in

the remaining months are between these two groups of low and high.

Power density is also varies with monthly average wind speed. Monthly average wind speed

of Kebredehar is a bit higher than average monthly wind speed of Deghabur.In the table 2.3

show that the minimum power densities occur in April and November, with 58.33 and 81.85

W/m2, respectively. Likewise, Degehabur highest power density values occur in the summer

months of June, July and August, with the maximum value of 693.68 W/m2 in July and the

power densities in the remaining months are between these two groups of low and high..

Estimates of wind power density are presented as wind class, ranging from 1 to 7. The speeds

are average wind speeds over the course of a year, although the frequency distribution of

wind speed can provide different power densities for the same average wind speed. See table

2.4 below.


Table 2.4: wind class category by wind speed and power density

Class 10 m (33 ft) 30 m (98 ft) 50 m (164 ft) Wind power

density (W/m2)

Speed m/s (mph)

Windpowerdensity (W/m2)

Speed m/s (mph)

Windpower density (W/m2)

Speed m/s

(mph)

1 0 - 100 0 - 4.4 0 - 160 0 - 5.1 0 - 200 0 - 5.6 (0 - 9.8) (0 - 11.4) (0 -

12.5)2 100 - 150 4.4 - 5.1 160 - 240 5.1 - 5.9 200 - 300 5.6 - 6.4

(9.8 - 11.5) (11.4 - 13.2)

(12.5 - 14.3)

3 150 - 200 5.1 - 5.6 240 - 320 5.9 - 6.5 300 - 400 6.4 - 7.0 (11.5 - 12.5) (13.2 -

14.6) (14.3 - 15.7)

4 200 - 250 5.6 - 6.0 320 - 400 6.5 - 7.0 400 - 500 7.0 - 7.5 (12.5 - 13.4) (14.6 -

15.7) (15.7 - 16.8)

5 250 - 300 6.0 - 6.4 400 - 480 7.0 - 7.4 500 - 600 7.5 - 8.0 (13.4 - 14.3) (15.7 -

16.6) (16.8 - 17.9)

6 300 - 400 6.4 - 7.0 480 - 640 7.4 - 8.2 600 - 800 8.0 - 8.8 (14.3 - 15.7) (16.6 -

18.3) (17.9 - 19.7)

7 400 - 1000 7.0 - 9.4 640 - 1600

8.2 - 11.0 800 - 2000 8.8 - 11.9

(15.7 - 21.1) (18.3 - 24.7)

(19.7 - 26.6)

Source: (wikipedia, 2010)

Refer table 2.3 and 2.4, specified that Degehabur annual average wind speed and power

density distribution categorized in first wind class whereas Keberedehar categorized in the

second class. But wind class each month of both towns varies from first to sixth wind class.

2.5 Wind Turbines

2.5.1 Different Types of Turbines

A wind turbine is a machine that converts the kinetic energy from the wind into mechanical

energy. If the mechanical energy is used directly by machinery, such as a pump or grinding

stones, the machine is usually called a windmill. If the mechanical energy is then converted

to electricity, the machine is called a wind generator (Gipe, 2004).

There are a number of different wind turbine types available. The horizontal axis turbine,


HAWT is by far the most common type of turbine. They come in two different types: the

upwind, which face the wind (tower behind rotor) and the downwind arrangement that works

away from the wind (tower in front). Another kind of turbine is the vertical axis, VAWT

arrangement that uses drag and lift as the driving forces; the horizontal also uses drag and lift,

but in other proportions.

The advantages with upwind turbines are that the tower does not act as an obstacle for the

wind hitting the rotor. Despite this, the flow behind the passing blade is affected by the tower

and causes a slight drop in power. When the blade passes the tower it also decreases the drag

on the construction which can cause an on / off bending process causing fatigue stress. This

has of course been taking into account when designing the turbine. The upwind design needs

a control system that helps the nacelle turn straight to the wind. In downwind turbines, the

tower shades a rotor blade each time it passes by and causes greater power losses compared

to the upwind design. An advantage with downwind turbines is that the nacelle is self-

adjusting and is not in need of a control system. One drawback with this is the problem with

untwisting the cable inside when the nacelle has turned same direction repeatedly. The

VAWT´s are not as commercial and economically competitive as the HAWT´s. Some of the

VAWT types suffer from low efficiency due to design difficulties as well as the problem with

operation close to the ground. Parts of the vertical turbines will therefore receive low quality

winds causing power losses. To keep the construction upright it also needs to be supported

with guy cables attached to the ground. The vertical turbine is not in need of yaw control,

which of course is an advantage and the wind always hits the turbine tangentially (Boyle,

1996).

The modern wind turbine is a sophisticated piece of machinery with aerodynamically

designed rotor and efficient power generation, transmission and regulation components. The

size of these turbines ranges from a few Watts (Small Wind Turbines) to several Million

Watts (Large Wind Turbines). The modern trend in the wind industry is to go for bigger units

of several MW capacities in places where the wind is favorable, as the system scaling up can

reduce the unit cost of wind-generated electricity. Most of today's commercial machines are

horizontal axis wind turbines (HAWT) with three bladed rotors.


Figure 2.9: Horizontal axis wind turbines (HAWT) are either upwind machine or down wind machines

(a) Upwind machine (b) Or down wind machines (c). Vertical axis wind turbines (VAWT) accept wind from any direction (Masters, 2004.)

2.5.2 Wind Turbines Components

The most common turbine type is the horizontal axis wind turbine. A cut-view (Figure 2.10)

helps the reader to get familiar with the components of a wind turbine.

Figure 2.10: Cut-view of a wind turbine. (Source: DOE/NREL)

Anemometer:

Measures the wind speed and transmits wind speed data to the controller.


Blades:

Most turbines have either two or three blades. Wind blowing over the blades causes the

blades to "lift" and rotate.

Brake:

A disc brake, which can be applied mechanically, electrically, or hydraulically to stop the

rotor in emergencies.

Controller:

The controller starts up the machine at wind speeds of about 8 to 16 miles per hour (mph) and

shuts off the machine at about 55 mph. Turbines do not operate at wind speeds above about

55 mph because they might be damaged by the high winds.

Gear box:

Gears connect the low-speed shaft to the high-speed shaft and increase the rotational speeds

from about 30 to 60 rotations per minute (rpm) to about 1000 to 1800 rpm, the rotational

speed required by most generators to produce electricity. The gear box is a costly (and heavy)

part of the wind turbine and engineers are exploring "direct-drive" generators that operate at

lower rotational speeds and don't need gear boxes( DOE/NREL)

Generator:

It usually an off-the-shelf induction generator that produces 60/50-cycle AC electricity.

High-speed shaft:

It drives the generator.

Low-speed shaft:

The rotor turns the low-speed shaft at about 30 to 60 rotations per minute.

Nacelle:

The nacelle sits atop the tower and contains the gear box, low- and high-speed shafts,

generator, controller, and brake. Some nacelles are large enough for a helicopter to land on.

Pitch:

Blades are turned, or pitched, out of the wind to control the rotor speed and keep the rotor

from turning in winds that are too high or too low to produce electricity.

Rotor:

The blades and the hub together are called the rotor.

Tower:

Towers are made from tubular steel (shown here), concrete, or steel lattice. Because wind

speed increases with height, taller towers enable turbines to capture more energy and generate

more electricity.


Wind direction:

This is an "upwind" turbine, so-called because it operates facing into the wind. Other turbines

are designed to run "downwind," facing away from the wind.

Wind vane:

Measures wind direction and communicate with the yaw drive to orient the turbine properly

with respect to the wind.

Yaw drive:

Upwind turbines face into the wind; the yaw drive is used to keep the rotor facing into the

wind as the wind direction changes. Downwind turbines don't require a yaw drive; the wind

blows the rotor downwind (DOE/NREL)

Yaw motor:

Powers the yaw drive.

2.5.3 General Workings

The blade, using aerodynamic lift, capture energy from wind in order to turn the shaft. In

small wind turbines the shaft usually drives the generator directly. The generator converts the

mechanical energy into electricity. The shaft power causes coils to spin past alternate poles of

magnets allowing electric current to flow. If a permanent magnet device is being used the

opposite occur: current flow as magnets spin past coil windings. Most small wind turbines

use a permanent magnet alternator. Large wind turbines usually use either induction

generator or a synchronous generator. In addition, in large wind turbines the shaft connected

to the generator via a gearbox those steps up the rotational speed for the generator.

In off-grid application it is difficult to keep the frequency of the resulting current constant, as

it depends on wind speed which is highly variable. Therefore the current is usually rectified

to give DC.

Most wind turbines have two or three blade. Two blade machines are somewhat less

expensive. Three bladed machines suffer less mechanical stress and are less vulnerable to

fatigue problem. The Yaw bearing allows a wind turbine to rotate in order to face to the wind

from any direction. A tower support wind turbine and places it above any obstruction.

2.5.4 Wind system design

If the generator is undersized, the turbine will reached peak power at relatively low wind

speed and stay until the cut out speed reached. If the turbine is oversized, then power will

increase until the cut out speed reached.


The energy output of a wind turbine can be calculated by determining the frequency

distribution local wind speed and then computing the expected range of power outputs for

each wind speed by using power curve.

The wind turbine load and hence speed governed electrically by voltage controller and

mechanical by counterweights which reduce the pitch of the blade in the event of excess wind

speed or energy production

2.5.3 Wind turbine in hybrid system

Wind turbine single-source systems tend to produce highly variable and therefore unreliable

power supply due to the irregular wind speeds. If the wind turbine is combined with other

sources a hybrid system the produced energy can become more regular improving system

performance and cost effectiveness.

2.5.4 Wind Turbines Efficiency and Power Curve

The theoretical limit of power extraction from wind, or any other fluid was derived by the

German aerodynamicist Albert Betz. Betz law, [Betz, 1966], states that 59% or less of the

kinetic energy in the wind can be transformed to mechanical energy using a wind turbine. In

practice, wind turbines rotors deliver much less than Betz limit. The factors that affect the

efficiency of a turbine are the turbine rotor, transmission and the generator. Normally the

turbine rotors have efficiencies between of 40% to 50%. Gearbox and generator efficiencies

can be estimated to be around 80% to 90%. Also efficiency of a turbine is not constant. It

varies with wind speeds. Many companies do not provide their wind turbine efficiencies.

Instead they provide the power curve.

A power curve is a graph that represents the turbine power output at different wind speeds

values. The advantage of a power curve is that it includes the wind turbines efficiency. The

power curve is normally provided by the turbine’s manufacture. Figure 2.6 presents four

types of a wind turbine power curve. From the first two types turbines, note that at speeds

from 0 to 2.9m/s the power output is zero. This occurs because there is not sufficient kinetic

energy in the wind to move the wind turbine rotor where as for the second two types of

turbines that which are WES18 and WES30 cut-in speed are 4 and 5m/s respectively.

Normally the manufactures provide a technical data sheet where the startup wind speed of the

turbine is given. In general lower start up wind speeds result in higher energy coming from

the turbine. the power curve provide in the turbines manufacturer, the EOLTEC types of

turbines is still rotate with a lower speed (that is cut-in speed is 3m/s) compare of WES types


of turbines (the cut-in speed are 4 and 5m/s) There is also another important data which the

turbines is operating without any mechanical failure and this parameter is called the cut-out

speed. From the figure 2.6 observed that the cut-out speed of EOLTEC types of turbine is

around 20m/s whereas WES types of turbine is 25m/s.

Figure 2.11: Different type wind turbines power curves being considered for the selected wind farm

0

50

100

150

200

250

300

1 3 5 7 9 11 13 15 17 19 21 23 25 27

Pow

er in

(kW

)

Wind Speed in (m/s)

EOLTEC Power Output

WES 18 Power Output

WES 30 Power Output


Figure 2.12: Power output of EOLTEC CHINOOK 17-65 Wind Turbines with steady wind speed characteristics.

The hourly output power of a WTG can be easily obtained from the simulated hourly wind

speeds using Equation (2.12). The simulated output power of a 65 kW wind generator with

operating parameters of cut-in, rated and cut-out wind speeds of 2.3 m/s, 11 m/s and 20 m/s.

The output power of the WTG is between 0 and its power rating of 65 kW. Major technical

data for an EOLTEC CHINOOK 17-65 Wind Turbines including the power curve are given

in Appendix-C. From the above characteristic curve, there are three important points at which

much attention is paid for the speeds and the corresponding turbine output powers for every

wind turbine. These are the cut-in speed, rated output speed and cut-out speed. The important

terms characterizing the turbine power-speed (Figure 2.12) characteristics are described

below:

• Cut-in speed – at very low wind speeds, there is insufficient torque exerted by the wind on

the turbine blades to make them rotate. However, as the speed increases, the wind turbine will

begin to rotate and generate electrical power. The speed at which the turbine first starts to

rotate is called the cut-in speed and is typically between 3 and 4 meters per second

(WindPowerProgram).


• Rated output power and rate output wind speed – as the wind speed rises above the cut-

in speed, the level of electrical output power rises rapidly. However, typically somewhere

between 12 and 17 meters per second, the power output reaches the limit that the electrical

generator is capable of. This limit to the generator output is called the rated power output and

the wind speed at which it is reached is called the rated output wind speed. At higher wind

speeds, the design of the turbine is arranged to limit the power to this maximum level and

there is no further rise in the output power. How this is done varies from design to design but

typically with large turbines, it is done by adjusting the blade angles so as to keep the power

at the constant level (WindPowerProgram)

• Cut-out speed – as the speed increases above the rate output wind speed, the forces on the

turbine structure continue to rise and, at some point, there is a risk of damage to the rotor. As

a result, a braking system is employed to bring the rotor to a standstill. This is called the cut-

out speed and is usually around 25 meters per second (WindPowerProgram) .

• A furling speed ( ) is approximately twice that of the rated speed .This means the

turbine control system is able to maintain a constant power output over an eight to one range

of wind power input.

As the scale and shape parameter have been calculated, two meaningful wind speeds—the

most probable wind speed and the wind speed carrying maximum energy—can easily be

obtained. The most probable wind speed denotes the most frequent wind speed for a given

wind probability distribution and the wind speed carrying maximum energy represents the

wind speed which carries the maximum amount of wind energy (Akpinar, 2004). They can be

expressed as:

2-10

2-11

2.6 Wind Speed Height Correction

For idealized smooth plane surface, the average wind speed increases with height

approximately as the 1/7th power:


2-12

Where the wind speed at the desired height is , is the wind speed measured at

a known height , and is a coefficient known as the wind shear exponent. The wind shear

exponent varies with pressure, temperature and time of day. A commonly use value use is

one-seventh (1/7) and which is more applicable over open land surfaces.

Thus, a wind turbine with hub elevation of 50m will, relatively a height of 30m, sees an

average wind speed some 7.6% higher. Because available power varies as velocity cubed, the

higher position can increase turbine power by 24.5%, an appreciable improvement. As a

result, selection of the tower height is a major cost-benefit tradeoff. Other factors complicate

this chose, for example: Topography and vegetation alter the wind speed. Crest of treeless

hills are advantageous, however, the flow above hills does not follow the 1/7th power law

There also another formula that wind speed on height correction. As you know wind speed

always affected by local factor which are hills, building and topography where the wind

turbine install. This formula is best estimation of the wind speed at hub height. For this thesis

it uses logarithmic law of wind speed correction since it include local factor which affect the

wind speed. The most general equation to calculate wind speed at hub height is as follow,

2-13

The surface roughness length is a parameter that characterizes the roughness of the

surrounding terrain. The table below contains representative surface roughness lengths taken

from Maxwell, McGowan, and Rogers (NREL, 2008): Table 2.5: Representative surface roughness lengths different terrain (source: HOMER)

Terrain Description z0

Very smooth, ice or mud 0.00001 mCalm open sea 0.0002 m Blown sea 0.0005 m Snow surface 0.003 m Lawn grass 0.008 m Rough pasture 0.010 m Fallow field 0.03 m Crops 0.05 m Few trees 0.10 m Many trees, few buildings 0.25 m Forest and woodlands 0.5 m Suburbs 1.5 m City center, tall buildings 3.0 m


2.7 Wind Power

The power (P) in the wind is a function of air density ( ), the area intercepting the wind (A),

and the instantaneous wind velocity (V), or the speed. Increasing these factors will increase

the power available from wind. Equation 2-14 shows the relationship between these

parameters but all this parameters is included on the power curve of any wind turbines.

2-14

Where P is the power output in (watts), is the air density in (kg/m³), A is the area where

wind is passing (m²) and V is the wind speed in (m/s).

To calculate the output of the wind turbine in a particular hour, it follows a three-step

process:

It takes that hour's wind speed from the wind resource data and adjusts it to the hub height

using either the logarithmic profile or the power law profile, as described in Wind Shear

Inputs.

It refers to the wind turbines power curve to calculate the power output under standard

conditions of temperature and pressure.

It multiplies that value by the air density ratio. It calculates the air density ratio using in Eq 2-

15 (NREL, 2008) :

2-15

The air density under standard conditions (sea level, 15 degrees Celsius) is 1.22kg/m3

The average power output from a wind turbine is the power produced at each wind speed

times the fraction of the time that wind speed is experienced, integrated over all possible

wind speeds.

The electric power output of a WTG in the up state depends strongly on the wind regime as

well as on the performance characteristics and the efficiency of the generator. Given the

hourly wind speed variations, the next step is to determine the power output of the WTG as a

function of the wind speed. This function is described by the operational parameters of the

WTG. The parameters commonly used are the cut-in wind speed (at which the WTG starts to

generate power), the rated wind speed (at which the WTG generates its rated power) and the


cut-out wind speed (at which the WTG is shut down for safety reasons). The hourly output of

a WTG can be obtained from the simulated hourly wind speed by applying Equation 2-

16.The following equations for the electrical power output of a model wind turbine:

2-16

The coefficients a and b are given by

The relationship can also be illustrated graphically as shown in Figure 2.12 and is often

referred to as the “Power Curve”. Actual power curve for a particular WTG shown in the

figure 2.12 can be obtained from the manufacturer.

The Rayleigh distribution is a special case of the Weibull distribution with k = 2 and is often

sufficiently accurate for analysis of wind power systems. This value of k should be used if the

wind statistics at a given site are not well known. If Weibull k is not known, use k = 2 for

inland sites, use 3 for coastal sites, and use 4 for island sites and trade wind regimes Wind

turbine power output daily profile for Kebridehar and Degehabur towns is presented in

Appendix-C, table C.1 and C2.

2.7.1 Swept Area

As shown in equation 2-2, the output power is also related to the area intercepting the wind,

that is, the area swept by the wind turbines rotor. Double this area and you double the power

available. For the horizontal axis turbine, the rotor swept area is the area of a circle:

2-17 Where D is the rotor diameter in meters. The relationship between the rotor’s diameter and

the energy capture is fundamental to understanding wind turbine design. Relatively small

increases in blade length or in rotor diameter produce a correspondingly bigger increase in


the swept area, and therefore, in power. Nothing tells you more about a wind turbines

potential than rotor diameter. The wind turbine with the larger rotor will almost invariably

generate more electricity than a turbine with a smaller rotor, not considering generator

ratings.

2.8 Annual wind energy production and capacity factor

The average power output of a turbine is a very important parameter of a wind energy system

since it determines the total energy production and the total income. It can be obtained by

multiplying the power produced at each wind speed and the fraction of the time that wind

speed has been experienced, integrated overall wind speeds.

The capacity wind turbines any site can be given as:

2-18

The capacity factors for Kebri Dehar and Degehabur towns are 22.8% and 17.8%

respectively.

