Techno-Economic analysis of a Solar PV Energy System in …1294536/... · 2019-03-07 · tariffs,...

Master of Science Thesis

KTH School of Industrial Engineering and Management

Energy Technology TRITA-ITM-EX 2018:633

Division of Heat and Power Technology

SE-100 44 STOCKHOLM

Techno-Economic analysis of a Solar

PV Energy System in Zimbabwe

Country Office

Montserrat Pitarch Ruiz

Disclaimer: This Master Thesis is confidential and must only to be read

by my thesis supervisor, the examiner and the commissioner. Any

publication or submission besides the Thesis Preparation Course official

hand-in submission link is unauthorized.

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Master of Science Thesis TRITA-ITM-EX

2018:633

Techno-Economic analysis of a Solar PV

Energy System in Zimbabwe Country Office

Montserrat Pitarch Ruiz

Approved

18/07/2018

Examiner

Reza Fakhraie

Supervisor

Reza Fakhraie

Commissioner Contact person

Abstrakt

Detta arbete utreder möjligheten att använde förnybar energi som alternative energikälla i UN byggnader enligt ”smarta UN-faciliteter” direktiven. I detta arbetet står UNDP Zimbabwe i Harare som testplattform. Detta projekt har utvecklats inom ramen för FN:s utvecklingsprogram (UNDP) ”Office of Information Management and Technology (OIMT)” med målet att kunna erbjuda den mest tillförlitliga och genomförbara solabaserad system som förnybar energi system.

Data för energiförbrukningssensorer i UNDP Zimbabwe samlades sedan mitten av 2017. En årlig profil för energiförbrukning presenterades som tillsammans med information om lokala nätavgifter, tillgängligt utrymme för solcellspaneler, användes för en vidare analys. Analysen inkluderar tre studiefall:

− Undersökningen av två solcellssystem med 44 kWp och 28 kWp kapacitet res.

− Gemföresle av multikristall kisel PV (BSF-teknik, 30.15 kWp solpanel PV-system) mot

monokristall kisel PV (PERC tekniken).

Dessa alternativ uppnår uppskattningsvis 37 %, 25 % respektive 27 % av den totala förväntade elförbrukningen av byggnaden, med tillhörande besparingar och fördelar.

Abstract

In pursuit of utilizing green energy in line with Smart UN Facilities and the Sustainable Development

Goals (SDGs), this Master Thesis presents the results of an analysis on potential solar photovoltaic (PV)

panel solutions for UNDP Zimbabwe Country Office in Harare. This project has been developed under

the United Nations Development Programme’s (UNDP) Office of Information Management and

Technology (OIMT) methodology in order to offer the most reliable and feasible renewable energy

system.

Using data gathered by power consumption sensors in the UNDP Zimbabwe Country Office (CO) since

mid-2017, a yearly load profile was created. This data has been coupled with information on local grid

tariffs, available space for solar PV panels at the premises, and UNDP Zimbabwe CO project objectives

in order to model three options: two Solar PV systems with 44 kWp and 28 kWp of rated capacity,

respectively, using multicrystalline silicon PV panels with BSF technology, and a 30.15 kWp Solar PV

system with monocrystalline silicon PV panels developed with the innovative PERC technology. These

options achieve an estimated 37%, 25% and 27% coverage of the total expected electricity consumption

of the building, respectively, with associated savings and benefits.

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Table of Contents

Abstrakt ........................................................................................................................................................................... 2

Abstract ........................................................................................................................................................................... 2

List of Tables .................................................................................................................................................................. 6

List of Figures ................................................................................................................................................................ 7

List of Nomenclatures, Abbreviations and Terminologies..................................................................................... 9

1 Introduction ........................................................................................................................................................11

1.1 Problem Statement ...................................................................................................................................11

1.2 Objectives ...................................................................................................................................................11

1.3 Scope ...........................................................................................................................................................11

2 Background – UNDP Green Energy Solutions ............................................................................................13

2.1 Smart UN Facilities concept ...................................................................................................................13

2.2 The Seven-Step Solution Process ...........................................................................................................15

3 Literature Review ...............................................................................................................................................16

3.1 Country Overview .....................................................................................................................................16

3.2 Climate ........................................................................................................................................................17

3.2.1 Solar irradiance .................................................................................................................................17

3.2.2 Temperature ......................................................................................................................................18

3.3 Wind Speed ................................................................................................................................................19

3.4 Energy resources and access to electricity.............................................................................................19

4 Hybrid Energy Systems .....................................................................................................................................21

4.1 Solar photovoltaic panels .........................................................................................................................22

4.1.1 Manufacturing process ....................................................................................................................24

4.1.2 Solar cell performance .....................................................................................................................25

4.2 Energy storage – Batteries .......................................................................................................................26

4.2.1 Battery performance ........................................................................................................................26

4.2.2 Lead Acid vs Lithium-Ion Batteries ..............................................................................................27

5 Methodology .......................................................................................................................................................30

5.1 Data Collection – Power Consumption Measuring and Monitoring................................................30

5.2 PCMM Data Management .......................................................................................................................31

5.3 Tools selection ...........................................................................................................................................32

5.3.1 PV*SOL ............................................................................................................................................32

5.3.2 Green Team Costs Database .........................................................................................................33

5.3.3 HOMER System Simulation Software .........................................................................................33