The annual energy production wind turbines given as,

E= 2-19

The selected wind turbine must match the wind characteristics at the site and it should yield

an optimum energy with a high capacity factor (CF) to meet the electrical energy demand.Table 2.6: Technical Data of EOLTEC CHINOOK 17-65 Wind Turbine

Technical Data of EOLTEC CHINOOK 17-65 Wind Turbine

Rated power 65 kW @ 10 m/s Cut-in wind speed 2.3 m/s Cut-out wind speed 20 m/s Rated wind speed 11 m/s Survival speed 50 m/s Number of rotor blades 3Rotor diameter 17 m Swept area 227 m2 Rotor speed (variable) 25-75 rpm Power control Active blade pitch control Hub height 32-40 m (40 m is used for simulations)


Predicted annual and monthly energy production from a single EOLTEC CHINOOK 17-65

Wind Turbine for the towns presented in table 2.6 below. Table 2.7: Predicted annual and monthly energy production from a single EOLTEC CHINOOK 17-65 Wind Turbine for the two towns

Degehabur ( annual wind speed 5.45 m/s) Kebri Dehar ( annual wind speed 5.91 m/s)

EOLTEC CHINOOK 17-65 EOLTEC CHINOOK 17-65

Predicted energy production Predicted energy production

Average output power 10.95 kW Average output power 13.80 kW

Daily energy production 262.7 kW.h Daily energy production 331.2 kW.h

Monthly energy production 7,990 kW.h Monthly energy production 10,074 kW.h

Annual energy production 95,884 kW.h Annual energy production 120,893 kW.h

Source Manufacturer excel sheet

The most common generator used in wind turbines is the induction generator. Asynchronous

generator became popular in medium-size and some households size wind turbines for

several reasons: they are readily available, robustness and mechanical simplicity, they are

inexpensive and they can supply utility-compatible electricity without sophisticated

electronic inverters.

Doubly Fed Induction Generator also the future technology. This technology is under

research and development phase and very promising with mechanical simplicity and cost as

well as which is easily connect to utility line.

Homer wind turbines types data base it has limited types of technology incorporate with this

software and only being considered three types wind turbines technology for this study.

The total annual energy production wind turbines of the two towns will be discuss on the

chapter six in result and discussion sub topic.

3. PHOTOVOLTAIC POWER SYSTEMS

3.1 Introduction

Photovoltaic (PV) solar cells made of semiconductors materials generates electrical power,

measured in Watts or Kilowatts, when they are illuminated by photons. Many PV have been

in continuous outdoor operation on Earth or in space for over 30 years (A. Luque, 2003).

3.2 History

The photovoltaic history starts in 1839 when a French physicist Alexander Edmond

Becquerel discovered the photovoltaic effect while experimenting with an electrolytic cell

made up of two metal electrodes. When the cells were exposed to light the generation of

electricity increased (USDE, 2004). In 1954 Bell Laboratories produced the first silicon cell.


It soon found applications in U.S. space programs for its high power-generation capacity per

unit weight. Since then it has been extensively used to convert sunlight into electricity for

earth-orbiting satellites. Having matured in space applications, PV technology is now

spreading into terrestrial applications ranging from powering remotes sites to feeding utility

grids around the world. Economically speaking in the past the PV cost was very high. For

that reason, PV applications have been limited to remote locations not connected to utility

lines. But with the declining prices in PV, the market of solar modules has been growing at

25 to 30% annually during the last 5 yr (Patel, 2006).

Figure 3.1: Solar Module Retail Price Index

The figure indicated the figure 3.1, as of September 2010; there are now 546 solar module

prices below $4.00 per watt (€2.92 per watt) or 42.6% of the total survey. This compares with

527 price points below $4.00 per watt (€3.12 per watt) in September (Solarbuzz, 2010).

The lowest retail price for a multicrystalline silicon solar module is $1.97 per watt (€1.44 per

watt) from a US retailer. The lowest retail price for a monocrystalline silicon module is also

$2.21 per watt (€1.61 per watt), from a German retailer (Solarbuzz, 2010)

Note, however, that "not all models are equal." In other words, brand, technical attributes and

certifications do matter.

The lowest thin film module price is at $1.40 per watt (€1.02 per watt) from a United States-

based retailer. As a general rule, it is typical to expect thin film modules to be at a price

discount to crystalline silicon (for like module powers). This thin film price is represented by

a 60 watt module (Solarbuzz, 2010).


3.3 Photovoltaic

The solar cells that are used on calculators and satellites are photovoltaic cells or modules.

This PV module consists of many PV cells wired in parallel order to increase current and in

series to produce a higher voltage. Use of 36 cell modules are the industry standard for large

power production. When we speak of a PV panel it means any number of PV modules and

when we speak of array it means any number of PV panels. Individual PV cells are typically

only a few inches in diameter, but multiple cells can be connected to one another in modules,

modules can be connected in arrays, and arrays can be connected in very large systems. See

figure 3.2.

Figure 3.2: PV Diagram

3.3.1 PV electricity

PV panel convert to sunlight to DC electricity. The PV generated electricity is ‘silent’, low in

maintenance and does not need in fuel or oil supplies. However, PV energy is available when

enough irradiance is accessible. PV panel is available in wide variety of rating up to

100Wp.In some cases, panel up to 300Wp each are manufactured. There is also AC PV

panels by including an inverter into the panel set-up to enable easy and modular AC bus

connection. A slight economy scale can often be noted for the different panel sizes up to

100Wp, however after that the size cost will increase circa linearly with size. The main

disadvantage PV is its high capital costs even though it is hope that the panel costs might

come down the future cost. PV can be cost-effective for small power requirements in areas


remote from the existing grid. According to recent figure show that PV panel last depending

on their types over 15-30 years.

3.3.2 General working principles Photovoltaic Cells

PV cells convert sunlight directly into electricity by taking advantage of the photoelectric

effect. Cells are constructed from semiconductor materials coated with light-absorbing

materials. When photons in sunlight strike the top layer of a PV cell, they provide sufficient

energy to knock electrons through the semiconductor to the bottom layer, causing a

separation of electric charges on the top and bottom of the solar cell. Connecting the bottom

layer to the top with a conductor completes an electrical circuit and allows the electrons to

flow back to the top, creating an electric current and enabling the cycle to repeat with more

sunlight (Clean Energy Associates). Figure 3.3 illustrates how photovoltaic cells work.

Figure 3.3: How Photovoltaic Cells Work (Clean Energy Associates)

3.3.3 Solar Module Power Characteristics and Operating issue

PV panels have a specific voltage-current relationship, which is depicted in an IV-curve. The

maximum power point (MPP) operation is where the maximum panel output power is

obtained with a given irradiation and temperature.


Manufactures typically provides I-V curves speciation at different levels of irradiance

keeping other variables such as temperature and wind speed constant figure3.2.PV panel

generates at constant irradiation levels roughly constant current from short circuit current to

just before the current value near the open circuit voltage. If the irradiance increases, the PV

panel output increase linearly. The maximum power point voltage stays nearly unaffected by

the level of irradiance, and open circuit voltage changes only slightly (Jimenez-98).

To account for the effect of panel temperature, manufactures will usually I-V curves for

various temperatures keeping irradiance level constants. The open circuit voltage (current is

zero) decreases with increasing temperature, while short circuit (voltage is zero) increase

only slightly, leading to decreased power production of the panel (Jimenez-98).

An I-V curve as illustrated in figures 3.4 is simply all of a module’s possible operating points,

(voltage/current combinations) at a given cell temperature and light intensity. Increases in

cell temperature increase current slightly, but drastically decrease voltage.

Maximum power is derived at the knee of the curve. Check the amperage generated by the

solar array at your battery’s present operating voltage to better calculate the actual power

developed at your voltages and temperatures (Kyocera, 2009).the detail technical

specification solar PV module indicated in Appendix-D.

.Figure 3.4: I-V curves showing the effect of solar isolation and temperatures on PV panel performance

3.3.4 Photovoltaic Cells and Efficiencies

PV cells are made up of semiconductor material, such as silicon, which is currently the most

commonly used. Basically, when light strikes the cell, a certain portion of it is absorbed


within the semiconductor material. This means that the energy of the absorbed light is

transferred to the semiconductor. The energy knocks electrons loose, allowing them to flow

freely. PV cells have one or more electric fields that act to force electrons that are freed by

light absorption to flow in a certain direction. This flowing of electrons is a current and by

placing metal contacts on the top and bottom of the PV cell we can draw that current off to be

used externally. For example, the current can power a calculator. This current, together with

the cell's voltage, which is a result of its built-in electric field or fields, defines the power in

watts that the solar cell can produce (Patel, 2006)

There are currently five commercial production technologies for PV cells:

• Single Crystalline Silicon: This is the oldest and more expensive production technique, but

it's also the most efficient sunlight conversion technology available. Cells efficiency averages

between 11% and 16%

• Polycrystalline or Multi-crystalline Silicon: This has a slightly lower conversion efficiency

compared to single crystalline and manufacturing costs are also lower. Cells efficiency

averages between 10% and 13%.But Kyocera’s advanced cell processing technology and

automated production facilities have produced multi-crystalline solar cells with efficiencies

of over 16.5%.

• String Ribbon: This is a refinement of polycrystalline silicon production. There is less work

in its production so costs are even lower. Cells efficiency averages 8% to 10%

• Thin Film “copper-indium-diselenide”: This is a promising alternative to silicon cells.

They are much more resistant to effect of shade and high temperatures, and offer the promise

of much lower cost. Cells efficiency averages 6% to 8%

• Amorphous: Made when silicon material is vaporized and deposited on glass or stainless

steel. The cost is lower than any other method. Cells efficiency averages 4% to 7% Cells

efficiency decreases with increases in temperature. Crystalline cells are more sensitive to heat

than thin films cells. The output of a crystalline cell decreases approximately 0.5% with every

increase of one degree Celsius in cell temperature. For this reason modules should be kept as

cool as possible, and in very hot condition amorphous silicon cells may be preferred because

their output decreases by approximately 0.2% per degree Celsius increase (Antony et al.

2007).


3.3.5 PV installation

The tilt angle of a PV array can be adjusted optimize various system objectives, such as

maximizing annual, summer or winter energy production. Using adjustable fixed mounts and

adjusting the title angle periodically through the year can further increase energy production

(Jimenez-98).fixed mount lower in cost than tracking mounts.

For best year round power output with the least amount of maintenance, you should set the

solar array facing true south at a tilt angle equal to your latitude with respect to the horizontal

position. If you plan to adjust your solar array tilt angle seasonally, a good rule of thumb is:

- Latitude minus 15° in the summer

- Latitude in the spring/fall

- Latitude plus 15° in the winter

To capture the maximum amount of solar radiation over a year, the solar array should be

tilted at an angle approximately equal to a site’s latitude, and facing within 15º of due south.

To optimize winter performance, the solar array can be tilted 15º more than the latitude angle,

and to optimize summer performance, 15º less than the latitude angle. At any given instant,

the array will output maximum available power when pointed directly at the sun

(KYOCERA, 2004)

To compare the energy output of your array to the optimum value, you will need to know the

site’s latitude, and the actual tilt angle of your array-which may be the slope of your roof if

your array is flush-mounted. If your solar array tilt is within 15º of the latitude angle, you can

expect a reduction of 5% or less in your system’s annual energy production. If your solar

array tilt is greater than 15º off the latitude angle, the reduction in your system’s annual

energy production may fall by as much as 15% from its peak available value. During winter

months at higher latitudes, the reduction will be greater (KYOCERA, 2004).

When installing PV panels the racks are mounted on a roof or pole and then the panels are

mouthed on racks. Care must be taken to ensure the panels will not shaded during the day as

even partial shading of a panel will often reduce its power output to near zero. The PV array

can then be connected to DC loads, directly or via battery and / or regulator. DC appliances

can be slightly more expensive than AC appliances for which also DC/AC inverter need to be

installed.

When the PV modules are installed in parallel they can be segregated into separate sets to

fine-tune the battery charging current. However, this is only feasible for big systems. Because

one PV module is not working properly any more can take out a whole string, PV panels need


to be kept clean, free overshadowing, and electrical connections need periodic inspection for

loose connections and corrosion.

3.3.6 Photovoltaic Modules

A PV module is composed of interconnected photovoltaic cells encapsulated between a

weather-proof covering (usually glass) and back plate (usually a plastic laminate). It will also

have one or more protective by-pass diodes. The output terminals, either in a junction box or

in a form of output cables, will be on the back. Most have frames. Those without frames are

called laminates. In some, the back plate is also glass, which gives a higher fire rating, but

almost doubles the weight.

The cells in the modules are connected together in a configuration designed to deliver a

useful voltage and current at the output terminals. Cells connected in series increases the

voltage output while cells connected in parallel increases the current. A group of several PV

modules are connected together are called a solar array.

3.3.7 Photovoltaic Manufactures

Photovoltaic’s modules are available in a range of sizes. Those used in grid tied or stand

alone systems range from 80W to 300W. The performance of PV modules and arrays are

generally rated according to their maximum DC power output (watts) under the Standard

Test Conditions (STC). Standard Test Conditions are defined by a module (cell) operating

temperature of 25ºC (77 F), an incident solar irradiant level of 1000 W/m² and under Air

Mass 1.5 spectral distribution. Since these conditions are not always present PV modules and

arrays operate in the field with performance of 85 to 90 percent of the STC rating. Tables 3-2

present the PV modules specification used in this thesis. All the data was taken from the

manufacture’s data sheet. Price of each module where obtained in August 2010 from the

vendors.


Table 3.1: Solar Module Power at STC Rating and Price

Solar Module Brand

Photo conversion

efficiency(%)Watt at

1000W/m2 US$/unit US$/watt Solar panel vendorKyocera Solar (KD-210GX-LP) 19 210 483.00 2.30 http://www.advancepower.netEvergreen (ES-A-200-fa3 ) 18 200 675.00 3.38 http://www.altersystems.comEvergreen (ES-190-RL) 18 190 560.00 2.95 http://www.altersystems.comMitsubishi ( PV-UE125MF5N ) 16 125 $580.00 4.64 http://www.altersystems.comSILRAY SOLAR PANELS 16 180 387.00 2.15 http://www.advancepower.netGE Solar Panels 17 165 730.95 4.42 http://www.altersystems.comSanyo Solar Panel (HIP-190BA3) 17 190 889.00 4.68 http://www.altersystems.comSharp Solar Panel (NE-170U1) 16 170 545.00 3.20 http://www.altersystems.comSharp Solar Panel (NU-U230F3 ) 17 230 725.00 3.15 http://www.altersystems.comYingli Solar Panel (YL175) 17 175 525.00 3.00 http://www.altersystems.com

Today’s photovoltaic modules are extremely safe and reliable products, with minimal failure

rates and projected service lifetimes of 20 to 30 years. Most major manufacturers offer

warranties of twenty or more years maintaining a high percentage of the initial rated power

output.

3.4 Solar resource

Solar energy is available everywhere on Earth, in varying amounts. Solar radiation that

reaches the earth’s surface in an unbroken line is called direct, while sunlight scattered by

clouds, dust, humidity and pollution is called diffused. The sum of the direct and diffuse

sunlight is called global-horizontal insolation. Concentrating solar technologies, which use

mirrors and lenses to concentrate sunlight, rely on direct radiation, while PV cells and other

solar technologies can function with diffused radiation.

Solar radiation provides a huge amount of energy to the earth. The total amount of energy,

which is irradiated from the sun to the earth's surface, equals approximately 10,000 times the

annual global energy consumption. On average, 1,700 kWh per square meter is insolated

every year (Patel, 2006).

The light of the sun, which reaches the surface of the earth, consists mainly of two

components: direct sunlight and indirect or diffuse sunlight, which is the light that has been

scattered by dust and water particles in the atmosphere. Photovoltaic cells not only use the

direct component of the light, but also produce electricity when the sky is overcast. To the

average total solar energy received over the year, rather than to refer to instantaneous

irradiance.

The daily radiation of Somalia region is very high although there are zonal and seasonal

variations. Solar radiation potential in Degehabur and Kebri Dehar are estimated to be 5.0 to


7.5 KWh/M2. As majority of the population in the region live in dispersed area solar energy

resources could be the most appropriate electricity resources. See Figure 3.3 below solar map

of Ethiopia including project area of the thesis.

Figure 3.5: solar maps Annual Daily Solar Radiation of Ethiopia (Including project area)

(Source: SWERA).

In order to capture as much solar energy as possible, the photovoltaic cell must be oriented

towards the sun. If the photovoltaic cells have a fixed position, their orientation with respect

to the south (northern hemisphere), and tilt angle, with respect to the horizontal plane, should

be optimized. For regions nearer to the equator, this tilt angle will be smaller, for regions

nearer to the poles it will be larger. A deviation of the tilt angle from the optimum angle, will

lead to less power to be capture by the photovoltaic system.

Degehabur and Kebri Dehar are located at the Latitude 8º 13' N and longitude 43º 34' W and

the Latitude 6º 45' N and longitude 44º 17' W, meaning that the tilt angle for the Degehabur

and Kebri Dehar should be 8º 13' N and 6º 45' N respectively.

3.4.1 Degehabur and Kebri Dehar Solar Resources

Solar resources is an important factor for know how many power can be generated by a

photovoltaic system. Solar radiation data was obtained from the NASA SMSE satellite

measurements. The NASA SMSE database was derived from the meteorology and solar

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3.5 Synthesize Hourly solar data from monthly average radiation

To synthesize data, you must enter twelve average monthly values of either solar radiation or

clearness index and builds a set of 8,760 solar radiation values, or one for each hour of the

year. To create the synthesized values using the Graham algorithm, this results in a data

sequence that has realistic day-to-day and hour-to-hour variability and autocorrelation

(NREL, 2008).

Since measured hourly solar radiation data is seldom available, it is often necessary to use its

capability to generate synthetic hourly solar data from monthly averages. The synthetic data

display realistic day-to-day and hour-to-hour patterns. If one hour is cloudy, there is a

relatively high likelihood that the next hour will also be cloudy. Similarly, one cloudy day is

likely to be followed by another cloudy day.

The figure 3.2 and figure 3.3 show that daily horizontal solar radiation for Degehabur and

Keberi Dehar towns respectively. Total daily solar radiation is considered as the most

important parameter in the performance prediction of renewable energy systems, particularly

in sizing photovoltaic (PV) power and solar heating systems. However, measuring and

recording equipment for solar radiation are costly. Therefore, numbers of stations in the

developing countries are very limited and insufficient for use to overcome this problem, some

mathematical models relating solar radiation have been proposed, the sunshine duration is

considered to be a good predictor of global solar radiation. Homer simulation software is

better solution for this problem

The global solar radiation determines the energy state of the active surface and the lower

atmosphere layers. The values of global solar radiation are determined in the first place by the

sun height and by the cloudiness. Substantial influence on its values makes the albedo, which

characterizes the reflection properties of the active surface. According to the figure 3.2 and

3.3 indicated that the global solar radiation increases from the sunrise till noon and decreases

till sundown. With the increase of the atmospheric haze the global solar radiation decreases

especially for large sun heights. Because of cloudiness influence during the warm part of the

year the global solar radiation after noon is lower compared to before noon at one and the

same sun height. Daily amounts of solar radiation are minimal in July and maximal in

February. The limits, in which the daily amounts of the global solar radiation are changing,

are small in winter. This is due to the small sun height and the considerable cloudiness. In

summer, when sun height is large, the variability of the solar radiation and cloudiness are


larger, the annual amounts of global solar radiation are varying in larger limits. The data

presented in Appendix-B, Table B.5 and table B.6, and summarized in figure 3.8 and 3.9

Figure 3.7: Global daily solar radiations on horizontal surfaces for Kebri Dehar town

Figure 3.8: Global daily solar radiations on horizontal surfaces for Degehabur town

The flow charts of which synthetic hourly horizontal radiation and calculates the global radiation incident on the PV array is as follows:


The major procedures in the program for generating synthetic hourly radiation are shown in

Figure 3.9

Figure 3.9: Diagram of the solar radiation calculation on panel surface

The procedures in Figure 3.10 can be briefly described as follows:

1. Calculate the radiation at the horizontal surface based on the day of the year and the site

latitude and then establish a clearness index.

2. The clearness index is then used to calculate the direct, diffuse and random components of

the radiation on a horizontal surface.

3. The total radiation is then calculated from the direct, diffuse and random values.

4. Finally the radiation on the surface of the panel is determined.


It requires monthly average meteorological data at a specific site location as its input for the

simulation of the solar radiation process at that site. The necessary data is the monthly

average values of solar radiation on the horizontal surface and the ambient temperature

Declination Angle

The declination is the angular position of the sun at solar noon, with respect to the plane of

the equator. Its value in degrees is given by Cooper’s equation (NREL, 2008):

°

The time of year affects the solar declination, which is the latitude at which the sun's rays are

perpendicular to the earth's surface at solar noon (NREL, 2008).

Where: is the day of the year

The equation of time accounts for the effects of obliquity (the tilt of the earth's axis of

rotation relative to the plane of the ecliptic) and the eccentricity of the earth's orbit. HOMER

calculates the equation of time as follows (NREL, 2008):

Where B is given by:

Where n is the day of the year, starting with 1 for January 1st and 365 for December 31st

Now, for a surface with any orientation, we can define the angle of incidence, meaning the

angle between the sun's beam radiation and the normal to the surface, using the following

equation (NREL, HOMER user manual, 2007):

An incidence angle of particular importance, which we will need shortly, is the zenith angle,

meaning the angle between a vertical line and the line to the sun. The zenith angle is zero

when the sun is directly overhead and 90° when the sun is at the horizon. Because a


horizontal surface has a slope of zero, we can find a equation for the zenith angle by setting

= 0° in the above equation, which yields (NREL, 2008):

Extraterrestrial Normal Radiation and clearness index

It states that the amount of solar radiation arriving at the top of the atmosphere over a

particular point on the earth's surface. It assumes that the output of the sun is constant in

time. But the amount of sunlight striking the top of the earth's atmosphere varies over the

year because the distance between the sun and the earth varies over the year due to the

eccentricity of earth's orbit. To calculate the extraterrestrial normal radiation, defined as the

amount of solar radiation striking a surface normal (perpendicular) to the sun’s rays at the top

of the earth's atmosphere, it uses the following equation (NREL, HOMER user manual,

2007):

Gon Gsc

Since HOMER simulates on a time step by time step basis, we integrate the above equation

over one time step to find the average extraterrestrial horizontal radiation over the time step

(NREL, 2008):

The above equation gives the average amount of solar radiation striking a horizontal surface

at the top of the atmosphere in any time step. The solar resource data give the average amount

of solar radiation striking a horizontal surface at the bottom of the atmosphere (the surface of

the earth) in every time step. The ratio of the surface radiation to the extraterrestrial radiation

is called the clearness index. The following equation defines the clearness index (NREL,

HOMER user manual, 2007):

Beam and diffuse radiation

The correct prediction of the power generated by PV arrays requires the determination of the

intensity of the global solar radiation on the surface of the arrays at a specific site location.

The total global radiation is normally composed of two components namely the direct and the


diffuse radiation. The direct component is the radiation received from the sun without having

been scattered by the atmosphere, while the diffused component is the radiation received

from the sun after its direction has been changed due to scattering. The contribution of the

direct and diffuse components to the total radiation mainly depends on the cloud cover

(Beckman, 1980). Now let us look more closely at the solar radiation on the earth's surface.

Some of that radiation is beam radiation, defined as solar radiation that travels from the sun

to the earth's surface without any scattering by the atmosphere. Beam radiation (sometimes

called direct radiation) casts a shadow. The rest of the radiation is diffuse radiation, defined

as solar radiation whose direction has been changed by the earth's atmosphere. Diffuse

radiation comes from all parts of the sky and does not cast a shadow. The sum of beam and

diffuse radiation is called global solar radiation, a relation expressed by the following


The distinction between beam and diffuse radiation is important when calculating the amount

of radiation incident on an inclined surface. The orientation of the surface has a stronger

effect on the beam radiation, which comes from only one part of the sky, than it does on the

diffuse radiation, which comes from all parts of the sky (NREL, 2008).

However, in most often cases you get only monthly the global horizontal radiation, not its

beam and diffuse components. The only necessary data to fill missing hourly solar is global

horizontal radiation for that reason, HOMER expects you to enter global horizontal radiation

in HOMER's Solar Resource Inputs window. That means that in every time step, HOMER

must resolve the global horizontal radiation into its beam and diffuse components to find the

radiation incident on the PV array. The well knows uses correlation of Erbs et al. (1982),

which gives the diffuse fraction as a function of the clearness index as follows (NREL,

HOMER user manual, 2007) :

3.1

For each time step, it uses the average global horizontal radiation to calculate the clearness

index, then the diffuse radiation. It then calculates the beam radiation by subtracting the

diffuse radiation from the global horizontal radiation.


3.6 Calculates the global radiation incident on the PV array

Now the final step to calculate the global radiation striking the tilted surface of the PV array.

This parameter is very important since the predication of solar array output which supplies

electric demand of the two towns. The HDKR model is the well know model to calculates the

global radiation incident on the PV array, which assumes that there are three components to

the diffuse solar radiation: an isotropic component which comes all parts of the sky equally, a

circumsolar component which emanates from the direction of the sun, and a horizon

brightening component which emanates from the horizon. Before applying that model we

must first define three more factors (NREL, HOMER user manual, 2007).

The following equation defines Rb, the ratio of beam radiation on the tilted surface to beam

radiation on the horizontal surface:

The anisotropy index, with symbol Ai, is a measure of the atmospheric transmittance of beam

radiation. This factor is used to estimate the amount of circumsolar diffuse radiation, also

called forward scattered radiation. The anisotropy index is given by the following equation

(NREL, 2008):

The final factor we need to define is a factor used to account for 'horizon brightening', or the

fact that more diffuse radiation comes from the horizon than from the rest of the sky. This

term is related to the cloudiness and is given by the following equation (NREL, 2008):

The HDKR model calculates the global radiation incident on the PV array according to the

following equation (NREL, 2008):

3.2

It uses this quantity to calculate the cell temperature and the power output of the PV array

In two axis tracking ,Referring figure 3.4 below, Degebabur town monthly daily average

solar radiation incidence on the PV array improved from range of 30 to 39 percent instead of


the panel installed the latitude angle (fixed mounted). The tracked array rises up to quickly to

full power and stays there on a clear sunny day. The fixed array only maintains the maximum

power for a few hours in the middle of the day. The tracked array will be greater in wattage

than the fixed mount arrays but it cost higher. The same is true for Keberidehar town and

slight difference the values improved same as Degehabur. The data presented in Appendix-B

through table B.7 to table B.10 and summarized in figure 3.12 for the two towns.

Figure 3.10: Calculates the global radiation incident on the PV array with tracking and without tracking system

3.6.1 Calculates the PV Cell Temperature and PV array power output

The PV cell temperature is the temperature of the surface of the PV array. During the night it

is the same as the ambient temperature, but in full sun the cell temperature can exceed the

ambient temperature by 30°C or more. The PV Array outputs depend of the temperatures of

each time step. It is negative effect is PV array output, meaning that the PV array out is

decreasing when the panel temperature increasing. It starts by defining an energy balance for

the PV array, using the following equation from Duffie and Beckman (1991) (NREL,

HOMER user manual, 2007):

3.3

The above equation states that a balance exists between, on one hand, the solar energy

absorbed by the PV array, and on the other hand, the electrical output plus the heat transfer to


the surroundings. We can solve that equation for cell temperature to yield (NREL, HOMER

user manual, 2007):

3.4

It is difficult to measure the value of ( ) directly, so instead manufacturers report the

nominal operating cell temperature (NOCT), which is defined as the cell temperature that

results at an incident radiation of 0.8 kW/m2, an ambient temperature of 20°C, average wind

speed of 1 m/s ,and no load operation (meaning = 0) with the cell or module in an

electrically open circuit state, the wind oriented parallel to the plane of the array, and all sides

of the array fully exposed to the wind (NREL, HOMER user manual, 2007).

The temperature coefficient of power indicates how strongly the PV array power output

depends on the cell temperature, meaning the surface temperature of the PV array. It is a

negative number because power output decreases with increasing cell temperature. Nominal

operating cell temperature (NOCT) and the temperature coefficient of power are depending

on PV Module Type. Table 3.2 below show that NOCT and p Module Characteristics for

Standard Technologies Table 3.2: PV Module Characteristics for Standard Technologies

PV Module Type r (%) NOCT(°C)

Average Value of p

(%/°C) Polycrystalline silicon 17.00 47.00 -0.48

Monocrystalline silicon 13.50 45.00 -0.46

Monocrystalline/amorphous silicon hybrid 16.40 48.00 -0.30

Thin film amorphous silicon 5.50 46.00 -0.20 Thin film CIS 8.20 47.00 -0.60

Source: Homer user manual and Kyocera PV manufacturer

Note: In this thesis work the author has used the Kyocera Polycrystalline-silicon module cell

due to its higher conversion efficiency as well as lower capital cost than the others. The detail

Kyocera PV module technical specification is presented in Appendix-D

We can substitute these values into the above equation and solve it for to yield the

following equation (NREL, HOMER user manual, 2007):


If we assume that is constant, we can substitute this equation into the cell temperature

equation to yield (NREL, HOMER user manual, 2007):

3.5

It assumes a value of 0.9 for in the above equation, as Duffie and Beckman (1991)

suggest. Since the term is small compared to unity, this assumption does not introduce

significant error (NREL, HOMER user manual, 2007).

It assumes that the PV array always operates at its maximum power point, as it would if it

were controlled by a maximum power point tracker. That means HOMER assumes the cell

efficiency is always equal to the maximum power point efficiency: (NREL, HOMER user

manual, 2007)

But depends on the cell temperature . It assumes that the efficiency varies linearly with temperature according to the following equation (NREL, HOMER user manual, 2007):

3.6

The temperature coefficient of power is normally negative, meaning that the efficiency of

the PV array decreases with increasing cell temperature. The maximum temperature data of

the two towns are presented in Appendix-A, table A.1.

We can substitute this efficiency equation into the preceding cell temperature equation and

solve for cell temperature to yield (NREL, HOMER user manual, 2007):

3.7

The final step is to Calculates the PV Array Power Output by using equation 3.8 below:

3.8

is the cell temperature under standard test conditions [25°C]

.The maximum installed power capacity of PV Array 600 KW and 700 KW for Kebridehar

and Degehabur towns respectively. PV Derating Factor is 80% and the ground reflectance


20% used for the simulation input. The ground reflectance data obtained from NASA. The

ground reflectance (also called albedo) is the fraction of solar radiation incident on the

ground that is reflected. A typical value for grass-covered areas is 20%. Snow-covered areas

may have a reflectance as high as 70% (NREL, 2008). This value is used in calculating the

radiation incident on the tilted PV panels, but it has only a modest effect. The solar radiation

incident on the PV array in the current time step of the two towns are indicated in section 3.6

Appendix-B, table B.8 and table B.11.By using Eq 3.8, above, we can computed the Daily

PV Array power outputs of the two towns and presented in Appendix-C, table C.3 and

C.4.This PV Array output power is the contribution hybrid energy generation to meet the

required demand of the selected towns.

4. Batteries, PV controller, Inverters and Energy Consumption

4.1 Introduction

A battery is a device that stores Direct Current (DC) electrical energy in electrochemical form

for later use. The amount of energy that will be storage or deliver from the battery is managed

by the controller or the inverter. The inverter converts the DC electrical energy to Alternative

Current (AC) electrical energy, which is the energy that most residential homes use

4.1.1 Batteries

Electrical energy is stored in a battery in electrochemical form and is the most widely used

device for energy store in a variety of application. The conversion efficiency of batteries is

not perfect. Energy is lost as heat and in the chemical reaction, during charging or recharging.

Because not all battery’s can be recharged they are divided in two groups. The first group is

the primary batteries which only converts chemical energy to electrical energy and cannot be

recharged. The second group is rechargeable batteries. Rechargeable batteries are used for

hybrid wind / PV system.

The internal component of a typical electrochemical cell has positive and negative electrodes

plates with insulating separators and a chemical electrolyte in between. The cells store

electrochemical energy at a low electrical potential, typically a few volts. The cell capacity,

denoted by C, is measured in ampere-hours (Ah), meaning it can deliver C A for one hour or

C/nA for n hours (A. Luque, 2003).

Many types of batteries are available today like for example: Lead-acid, Nickel cadmium,

Nickel-metal, Lithium-ion, Lithium-polymer and Zinc air. Lead-acid rechargeable batteries

continue to be the most used in energy storage applications because of its maturity and high

performance over cost ratio, even though it has the least energy density by weight and


volume. These lead acid batteries come in many versions. The shallow- cycle version is the

one use in automobiles, in which a short burst of energy is drawn from the battery to start the

engine. The deep-cycle version, on the other hand, is suitable for repeated full charge and

discharge cycles. Most energy store applications require deep-cycle batteries (Patel, 2006).

Table 4.1 show the lead acid batteries used in this thesis. These specifications are taken from

manufactures data sheet and the prices were obtained in January 2010. Table 4.1: manufactures data sheet and the prices

Capacity Capacity Capacity

No

.

Flooded Lead-Acid battery

Price Volts C@100H(AH)

C@72H (HA)

C@20H(AH)

Supplier

1 MK 8L16 266 6 420 370 Alternative Energy Store

2 Surrette 12-Cs-11Ps

1178 12 503 475 357 Alternative Energy Store

3 Surrette 2Ks33Ps


4 Surrette 4-CS-17PS


5 Surrette 4-Ks-21Ps


6 Surrette 4-Ks-25Ps


7 Surrette S-460 340 6 460 441 350 Alternative Energy Store

8 Surrette S-530 375 6 530 504 400 Alternative Energy Store

9 Trojan L16H 6 420 Alternative Energy Store

10 Trojan T-105 6 225 Alternative Energy Store

11 US Battery US185

12 195 Alternative Energy Store

12 US Battery Us2200

6 225 Alternative Energy Store

4.1.2 Battery Electricity

Battery is a electro-chemical devices that is store energy in chemical form. They are used to

excess energy in the later use. Most batteries used in the hybrid are of the depth of the lead –

acid types. They are several other appropriate types (nickel-cadmium, nickel-Iron, Iron-air

and sodium-sulfur) but these are generally either too expensive or too unreliable for practical

application as most of them are still experiment stage. The lead-acid battery widely used and,

although complex, is well known. Its major limitation is that it must be operated within strict


boundaries as it is susceptible to damage under a certain condition- such as overcharging,

undercharging and remaining for long periods a low state of charge (Jimens-

98),(Slabbert,Seeling and Hochmuse-97). Battery cost can form a minor part of the system

initial costs, but adverse condition, battery maintenance and replacement can become a

significance portion of system lifecycle cost and can prove to be expensive a long run. If the

operating condition is favorable, however, these batteries can last up till 15 years in an

autonomous. Indivual batteries used in renewable energy and hybrid systems are in capacities

ranging from 50 ampere-hours at 12 volts to thousand of ampere-hours at two volts (i.e. from

0.5 kWh to several kWh)

4.1.3 General working

Batteries consist of one or more 2V-cells wired in series. Each cell consists of plates that

immersed in an electrolyte. When a discharging a chemical reaction between the plates and

electrolyte produce electricity. This reaction reversed when the battery charged.

The thickness of the battery’s plates determines the maximum depth of discharge beyond

which the battery suffers damage. Shallow cycle batteries, such as car batteries, have thin

plates and are design to produce a large current for short period of time. These should not be

a deeper discharge than 10-20% depth of discharge after which the battery ruined easily

(Jimenez-98).shallow cycle batteries are usually not suited for hybrid and renewable system

but often used anyway in small home systems in developing countries due to a lack of any

alternatives. Deep cycle batteries have thick, often tubular plates and can be often be

discharged up to 70%-80%. However, this types of battery cannot be quickly charged and

discharge (Jimenez-98).

4.1.4 Storage capacity

The storage capacity of the battery is generally is given in ampere-Hours or after the with

multiplication the battery’s nominal voltage in kWh. The value for the storage capacity

depends on its operation, age and treatment. The storage capacity is increased when the

battery charging and discharging rates are slow. Most battery manufacturer give the storage

capacity for a given discharge time, usually 20 or 100 hours. Some of the energy used to

charge the battery is lost which accounted for by the round trip efficiency (typically 50%-

80%).

The capacity of a battery is defined as the amount of energy that can be withdrawn from it

starting from a fully-charged state. But the capacity of a battery depends on the rate at which


energy is withdrawn from it. The higher the discharge current, the lower the capacity. One

can create a capacity curve by measuring a battery's capacity at several different constant

discharge currents.

For in this thesis Surrette4KS25P of battery type is selected since the capacity and life time of

the battery is suited for this project.

4.1.5 Battery modeling

Validated battery modeling is essential for accurate predictions of the ability of the renewable

system to meet the load demand, especially in the autonomous case. A common technique is

to estimate the state of charging (SOC) of the battery in Ampere-hours or the percentage to

express its condition. A battery said to have a certain capacity in Ah (100% SOC) and the

amount of charge taken from it under operation (% depth of discharge) will leave it at a new

% SOC

Unfortunately a quantity such as SOC is not directly measurable. As an alternative approach

the battery states of voltage can be used to give an indication of the SOC in order to judge the

condition of battery. For this thesis uses the Kinetic Battery Model (Maxwell and McGowan,

1993) to determine the amount of energy that can be absorbed by or withdrawn from the

battery bank each time step. The kinetic battery model, so named because it is based on the

concepts of electrochemical kinetics, models a battery as a two tank system. The first tank

contains "available energy", or energy that is readily available for conversion to DC

electricity. The second tank contains "bound energy", or energy that is chemically bound and

therefore not immediately available for withdrawal.

The battery bank is a collection of one or more individual batteries. The models a single

battery as a device capable of storing a certain amount of dc electricity at fixed round-trip

energy efficiency, with limits as to how quickly it can be charged or discharged, how deeply

it can be discharged without causing damage, and how much energy can cycle through it

before it needs replacement. It assumes that the properties of the batteries remain constant

throughout its lifetime and are not affected by external factors such as temperature.

The key physical properties of the battery are its nominal voltage, capacity curve, lifetime

curve, minimum state of charge, and round-trip efficiency. The capacity curve shows the

discharge capacity of the battery in ampere-hours versus the discharge current in amperes.

Manufacturers determine each point on this curve by measuring the ampere-hours that can be

discharged at a constant current out of a fully charged battery. Capacity typically decreases


with increasing discharge current. The lifetime curve shows the number of discharge–charge

cycles the battery can withstand versus the cycle depth. The number of cycles to failure

typically decreases with increasing cycle depth. The minimum state of charge is the state of

charge below which the battery must not be discharged to avoid permanent damage. In the

system simulation, it does not allow the battery to be discharged any deeper than this limit.

The round-trip efficiency indicates the percentage of the energy going into the battery that

can be drawn back out (PAUL GILMAN and PETER LILIENTHAL)

Figure 4.1: Kinetic battery model concepts

To calculate the battery’s maximum allowable rate of charge or discharge, it uses the kinetic

battery model, which treats the battery as a two tank system, as illustrated in Figure 4.1.

According to the kinetic battery model, part of the battery’s energy storage capacity is

immediately available for charging or discharging, but the rest is chemically bound. The rate

of conversion between available energy and bound energy depends on the difference in

‘‘height’’ between the two tanks. Three parameters describe the battery. The maximum

capacity of the battery is the combined size of the available and bound tanks. The capacity

ratio is the ratio of the size of the available tank to the combined size of the two tanks. The

rate constant is analogous to the size of the pipe between the tanks (PAUL GILMAN and

PETER LILIENTHAL)

The kinetic battery model explains the shape of the typical battery capacity curve, such as

shown in Figure 4.2. At high discharge rates, the available tank empties quickly, and very

little of the bound energy can be converted to available energy before the available tank is

empty, at which time the battery can no longer withstand the high discharge rate and appears

fully discharged. At slower discharge rates, more bound energy can be converted to available

energy before the available tank empties, so the apparent capacity increases. It performs a


curve fit on the battery’s discharge curve to calculate the three parameters of the kinetic

battery model. The line in Figure 4.2 corresponds to this curve fit.

Modeling the battery as a two-tank system rather than a single-tank system has two effects.

First, it means the battery cannot be fully charged or discharged all at once; a complete

charge requires an infinite amount of time at a charge current that asymptotically approaches

zero (PAUL GILMAN and PETER LILIENTHAL). Second, it means that the battery’s

ability to charge and discharge depends not only on its current state of charge, but also on its

recent charge and discharge history. A battery rapidly charged to 80% state of charge will be

capable of a higher discharge rate than the same battery rapidly discharged to 80%, since it

will have a higher level in its available tank. It tracks the levels in the two tanks each hour,

and models both these effects (PAUL GILMAN and PETER LILIENTHAL) .