5.4 Levelized Cost of Energy .........................................................................................................................34

6 Baseline scenario.................................................................................................................................................35

6.1 Energy supply ............................................................................................................................................35

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6.2 Energy consumption ................................................................................................................................35

6.3 Drone pictures of the compound ...........................................................................................................37

7 Solar PV panels configuration ..........................................................................................................................38

7.1 Option 1 – 44 kWp ...................................................................................................................................38

7.2 Option 2 – 28 kWp ...................................................................................................................................39

7.3 Option 3 – 30.15 kWp..............................................................................................................................39

8 Model overview ..................................................................................................................................................40

8.1 Cost estimation ..........................................................................................................................................40

8.1.1 System with Li-Ion batteries and multicrystalline PV panels ....................................................40

8.1.2 System without Li-Ion batteries and multicrystalline PV panels ..............................................41

8.1.3 System without Li-Ion batteries and monocrystalline PV panels ............................................42

8.2 HOMER inputs .........................................................................................................................................43

9 Optimization results...........................................................................................................................................45

9.1 Technical results ........................................................................................................................................45

9.1.1 Option 1 – 44 kW ............................................................................................................................45

9.1.2 Option 2 – 28 kW ............................................................................................................................47

9.1.3 Option 3 – 30.15 kW .......................................................................................................................48

9.2 Economic results .......................................................................................................................................50

9.2.1 Option 1 – 44 kW ............................................................................................................................50

9.2.2 Option 2 – 28 kW ............................................................................................................................51

9.2.3 Option 3 – 30.15 kW .......................................................................................................................52

10 Planning ...............................................................................................................................................................53

10.1 Site Survey ..................................................................................................................................................53

10.2 Procurement...............................................................................................................................................53

10.3 Installation and commissioning ..............................................................................................................53

10.4 Training .......................................................................................................................................................54

11 Sensitivity analysis ..............................................................................................................................................55

12 Scenarios comparison ........................................................................................................................................56

13 Sustainability assessment ...................................................................................................................................58

13.1 Economic ...................................................................................................................................................58

13.2 Social ...........................................................................................................................................................59

13.3 Environmental ...........................................................................................................................................59

14 Conclusions .........................................................................................................................................................61

Acknowledgements .....................................................................................................................................................62

Bibliography .................................................................................................................................................................63

Annex A Roughness Class .........................................................................................................................................67

Annex B Aeolos-H 3kW Micro-wind turbine specifications ...............................................................................68

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Annex C Micro-wind turbine main parameters .....................................................................................................69

Annex D PCMM Data Credentials for Zimbabwe CO ........................................................................................70

Annex E Yearly load profile Matlab code ...............................................................................................................71

Annex F PCMM Data Collection .............................................................................................................................81

Annex G JA Solar PV panel datasheet.....................................................................................................................82

Annex H JINKO Solar PV panel datasheet ............................................................................................................83

Annex I Energy Efficiency measures .......................................................................................................................85

Annex J Compound picture .......................................................................................................................................87

Annex K UNDP Zimbabwe CO Blueprint ............................................................................................................88

Annex L 44 kWp PV*SOL results ............................................................................................................................88

Annex M 28 kWp PV*SOL results ....................................................................................................................... 106

Annex N 30.15 kWp PV*SOL results .................................................................................................................. 109

Annex O Costs estimation with Li-Ion battery ................................................................................................... 112

Annex P Costs estimation without Li-Ion battery .............................................................................................. 113

Annex Q Costs estimation of PV system with monocrystalline PV panel using PERC technology .......... 114

Annex R 44 kWp solar PV system results HOMER .......................................................................................... 115

Annex S 28 kWp solar PV system results HOMER ........................................................................................... 116

Annex T 30.15 kWp solar PV system results HOMER ..................................................................................... 117

Annex U Preventive Maintenance of a Solar PV System .................................................................................. 118

Annex V Sensitivity analysis ................................................................................................................................... 121

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List of Tables

Table 1. Monthly average Solar Global Horizontal Irradiance (GHI) data from Harare, Zimbabwe. (NASA

Surface meteorology and Solar Energy - Available Tables, 2017) .......................................................................17

Table 2. Monthly average Temperature data from Harare, Zimbabwe. (NASA Surface meteorology and

Solar Energy - Available Tables, 2017) ....................................................................................................................18

Table 3. Installed generation capacity in Zimbabwe (RECP, 2017) ....................................................................20

Table 4. Comparison table with main properties of Lead acid and Li-ion Batteries (Joe O’Connor, 2017) 29

Table 5. UNDP Zimbabwe CO Load consumption characteristics (Own source) .........................................36

Table 6. Project costs summary in UNDP Zimbabwe CO (Own Source) ........................................................44

Table 7. System proposals for UNDP Zimbabwe CO, Harare. (HOMER Energy, 2018) .............................45

Table 8. Electrical production (HOMER Energy, 2018) ......................................................................................45

Table 9. Electrical consumption (HOMER Energy, 2018) ..................................................................................45

Table 10. Generic flat plate PV production (HOMER Energy, 2018) ...............................................................46

Table 11. Generic flat plate PV performance (HOMER Energy, 2018) ............................................................46

Table 12. Electrical production (HOMER Energy, 2018) ....................................................................................47

Table 13. Electrical consumption (HOMER Energy, 2018) ................................................................................47



Table 16. Electrical production (HOMER Energy, 2018) ....................................................................................48

Table 17. Electrical consumption (HOMER Energy, 2018) ................................................................................48



Table 20. Cost summary of HOMER simulation (HOMER Energy, 2018) .....................................................50



Table 23. Tasks duration of UNDP Zimbabwe CO in Harare (Own source) ..................................................54

Table 24. Carbon footprint result per electricity generated (Own source) ........................................................60

Table 25. CO2 equivalent payback time for each system proposal in UNDP Zimbabwe Country Office

(Own source) ................................................................................................................................................................60

Table 26. Summary of Solar PV proposal for UNDP Zimbabwe CO. (Own source) ....................................61

Table 27. Roughness definition according to landscape type (Danish Wind Industry Association, 2003) ..67

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List of Figures

Figure 1. Benefits of a solar powered Country Office (UNDP, 2018) ...............................................................13

Figure 2. Technologies involved in the Smart UN Facilities (OIMT/CIAS, 2017) .........................................14

Figure 3. 7 Step process of Green Energy Solution (UNDP, 2018) ...................................................................15

Figure 4. Zimbabwe Map - Climate Classification. (Peel, Finlayson and McMahon, 2007) ...........................16

Figure 5. Global Horizontal Irradiation in Zimbabwe (World Bank Group, 2018) .........................................17

Figure 6. Average air temperature in Zimbabwe (World Bank Group, 2018) ..................................................18

Figure 7. Average min and max temperatures in Harare, Zimbabwe (World Weather and Climate

Information, 2016) ......................................................................................................................................................18

Figure 8. Average Monthly Wind Speed at 50m height in Harare, Zimbabwe. (NASA Surface meteorology

and Solar Energy - Available Tables, 2017) .............................................................................................................19

Figure 9. Energy system scheme proposal for Zimbabwe Country Office in Harare. (HOMER Energy,

2018) ..............................................................................................................................................................................21

Figure 10. The left figure represents a standard solar cell structure and the right one is the PERC solar cell

structure (Vogt et al., 2017) .......................................................................................................................................23

Figure 11. Worldwide market shares for different solar cell technologies (ITRPV, 2018) .............................23

Figure 12. Simplified process flow for BSF (upper) and PERC (lower) (Green, 2015) ..................................24

Figure 13. Evolution of the solar panel price and global solar panel installations (Zachary Shahan, 2018).25

Figure 14. Hot Climate, Cycle Life comparison between Lead Acid (AGM) and Li-Ion (Lithium) (Mobbs,

2016) ..............................................................................................................................................................................27

Figure 15. Battery Density Comparison (Joe O’Connor, 2017)...........................................................................28

Figure 16. Total Lifecycle Cost of Batteries (Joe O’Connor, 2017) ....................................................................28

Figure 17. A standard Power Consumption and Monitoring (PCMM) device from the UNDP LTA vendor

Eyedro (Eyedro, 2017) ...............................................................................................................................................30

Figure 18. UNDP Eyedro Portal illustrating the total daily power consumption in Zimbabwe CO.

(UNDP, 2017) ..............................................................................................................................................................31

Figure 19. Average weekdays hourly data extracted from the PCMM load consumption in UDNP

Zimbabwe CO, Harare. (HOMER Energy, 2018) .................................................................................................32

Figure 20. Hourly energy consumption in UNDP Zimbabwe CO (May 21st, 2018). (UNDP, 2017) ...........35

Figure 21. Seasonal profile of UNDP Zimbabwe CO, Harare. (HOMER Energy, 2018) ..............................36

Figure 22. UNDP Zimbabwe Country Office Building (UNDP, 2017) ............................................................37

Figure 23. 44 kWp PV multicrystalline panels layout (Own source)...................................................................38

Figure 24. 28kWp multicrystalline PV panels layout (Own source) ....................................................................39

Figure 25. 30.15kWp monocrystalline PV panels layout (Own source) .............................................................39

Figure 26. Costs breakdown of Hybrid System in UNDP Zimbabwe, including 44 kWp PV and 42 kWh

Li-Ion batteries (UNDP, 2017) .................................................................................................................................40

Figure 27. Total project costs of installations with lower PV capacity than 90kWp (UNDP, 2017) ............41

Figure 28. Costs breakdown of a 44 kWp PV solar system in UNDP Zimbabwe, Harare.(UNDP, 2017) .41

Figure 29. Total project costs for PV only installations (UNDP, 2017) ............................................................42

Figure 30. Costs breakdown of a 30.15 kWp PV solar system using monocrystalline PV panels in UNDP

Zimbabwe, Harare.(UNDP, 2017) ...........................................................................................................................42

Figure 31. Total project costs for PV-Only installations (UNDP, 2017) ...........................................................43

Figure 32. Monthly average electric production (HOMER Energy, 2018) ........................................................46

Figure 33. Consumption and production of the required electricity in week 34 (HOMER Energy, 2018) .46





Figure 38. Cumulative cash flow comparison between PV system proposal and current system (HOMER

Energy, 2018) ...............................................................................................................................................................50

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Energy, 2018) ...............................................................................................................................................................51


Energy, 2018) ...............................................................................................................................................................52

Figure 41. Planning of UNDP Zimbabwe CO (Own source) .............................................................................53