Figure 4.2: Capacity curve for deep-cycle battery model Surrette4KS25P

Figure 4.2 shows a lifetime curve typical of a deep-cycle lead–acid battery. The number of

cycles to failure (shown in the graph as the lighter-colored points) drops sharply with

increasing depth of discharge. For each point on this curve, one can calculate the lifetime

throughput (the amount of energy that cycled through the battery before failure) by finding

the product of the number of cycles, the depth of discharge, the nominal voltage of the

battery, and the aforementioned maximum capacity of the battery. The lifetime throughput

curve, shown in Figure 4.3 as black dots, typically shows a much weaker dependence on the

cycle depth. It makes the simplifying assumption that the lifetime throughput is independent

of the depth of discharge. The value that it suggests for this lifetime throughput is the average

of the points from the lifetime curve above the minimum state of charge, but the user can

modify this value to be more or less conservative (PAUL GILMAN and PETER

LILIENTHAL).


The assumption that lifetime throughput is independent of cycle depth means that it can

estimate the life of the battery bank simply by monitoring the amount of energy cycling

through it, without having to consider the depth of the various charge–discharge cycles. It

calculates the life of the battery bank in years as (NREL, Homer user manual, 2008):

Figure 4.3: Lifetime curve for deep-cycle battery model Surrette4KS25P

The higher the DOD, the lower will be the cycles and the lifetime of the batteries (can be

seen from Figure 4.3).

4.1.6 Battery regulators

Battery regulator used to control the operation of the batteries used in an off-grid/hybrid

system and thus protect them from unfavorable condition. The main functions are top-of-

charge regulation to prevent overcharging and load disconnection to prevent excessive

discharging. Additionally they may indicate the status of the system and may also give a

boost charge from time to time to avoid the stratification of the battery.

Regulators measure voltage levels an approximation to state of charge but this may vary with

charge/discharge currents, temperature compensation and ampere hour counting determined

state of charge more accurately. Set points are selected to maximizing battery life time.

4.1.7 Battery Sizing

Battery sizing consists in calculating the number of batteries needed for a hybrid renewable

energy system. This mainly depends on the days of autonomy desired. Days of autonomy are

the number of days a battery system will supply a given load without being recharged by a

PV array, wind turbine or another source. If the load being supplied is not critical then 2 to 3


autonomy day are commonly used. For critical loads 5 days of autonomy are recommended.

A critical load is a load that must be used all the time.

Another important factor is maximum depth of discharge of the battery. The depth of

discharge refers to how much capacity will be use from the battery. Most systems are

designed for regular discharges of up to 40 to 80 percent. Battery life is directly related to

how deep the battery is cycled. For example, if a battery is discharged to 50 percent every

day, it will last about twice long as if is cycled to 80 percent, (PVDI 2007).

Atmospheric temperature also affects the performance of batteries. Manufacturers generally

rate their batteries at 25ºC. The battery’s capacity will decrease at lower temperatures and

increase at higher temperature. The battery’s life increases at lower temperature and

decreases at higher. It is recommended to keep the battery’s storage system at 25 ºC. At 25 ºC

the derating factor is one.

The following procedure shows how to calculate the number of batteries needed for a hybrid

energy system, (Sandia 2004). Equation 4-3 shows how to calculate the required battery bank

capacity for a hybrid renewable energy system. The depth of discharge of the battery (60% is

considered here for this study), is the days of autonomous (two day of autonomous is

considered here) and is the battery efficiency (80% in this case) yielding capacities of

5MWh/day and 4.6MWh/day for Keberi Dehar and Degeabur towns, respectively. The

nominal battery capacity is 1.25 times the calculated value. The charging/discharging of the

battery in the linear region (40% - 90% peak capacity) gives highest efficiency and controls

must be designed in this manner. The required battery bank capacity for Degehabur town is

But the nominal battery capacity is 1.25*94,697Ah=118371 Ah. Likewise, the nominal capacity of Kebri Dehar town is 1.25*87,121Ah=108,902Ah.

Where is the Amp-hour consume by the load in a day (Ah/Day), is the number

of autonomy days, is the maximum depth of discharge, is the derate factor and is

the required battery bank capacity in (Ah).

Equation 4-2 presents how to calculate the number of batteries to be connected in parallel to

reach the Amp hours required by the system


The number of batteries that needs to be in parallel for Degehabur and Kebri Dehar towns is

62 and 57 respectively.

Equation 4-3 presents how to calculate the number of batteries to be connected in series to

reach the voltage required by the system. The system voltage of the selected towns is 400

volts then the number of batter to be connected in series is

The battery bank autonomy is the ratio of the battery bank size to the electric load. It

calculates the battery bank autonomy using the following equation:

the battery bank of antonmy of the two towns are the same. By using equation 4.7 we get 48

Hours.The total numbers of batteries needed is obtained multiplying the total number of

batteries in series and the total number of batteries in parallel as shown in equation 4-4.

The total number of batter need for Degehabur and Kebri Dehar towns is 2300 and 2050

repectively.the battery type of this project is Surrette 4KS25P (each battery, 4V, 1900Ah

capacity)

4.1.7 Batter life time

Battery life time is measured both interns of energy taken out from the battery and float life.

A battery dead when all available energy has been taken out or when the average battery has

been reduced to 80% of its original value.

The main factors affecting battery life time are grid corrosion, buckling of plates, sulfation,

and stratification of the electrolytes. These factors are causing loss of active materials and

internal short circuit. If less active materials is available in the ration of reaction components

is becoming non-optimal resulting in a drop of capacity and the charging efficiency reduced.

The Internal short circuits lead to harmful deep of discharge of the concerned cell and hence

ruin the whole battery.


For many batteries, especially the lead acid types, as long as the battery state of charge is kept

within the manufacturer’s recommended limit, the lifetime cumulative energy flows vary

widely.

Typical float lives for a good quality lead acid batteries ranges between 5 and 15 years at

20 . High the ambient temperatures severely shorten a battery’s float life. A rule of thumb is

that every 10 increase in average ambient temperature will halve the battery float life.

4.1.8 Battery Design

When selecting a battery types, usually lead acid types of batteries are chosen. In general lead

acid batteries are more cost effective than NiCad batteries, but the latter may be the better

choice if the greater battery raggedness is an important consideration (Jimenez-98).

The selection battery voltage depends on inverter and generation controller equipment

generally available. They comes specific voltages from 2, 4, 6,12,24,48 up to 120 and

240VDC and thus batteries must be selected and combined in series to meet this voltage

requirement. The number of battery strings that can be connected in parallel is limited to

about five without rigorous monitoring and high maintenance cost. This means that once the

general battery bank capacity has been selected the size of the individual battery types must

be chosen accordingly.

When designing the system depth of discharge (DOD) a trade-off must be made between a

low DOD where the battery will be less affected by sulphation, but may face frequent load

interruption and will be cycle more often; and a high DOD where although the supply may be

more reliable and the cycling reduce, the battery lifetime may be shortened due to increased

sulphation.

4.1.9 Battery in hybrid system

Battery operation in a hybrid system as opposed to a single-source application may result in

certain advantages with respect to battery lifetime optimization. This can be attributed to the

fact that there is often more sophisticated control installed in a hybrid system due to the

interaction of many components. This requires better regulation of components and will

results in better treatment the battery. Moreover, there are more energy sources available

resulting in the battery not being utilized to as high a degree as in single-source systems.

Reduced cycling lead to increased lifetime and more time (and source) available for

recharging and boost charging. Batteries are costly and can often be sized smaller in hybrid

system than in a single-source system.


4.2 PV Controllers

The photovoltaic controller works as a voltage regulator. The primary function of a

controller is to prevent the battery from being overcharged by a photovoltaic array system. A

charge controller constantly monitors the battery’s voltage. When the batteries are fully

charged, the controller will stop or decrease the amount of current flowing from the

photovoltaic array into the battery. The controllers average efficiencies range from 95% to

98%. For this thesis the efficiency that will be use for the analysis will be 95%, (A. Luque,

2003).

Charge controllers for PV system come in many sizes, typically from just a few amps to as

much as 80 amps. If high current are required, two or more controllers can be used.

When using more than one controller, it is necessary to divide the array into sub-arrays. Each

sub-array will be wired into the same battery bank. There are five different types of PV

controllers: shunt controller, single-stage series controllers, diversion controller, pulse width

modulation (PWM) controller and the maximum power point tracking controllers (MPPT).

The one we will be using in this thesis are the MPPT controllers.

4.2.1 MPPT Charge Controllers

The Maximum Power Point Tracking (MPPT) charge controllers are the best of today's PV

systems. As the names implies, this feature allows the controller to track the maximum power

point of the array throughout the day in order to deliver the maximum available solar energy

to the batteries or the system. The result is additional 15-30% more power out of an array

versus a PWM controller. Before MPPT was available as an option in controllers, the array

voltage would be pulled down to just slightly above the battery voltage while charging

battery. For example, in a 12V battery charging system, an array’s peak power point voltage

is around 17-18V. Without MPPT, the array would be forced to operate around the voltage of

the battery. These results in a loss of the power coming from the array. Table 4-2 present the

MPPT PV controllers be used in this study. Table 4.2: MPPT Charge Controllers Manufactures

MPPT Charge Controllers

Price

MaxOutput Nom. Battery

Max PV Open

Manufacture Model current

(A) Voltage (V)

Circuit Voltage Allowed (VOC)

Blue Sky Solar Solar Boost

3048DiL 470 30 12,24 140V


Outback Solar Flexmax 80 667 80 12,24,36,48,60 150V Outback Solar Flexmax 60 597 60 12,24,36,48,60 150V

4.2.2 General working princples

Maximum power trackers are high- frequency DC-DC converters used to force the output of

PV arrays to their maximum instantaneous power. They can improve the efficiency. They can

couple to the battery regulators, directly to DC water pumps or to AC water pumps via an

inverter. Best result are achieved with direct DC pumps coupling where the potentially the

biggest operating mismatch occur. Smaller improvements are realized with battery coupling

as the natural battery/array operating point is usually close to the array MPP (Jimenez-98),

(Slabbert, Seeling-Hochmuth-97).

4.3 Inverters

An inverter converts the direct current (DC) electricity from sources such as batteries, solar

modules, or wind turbine to alternative current (AC) electricity. The electricity can then be

used to operate AC equipment like the ones that are plugged in to most house hold electrical

outlets. The normal output AC waveform of inverters is a sine wave with a frequency of

50Hz for Ethiopia grid system.

When AC appliances are used, an inverter is required between them and the battery/DC

supply system. The inverter is normally only single phase for small power rating. Three phase

inverters are more costly than single phase inverters.

The efficiency of converting the direct current to alternative current of most inverters today is

90 percent or more. Many inverters claim to have higher efficiencies but for this thesis the

efficiency that will be used is 95%. Table 4-3 presents inverters used in this thesis. All the

inverters have output voltage of 220V and produce a sine wave AC output signal of 50Hz.

All the inverters are grid-tied with battery backup. Meaning can do the work as standalone

inverters or grid tied inverters.


Table 4.3: Inverters Manufactures

Inverter Manufacture Model

Power (W)

DC Input Voltage (VDC)

ACoutput

Voltage (VAC)

Nominal Frequency

(Hz) Price ($)

Xantrex XW6048 6,000 50 120/240 60 3,495 Xantrex XW4548 4,500 50 120/240 60 2,612 Xantrex XW4024 4,000 25 120/240 60 2,330

4.3.1 General working

The harmonic distortion of inverters is an important issue especially when the powering

components like refrigerators and computer and is an indication as to what extent the inverter

output wave form is non-sinusoidal. Inverter output wave form can be square wave, modified

sine wave. Square wave and quasi-wave inverters will introduce distortion as compared with

a 50Hz sine wave, but less expensive than sine wave inverters (Jimenez-98). They can

suitable power resistive load such as resistance heaters or incandescent lights. Modified sine

wave inverters produce a staircase square wave that more approximately a sine wave. They

can supply most electronic devices and motors. However, some sensitivity electronic may

require sine wave inverters. These inverters can produce utility grid power but cost than the

other types of inverters (Jimenez-98).

4.3.2 Inverter Sizing

Inverter sizing consists in calculating the number of inverters needed for the PV and wind

turbine system. In small hybrid systems one inverter will be enough to supply the power but

for a larger hybrid system more inverters may be needed. When you select an inverter you

must have a DC voltage equal to your inverter DC voltage and have an AC voltage and

frequency equal to your home and utility values.

Equation 4-8 shows how to calculate the number of inverters needed for a standalone hybrid

system

Number of Inverters required 4-8

The installed capacity of the inverter is 300KW to accommodate the peak load demand for

the selected towns. The total power output from battery through inverter to supply AC energy

demand and at the same time excess AC power production from wind turbines through


inverters change to DC power source which charge the battery for the future use. Inverter

output power daily profile for Kebridehar and Degehabur towns are presented in Appndex-C,

table C.5 and C.6

4.4 Energy consumption for Kebridehar and Degehabur towns

4.4.1 Introduction

Energy consumption is the electrical power your loads consume in a period of time. It is

measured in kWh. Loads are usually the largest single influence on the size and cost of a PV

and wind turbine system. In order to reduce the cost of the PV and wind turbine system it is

necessary to use more efficient, lower demand appliance and to eliminate, partially or

completely, the use of other loads.

It is assumption was made for load profile of the two towns and the load profiles are the same

throughout the years since the scarcity of the data which was not taken during data collected

time. The primary electrical load data for Kebri Dehar and Degehabur towns are shown in

Fig.4.4 and Fig 4.5. The annual peak load of 350kW was observed on each month of the

years around 18:00 h. Fig. 2 describes the monthly average variation of load of the two towns

are the same. The higher demand exists between 18:00 and 20:00 PM and while relatively

smaller load requirements are found between 00:00h and 6:00h .The daily energy

consumption is relatively lower in most of the time during 24 h except around 18:00 h to

20:00 h. The minimum load of 145 KW and 140 KW for Degehabur and Kebri Dehar

respectively. The minimum load which occurs in the morning and at after mid night, whiles

the majority of the load occurs in the evening. This evening load, with a peak load of 343

KW for Degehabur and 290 KW for Kebridehar, would likely include Residential, street

light, commercial and mini industrial load .The total daily load averages 5 Mega watt-hours

per day and 4.6 Mega Watt-hours per day for Degehabur and Kebri Dehar towns respectively.

But the load profiles the towns are presented in Appendix B and summarized in Figure 4.4

and figure 4.5 below.


Figure 4.4: Hourly load profiles for Kebri Dehar town

Figure 4.5: Hourly load profiles for Degehabur town

Deferrable load is electrical load that must be met within some time period, but the exact

timing is not important. Loads are normally classified as deferrable because they have some

storage associated with them. Water pumping is a common type for deferrable load - there is

some flexibility as to when the pump actually operates, provided the water tank does not run

dry. The peak deferrable load for Degehabur town is 150 kW, which is the rated power of the

pump. It would take the pump 6 hours at full power to fill the tank, so the storage capacity is

6 hours times 50 kW, which is 300 kWh. It would take the pump 6 hours at full power to

meet the daily requirement of water, so the average deferrable load is 6 hours per day times

50 kW, which is 300 kWh/day. For the case Kebri dehar town, it follows the same procedure


but the only difference is that the power needed to fill tank with water for the daily

requirement so, it is around 100 Kwh/per day is required to fill this tank .The annual energy

consumption of the two of towns is presented in table 4.4 Table 4.4: Total annual energy consumption for Degehabur and Kebri Dehar towns

Name of town

Energy consumption

per day (Kwh/day)

Annual Deferrable load (Kwh)

Annual AC primary energy consumption

(Kwh)

total annual energy

Consumption (Kwh/yr)

Degehabur 5001 109,425 1,728,617 1,838,042

Kebri Dehar 4565 36,726 1,592,833 1,629,559

The deferrable load is second in priority behind the primary load, but ahead of charging the

batteries. There are two types of dispatch strategies that HOMER follows. Under the load

following strategy, HOMER serves the deferrable load only when the system is producing

excess electricity or when the storage tank becomes empty. Under the cycle charging

strategy, HOMER will also serve the deferrable load whenever a generator is operating and

able to produce more electricity than is needed to serve the primary load. Regardless of

dispatch strategy, when the level of the storage tank drops to zero, the peak deferrable load is

treated as primary load. The dispatch able power sources (generator, grid or battery bank)

will then serve as much as possible of the peak deferrable load (NREL, Homer user manual,

2008).

4.5 Load forecast for Degehabur and Kebri Dehar

4.5.1 Methodology

A forecast of the electricity demand of the towns is made based on a method known as

energy approach. In this approach the annual energy sales for residential and non-residential

customers are forecasted using appropriate growth rates and the total sum of each category is

converted to the peak demand requirement using loss rate and load factor. Other parameters

like population growth rates, customer growth (market penetration) and growth rates of

consumption per connection used in the forecast are described in the sections below.

-Residential Consumption

The domestic energy consumption is forecasted based on the population of the towns, market

penetration rates, and consumption per connection. First year population figure is projected

by an annual growth rate of 4.9%, until the end of the forecast period. This population figure


is converted to total number of households using an assumed average number of occupants

per house.

- Market Penetration

At the first year of electrification all households may not be connected. Therefore, market

penetration rate is necessary. At the first year of interconnection 15% market penetration rate

(MP), for each town depending on its smallness and largeness is taken to change the total

number of households to potential residential customers. The MP of each town is made to

grow at a rate of 14.87% to reach an ultimate rate of 40 % in the first 12 years of

interconnection thereafter it is assumed to remain at this rate till the end of the forecast

period.

- Average Energy Consumption Per Residential Connection

The number of potential residential customers is changed to annual residential consumption

by assuming an average per household consumption, which starts with 303.7 kWh on the first

year. This average consumption per household is made to grow at a rate of 2.57% for the

whole forecast period.

- Non Residential Consumption

Consumption patterns in population centers with electrical supply were reviewed in the

Ethiopian Power System Expansion Master Plan study to determine relationship between

residential and nonresidential use. Nonresidential use Includes Commercial, street lighting

and small industrial activities. No large industrial activity is assumed for the towns under

study. Annual nonresidential consumption is estimated at 175.8 kWh for small commercial,

6.4 kWh for street lighting and 216.4 kWh for small industrial. There are no explicit forecasts

for the number of customers in each of these categories, therefore these average consumption

rates are based on the number of domestic customers. Total nonresidential consumption is

398.6kWh/year. Commercial Consumption per customer is assumed to rise at 2.98%/year,

while the annual growth in industrial consumption per customer is 1.65%. The domestic

growth rate (2.57%) was also adopted for the street lighting load.

4.5.2 Energy Requirement and Peak Power Demand

The annual energy requirement is calculated using a loss rate of 6% as expected to be

supplied from stand alone system. The peak power demand is calculated using a load factor

of 58%.

Main Re

4.5.3 F

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5. Hybrid energy systems

5.1 Introduction

Hybrid energy system is an excellent solution for electrification of remote rural areas where

the grid extension is difficult and not economical. Such system incorporates a combination of

one or several renewable energy sources such as solar photovoltaic, battery and wind energy.

A hybrid system uses a combination of energy producing components that provide a constant

flow of uninterrupted power. Hybrid, wind turbine and photovoltaic modules, offer greater

reliability than any one of them alone because the energy supply does not depend entirely on

any one source. For example, on a cloudy stormy day when PV generation is low there's

likely enough wind energy available to make up for the loss in solar electricity (Science Direct,

2005).

Wind and solar hybrids also permit use of smaller, less costly components than would

otherwise be needed if the system depended on only one power source. This can substantially

lower the cost of a remote power system. The use of renewable energy sources presents a

tremendous potential for many applications and especially off-grid standalone systems. In

this context, one of the most promising applications of renewable energy technology is the

installation of hybrid energy systems (HES) in remote areas, where the grid extension is

costly and the cost of fuel increases drastically with the remoteness of the location (green,

2010).

Despite advances by hybrid power systems in improving reliability and reducing the overall

size of the power system, initial costs remain relatively high. It heaves the potential user to

reduce demand as much as possible to keep costs down. Advances in energy efficiency

permits users to meet their energy needs from smaller, less expensive power systems than

once was possible.