Figure 42. Delivery and installation schedule of UNDP Zimbabwe CO (Own source) .................................54

Figure 43. Sensitivity analysis of grid electricity price in UNDP Zimbabwe CO, Harare (Own source) .....55

Figure 44. Technical results comparison for UNDP Zimbabwe CO (Own source) .......................................56

Figure 45. Economic and environmental results comparison for UNDP Zimbabwe CO (Own source) ....56

Figure 46. Sustainability triangle. (Kurry, 2011) .....................................................................................................58

Figure 47. Weekly energy consumption in UNDP Zimbabwe from July 1st 2017 to March 26th 2018

(Eyedro, 2017) ..............................................................................................................................................................81

Figure 48. Monthly average consumption of UNDP Zimbabwe CO in Harare (Eyedro, 2017) ...................81

Figure 49. Building 9 and 10 of UN Zimbabwe Country Office in Harare ((UNDP, 2017) ..........................87

Figure 50. UNDP Zimbabwe Office Blueprints. (UNDP, 2017) .......................................................................88

Figure 51. Projects Costs of UNDP Zimbabwe CO, including Li-Ion batteries and multicrystalline PV

panels in the system (UNDP, 2017) ...................................................................................................................... 112

Figure 52. Project Cost Estimation from Database (UNDP, 2017) ................................................................ 112

Figure 53. Projects Costs of UNDP Zimbabwe CO, without Li-Ion batteries and multicrystalline PV

panels in the system (UNDP, 2017) ...................................................................................................................... 113

Figure 54. Project costs estimation from Database (UNDP, 2017) ................................................................. 113

Figure 55. Projects Costs of UNDP Zimbabwe CO, without Li-Ion batteries and monocrystalline PV

panels with PERC technology in the system (UNDP, 2017) ............................................................................ 114

Figure 56. PV power output (HOMER Energy, 2018) ...................................................................................... 115

Figure 57. Consumption and production in week 26 at UNDP Zimbabwe CO (HOMER Energy, 2018)

..................................................................................................................................................................................... 115



..................................................................................................................................................................................... 116



..................................................................................................................................................................................... 117

Figure 62. 44 kWp Solar PV system cashflow comparison with a grid price of 0.22 $/kWh (HOMER

Energy, 2018) ............................................................................................................................................................ 121

Figure 63. 44 kWp Solar PV system cashflow comparison with a grid price of 0.30 $/kWh (HOMER

Energy, 2018) ............................................................................................................................................................ 121

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List of Nomenclatures, Abbreviations and Terminologies

𝑨𝑷𝑽: Area of the module (m2)

𝑨𝒕: Annual total costs of the system operation ($)

AC: Alternating Current

AEP: Annual energy production (kWh)

AGM: Absorbed Glass Mat

AR: Antireflective

BOM: Bill of Material

BSF: Back Surface Field

𝜷: Temperature coefficient (1/ºC)

CED: Cumulative Energy Demand (kWh)

CIAS: Country Office ICT Advisory Support

CO: Country Office

CO2: Carbon Dioxide

DC: Direct Current

𝑫𝑶𝑫: Depth of discharge (%)

𝑬𝒃𝒂𝒕: Capacity required of the battery (Wh)

𝑬𝒅𝒂𝒊𝒍𝒚: desired energy to be stored (Wh)

EPBT: Equivalent payback time

FAO: Food and Agriculture Organization

GHI: Global Horizontal Irradiance

GSI: Global Solar Irradiance

HES: Hybrid Energy System

HOMER: Hybrid Optimization of Multiple Energy Resources

𝒊: Real discount rate (%)

𝑰𝟎: Investment expenditure ($)

𝑰𝜶: Solar radiation on a tilted plane (W/m2)

ICT: Information Communicating Technology

IoT: Internet of Things

ITRPV: International Technology Roadmap for Photovoltaic

LA: Lead Acid

LCA: Life Cycle Assessment

LCOE: Levelized Cost of Energy

Li-Ion: Lithium-Ion

LTA: Long Term Agreement

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𝑴𝒕,𝒆𝒍: Electricity produced in the respective year (kWh)

MEPD: Ministry of Energy and Power Development

𝒏: Economic operational lifetime (years)

NASA: National Aeronautics and Space Administration

𝜼𝑷𝑪: Power conditioning efficiency (%)

𝜼𝑷𝑽: Solar PV efficiency (%)

𝜼𝒓: Reference module efficiency (%)

O&M: Operation and Maintenance

OIMT: Office of Information Management and Technology

𝑷𝑷𝑽: PV power output (W)

PBT: Payback time

PCMM: Power Consumption Measuring and Monitoring

PERC: Passivated Emitter and Rear Cell

PV: Photovoltaic

𝒓: Yearly degradation rate of the system (%)

RF: Renewable Fraction

RfQ: Request for Quotation

SDG: Sustainable Development Goals

𝒕: Year of lifetime (1, 2, …n)

𝑻𝒄,𝒓: Reference temperature (ºC)

𝑻𝒄: Cell temperature (ºC)

UAT: User Acceptance Testing

UN: United Nations

UNDP: United Nations Development Programme

UNEP: United Nations Environment Programme

UNICEF: United Nations Children's Fund

VRLA: Valve-regulated lead-acid

ZESA: Zimbabwe Electricity Supply Authority

ZPC: Zimbabwe Power Company

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1 Introduction

1.1 Problem Statement

United Nations Development Programme (UNDP) Zimbabwe Country Office (CO) has an average daily

energy consumption of 390.80 kWh. Currently, these premises rely on the grid, which 95% of its

electricity production comes from fossil fuel sources (Climatescope, 2017).

The grid energy system in Zimbabwe offers medium reliability, which means they can suffer an outage

once every two weeks. For this reason, they have one backup generator in the premises to ensure energy

security when there is a blackout.

Even the grid is quite reliable, the backup generator ensures energy security to the CO; however, diesel

generators are expensive due to fluctuating fuel prices, operation and maintenance, and they produce

environmental and noise pollution. Moreover, the generator operator needs to regularly perform a

maintenance check to ensure it works when they are required.

For the aforementioned reasons, UNDP in Copenhagen will offer a Solar PV system to reduce their

energy dependency on fossil fuel sources and reduce the greenhouse gases emissions. This way,

Zimbabwe CO will improve their current energy situation as they will include a renewable energy system

for this compound.

1.2 Objectives

UNDP Zimbabwe CO has, in cooperation with Green Energy Solutions team, taken initial steps towards

implementing a Solar PV system in the UNDP compound in Harare. The purpose of this project is to

outline how the participating UN Agency can benefit from implementing a green energy solution

compared to a traditional setup.

Switching to renewable energy also implies strong environmental incentives. Adding solar will save CO2

emissions annually, effectively reducing Zimbabwe CO carbon footprint and burden on the environment.

This supports the United Nations Sustainable Development Goals while also promoting green energy

solutions and inspire other UN Agencies and local economies to adopt similar solutions.

A solar installation in Zimbabwe CO will lead to a reduction in non-renewable energy costs. Furthermore,

it will offer a more resilient system to crisis ensuring a level of business continuity and work environment.

1.3 Scope

The aim of this project is to offer a renewable energy solution for Zimbabwe CO. As this project is based

on the creation of Green Energy Solutions, it will include the design of a Solar PV energy system in

UNDP Zimbabwe. The business case will include the following energy analysis obtaining a cost-effective

solution in each case:

− Solar PV Energy Solution: a grid-connected system including only Solar PV panels. The

maximum solar PV capacity that could be fitted into the premises will be offered, squeezing all

shadow-free areas.

− Hybrid Energy Solution: the feasibility of a grid-connected system including Solar PV panels

and a set of Li-Ion Batteries will also be considered.

Nevertheless, the scope of the project is defined due to the following constraints:

− A limited budget is allocated for this purpose as the Country Office wants to reduce carbon footprint and be a showcase in Zimbabwe. For this reason, a system with only PV panels is

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deemed more suitable as Lithium-Ion batteries are nowadays very expensive. However, a Hybrid Energy System with PV panels and Li-Ion batteries will also be analyzed if it is feasible for this Country Office. The system proposal aims to be a showcase for the country and highlight the importance of renewable energy as their current electricity price is very cheap and the reliability in the capital of Zimbabwe is moderate-high.

− Limited space for mounting solar panels in the premises dictates the size of the maximum solar system that could be installed.

− Missing information from Zimbabwe CO such as rooftop tilted angle and technical details from the backup generator, therefore some assumptions were considered when developing the business case. Nevertheless, a drone was sent to take plan view pictures of the compound and the pictures received were useful to analyze the solar assessment.

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2 Background – UNDP Green Energy Solutions

United Nations Development Programme (UNDP) branch offices are located in 170 countries across the

world, including regions having poor electricity infrastructure and high diesel prices. The continuous

power supply is a requirement for daily operations and crucial for communication.

In the wake of the Ebola crisis, the Office of Information Management and Technology (OIMT), located

in Copenhagen, leveraged its experience in implementing technical solutions in Country Offices to roll out

solar solutions to the affected countries (UNDP, 2018). Offices in Guinea, Sierra Leone and Liberia

could not rely on the grid to meet their energy requirements and the diesel shortage restricted the access

to power supply to the affected Country Offices (COs).

The unreliable energy scenario was coupled with increasing energy requirements to support the influx of

additional crisis response staff, creating the necessity for alternative energy resources. While these projects

were hindered by the uncertainty nature of the crisis, the solar power solution program for Country

Offices was initiated to support their operations.

From this learning experience, the Green Energy Team in UNDP/OIMT developed and refined its Seven

Step Solar Solution aimed at guiding COs from the initial self-assessment stage to the operation and

maintenance of a tailored solar system (OIMT, 2018).

UNDP’s work is aligned with the Sustainable Development Goal 7 to ensure access to affordable,

modern, reliable and sustainable energy including three major action areas: energy access, renewable

energy and energy efficiency (UNDP, 2016). The progress of creating sustainable COs includes three

interrelated challenges:

− Social: there are divergences to access reliable energy services as UNDP is working with conflict

and fragile areas.

− Economic: promote job creation and economic growth with the solar energy solutions.

− Environmental: reduce green-house gases emissions by reducing the energy usage from the

corresponding grid or diesel generator with the implementation of renewable energy systems.