5.2 Stand Alone Hybrid System

The stand-alone hybrid power system is used primarily in remote areas where utility lines are

uneconomical to install due to the terrains right-of-way difficulties, or environmental

concerns. Building new transmission lines is expensive even without these constraints. A

130-kV line costs in Ethiopia more than $125,000 per kilometer. A stand-alone system would

be more economical for remote villages/towns than the rural towns are found many

kilometers far from the nearest transmission line.

Solar and wind power outputs can fluctuate on an hourly or daily basis. The standalone

system, therefore, must have some means of storing excess energy on a sunny day or a windy


day for use on a rainy day or without wind. Alternatively, wind turbines and PV modules can

be used in a hybrid configuration with a Diesel engine generator in remote areas or with a

fuel cell in urban areas. For this thesis it only focuses on PV modules and wind turbine

configurations with storage battery system.

According to the World Bank, more than 2 billion people live in villages that are not yet

connected to utility lines (Lifelong Learning Programme Erasmus IP - RESchool, 2003).

These villages are the largest potential market for stand-alone hybrid systems using wind

turbines and PV modules for meeting their energy needs. Additionally, wind turbines and PV

modules systems create more jobs per dollar invested than many other industries. On top of

this fact they are bringing much needed electricity to rural areas and helps minimize

migration to already strained cities in most countries.

5.2.1 Typical Stand Alone Hybrid Components and Efficiencies

In this project, a parallel hybrid configuration (Figure 5.1) has been chosen due to its ability

in meeting the load optimally. Parallel hybrid configuration allows the system to decide

which component(s) to operate under a specific load. The wind turbine is coupled with

asynchronous and it operated variable wind speed. The wind turbine output directly

connected to AC bus through controller. During wind speed and solar radiation become low;

the inverter takes power from the battery, converts it to alternating current (AC) then supplies

the load. When solar and wind energy could be higher thus exports excess power to the

battery bank. In this instance, the inverter changes its function to become a battery charger.

But the maximum depth of discharge of the batteries is 60% and Controllers Keep the

batteries from overcharging and discharging rate. The maximum day autonomy is 48 hours,

meaning the batteries have a capacity to accommodate all required energy demand without

getting any power input from the system.


Figure 5.1 PV/Battery/Wind Stand Alone System

Typical components for a standalone hybrid system are:

• Wind Turbine: Provides energy from the wind. It is variable wind speed generator coupled

with wind turbines.

• Solar modules: Provide energy from solar radiation.

• Inverters: it is an electronic circuit use to convert direct current (DC) to alternating current

(AC). Its average efficiency is 90%.

• Controllers (MPPT): Keep the batteries from overcharging and maintain the solar module at

the maximum power point output. Its average efficiency is 95%

• Batteries: Supply energy to the system when is needed and store it when is not needed. Its

average efficiency is 90%

• Wires: Electrically connect equipment together. Their average efficiency is 98%.

• Loads: Consume the power generated by the wind turbine and photovoltaic modules.

Since the subsystem, or the components, are sequential regarding the energy flow the overall

efficiency of the system is the product of individual components efficiency.

(Inverter Efficiency) (Controller Efficiency) (Wires Efficiency) (Battery Efficiency). 5.1

Where is the total stand alone system efficiency. Table 5.1 shows the average efficiency for inverter, controllers, batteries and wires used in this work. Table 5.1: Average Efficiency of hybrid system components

Using the values from table 5.1 above and equation 5.1, the total stand alone system Efficiency is:

0.80

The total efficiency of the system is approximately 80%. This means that 80% of all the

electricity produced is delivered to the loads and 20% is consumed by the wires and the

internal components, inverters, controllers and batteries.

5.2.2 Proposed Stand Alone Sizing Optimization Procedure

Hybrid system optimization modeling methodology

Inverter 0.94 Controller 0.96

Wires 0.98 Battery 0.90


Designing optimized hybrid systems involves careful consideration of dozens of variables

including:

• Villages electricity load profile (kW for every hour of the day)

• Location-specific solar resource (taking into account that some days are cloudy and

some are sunny)

• Location-specific wind resource (monthly averages, diurnal variation)

• physical characteristics of batteries considered (capacity and voltage, cycle life,

round-trip efficiency, minimum state of charge, lifetime throughput, maximum charge

rate and maximum discharge rates)

• physical characteristics of solar panels (derating factor, slope, lifetime)

• physical characteristic of generators (output vs. fuel consumption curve, minimum

load ratio, lifetime operating hours)

• physical characteristic of wind turbines (power curve)

• Diesel fuel price

• initial, O&M and replacement costs of all components

The optimization and simulation tasks involve answering the questions,

“Which components does it make sense to include in the system design?”, “How many and of

what size each component should be used?” and “What will be the total costs involved?” The

large number of technology options and the variation in technology costs and availability of

energy resources make these decisions complex.

5.3 Economic Evaluation of the Hybrid System

5.3.1 Annual real interest rate

The annual real interest rate is one of the HOMER’s inputs which are also called the real

interest rate or just interest rate. It is the discount rate used to convert between one-time costs

and annualized costs. It is found in the Economic Inputs window. The annual real interest rate

is related to the nominal interest rate by the equation given below (NREL, HOMER user

manual, 2007):

In this equation, is the real interest rate, is the nominal interest rate (the rate at which

you could get a loan), and is the annual inflation rate.


For Ethiopia, = 10% (19.09.2010) and = 6.0% (year 2009 annual inflation rate) are used.

With these values, according to Eq. (3) real interest rate is found around 4.0% as shown

below:

In HOMER simulations, 4.0% is used for real interest rate.

5.3.2 Levelized cost of energy

HOMER defines the levelized cost of energy (COE) as the average cost/kWh of useful

electrical energy produced by the system. To calculate the COE, HOMER divides the

annualized cost of producing electricity (the total annualized cost minus the cost of serving

the thermal load) by the total useful electric energy production. The equation for the COE is

as follows (NREL, HOMER user manual, 2007):

The total annualized cost is the sum of the annualized costs of each system component, plus

the other annualized cost.

The annualized cost of a component is equal to its annual operating cost plus its capital and

replacement costs annualized over the project lifetime. The annualized cost of each

component is equal to the sum of its: annualized capital cost, annualized replacement cost,

annual O&M cost and annual fuel cost (if applicable) (NREL, HOMER user manual, 2007).

It calculates the annualized capital cost of each component using the following equation:

5.3.3 Net present cost (NPC)

The present value of the cost of installing and operating the system over the lifetime of the

project (also referred to as lifecycle cost). Project lifetime in this study is considered as 25

years.

The total net present cost is HOMER’s main economic output. All systems are ranked

according to net present cost, and all other economic outputs are calculated for the purpose of

finding the net present cost. The net present cost is calculated according to the following



Project lifetime [year] (25 years in this study).

The capital recovery factor is a ratio used to calculate the present value of an annuity (a series

of equal annual cash flows). The equation for the capital recovery factor is

The system costs which are given in Table5.3 will be used for HOMER simulations.

Personnel outgoings, transport cost, ground rent or price, tax and other cost are neglected in

the simulations. 10$ will be used for annual operating and maintenance costs of PV modules,

batteries and converters in simulations. Although, in ideal working conditions; PV panels,

batteries, inverters and charge regulators are inexpensive. Operating and maintenance costs

are indefinite in real working condition.

Costs of hybrid system include: components initial costs, components replacement costs,

system maintenance costs, fuel and/or operation costs, and salvage costs or salvage revenues.

Initial costs include purchasing the following equipments required by the hybrid system:

wind turbine, PV modules, batteries, diesel generator, charge controllers, bidirectional

inverter, management unit, cables, and other accessories used in the installation including

labors. Table 5.2: System cost values that used in simulations

Component Capital Cost ($)

Replacement Cost ($)

Operating and maintenance cost

($/year) Wind turbines+ tower

erection and foundation 1 kW 1500 1300 2% of Capital cost

PV modules including tracking system, installation 1 kW 6000 5000 $5

Batteries 1 Num 1700 1500 $10 Converters (Inverter + rectifier + charge

controller) 1 kW 900 900 $10 Diesel generators 1KW 500 400 0.05 ($/hr)

Wind turbine capital cost (including installation and tower) is considered as 1500 $/kW. For

every kW of PV modules, capital cost is assumed as 6000$. Surrette 4KS25P (1900 Ah)


batteries are chosen in HOMER simulations. For every battery, 1700$ is suggested as capital

cost. Converters’ capital cost is assumed as 900$ for 1 kW. Converter cost includes inverter,

rectifier and charge controller cost. Diesel generators’ capital cost is assumed as 500$ for 1

kW. The fuel prices 1$/liter. Solar PV modules are chosen with two axis tracing system in

discussed with figure 3.4.

To determine optimum component sizes, the team used “HOMER: The Micro power

Optimization Model”, developed by the US National Renewable Energy Laboratory.

HOMER simulates the operation of a proposed system by making energy balance

calculations for each of the 8,760 hours in a year. For each hour, HOMER compares the

electricity demand in the hour to the energy that the system can supply in that hour, and then

calculates the flow of energy to and from each component of the system. This requires an

hour by- hour simulation of the solar and wind power available, as well as hour the by-hour

estimation of the electricity load. HOMER also decides for each hour on how to operate the

generators and whether to charge or discharge the batteries.

HOMER performs these energy balance calculations for each system configuration that the

software user specifies. Because we consider a range of different capacities for PV, batteries,

and inverters on two towns, there is thousands of different system configurations considered.

HOMER then determines whether each configuration is feasible, i.e., whether it can meet the

electric demand under the conditions that you specify, and estimates the cost of installing and

operating the system over the lifetime of the project. The system cost calculations account for

costs such as capital, replacement, operation and maintenance, fuel, and interest .After

simulating all of the possible system configurations, HOMER displays a list of

configurations, sorted by net present cost (sometimes called lifecycle cost).The total net

present cost of a system is the present value of all the costs that it incurs over its lifetime,

minus the present value of all the revenue that it earns over its lifetime. Costs include capital

costs, replacement costs, O&M costs, fuel costs, emissions penalties, and the costs of buying

power from the grid. Revenues include salvage value and grid sales revenue. For instance, if i

= 7% and N = 5 years, the capital recovery factor is equal to 0.2439. A $1000 loan at 7%

interest could therefore be paid back with 5 annual payments of $243.90. The present value

of the five annual payments of $243.90 is $1000.

The project life time was taken as 25 years and the annual real interest rate as 4%.each

components are own life time and it expecting to replace at the end of life time (refer

Appendix-E lifetime of each components of the hybrid system).


The initial capital cost of a component is the total installed cost of that component at the

beginning of the project .The levelized cost of energy (COE), net present cost and initial cost

of the two towns are presented in chapter six.

5.4 Breakeven Grid Extension Distance

The distance from the grid which makes the net present cost of extending the grid equal to the

net present cost of the stand-alone system. Farther away from the grid, the stand-alone system

is optimal. Nearer to the grid, grid extension is optimal.

HOMER calculates the breakeven grid extension distance using the following equation:

5.4

Table 5.4 presented below the total distance of two towns far from the nearest gird and cost

associated with gird extension. Table 5.3: Grid extension cost for Kebri Dehar and Degehabur towns

Town of Name

Nearest substation

Voltage level

Unit cost per

km

TotalTransmission

line cost O and M cost Total cost

Kebridehar Gode 132 125,000 23,750,000 475,000 24,225,000

Degehabur Jijaga 132 125,000 20,000,000 400,000 20,400,000

The total capital costs of grid extension are 24.23 Million US dollar and 20.40 Million US

dollar, for Kebri Dehar and Degehabr respectively. Moreover, the breakeven distances from

the grid extension are 63 Kilometers and 77 Kilometers for Kebri Dehar and Degehabur

respectively, meaning which is the net present cost of grid extension equals the net present

cost of the stand-alone system. But both towns are located far from breakeven grid extension

and the detail for this we will discuss next chapter. If you go farther from this point

(breakeven grid extension distance) the stand alone is the optimal solution power supply of

the selected towns.

5.5 System architecture

Hybrid systems are fundamentally of two types: direct current (DC) bus and alternating

current (AC) bus. The key difference between the systems is that in a DC bus system, all

electricity from renewable energy sources must be produced nearby the battery bank (located

in the power house). In an AC bus system, electricity generation can occur anywhere along

the AC transmission system. Thus, solar panels can be distributed in several different

Main Re

locations

and expa

of the K

supplied

consideri

from ren

conventio

by.

Figure 5.2

eport Final M

s in the town

andable. The

Kebri Dehar

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Figure 5.3: Equipment to consider and hybrid system configuration for Kebri Dehar town

5.5.1 Key model input assumptions for thesis

The “optimal system” determined by HOMER depends on the input assumptions. Key

assumptions are summarized in the table below 5.4, and followed by a more detailed

discussion. Table 5.4: Key model input assumptions for model

Variable Value Data source O and M cost of PV $5/kW

Nominal interest rate 10% EEPCO

PV cost ( Including PV panels ,mounting hardware ,tracking

system, control system (maximum power point tracker)

,wiring and installation $6000/kW Green energy

Solar resource for Degehabur 6.34 kWh/day NREL datasetSolar resource for Kebridehar 6.19 kWh/day NREL dataset

Wind resource for Degehabur Annual average 5.45 m/s with monthly variation NREL dataset

Wind resource for KebrideharAnnual average 5.91 m/s with monthly variation NREL dataset

Wind turbine cost (65kW AC power)

97500/turbine including tower erection and

foundation Manufacturer

Battery cost (Types S4KS25P) $1700/Battery Green energy Converter cost $3600 for 4 kW Homer data base

Existing Diesel Generator 375 and 400 kW initial

cost $0 EEPCO

Diesel cost 1$/Liter (without subsidy)

Local interview

The sizes considered for each component are shown in Figure 5.2 and 5.3. These sizes were

iteratively determined to be sufficiently broad that HOMER did not indicate that the “search

space” of any particular item was possibly too small, while at the same time trying to reduce

the number of possible options in order to keep the computational requirements, and thus

model run time, at a manageable level.


Figure 5.4: sizes considered for components for KebriDehar town HOMER model run

Figure 5.5: sizes considered for components for Degehabur town HOMER model run

Notes that: Eoltec is 65Kw wind turbine

Label stand for Diesel generator

S4KS25P is energy storage battery

Homer builds the search space, or set of all possible configuration, from this table and then

simulates the configuration and sorts them by net present cost.

Figure 5.6 shows the architecture of HOMER, which was taken from Fung et al. with a small

modification. There are three main parts of HOMER; inputs, HOMER simulation and

outputs.


Figure 5.6: Architecture of HOMER simulation and optimization

6. Results and Discussion

6.1 General

In this chapter we use all concept, formulas and tables presented in previous chapters to

evaluate of hybrid renewable energy system, wind turbine and photovoltaic modules, for

Degehabur and Kebri Dehar towns. Two towns considered for this study; the town of Kebri

Dehar and Degehabur where the wind resource is moderate. The solar radiation of the towns

almost the same but slight difference. The energy demands of the two towns are different. In

each location it assumes to be serving a residential load and non residential load of a total

4.6MWh for Kebrid Dehar and 5.0 MWh for Degehabur towns per day. These load

comprised of street light, commercial and residential as well as mini industrial energy

consumption .In the economic analysis use a life time period of 25 years with an inflation rate

of 6% and nominal interest rate on the loan to finance the hybrid system of 10%.

For each location we use solar and wind data, and our optimization procedure to design

hybrid renewable power system. The author considers a standalone system and grid

connected system. For the stand alone system author seek to determine the most economic

combination of PV modules and wind turbines to serve the residential and non residential


load. It assumes batteries have a life time of 12 years, thus a replacement of batteries is

considered at the end of year 12 and each component are to replace at the end of life time.

6.2. Simulation results

The simulation software provides the results in terms of optimal systems and the sensitivity

analysis. In this software the optimized results are presented categorically for a particular set

of sensitivity parameters like wind speed, maximum annual capacity shortage (MACS), net

present cost and fuel price in the present case.

The optimization and sensitivity results are presented in the forthcoming paragraphs.

6.2.1 Optimization results

The optimization results for Kebri Dehar and Degehabur towns summarized in figure 6.1 and

figure 6.2.Figure 6.1 and 6.2 below shows the results from HOMER modeling for Kebri

Dehar and Degehabur towns. The modeling simulates 8,760 hours (one year) of operation

and thousands of different system configurations. The system with the overall least cost of

energy is the one highest on the list. The first five columns of the HOMER results table

shows graphic icons representing which components are present in the optimized system. The

remaining columns show the optimized capacity of each component, the initial capital cost,

the total net present cost, the cost of energy (in $ per kWh), renewable energy fraction, total

liters of diesel consumed per year, and the number of hours diesel generator operates and life

time of the battery.

Figure 6.1: Overall optimization results table showing system configurations sorted by total

net present cost for Kebridehar town


Fig 6.1 Optimization results for wind speed of 5.91 m/s daily radiation of 6.19 Kwh/m2 /day, diesel price of 1 $/L, wind operating reserve of 5% and maximum annual capacity shortage of 5% for Kebridehar town.

Figure 6.2: Overall optimization results table showing system configurations sorted by total net present cost Degehabur town.

Fig 6.2 Optimization results for annual wind speed of 5.45 m/s daily radiation of 6.34

kWh/m2 /day, diesel price of 1 $/L, wind operating reserve of 5%,solar operating reserve of

5% and maximum annual capacity shortage of 5% for Degehabur town.

Based on the HOMER modeling, the optimal system for Kebri Dehar town in figure 6.1 a

second row, a hybrid solar/wind /battery (no diesel system), with 600 kW of solar, a 6*65 kW

wind turbines ,2050 S4KS25P batteries (each 1900AH capacity) and 300 kW bi-directional

inverter are required power supplied for Kebridehar town . This “optimal” system uses 100%

renewable energy, and the cost of electricity is $0.422/kWh including depreciation on capital

and levelized O&M with net present cost of $10,283,954.

Likewise, based on the HOMER simulation results, the optimal system for Degehabur town

in figure 6.2 a second row, a hybrid solar/wind /battery (no diesel system), with 700 kW of

solar, a 8*65 kW wind turbines ,2300 S4KS25P batteries (each 1900AH capacity) and 300

kW bi-directional inverter are required power supplied for Degehabur town. This “optimal”

system uses 100% renewable energy, and the cost of electricity is $0.441/kWh including

depreciation on capital and levelized O&M with net present cost of $ 12,675,183.

The energy yield from different components of the wind/solar/battery hybrid system is shown

in Fig. 6.3 of the total primary energy requirement for Kebri Dehar town, the wind machines

produced 775,961 kWh (37% of the total energy served) while solar PV produced almost

63% of the energy i.e. 1,336,155 kWh. Although an excess energy of 291,476 kWh (13.8%)

Main Re

was prod

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Master The

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6.3 Comparison with “diesel only” for Kebri Dehar and Degehabur towns

In order to supply the same 24-hour electricity service using only diesel generators, (Figure

6.1 and 6.2) indicates that optimal hybrid system, the diesel-only system would require a 375

kW and 400 kW capacity diesel generators for Kebri Dehar and Degehabur towns

respectively, with the generator operating 8,760 hours/year. Referring figure 6.1, for Kebri

Dehar town, the diesel-only electricity supply, levelezed cost of energy is $0.564/kWh with

a net present cost of $14,995,565. In contrast, the optimal hybrid solution discussed above

provides electricity at $0.422/kWh with a net present cost of only $10,283,954. The optimal

solution thus saves the difference of about $4,711,611 over the lifetime of the project,

compared to an optimized “diesel only” option providing the same level of electricity service.

For the case of Degehabur town, the diesel-only system a levelized cost of energy is

$0.543/kWh with a net present cost of $16,395,988 and the optimal hybrid solution provides

electricity at $0.441/kWh with a net present cost of only $ 12,675,183. The optimal solution

thus saves the difference of about $ 3,720,805 over the lifetime of the project, compared to an

optimized “diesel only” option providing the same level of electricity service. Moreover, the

“diesel only” options (from both towns) produce total of around 3835 tons of CO2 per year,

whereas the hybrid system (100% renewable energy) has Zero direct CO2 and other green

houses gases emission to the atmosphere. Table 6.7 below, presented cost of energy, net

present cost and comparison of the green house gas contribution diesel-only and hybrid

system. Table 6.1: Comparison of net present cost, energy cost and green house gas emission diesel-only option with hybrid system

Name of Town Components

Net Present cost ($)

Cost of Energy ($/kWh

)

DieselConsumed per year (Liters)

Carbon dioxide emission (tons

per year)

Otheremission gas

1. Kebri Dehar Diesel only 14,995,565 0.564 692,144 1823 49

Wind/Solar/Battery 10,715,823 0.422 0 0 0

2. Degehabur Diesel only 16,395,988 0.543 763,938 2012 54

Wind/Solar/Battery 12,675,183 0.441 0 0 0


Note that: other emission gases are carbon monoxide, unburned hydrocarbons, particulate matter, sulfur dioxide and Nitrogen oxides.

6.4 Economic analysis

The project life time was taken as 25 years and the annual real interest rate and the nominal

rate as 4% and 10%, respectively and each component are its own life time and it expecting

to replace at the end of life time.

The initial capital cost of a component is the total installed cost of that component at the

beginning of the project .The levelized cost of energy (COE), net present cost and initial cost

of the two towns are presented in the forthcoming paragraphs.

Table 4.9 and Figure 4.21 summaries the economic performance of the winning system for

Degehabur town. The capital cost constituted the largest portion of the total NPC at 67.20%,

followed by replacement cost (27.80%) and O&M costs (5.01%).

The component incurring the largest cost is the battery bank (54.97%) followed by the PV

modules (33.57%), wind turbines generator (8.08%) and inverter (3.38%) Table 6.2: Economic performance of the hybrid stand alone system for Degehabur town

Component Capital ($) Replacement ($)

O&M ($) Fuel ($)

Salvage ($) Total ($)

PV 4,200,000 0 54,677 0 0 4,254,677

WindTurbines

780,000 0 243,705 0 0 1,023,705

Batteries 4,140,229 3,734,399 359,308 0 -1,265,474 6,968,462

Converter 270,000 149,922 42,180 0 -33,761 428,341

System 9,390,229 3,884,321 699,870 0 -1,299,235 12,675,184

Levelized COE

US$0.441/kWh

The COE for the optimum system for Degehabur town found to be US$0.441/kWh. If

compared to the current electricity tariff of small residences in Ethiopia which is US$0.06

/kWh which is heavily subsidized, the COE of the RAPS system is 7.4 times higher.

However, if compared to the COE of a diesel generator only system at US$0.543/kWh, which

is a popular option for electrification of rural towns and village located far from national grid

system in Ethiopia today, the Remote Area Power Supply (RAPS) system offers a competitive

COE which is lower by 19%.Unfortunately, the capital cost of the hybrid system is far above

the generator only option. But the net present cost of diesel-only option found to be higher

than the optimal hybrid system


Figure 6.5: Lifecycle costs of hybrid system by components for Degehabur town

Likewise, for Kebridehar towns Table 4.9 and Figure 4.21 summaries the economic

performance of the winning system for Kebridehar town. The capital cost constituted the

largest portion of the total NPC at 67.3%, followed by replacement cost (27.70%) and O&M

costs (5.05%).The component incurring the largest cost is the battery bank (55%) followed by

the PV modules (34%) ,wind turbines generator (7%) and inverter (4%). Table 6.3: Economic performance of the hybrid stand alone system for Kebridehar town.

Component Capital ($) Replacement ($)

O&M ($) Fuel($)

Salvage ($)

Total ($)

PV 3,600,000 0 46,866 0 0 3,646,866

Wind Turbines

585,000 0 182,778 0 0 767,779

Batteries 3,485,000 3,120,262 320,253 0 -1,057,362 5,868,153

Converter 270,000 149,922 46,866 0 -33,761 433,027

System 7,940,000 3,270,183 596,764 0 -1,091,122 10,715,826

Levelized COE

US$0.422/kWh

The COE for the optimum system for Kebridehar town found to be US$0.422/kWh. If

compared to the current electricity tariff of small residences in Ethiopia which is US$0.06

/kWh which is heavily subsidized, the COE of the RAPS system is 7.03 times higher.

However, if compared to the COE of a diesel generator only system at US$0.564/kWh, which

is a popular option for rural electrification in Ethiopia today, the Remote Area Power Supply

(RAPS) system offers a competitive COE which is lower by 25%.Unfortunately, the capital

cost of the hybrid system is far above the generator only option. But the net present cost of

diesel-only option found to be higher than the optimal hybrid system


Figure 6.6: Lifecycle costs of hybrid system by components for Kebridehar town

6.5 Sensitivity results

The HOMER software simulates all the systems in their respective search space for each of

the sensitivity values. An hourly time series simulation is performed for one complete year. A

feasible system is defined as the hybrid system which meets the required load. The software

eliminates all infeasible systems and presents the results in ascending order of NPC. In the

present case wind speed (5.91 and 5.45 m/s, diesel price (1.0, 1.3 and 1.5 $/L). A total of

3600 sensitivity cases were tried for each system configuration. Overall 50 systems were

simulated for 600 sensitivities which mean a total of 4320 combinations were tried.

6.5.1 Cost of energy sensitivity to diesel price – Kebri Dehar and Degehabur towns

Costs of energy appear to vary in line with diesel prices (Figure 6.5) – not surprising

considering that diesel is the main energy source in the system. Base case cost of energy for

KebriDehar is $0.564/kWh $0.543/kWh for Kebri Dehar and Degehabur towns respectively

and if diesel remains at $1.0/liter. In the highest diesel price scenario ($1.5/liter) the cost of

energy is $0.771/kWh and 0.738/kWh for Kebri- Dehar and Degehabur towns respectively.

But the hybrid energy supply systems which show in the figure 6.5 the diesel price is not

affect the cost of energy meaning all energy supply comes from renewable energy resource.

The overall cost savings from a hybrid system compared with a diesel-only option increases

with increasing diesel price (Figure 6.7) since hybrid systems totally not fuel dependant.

Main Re

Figure 6.7price sce

6.5.2 Lif

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8: Net preseenarios

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ebri Dehar

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16,337,213.

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20,521,246,

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22,304,360,

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The hybrid life cycle costs of the two towns are keep constant since the diesel price is not

totally meaningful change on the system throughout project life time.

6.6 Comparison of the Grid extension with standalone (Off Grid) and

diesel-only system

The distance from the grid which makes the net present cost of extending the grid equal to the

net present cost of the stand-alone system. Farther away from the grid, the stand-alone system

is optimal. Nearer to the grid, grid extension is optimal. The unit cost of 132 kV single circuit

transmission line (steel lattices supported) with Optical Fiber Ground Wire (OPGW) is

125,000USD per km. The operating and maintenance cost of the transmission is 2% of capital

cost is around $2500/Km per year. Degebabur is located 160 Km and Kebri Dehar is located

190 km from nearest national grid system.

The total capital costs of grid extension are 24.23 Million US dollar and 20.40 Million US

dollar, for Kebri Dehar and Degehabr respectively. Moreover, the breakeven distances from

the grid extension are 63 Kilometers and 77 Kilometers for Kebri Dehar and Degehabur

respectively, meaning which is the net present cost of grid extension equals the net present

cost of the stand-alone system. Both towns are located very far from breakeven grid

extension If you go farther from this point (breakeven grid extension distance) the stand alone

is the optimal solution power supply of the selected towns. Figure 6.7 and figure 6.8 show the

net present cost comparison of standalone system with grid extension of Kebri Dehar and

Degehabur towns. Therefore, the standalone system is the optimal solution of power supply

for the two towns since the net present cost of grid extension much higher than standalone

Main Re

Figure 6.9

Figure 6.

Solar PV

than 63 K

and their

below in

1.172 and

the diese

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see table `Table 6.4

eport Final M

9: Compari

10: Compar

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Kilometers a

r energy cos

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d 0.869 $/kW

el only optio

n energy cos

6.4. 4: Energy c

Name of Towns

Kebri Deh

Degehabu

Master The

son of Grid e

rison of Grid

tery options

and 77 Kilo

sts were com

t, the grid ex

Wh for Kebr

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Gr

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ri Dehar and

nd still it wa

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Diesel-Only PV/Wind/Batt

rid Extension

Diesel-Only PV/Wind/Batt

rid Extension

ith standalon

with standalo

better than

Keberi Deha

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1,04tery 811

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441$/kWh,

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with hybrid an

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To(k

9,8965,941

95,284

49,540 1,363

80,239

f Kebri Deha

of Degehabu

sion for dist

ehabur towns

respectively

Dehar and D

pectively. M

nsion arrang

(Solar PV/W

nd diesel-on

otal Demand kWh/Year)

1,702,951 1,625,057

1,702,951

1,934,455 1,839,797

1,934,455

r town

ur town.

tances greate

s respectivel

y. In table 6

Degehabur ar

oreover, eve

gement. Gri

Wind/Battery

ly system

Cost of Energy ($/kWh)

0.564 0.422

1.172

0.543 0.441

0.869

er

ly

.4

re

en

id

y)


7. Conclusion and Recommendation

7.1 Conclusions

This study aimed to identify options and to design feasible systems to provide electricity for

Degehabur and Kebridear towns in Somalia region, Ethiopia by harnessing power from

renewable energy resources.

Two power supply options have been identified. The first option was a hybrid (standalone

Solar/wind/battery) system and the second option was to construct new transmission line

from nearest substation to selected towns. The HOMER simulation program developed by the

NREL has been used as the design tool for both options.

HOMER modeling results indicate that both Kebri Dehar and Degehabur towns electricity

needs could be met at considerable overall cost savings with a hybrid (wind/solar/Battery)

system compared with existing separate diesel generator systems. At existing diesel prices

and in a “base case” load scenario, the optimum system comprises a hybrid solar/wind/battery

system., based on the simulation result, the optimal system for Kebri Dehar town, a hybrid

solar/wind /battery (no diesel system), with 600 kW of solar, a 6*65 kW wind turbines, 2050

S4KS25P batteries (each 1900AH capacity) and 300 kW bi-directional inverter. This

“optimal” system uses 100% renewable energy, and the cost of electricity is $0.422/kWh

including depreciation on capital and levelized O&M with net present cost of $10,715,823.

Likewise, according to simulation results, the optimal system for Degehabur town, a hybrid

solar/wind /battery (no diesel system), with 700 kW of solar, a 8*65 kW wind turbines ,2300

S4KS25P batteries (each 1900AH capacity) and 300 kW bi-directional inverter has been

selected. This “optimal” system uses 100% renewable energy, and the cost of electricity is

$0.441/kWh including depreciation on capital and levelized O&M with net present cost of $

12,675,183.

The sensitivity Costs of energy appear to vary in line with diesel prices – not surprising

considering that diesel is the main energy source in the system. Base case cost of energy for

Kebri Dehar is $0.568/kWh and $0.541/kWh for Kebri Dehar and Degehabur towns,

respectively and if diesel remains at $1.0/liter. In the highest diesel price scenario ($1.5/liter)

the cost of energy was $0.771/kWh and 0.738/kWh for Kebri- Dehar and Degehabur towns,

respectively.

The sensitivity of life-cycle cost (net present cost) to different diesel price scenarios was

considered. Scenarios of high diesel price result in net present cost for Kebri Dehar town

approaching $ 20,521,246, whereas the lowest end scenario is just over $15,114,885.then for


case of Degehabur town, scenarios of high diesel price result in net present cost approaching

$ 22,304,360, while the lowest end scenario is just over $16,337,213.

Though the optimum system configuration changes under different diesel price assumptions,

the hybrid system remains most economically feasible solution than the existing

arrangements (diesel-only), under all scenarios considered. The COE for Degehabur and

Kebridehar towns the above mention hybrid system, the COE of the RAPS system is 7 times

higher than a current electricity tariff which is heavily subsidized, but 25% lower than a

diesel only system’s COE for Kebridehar and 19% a diesel only system’s COE for

Degehabur towns.

Moreover, the “diesel only” options (from both towns) produce total of around 3835 tons of

CO2 per year, whereas the hybrid system (100% renewable energy) have illegible green

houses gases emission to the atmosphere.

The second option looked at power supplied from the nearest substation. This options also

performed better for grid extension for distances less than 78 and 64km for Keberidehar and

Degeabur towns ( both towns are located very far from this point) ,respectively and their

energy costs were computed as 1.172 and 0.869 $/kWh for Kebri Dehar and Degehabur

towns respectively. The total capital costs of grid extension are 24.23 Million US dollar and

20.40 Million US dollar, for Kebri Dehar and Degehabr respectively. Furthermore, the

breakeven distances from the grid extension are 63 Kilometers and 77 Kilometers for Kebri

Dehar and Degehabur respectively, meaning which is the net present cost of grid extension

equals the net present cost of the stand-alone system. If you go further from these points

(breakeven grid extension distance), a hybrid (stand-alone) system are optimal solution the

power supply the selected towns .But both towns are located 150 km from the nearest

national grid.

Degehabur and Kebridehar towns located 160 km and 190 km, respectively from existing

substation and a 132 kV voltage level is selected. The voltage selection criteria of

transmission lines are mainly based on the power to be transmitted and on the distance

between delivery and receiving ends. The power supplied from the nearest substation for both

towns total net present cost of $44.46 Million, whereas hybrid (solar/wind/battery) total net

present cost is $23 Million system. A grid extension power supply option almost $22 million

higher than standalone system throughout project life time. The grid extension of energy cost

for Kebri Dehar and Degehabur are 1.172 and 0.869 $/kWh for Kebri Dehar and Degehabur


towns respectively. However, even the diesel only option performed better than grid

extension for distances greater than breakeven point.

Finally the Author proposed that standalone system (solar/wind/battery) is economically

feasible and environmentally friendly to replace the existing diesel-only power supply system

for Kebri Dehar and Degehabur towns.

7.2 Recommendation and Further work

The study recommends collecting wind speed data at the actual site at three

different heights using a wind mast of 40m for at least one complete year. This

data then must be used for final feasibility of the hybrid system.

This study shows only focus on two selected towns of Somalia region in

Ethiopia and it doesn’t cover all towns and villages around Somalia region.

So, the future researchers should expand this research work in other sites and

make the rural people beneficial with renewable energy resource.

In spite of the huge hydroelectric potential of Ethiopia, severe power cuts in

recent years have a heavy impact on the country’s economy.Solar thermal

technology recommended to be incorporated for the future grid connected

application to create a strength, reliability and maintaining sustainable energy

supply of the country. Somalia region has a great solar resource potential and

has not yet properly exploited this resource.

The Software HOMAR used for optimization in this study is found privately

not capacities solve different types of sensitivity analysis and other advanced

features, and it is commercially available for the future use of similar

assignments. Thus it is advisable if EEPCo purchases this software or other

comprehensive optimization software of highbred nature

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List of Appendixes

Appendix–A

Figure A.1 Location map of project area

Source:EEPCO


Table A.1 Maximum Temp. in oC for Keberidehar and Degehabur town

For Kebri Dehar Town For Degehabur Town

Year Year Year Year

Month 2007 2008 Month 2007 2008

Jan 35.1 35.1 Jan 31.4 31.7 FEB 36.1 35.7 FEB 32 33.0

MAR 36.8 36.7 MAR 33.7 32.8 APR 36.0 34.5 APR 32.3 32.3 MAY 33.3 32.5 MAY 31.3 30.1 JUN 32.0 31.9 JUN 29.1 31.9 JUL 31.1 32.2 JUL 28.3 28

AUG 31.7 32.0 AUG 30.8 29.7 SEP 33.9 33.7 SEP 30.7 32.2 OCT 33.3 32.0 OCT 32.5 31.4 NOV 34.0 34.3 NOV 31.6 30.9

DEC 34.6 34.9 DEC 31.2 31.3

Average 34.0 34.8 Average 31.2 31.1

Annual Average 34.8 Annual Average 31.2

Source: NMSA


Table A.3 Energy and Power Forecast For Degehabur town

RurDom RurComm RurLV RurStrL RurTotal

Year KWh KWh KWh KWh KWh

2009 601,933 348,436 428,905 12,685 1,391,959

2010 711,854 413,493 503,282 15,001 1,643,630

2011 839,480 489,103 589,490 17,691 1,935,764

2012 987,698 576,987 689,456 20,814 2,274,955

2013 1,159,465 678,917 805,136 24,434 2,667,952

2014 1,358,728 797,245 939,172 28,633 3,123,778

2015 1,589,838 934,569 1,094,463 33,503 3,652,373

2016 1,857,560 1,093,742 1,274,173 39,145 4,264,620

2017 2,168,000 1,278,403 1,482,382 45,687 4,974,472

2018 2,527,418 1,492,299 1,723,242 53,261 5,796,220

2019 2,944,057 1,740,346 2,002,266 62,041 6,748,711

2020 3,146,666 1,865,693 2,125,759 66,311 7,204,429

2021 3,360,556 1,998,291 2,255,617 70,818 7,685,282

2022 3,586,624 2,138,708 2,392,379 75,582 8,193,293

2023 3,825,184 2,287,177 2,536,158 80,610 8,729,128

2024 4,077,163 2,444,290 2,687,503 85,920 9,294,875

2025 4,342,907 2,610,303 2,846,539 91,520 9,891,269

2026 4,623,681 2,786,011 3,014,042 97,437 10,521,170

2027 4,919,870 2,971,701 3,190,151 103,679 11,185,401

2028 5,232,175 3,167,848 3,375,225 110,260 11,885,508

2029 5,561,921 3,375,289 3,570,062 117,209 12,624,480

2030 5,909,556 3,594,362 3,774,821 124,535 13,403,274


TableA.2 Energy and Power Forecast For Keberdehar town

RurDom RurComm RurLV RurStrL RurTotal Year KWh KWh KWh KWh KWh

2009 529,045 306,244 376,969 11,149 1,223,407

2010 625,856 363,539 442,482 13,189 1,445,066

2011 738,213 430,101 518,382 15,557 1,702,253

2012 868,339 507,261 606,138 18,299 2,000,037

2013 1,019,424 596,916 707,893 21,483 2,345,716

2014 1,194,740 701,022 825,825 25,177 2,746,765

2015 1,397,947 821,767 962,367 29,460 3,211,540

2016 1,633,405 961,756 1,120,421 34,421 3,750,003

2017 1,906,195 1,124,025 1,303,371 40,170 4,373,762

2018 2,222,137 1,312,049 1,515,093 46,828 5,096,106

2019 2,588,412 1,530,112 1,760,387 54,547 5,933,458

2020 2,766,393 1,640,228 1,868,852 58,297 6,333,770

2021 2,954,717 1,756,966 1,983,219 62,266 6,757,167

2022 3,153,348 1,880,347 2,103,367 66,452 7,203,514

2023 3,363,158 2,010,920 2,229,826 70,873 7,674,778

2024 3,584,738 2,149,077 2,362,917 75,543 8,172,273

2025 3,818,390 2,295,042 2,502,747 80,467 8,696,645

2026 4,065,335 2,449,576 2,650,078 85,671 9,250,660

2027 4,325,612 2,612,759 2,804,818 91,155 9,834,345

2028 4,600,171 2,785,199 2,967,521 96,941 10,449,833

2029 4,889,986 2,967,525 3,138,751 103,049 11,099,311

2030 5,195,753 3,160,207 3,318,865 109,492 11,784,317


Appendix-B

TableB.1

Monthly Averaged Wind Direction At 50 m Above The Surface Of The Earth (degrees) for Kebridehar and Degehabur town

Lat 6.75 Lon 44.283 for Kebridehar

Lat 6.75 Lon 44.283 for Degehabur

Month 10-year Average

Month 10-year Average

Jan 64 Jan 57

Feb 67 Feb 62

Mar 74 Mar 68

Apr 79 Apr 70 May 100 May 75

Jun 175 Jun 126

Jul 204 Jul 205

Aug 211 Aug 218

Sep 214 Sep 224

Oct 212 Oct 225

Nov 208 Nov 225

Dec 202 Dec 222

Notes

measured clockwise from True North

direction the wind is coming from

The monthly average wind direction for a given month, averaged for that month over the 10-year period (July 1983 - June 1993).

Wind direction values are for 50 meters above the surface of the earth.