2.1 Smart UN Facilities concept

The UNDP champions the new path of mainstreaming sustainable solutions all over the world. The idea

behind is not only to increase energy security and reliability in Country Offices but also to showcase the

feasibility of Hybrid Energy Systems (HES) and embody the new Green Energy Era by implementing

Smart UN Facilities. In Figure 1, the main benefits of implementing solar energy solutions are rolled out.

The Smart UN Facilities project has ushered in a movement towards green energy solutions for all offices.

The concept is to build modern premises around the globe that are fully aligned with Sustainable

Development Goals (SDGs) and to lead as an example to trigger a movement in the world (UN, 2018).

Figure 1. Benefits of a solar powered Country Office (UNDP, 2018)

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The UNDP Office of Information Management and Technology is committed to develop Smart UN

Facilities as an interconnected combination of Smart Technologies and People in pursuit of economic and

social development. This is aligned with the new sustainable development agenda that comprises to end

poverty, promote prosperity and improve people’s well-being while protecting the environment (UNDP,

2016).

The advent of Internet of Things (IoT) has revolutionized the way of doing business in organizations and

has given rise to the concept of Smart UN Facilities. In view of the benefits, it leads to make the first step

in transitioning into a low-carbon and digital organization through smart integration of various equipment.

As it is depicted below, Figure 2 shows the main technologies that set and establish the Smart UN

Facilities including Energy & Mobility, ICT Infrastructure & Business Solutions, Internet of Things & Monitoring

and Security (OIMT/CIAS, 2017).

Figure 2. Technologies involved in the Smart UN Facilities (OIMT/CIAS, 2017)

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2.2 The Seven-Step Solution Process

One of the main branches in the Smart UN Facilities concept is Energy & Mobility, which includes the

deployment of a Hybrid Solar PV System to fulfill the energy requirements in the Country Office. To do

so, OIMT UNDP has stablished the Seven-Step Solution Process to follow while developing and

implementing a customized Solar System for a Country Office since the very beginning (see Figure 3

below).

Figure 3. 7 Step process of Green Energy Solution (UNDP, 2018)

The first step is Pre-Site Survey and Self-Assessment, where the Green Energy Team helps Country Offices to

understand their energy need by painting a comprehensive picture of their energy consumption from

thorough data collection and management. This phase should take less than a half year to collect enough

energy consumption data of the premises so that an annual profile can be created. Moreover, Country

Offices would include technologies related to Internet of Things & Monitoring, which is one of the main lines

to achieve Smart UN Facilities.

The next step is creating a Business Case with our knowledge, providing expert advisory on energy

optimization and suggest tailored solar energy solutions to become sustainable COs under a limited

available budget.

The following steps, additional parties are involved in order to proceed with Procurement, Site Survey, Design

and Installation. A Long-Term Agreement (LTA) with Renewable Energy Vendors is established to

implement and manage green energy solutions for UNDP Country Offices around the globe.

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3 Literature Review

3.1 Country Overview

Zimbabwe is a landlocked country in South Africa and it has a total area of 390,757 km2 and it is bordered

by Mozambique, Zambia, Botswana and South Africa. Zimbabwe lies between latitudes 15º and 23ºS, and

longitudes 25º and 34ºE. Its capital and largest city is Harare, with a population of 1.56 million (United

Nations Population Division, 2017).

Zimbabwe has a population of 16.91 million, with Christianity the majority religion (Pew Research Center,

2015). It is a United Nations member state since August 1980, after the peace agreement of 15-year

guerrilla war (United Nations, 2017). Robert Mugabe became Prime Minister of Zimbabwe in 1980 when

his party won the elections (ZANU-PF) and he was President of Zimbabwe from 1987 since he resigned

in 2017. During his authoritarian regime, the country has suffered human right violations. It was ranked

number 13th on the Fragile States Index (The Fund for Peace, 2017) and it is one of the latest countries in

the World Happiness Report, being the 144 out of 155 countries (SDSN, 2018).

It has a tropical climate with local variations depending on the altitude (see Figure 4). The southern part is

featured with arid and desert climate whereas the rest of the country has a subtropical climate in which the

Eastern Highlands are characterized with cooler temperatures and highest rainfall compared to the other

locations in the country.

The dry season is from May to September where there is very little rain. In contrast, the rainy season starts

in late October and it is extended until March. Season is affected by the Intertropical Convergence Zone

where the northeast and southeast trade winds converge, leading to slightly lower temperatures, higher

humidity and more cloud coverage (World Weather & Climate Information, 2016).

Regarding the ecoregions in Zimbabwe, it is mostly savannah is although it is covered in tropical and

hardwood forest in the mountainous Eastern Highlands. However, deforestation has led to erosion and

land degradation due to population growth and urban expansion (Chipika and Kowero, 2000).

Figure 4. Zimbabwe Map - Climate Classification. (Peel, Finlayson and McMahon, 2007)

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3.2 Climate

3.2.1 Solar irradiance

Global Solar Irradiance (GSI) or Global Horizontal Irradiance (GHI) is a measure of the rate of total

incoming solar energy (both direct and diffuse) on a horizontal plane at Earth surface. Zimbabwe benefits

from an excellent solar irradiance all year long. The map below shows the Global Solar Irradiation of

Zimbabwe.