Source:NASA


Table B.2 Probability density vs. wind speed at hub height in Kebridehar and Degehabur towns

Kebridehar town Degehabur town

NO.

Wind speed at 50meter Hub

Height In [m/s]

Wind Probability Density in

[%]

Wind speed at 50meter Hub

Height In [m/s] Wind Probability

Density [%] 1 0.0 0.000 0.000 0.000 2 0.5 2.246 0.500 2.665 3 1.5 6.413 1.500 7.509 4 2.5 9.751 2.500 11.205 5 3.5 11.917 3.500 13.349 6 4.5 12.795 4.500 13.870 7 5.5 12.492 5.500 13.009 8 6.5 11.281 6.500 11.203 9 7.5 9.513 7.500 8.943

10 8.5 7.537 8.500 6.658 11 9.5 5.633 9.500 4.642 12 10.5 3.982 10.500 3.039 13 11.5 2.669 11.500 1.872 14 12.5 1.698 12.500 1.087 15 13.5 1.026 13.500 0.595 16 14.5 0.590 14.500 0.308 17 15.5 0.323 15.500 0.151 18 16.5 0.169 16.500 0.070 19 17.5 0.084 17.500 0.030 20 18.5 0.040 18.500 0.013 21 19.5 0.018 19.500 0.005 22 20.5 0.008 20.500 0.002 23 21.5 0.003 21.500 0.001 24 22.5 0.001 22.500 0.000 25 23.5 0.000 23.500 0.000

26 24.5 0.000 24.500 0.000


Table B.3 Wind speed daily profile for Degehabur

Time in Hours JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

m/s m/s m/s m/s m/s m/s m/s m/s m/s m/s m/s m/s

0.50 4.35 4.78 3.80 2.60 3.57 5.11 6.46 5.97 4.47 3.10 3.72 4.40 1.50 4.28 4.51 3.78 2.55 3.47 5.10 6.36 5.78 4.44 3.18 3.65 4.26 2.50 4.21 4.24 3.76 2.49 3.37 5.10 6.27 5.60 4.41 3.25 3.58 4.12 3.50 4.12 4.17 3.65 2.67 3.35 5.48 5.62 5.54 4.54 3.11 3.48 4.18 4.50 4.15 4.17 3.60 2.75 3.40 5.68 5.73 5.74 4.40 3.22 3.59 4.25 5.50 4.17 4.18 3.55 2.84 3.45 5.88 5.84 5.93 4.26 3.32 3.70 4.31 6.50 4.71 4.39 3.97 2.94 3.83 6.55 6.67 7.10 4.43 3.71 3.60 5.22 7.50 4.97 4.66 3.98 3.09 4.13 6.87 7.09 7.38 4.63 3.86 3.82 5.46 8.50 5.23 4.92 3.99 3.25 4.43 7.18 7.50 7.66 4.82 4.01 4.03 5.70 9.50 6.02 5.80 4.71 3.63 4.94 7.83 8.52 7.65 5.71 4.02 4.75 5.97

10.50 6.29 6.03 4.89 3.69 5.27 8.17 8.83 7.73 6.09 4.00 4.98 6.20 11.50 6.57 6.27 5.07 3.75 5.60 8.50 9.14 7.80 6.46 3.97 5.22 6.42 12.50 7.62 6.84 5.18 4.22 5.61 8.98 9.52 8.07 6.93 4.30 5.82 6.53 13.50 7.83 6.74 5.29 4.28 5.56 9.04 9.51 8.24 7.04 4.39 5.91 6.40 14.50 8.03 6.64 5.40 4.34 5.50 9.10 9.49 8.42 7.14 4.48 5.99 6.27 15.50 7.48 6.42 5.49 4.43 5.65 9.21 9.06 8.25 6.99 4.76 6.14 6.36 16.50 7.32 6.49 5.40 4.30 5.72 9.04 8.86 8.11 6.87 4.66 6.11 6.33 17.50 7.16 6.56 5.31 4.17 5.80 8.86 8.66 7.96 6.74 4.57 6.08 6.31 18.50 6.75 5.91 4.91 3.93 5.48 7.94 8.29 7.84 6.32 4.35 5.79 6.08 19.50 6.35 5.64 4.90 3.82 5.17 7.84 8.04 7.62 6.20 4.23 5.40 5.94 20.50 5.95 5.36 4.90 3.72 4.87 7.74 7.79 7.40 6.07 4.10 5.01 5.79 21.50 4.90 5.08 4.24 3.13 4.16 6.66 6.99 6.55 5.60 3.64 4.39 5.29 22.50 4.78 4.86 4.22 2.94 3.98 6.40 6.66 6.25 5.44 3.49 4.19 5.06

23.50 4.67 4.64 4.21 2.74 3.80 6.14 6.33 5.96 5.28 3.34 3.98 4.83


Table B.4 Wind speed daily profile for Kebridehar

Time in Hours JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

m/s m/s m/s m/s m/s m/s m/s m/s m/s m/s m/s m/s

0.50 4.52 5.03 3.90 2.80 4.58 5.83 7.07 6.63 5.19 3.41 3.26 4.411.50 4.46 4.76 3.89 2.74 4.46 5.82 6.98 6.42 5.15 3.49 3.20 4.302.50 4.39 4.49 3.87 2.68 4.35 5.82 6.88 6.21 5.10 3.57 3.15 4.183.50 4.31 4.40 3.76 2.88 4.31 6.24 6.18 6.17 5.26 3.42 3.06 4.224.50 4.33 4.42 3.71 2.96 4.38 6.44 6.30 6.39 5.10 3.54 3.15 4.265.50 4.36 4.44 3.67 3.05 4.44 6.63 6.42 6.62 4.95 3.65 3.24 4.306.50 4.92 4.69 4.09 3.16 4.93 7.39 7.34 7.90 5.14 4.05 3.19 5.207.50 5.20 4.96 4.11 3.32 5.30 7.74 7.80 8.23 5.36 4.22 3.35 5.458.50 5.48 5.24 4.12 3.49 5.67 8.08 8.26 8.55 5.57 4.39 3.52 5.709.50 6.27 6.17 4.85 3.88 6.35 8.80 9.38 8.59 6.62 4.44 4.19 5.97

10.50 6.56 6.42 5.04 3.96 6.73 9.18 9.72 8.68 7.06 4.41 4.39 6.2011.50 6.84 6.67 5.24 4.04 7.10 9.56 10.07 8.78 7.50 4.39 4.58 6.4212.50 7.90 7.25 5.37 4.54 7.18 10.10 10.46 9.13 8.01 4.74 5.10 6.5213.50 8.11 7.15 5.49 4.59 7.12 10.17 10.45 9.31 8.11 4.84 5.17 6.4114.50 8.32 7.04 5.61 4.65 7.06 10.24 10.44 9.50 8.22 4.94 5.24 6.2915.50 7.76 6.84 5.68 4.75 7.22 10.35 10.01 9.33 8.06 5.23 5.37 6.3616.50 7.60 6.91 5.58 4.62 7.32 10.17 9.79 9.15 7.93 5.13 5.33 6.3417.50 7.43 6.97 5.48 4.49 7.41 9.98 9.58 8.97 7.80 5.03 5.30 6.3118.50 7.03 6.29 5.06 4.21 7.02 8.93 9.15 8.78 7.31 4.79 5.06 6.0819.50 6.62 6.00 5.04 4.09 6.62 8.83 8.86 8.54 7.14 4.65 4.71 5.9420.50 6.21 5.71 5.03 3.97 6.23 8.72 8.58 8.29 6.98 4.51 4.36 5.7921.50 5.10 5.38 4.38 3.37 5.36 7.50 7.69 7.30 6.42 3.97 3.83 5.3022.50 4.98 5.15 4.35 3.16 5.12 7.21 7.33 6.98 6.25 3.82 3.66 5.05

23.50 4.86 4.93 4.32 2.95 4.89 6.92 6.96 6.66 6.07 3.66 3.49 4.81


Table B.5 Global Horizontal Solar Radiation Daily profile for Degehabur town

Timein

Hours JAN FEB MAR APR MAY JUNE JULY AUG SEP OCT NOV DEC kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.50 0.10 0.10 0.12 0.15 0.17 0.15 0.12 0.14 0.16 0.17 0.16 0.12 7.50 0.24 0.23 0.27 0.28 0.30 0.26 0.24 0.27 0.31 0.31 0.31 0.25 8.50 0.39 0.37 0.42 0.41 0.42 0.37 0.35 0.39 0.45 0.45 0.45 0.39 9.50 0.73 0.70 0.78 0.68 0.66 0.62 0.58 0.65 0.75 0.78 0.72 0.69

10.50 0.80 0.79 0.84 0.74 0.72 0.68 0.64 0.71 0.81 0.82 0.79 0.77 11.50 0.86 0.88 0.90 0.81 0.77 0.74 0.70 0.77 0.86 0.86 0.85 0.84 12.50 0.90 0.92 0.93 0.83 0.78 0.76 0.72 0.82 0.88 0.86 0.85 0.82 13.50 0.83 0.87 0.88 0.77 0.73 0.69 0.67 0.77 0.81 0.79 0.78 0.76 14.50 0.75 0.81 0.82 0.71 0.68 0.63 0.62 0.71 0.74 0.71 0.72 0.70 15.50 0.46 0.51 0.49 0.43 0.44 0.40 0.37 0.45 0.47 0.42 0.41 0.42 16.50 0.32 0.36 0.35 0.31 0.31 0.29 0.27 0.33 0.32 0.27 0.26 0.27 17.50 0.18 0.21 0.21 0.18 0.17 0.18 0.17 0.20 0.18 0.13 0.11 0.13 18.50 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.00 0.00 0.00 19.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 21.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 22.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

23.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


Table B.6 Global Horizontal Solar Radiation Daily profile for Kebridehar town

JAN FEB MAR APR MAY JUNE JULY AUG SEP OCT NOV DEC

Time in Hours

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.50 0.11 0.11 0.13 0.15 0.17 0.15 0.12 0.14 0.17 0.16 0.17 0.13 7.50 0.25 0.26 0.28 0.28 0.28 0.26 0.24 0.26 0.31 0.29 0.31 0.27 8.50 0.39 0.40 0.43 0.40 0.39 0.37 0.35 0.38 0.45 0.42 0.44 0.40 9.50 0.73 0.73 0.78 0.66 0.62 0.62 0.58 0.63 0.74 0.73 0.70 0.70

10.50 0.79 0.82 0.84 0.73 0.67 0.68 0.64 0.69 0.79 0.76 0.76 0.78 11.50 0.85 0.91 0.90 0.79 0.72 0.74 0.70 0.74 0.84 0.79 0.82 0.85 12.50 0.88 0.95 0.92 0.81 0.73 0.75 0.71 0.78 0.85 0.79 0.81 0.83 13.50 0.80 0.90 0.86 0.75 0.68 0.68 0.66 0.73 0.79 0.72 0.75 0.76 14.50 0.72 0.84 0.81 0.68 0.62 0.62 0.61 0.68 0.72 0.65 0.69 0.70 15.50 0.44 0.52 0.48 0.41 0.40 0.39 0.36 0.42 0.45 0.38 0.39 0.42 16.50 0.30 0.37 0.34 0.29 0.28 0.28 0.26 0.30 0.30 0.25 0.25 0.27 17.50 0.17 0.21 0.20 0.16 0.15 0.16 0.16 0.18 0.16 0.11 0.11 0.13 18.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 21.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 22.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 23.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


Table B.7 Global Solar radiation incident on PV Array Daily Profile with tracking system for

Degehabur town

Timein


kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.50 0.45 0.44 0.54 0.58 0.63 0.54 0.43 0.57 0.70 0.69 0.71 0.54 7.50 0.65 0.59 0.66 0.61 0.63 0.55 0.48 0.60 0.73 0.71 0.75 0.65 8.50 0.85 0.74 0.77 0.64 0.64 0.56 0.54 0.62 0.75 0.74 0.78 0.76 9.50 1.06 0.94 0.99 0.79 0.77 0.73 0.68 0.77 0.89 0.95 0.93 0.95

10.50 1.04 0.96 0.97 0.81 0.79 0.75 0.70 0.78 0.89 0.93 0.94 0.97 11.50 1.01 0.98 0.95 0.82 0.80 0.78 0.73 0.79 0.88 0.91 0.95 0.99 12.50 1.04 1.00 0.96 0.84 0.80 0.79 0.73 0.83 0.90 0.92 0.96 0.97 13.50 1.00 0.99 0.96 0.82 0.79 0.75 0.71 0.82 0.88 0.91 0.98 0.97 14.50 0.97 0.99 0.96 0.81 0.79 0.72 0.69 0.81 0.87 0.90 0.99 0.97 15.50 0.82 0.83 0.75 0.63 0.68 0.58 0.50 0.66 0.75 0.77 0.85 0.84 16.50 0.79 0.78 0.72 0.60 0.62 0.55 0.47 0.64 0.73 0.66 0.68 0.71 17.50 0.76 0.74 0.68 0.56 0.56 0.51 0.44 0.62 0.71 0.56 0.52 0.58 18.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 21.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 22.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

23.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


Table B.8 Global Solar radiation incident on PV Array Daily Profile without tracking system for

Degehabur town

Timein


kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.50 0.16 0.14 0.13 0.14 0.15 0.13 0.10 0.12 0.16 0.19 0.20 0.17 7.50 0.30 0.27 0.28 0.27 0.27 0.24 0.22 0.25 0.31 0.33 0.34 0.30 8.50 0.43 0.40 0.43 0.40 0.40 0.35 0.33 0.38 0.45 0.47 0.48 0.43 9.50 0.79 0.73 0.79 0.67 0.64 0.59 0.56 0.64 0.75 0.80 0.77 0.74

10.50 0.86 0.82 0.86 0.73 0.70 0.65 0.62 0.70 0.81 0.84 0.83 0.82 11.50 0.92 0.92 0.92 0.80 0.75 0.72 0.68 0.76 0.87 0.88 0.89 0.90 12.50 0.96 0.96 0.95 0.82 0.76 0.73 0.70 0.80 0.88 0.88 0.89 0.88 13.50 0.88 0.91 0.89 0.76 0.70 0.67 0.65 0.75 0.81 0.81 0.83 0.82 14.50 0.80 0.85 0.83 0.70 0.65 0.60 0.60 0.70 0.75 0.74 0.77 0.75 15.50 0.50 0.54 0.50 0.43 0.42 0.38 0.36 0.44 0.47 0.44 0.45 0.46 16.50 0.36 0.38 0.36 0.30 0.28 0.27 0.26 0.31 0.32 0.29 0.30 0.32 17.50 0.22 0.23 0.22 0.17 0.15 0.15 0.15 0.19 0.18 0.15 0.16 0.18 18.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 21.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 22.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

23.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


Table B.9 Global Solar radiation incident on PV Array Daily Profile without tracking system for

Kebridehar town

JAN FEB MAR APR MAY JUNE JULY AUG SEP OCT NOV DEC

Timein

Hours kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.50 0.15 0.14 0.13 0.14 0.15 0.13 0.11 0.13 0.17 0.17 0.20 0.17 7.50 0.29 0.28 0.28 0.27 0.26 0.24 0.22 0.25 0.31 0.30 0.33 0.31 8.50 0.43 0.42 0.43 0.40 0.38 0.35 0.33 0.37 0.45 0.43 0.46 0.44 9.50 0.77 0.76 0.79 0.65 0.60 0.59 0.56 0.62 0.74 0.74 0.73 0.74

10.50 0.83 0.85 0.85 0.72 0.65 0.65 0.62 0.67 0.79 0.78 0.79 0.82 11.50 0.89 0.94 0.91 0.78 0.70 0.72 0.68 0.73 0.85 0.81 0.85 0.90 12.50 0.92 0.99 0.93 0.80 0.71 0.73 0.69 0.77 0.85 0.81 0.85 0.87 13.50 0.84 0.93 0.87 0.74 0.66 0.66 0.64 0.72 0.79 0.74 0.79 0.81 14.50 0.76 0.87 0.82 0.68 0.61 0.59 0.59 0.67 0.72 0.67 0.73 0.74 15.50 0.47 0.55 0.48 0.41 0.39 0.37 0.35 0.41 0.45 0.39 0.42 0.45 16.50 0.34 0.39 0.34 0.28 0.26 0.26 0.25 0.29 0.30 0.26 0.28 0.31 17.50 0.20 0.23 0.20 0.16 0.13 0.14 0.15 0.17 0.16 0.12 0.14 0.17 18.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 21.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 22.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

23.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


Table B.10 Global Solar radiation incident on PV Array Daily Profile with tracking system for

Kebridehar town

JAN FEB MAR APRI

L MAY JUNE JULY AUG SEP OCT NOV DEC

Timein

Hours kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

kW/m2

0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.50 0.48 0.52 0.56 0.56 0.56 0.54 0.43 0.54 0.68 0.56 0.70 0.59 7.50 0.64 0.66 0.66 0.58 0.57 0.55 0.49 0.57 0.71 0.60 0.71 0.68 8.50 0.80 0.80 0.76 0.61 0.58 0.56 0.54 0.59 0.73 0.64 0.72 0.76 9.50 1.02 0.98 0.97 0.76 0.71 0.73 0.68 0.74 0.87 0.86 0.87 0.95

10.50 1.00 0.99 0.95 0.78 0.73 0.75 0.70 0.75 0.86 0.84 0.89 0.96 11.50 0.98 1.01 0.94 0.80 0.74 0.78 0.73 0.76 0.86 0.82 0.90 0.98 12.50 1.00 1.02 0.94 0.82 0.75 0.78 0.73 0.80 0.87 0.83 0.91 0.96 13.50 0.96 1.02 0.94 0.80 0.74 0.75 0.71 0.79 0.86 0.82 0.92 0.96 14.50 0.93 1.02 0.94 0.78 0.73 0.72 0.69 0.77 0.85 0.80 0.93 0.96 15.50 0.77 0.87 0.73 0.60 0.62 0.58 0.49 0.61 0.71 0.66 0.80 0.83 16.50 0.73 0.83 0.70 0.57 0.56 0.54 0.46 0.59 0.69 0.56 0.64 0.70 17.50 0.70 0.79 0.66 0.54 0.51 0.51 0.44 0.57 0.66 0.45 0.48 0.57 18.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 21.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 22.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

23.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Ma

in R

ep

ort

Fin

al M

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sis

A

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Tim

e

JAN

FE

B M

AR

APR

M

AY

JUN

E JU

LY

AU

G

SEP

OCT

N

OV

DEC

in

Hou

rs

kW

kW

kW

kW

kW

kW

kW

kW

kW

kW

kW

kW

0.50

41

.81

60.6

8 27

.61

8.58

22

.19

75.2

4 13

1.40

11

8.42

50

.73

18.4

3 28

.16

42.4

3 1.

50

40.9

5 51

.31

29.9

8 8.

58

21.1

5 74

.58

126.

93

114.

62

48.6

5 17

.66

29.1

9 38

.70

2.50

40

.08

41.9

4 32

.35

8.58

20

.11

73.9

3 12

2.45

11

0.83

46

.58

16.9

0 30

.21

34.9

8 3.

50

44.6

2 46

.36

30.4

1 9.

67

21.6

6 92

.09

106.

82

97.4

8 48

.41

17.5

3 27

.28

37.4

4 4.

50

45.3

1 43

.05

28.4

8 10

.22

22.1

8 10

3.31

11

3.57

10

4.01

46

.10

18.7

6 28

.13

38.7

4 5.

50

46.0

1 39

.73

26.5

5 10

.77

22.7

0 11

4.52

12

0.32

11

0.55

43

.79

20.0

0 28

.98

40.0

3 6.

50

48.8

4 47

.24

32.7

4 12

.22

30.4

6 15

3.01

15

2.17

16

2.64

45

.90

29.5

7 24

.48

80.3

0 7.

50

56.1

4 55

.73

33.4

7 14

.56

38.2

3 16

9.02

17

0.84

17

3.71

51

.68

31.7

6 30

.77

88.3

5 8.

50

63.4

5 64

.22

34.2

1 16

.91

46.0

0 18

5.04

18

9.50

18

4.78

57

.46

33.9

5 37

.06

96.4

0 9.

50

113.

14

104.