Month Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

GHI

(kWh/m2/day) 5.84 5.78 5.62 5.54 5.14 4.70 4.99 5.77 6.56 6.68 6.25 5.71

Table 1. Monthly average Solar Global Horizontal Irradiance (GHI) data from Harare, Zimbabwe. (NASA Surface meteorology and Solar Energy - Available Tables, 2017)

Figure 5. Global Horizontal Irradiation in Zimbabwe (World Bank Group, 2018)

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3.2.2 Temperature

Zimbabwe has tropical climate, featured by a rainy season of high humidity and large amounts of rainfall

followed by a drier season. The average temperature is 20.59ºC with October being the warmest month

with temperatures ranging between 15ºC and 29ºC, and July is the coolest month with an average

temperature from 7ºC and 21ºC (World Weather and Climate Information, 2016). Figure 6 and Figure 7

below show the average air temperature in Zimbabwe and the average monthly air temperature in its

capital, Harare.

Month Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

Air temperature

(ºC) 22.14 21.93 21.62 20.37 18.32 16.29 16.22 18.66 22.25 23.66 23.47 22.17

Table 2. Monthly average Temperature data from Harare, Zimbabwe. (NASA Surface meteorology and Solar Energy - Available Tables, 2017)

Solar panels are affected by the operating temperature, which depends on the level of sunlight and the

ambient temperature. Environmental factors and temperature can reduce efficiency and limit the solar

panel energy output.

Solar PV panels are usually power tested at 25ºC. The temperature coefficient gives an indication of the

variation of efficiency as temperature goes up or down by a degree. For instance, if the temperature

coefficient of a panel is -0.5%, then for every 1ºC rise, the panel will reduce by 0.5% (Schinckel, 2016).

Therefore, on a hot day, when the temperature of the panel can reach 29ºC, such panel would see its

maximum power output reduced by only 2%. This means that the average air temperature in Harare along

the year lets maximize the power output from the Solar PV panels as they are very close to Standard Test

Conditions (STC). Conversely, with the same irradiance on a colder winter day, the panels would actually

be more efficient.

Figure 6. Average air temperature in Zimbabwe (World Bank Group, 2018)

Figure 7. Average min and max temperatures in Harare, Zimbabwe (World Weather and Climate Information, 2016)

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3.3 Wind Speed

Wind speed in Harare is very low on average. As it can be seen in Figure 8, wind speed from NASA

Surface meteorology and Solar Energy database has been extracted and the average annual wind speed is

4.51 m/s at 50 m above the surface of the earth for terrain similar to airports.

Figure 8. Average Monthly Wind Speed at 50m height in Harare, Zimbabwe. (NASA Surface meteorology and Solar Energy - Available Tables,

2017)

As the available micro-wind turbine of UNDP Long Term Agreement (LTA) holders has an average hub

height of 10 m, the previous data should be adapted with the logarithmic power law formula and an

average-scaled annual wind speed of 3 m/s is obtained.

𝑣(𝑧) = 𝑣𝑟 ·log

𝑧𝑧0

log𝑧𝑟𝑧0

The previous equation stands for the logarithmic power law formula where 𝑣𝑟 and 𝑧𝑟 are the reference

velocity and height, respectively, 𝑧0 is the roughness length and z is the specific hub height from the

desired wind turbine to be installed. In Annex A, there is the roughness classification depending on the

landscape type, in this case it is considered 0.4 m of roughness (Danish Wind Industry Association, 2003).

For this reason, it is not feasible to install micro-wind turbines in Zimbabwe CO as the average wind

speed at hub height is almost null and the required cut-in wind speed for the available wind turbine model

(Aeolos-H 3kW) is 3 m/s and the optimal wind speed to run efficiently the micro-wind turbine is between

9-10 m/s. See Annex B to check the technical specifications of Aeolos-H 3kW Micro-Wind Turbine and

Annex C to see the main parameters to be considered when including Micro-Wind Turbines in a Hybrid

Energy System.

3.4 Energy resources and access to electricity

Zimbabwe electricity generation is heavily reliant on its coal and water resources, only 5% of the electricity

generation comes from renewable energy sources (Climatescope, 2017) and the equivalent carbon

footprint emissions from the grid in Zimbabwe are 0.575 kg CO2/kWh (UNEP, 2017).

The main supply is produced at the Hwange thermal Power Station, Kariba Dam Hydroelectric Power

Station and three smaller coal-fired power stations which all of them are managed by the state-owned

Zimbabwe Electricity Supply Authority (ZESA) subsidiary, the Zimbabwe Power Company (ZPC) (see

Table 3). Luckily, the current tariff electricity price is very cheap and it accounts for 0.145 $/kWh (RECP,

2017).

0

1

2

3

4

5

6

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

Aver

age

Win

d S

pee

d (

m/

s)

Month

-20-

However, Zimbabwe suffered a record-breaking crisis in which the electricity supply dropped to less than

half of country’s demand. The root cause of this crisis were technical faults at Hwange Power Station as

well as very low water levels at the Kariba Dam. More precisely, lake levels during 2015 and 2016 in

Kariba Reservoir were at their lowest average in the las twenty years (Zambezi River Authority, 2018). For

instance, in February 2016, Kariba lake level was at only 11% of its operating volume which reduces the

power output up to 82.5 MW. Apart from that, several maintenance and repairs have caused regular

power interruptions during more than 18 hours in the country.