24

53.2

7 24

.50

65.3

7 19

5.60

24

2.74

17

9.04

89

.41

31.8

1 51

.71

108.

08

10.5

0 12

3.78

11

3.74

58

.17

27.1

2 75

.09

206.

78

255.

38

181.

17

107.

04

31.0

2 57

.35

123.

05

11.5

0 13

4.43

12

3.24

63

.08

29.7

3 84

.82

217.

96

268.

02

183.

29

124.

67

30.2

3 62

.98

138.

02

12.5

0 18

4.99

13

1.75

67

.76

40.3

4 85

.86

256.

62

276.

18

206.

74

142.

95

41.1

7 93

.10

137.

04

13.5

0 19

4.39

13

1.75

72

.32

38.8

9 83

.69

254.

00

276.

75

214.

71

149.

95

44.7

0 94

.31

125.

98

14.5

0 20

3.80

13

1.75

76

.88

37.4

5 81

.53

251.

38

277.

31

222.

69

156.

95

48.2

3 95

.52

114.

92

15.5

0 17

4.11

11

3.86

80

.37

36.5

6 84

.10

250.

35

250.

82

202.

96

164.

51

50.6

1 10

3.56

11

3.71

16

.50

165.

38

121.

02

76.7

9 34

.14

85.2

7 24

5.17

23

8.84

19

9.57

15

2.92

47

.25

100.

34

117.

45

17.5

0 15

6.64

12

8.17

73

.22

31.7

3 86

.44

240.

00

226.

86

196.

17

141.

34

43.9

0 97

.11

121.

19

18.5

0 13

1.23

10

2.61

62

.06

26.0

9 76

.14

202.

32

215.

00

199.

13

120.

35

36.8

9 87

.29

111.

72

19.5

0 11

6.11

93

.65

62.3

3 24

.54

63.8

2 19

9.70

19

9.34

19

6.97

11

5.79

34

.49

74.5

9 10

5.16

20

.50

100.

99

84.6

9 62

.61

23.0

0 51

.51

197.

07

183.

68

194.

82

111.

23

32.1

0 61

.89

98.6

0 21

.50

65.1

0 72

.44

35.9

6 13

.99

32.3

3 14

5.17

15

8.84

14

9.39

98

.93

24.1

5 46

.35

80.9

7 22

.50

61.0

2 63

.30

35.8

3 12

.07

29.2

8 13

0.94

13

9.21

13

7.88

88

.34

22.3

1 40

.71

70.5

5

23.5

0 56

.95

54.1

6 35

.70

10.1

6 26

.24

116.

71

119.

57

126.

38

77.7

5 20

.47

35.0

7 60

.12

Ma

in R

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ort

Fin

al M

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r T

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T

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Tim

e

JAN

FE

B M

AR

APR

M

AY

JUN

E JU

LY

AU

G

SEP

OCT

N

OV

D

EC

in H

ours

kW

kW

kW

kW

kW

kW

kW

kW

kW

kW

kW

kW

0.50

36

.73

54.9

6 23

.40

8.54

37

.53

85.2

3 12

5.77

11

2.01

61

.82

19.1

2 14

.02

32.9

9 1.

50

36.0

1 46

.91

25.3

5 8.

45

35.9

6 85

.15

122.

95

109.

88

59.1

0 18

.42

14.4

2 30

.53

2.50

35

.30

38.8

6 27

.30

8.37

34

.40

85.0

6 12

0.14

10

7.76

56

.38

17.7

2 14

.82

28.0

7 3.

50

39.2

3 42

.18

25.6

2 9.

51

37.1

4 98

.09

104.

97

103.

41

58.9

1 18

.21

13.5

2 29

.80

4.50

39

.52

39.7

0 24

.09

10.0

3 37

.79

105.

89

109.

81

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243.

06

192.

87

175.

95

18.5

20

6.91

23

7.29

25

7.64

26

7.96

23

1.04

16

3.32

15

8.1

164.

23

221.

26

273.

11

250.

88

228.

91

19.5

21

3.31

23

4.53

25

0.5

234.

97

219.

31

155.

06

153.

27

159.

78

215.

39

266.

14

250.

63

225.

26

20.5

21

9.71

23

1.77

24

3.35

20

1.98

20

7.59

14

6.8

148.

44

155.

33

209.

53

259.

17

250.

39

221.

61

21.5

22

1.99

21

7.47

23

1.45

13

7.6

185.

93

153.

27

149.

07

163.

54

186.

61

223.

33

236.

99

208.

17

22.5

19

2.16

19

0.78

19

9.79

11

1.46

15

0.75

13

6.91

13

1.82

14

3.74

16

4.4

190.

35

208.

48

186.

77

23.5

16

2.33

16

4.09

16

8.13

85

.33

115.

57

120.

55

114.

56

123.

94

142.

19

157.

37

179.

97

165.

36

Ma

in R

ep

ort

Fin

al M

aste

r T

he

sis

T

ab

le C

.6 I

nv

erte

r ou

tpu

t p

ow

er d

ail

y p

rofi

les

for

Keb

rid

ehar

tow

n

Tim

e

JAN

FE

B M

AR

APR

M

AY

JUN

E JU

LY

AU

G

SEP

OCT

N

OV

D

EC

in

Hou

rs

kW

kW

kW

kW

kW

kW

kW

kW

kW

kW

kW

kW

0.5

103.

34

93.9

7 11

3.97

51

.38

94.6

3 76

.83

55.9

1 72

.36

86.5

0 77

.43

85.7

8 10

3.04

1.

5 10

4.38

98

.30

112.

29

49.3

4 94

.03

78.1

5 57

.68

72.4

3 88

.31

69.7

7 82

.57

104.

00

2.5

105.

43

102.

63

110.

61

47.2

9 93

.44

79.4

7 59

.46

72.5

1 90

.11

62.1

0 79

.37

104.

96

3.5

106.

38

99.1

5 10

9.66

28

.85

87.4

3 75

.53

71.1

5 68

.99

89.8

2 53

.27

69.5

5 10

4.25

4.

5 10

7.91

10

1.37

10

9.85

18

.46

83.9

8 71

.53

71.5

1 65

.73

91.1

5 48

.89

61.8

2 10

2.27

5.

5 10

9.45

10

3.59

11

0.04

8.

08

80.5

4 67

.54

71.8

8 62

.46

92.4

8 44

.52

54.0

9 10

0.28

6.

5 13

2.33

10

8.31

11

7.20

11

2.58

98

.47

87.3

5 80

.75

75.7

3 13

5.66

10

5.22

11

9.73

95

.16

7.5

115.

89

103.

02

118.

59

117.

84

92.6

4 74

.44

71.8

7 65

.37

117.

87

108.

30

123.

04

92.9

2 8.

5 99

.44

97.7

2 11

9.98

12

3.10

86

.81

61.5

4 62

.99

55.0

2 10

0.09

11

1.38

12

6.35

90

.68

9.5

92.0

2 96

.65

123.

73

139.

14

86.8

4 71

.43

58.9

0 67

.22

94.3

5 13

3.34

13

6.96

90

.90

10.5

97

.17

100.

08

134.

10

150.

76

92.6

4 62

.00

60.3

7 66

.75

93.9

5 14

7.94

14

8.64

95

.73

11.5

10

2.33

10

3.52

14

4.47

16

2.39

98

.43

52.5

7 61

.85

66.2

9 93

.56

162.

53

160.

33

100.

56

12.5

72

.62

94.2

6 14

4.39

15

4.68

97

.08

43.7

4 53

.22

64.4

5 80

.65

155.

88

151.

97

109.

96

13.5

76

.86

103.

31

151.

51

167.

86

104.

14

47.0

0 50

.29

67.3

9 85

.36

162.

30

159.

89

125.

94

14.5

81

.09

112.

36

158.

63

181.

03

111.

20

50.2

6 47

.36

70.3

3 90

.07

168.

72

167.

82

141.

92

15.5

90

.25

121.

78

149.

01

174.

92

108.

77

50.9

8 54

.35

69.7

8 86

.48

160.

26

157.

88

134.

91

16.5

98

.16

118.

45

154.

01

177.

18

107.

52

56.5

5 58

.96

74.4

7 93

.83

165.

79

160.

86

134.

53

17.5

10

6.07

11

5.12

15

9.02

17

9.44

10

6.27

62

.11

63.5

7 79

.17

101.

18

171.

32

163.

84

134.

15

18.5

17

4.02

20

0.93

22

9.97

23

5.81

16

1.35

11

5.75

11

9.59

12

1.68

16

5.11

23

6.32

22

7.76

20

5.47

19

.5

174.

47

191.

69

214.

72

202.

54

161.

03

106.

58

109.

61

116.

68

155.

85

216.

46

212.

08

194.

94

20.5

17

4.93

18

2.45

19

9.47

16

9.28

16

0.72

97

.41

99.6

3 11

1.68

14

6.59

19

6.60

19

6.40

18

4.42

21

.5

173.

56

167.

54

188.

80

114.

53

158.

12

99.2

1 10

2.10

11

2.71

12

7.53

16

9.90

17

4.79

16

7.19

22

.5

159.

36

155.

84

173.

37

100.

37

147.

17

96.6

8 96

.40

107.

97

118.

77

150.

07

155.

34

159.

50

23.5

14

5.17

14

4.13

15

7.93

86

.22

136.

21

94.1

5 90

.70

103.

23

110.

01

130.

25

135.

88

151.

81


Appendix D: Kyocera Photovoltaic modules technical specification


Appendix E: HOMER Input Summary

HOMER Input Summary for Degehabur town

File name: Degehabur town final runFile version: 2.68 beta Author: Bizuayehu Tesfaye

AC Load: Primary Load 1

Data source: Synthetic Daily noise: 0% Hourly noise: 0% Scaled annual average: 5,001 kWh/dScaled peak load: 343 kW Load factor: 0.608

AC Deferrable Load

Month Average Load

(kWh/d)Jan 500 Feb 500 Mar 500 Apr 500 May 500 Jun 500 Jul 500 Aug 500 Sep 500 Oct 500 Nov 500 Dec 500


Scaled annual average: 300 kWh/dStorage capacity: 500 kWh Peak load: 150 kW Min. load ratio: 0.9%

PV

Size (kW) Capital ($) Replacement ($) O&M ($/yr)

1.000 6,000 5,000 52.000 12,000 10,000 103.000 18,000 15,000 15

Sizes to consider: 0, 600, 650, 700, 750, 800, 900, 1,200 kWLifetime: 25 yr Derating factor: 80% Tracking system: Two Axis Slope: 8.22 deg Azimuth: 0 deg Ground reflectance: 20%

Solar Resource

Latitude: 8 degrees 13 minutes NorthLongitude: 43 degrees 34 minutes EastTime zone: GMT +3:00

Data source: Synthetic

Month Clearness Index Average Radiation

(kWh/m2/day) Jan 0.719 6.557Feb 0.693 6.748Mar 0.682 7.024Apr 0.600 6.290May 0.597 6.154Jun 0.571 5.778Jul 0.538 5.467Aug 0.602 6.223Sep 0.655 6.748Oct 0.667 6.574Nov 0.694 6.399Dec 0.694 6.159


Scaled annual average: 6.34 kWh/m²/d

AC Wind Turbine: Elotec

Quantity Capital ($) Replacement ($) O&M ($/yr)

1 97,500 83,850 1,9502 195,000 167,700 3,900

Quantities to consider: 0, 2, 3, 4, 5, 7, 8, 9, 10, 15, 20, 25, 30, 32, 35, 38, 40 Lifetime: 25 yr Hub height: 40 m

Wind Resource


Month Wind Speed

(m/s) Jan 5.75 Feb 5.39 Mar 4.51 Apr 3.43 May 4.59 Jun 7.27 Jul 7.64 Aug 7.11 Sep 5.64 Oct 3.88


Nov 4.71 Dec 5.49

Weibull k: 2.00 Autocorrelation factor: 0.850 Diurnal pattern strength: 0.250 Hour of peak wind speed: 15 Scaled annual average: 5.45 m/s Anemometer height: 50 m Altitude: 1,095 m Wind shear profile: LogarithmicSurface roughness length: 0.01 m

AC Generator: Diesel Generator

Size (kW) Capital ($) Replacement ($) O&M ($/hr)

30.000 0 15,000 1.500100.000 0 50,000 5.000400.000 0 200,000 20.000

Sizes to consider: 0, 400 kW Lifetime: 15,000 hrs Min. load ratio: 40% Heat recovery ratio: 0% Fuel used: Diesel Fuel curve intercept: 0.08 L/hr/kWFuel curve slope: 0.25 L/hr/kW


Fuel: Diesel

Price: $ 1/L Lower heating value: 43.2 MJ/kg Density: 820 kg/m3 Carbon content: 88.0% Sulfur content: 0.330%

Battery: Surrette 4KS25P


1 1,700 1,500 10.002 3,500 3,100 20.00

700 1,260,000 1,120,000 7,000.00Quantities to consider: 0, 2,300, 2,500, 3,000, 3,500Voltage: 4 V Nominal capacity: 1,900 Ah Lifetime throughput: 10,569 kWh

Converter


10.000 9,000 9,000 90100.000 90,000 90,000 900300.000 270,000 270,000 2,700400.000 360,000 360,000 3,600

Sizes to consider: 0, 10, 100, 300, 400, 500, 600, 700 kW Lifetime: 15 yr Inverter efficiency: 90% Inverter can parallel with AC generator: Yes Rectifier relative capacity: 100% Rectifier efficiency: 85%

Grid Extension

Capital cost: $ 125,000/km O&M cost: $ 2,500/yr/km Power price: $ 0/kWh

Economics


Annual real interest rate: 4% Project lifetime: 25 yr Capacity shortage penalty: $ 0/kWhSystem fixed capital cost: $ 0 System fixed O&M cost: $ 0/yr

Generator control

Check load following: Yes Check cycle charging: Yes Setpoint state of charge: 80% Allow systems with multiple generators: YesAllow multiple generators to operate simultaneously: YesAllow systems with generator capacity less than peak load: Yes

Emissions

Carbon dioxide penalty: $ 0/tCarbon monoxide penalty: $ 0/tUnburned hydrocarbons penalty: $ 0/tParticulate matter penalty: $ 0/tSulfur dioxide penalty: $ 0/tNitrogen oxides penalty: $ 0/t

Constraints

Maximum annual capacity shortage: 5%Minimum renewable fraction: 0%Operating reserve as percentage of hourly load: 0%Operating reserve as percentage of peak load: 0%Operating reserve as percentage of solar power output: 5%Operating reserve as percentage of wind power output: 5%

HOMER Input Summary for Kebridehar town

File name: Keberdehar final run.hmrFile version: 2.68 beta Author:

AC Load: Primary Load 1


Data source: Synthetic Daily noise: 0% Hourly noise: 0% Scaled annual average: 4,565 kWh/dScaled peak load: 290 kW Load factor: 0.656

AC Deferrable Load

Month Average Load

(kWh/d)Jan 120 Feb 0 Mar 0 Apr 0 May 0 Jun 400 Jul 400 Aug 400 Sep 200 Oct 0 Nov 0 Dec 0 Scaled annual average: 100 kWh/dStorage capacity: 300 kWh Peak load: 100 kW Min. load ratio: 0.9%

PV


1.000 6,000 5,000 52.000 12,000 10,000 10


3.000 18,000 15,000 15Sizes to consider: 0, 530, 550, 600, 650, 750, 800, 900, 1,000 kWLifetime: 25 yr Derating factor: 80% Tracking system: Two Axis Slope: 6.75 deg Azimuth: 0 deg Ground reflectance: 20%

Solar Resource

Latitude: 6 degrees 45 minutes NorthLongitude: 44 degrees 17 minutes EastTime zone: GMT +3:00


Month Clearness Index Average Radiation

(kWh/m2/day) Jan 0.693 6.440Feb 0.712 7.027Mar 0.671 6.951Apr 0.585 6.108May 0.559 5.699Jun 0.571 5.697Jul 0.538 5.403Aug 0.577 5.938Sep 0.636 6.571Oct 0.608 6.049Nov 0.660 6.197Dec 0.687 6.232Scaled annual average: 6.19 kWh/m²/d


AC Wind Turbine: Elotec


1 97,500 83,850 1,9502 195,000 167,700 3,900

Quantities to consider: 0, 3, 5, 6, 10, 15, 20, 25, 30, 32, 33Lifetime: 25 yr Hub height: 40 m

Wind Resource


Month Wind Speed

(m/s) Jan 5.98 Feb 5.72 Mar 4.65 Apr 3.68 May 5.88 Jun 8.19 Jul 8.40 Aug 7.96 Sep 6.51 Oct 4.26 Nov 4.12 Dec 5.49


Weibull k: 2.00 Autocorrelation factor: 0.850 Diurnal pattern strength: 0.250 Hour of peak wind speed: 15 Scaled annual average: 5.91 m/s Anemometer height: 50 m Altitude: 493 m Wind shear profile: LogarithmicSurface roughness length: 0.01 m

AC Generator: Generator 1

Size (kW) Capital ($) Replacement ($) O&M ($/hr)

30.000 0 15,000 1.500100.000 0 50,000 5.000375.000 0 187,500 18.750

Sizes to consider: 0, 375 kW Lifetime: 15,000 hrs Min. load ratio: 40% Heat recovery ratio: 0% Fuel used: Diesel Fuel curve intercept: 0.08 L/hr/kWFuel curve slope: 0.25 L/hr/kW

Fuel: Diesel


Price: $ 1/L Lower heating value: 43.2 MJ/kg Density: 820 kg/m3 Carbon content: 88.0% Sulfur content: 0.330%

Battery: Surrette 4KS25P


1 1,700 1,500 10.002 3,400 3,000 20.00

Quantities to consider: 0, 2,050, 2,500, 3,000, 3,500, 4,000Voltage: 4 V Nominal capacity: 1,900 Ah Lifetime throughput: 10,569 kWh Min battery life: 10 yr

Converter


4.000 3,600 3,600 40Sizes to consider: 0, 4, 300, 400 kWLifetime: 15 yr Inverter efficiency: 95% Inverter can parallel with AC generator: Yes Rectifier relative capacity: 100% Rectifier efficiency: 90%

Grid Extension

Capital cost: $ 125,000/km O&M cost: $ 2,500/yr/km Power price: $ 0/kWh

Economics

Annual real interest rate: 4% Project lifetime: 25 yr Capacity shortage penalty: $ 0/kWhSystem fixed capital cost: $ 0 System fixed O&M cost: $ 0/yr


Generator control

Check load following: Yes Check cycle charging: Yes Setpoint state of charge: 80% Allow systems with multiple generators: YesAllow multiple generators to operate simultaneously: YesAllow systems with generator capacity less than peak load: Yes

Emissions

Carbon dioxide penalty: $ 0/tCarbon monoxide penalty: $ 0/tUnburned hydrocarbons penalty: $ 0/tParticulate matter penalty: $ 0/tSulfur dioxide penalty: $ 0/tNitrogen oxides penalty: $ 0/t

Constraints

Maximum annual capacity shortage: 5%Minimum renewable fraction: 0%Operating reserve as percentage of hourly load: 0%Operating reserve as percentage of peak load: 0%Operating reserve as percentage of solar power output: 5%Operating reserve as percentage of wind power output: 5%

Bizuayehu Tesfaye

REYST report 05-2011

Bizuayehu Tesfaye Im

proved Sustainable Power Supply

RE

YS

T rep

ort 05-2011

Improved Sustainable Power Supplyfor Dagahabur and Kebridahar Town

of Somalia Region in Ethiopia

REYKJAVIK ENERGY GRADUATE SCHOOL OF SUSTAINABLE SYSTEMS

Reykjavík Energy Graduate School of Sustainable Systems (REYST) combines the expertise of its partners: Reykjavík Energy, Reykjavík University and the University of Iceland.

Objectives of REYST:Promote education and research in sustainable energy

earth sciences

REYST is an international graduate programme open for students holding BSc degrees in engineering, earth sciences or business.

REYST offers graduate level education with emphasis on practicality, innovation and interdisciplinary thinking.

REYST reports contain the master’s theses of REYST graduates who earn their degrees from the University of Iceland and Reykjavík University.

improved sustainable power supply for dagahabur and...

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