To avoid further problems with power outages, ZPC announced the expansion of 600 MW at Hwange

Thermal Power Station and 300 MW at Kariba Hydro Power Station in 2014. However, no development

has been undertaken and to summarize, less than 50% of the installed capacity in Zimbabwe is only

available to generate power.

Power Station Owner Installed Capacity

(MW)

Kariba Dam Hydroelectric Power

Station

ZPC 750

Hwange Thermal Power Station ZPC 920

Rusitu Hydro Rusitu Power Corporation 0.75

Munyati (Coal) ZPC 100

Bulawayo (Coal) ZPC 90

Harare (Coal) ZPC 80

Triangle (Bagasse) Triangle Ltd 45

Hippo Valley Estates (Bagasse) Hippo Valley Estates 33

Green Fuel (Bagasse) Green Fuel 18

Border Timbers (Wood waste) Border Timbers 0.5

Table 3. Installed generation capacity in Zimbabwe (RECP, 2017)

For the aforementioned reasons, the country suffers regular power shortages with a deficit of 60%. ZESA

generation capacity was measured in February 2016 and it was about 845 MW compared to the projected

national demand of 2,200 MW and installed capacity of 1,940 MW (RECP, 2017).

52% of the total population in Zimbabwe has access to electricity, which includes 78% of urban

population and 40% of rural population. To ensure energy access all over the country, the Government of

Zimbabwe has a target of achieving 85% electricity access by 2020, but following the previous proposals

of capacity expansion, the Government needs to move up faster to achieve some of its goals.

Regarding the governmental framework, although the Ministry of Energy and Power Development

(MEPD) has stat that renewable energy is required for energy development in the country, the legal

framework does not promote any investment in this field, however MEPD is planning to develop some

energy policies to expand renewables in Zimbabwe.

Finally, when it comes to greenhouse gases emissions, the total amount of carbon dioxide emissions

emitted by burning fossil fuels along the process of producing and consuming energy account for 8914

million tons in 2014 (Trading Economics, 2015).

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4 Hybrid Energy Systems

The primordial task for reaching a low-carbon energy future is to increase the share of renewable energy

and improve energy efficiency (International Energy Agency, 2017). Renewable sources such as solar

photovoltaic (PV) energy or wind power are crucial to meet future energy requirements while

decarbonizing the energy sector.

This is extremely important when working with developing countries with poor energy infrastructures as

renewable energy technologies have tremendous potential to shift the current energy situation is such

countries. Moreover, it is reported that the main increase in energy demand occurs amongst developing

countries. A combination of economic growth, adaptation to occidental lifestyle patterns, along with

expanding population, fosters the increase in world energy demand and use (U.S. Energy Information

Administration, 2017a).

Analyzing off-grid systems in developing countries, the cost reduction for RE technologies draws

attention when implementing Hybrid Energy Systems (HES), moving away from single source generation

systems based either on diesel generators or unreliable grid systems (Léna, 2013). These HES installations

will establish energy security and will result in economic, social and environmental benefits (Costantini et

al., 2007).

Hybrid Energy Systems (HES) consist of two or more renewable or non-renewable energy sources, such

as wind, solar PV and diesel generators, to provide increased system efficiency as well as greater balance

when supplying energy.

Renewable energy sources such as solar or wind are widely available and have low direct environmental

impact. However, they are intermittent with daily and even seasonal cycles in terms of resource potential.

For this reason, innovative ways to optimize their usage is required.

The potential from a solar PV system is insufficient to ensure energy stability and security as it a non-

continuous source of energy. It occurs the same with standalone wind systems, they can likewise not

guarantee constant load for the same reasons. Independent use of these energy sources is therefore not an

optimal solution and results in costly oversizing for system reliability. However, integrating different

intermittent sources allow to partly overcome inherent limitations (Notton, Diaf and Stoyanov, 2011).

Figure 9. Energy system scheme proposal for Zimbabwe Country Office in Harare. (HOMER Energy, 2018)

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Hybrid systems provide a high level of energy reliability by combining diverse energy resources, thus

reducing the risk of outages. In off-grid energy systems, battery storage along with a combination of

renewable sources is used to overcome intermittency (Nandi and Ghosh, 2010).

In case of Zimbabwe CO, as it is a grid-connected system, including batteries in the energy system is

optional as they have a reliable grid and a backup generator (see Figure 9 above). From the economic

point of view, batteries will make the project more expensive and less feasible, so the best system would

include only Solar PV panels to reduce the current dependency on the grid based of fossil fuels.

In HES some additional equipment to balance the system is required to safely transmit the electricity to

the consuming load (U.S. Energy Information Administration, 2017a). This equipment includes:

− Battery: Storage facilities are used when the system itself is not producing electricity. Batteries are

an important component in renewable energy systems serving as an energy buffer. This is

necessary due to the intermittency of renewable energy power generation as it mainly depends on

weather conditions and daily fluctuations.

− Controller: It regulates the flow of electricity from the generating source to the battery, ensuring

it is fully charged in a controlled way, without overcharging it. When the load is consuming

power, the controller is in charge of letting the energy flow from the generating source into the

load, the battery or both.

− Converter: Electricity is either produced as Alternating Current (AC) or Direct Current (DC),

depending on the source. Standard micro-wind turbines and diesel generators produce AC, where

solar PV produce DC. In addition, energy is stored in batteries using DC. So, it is essential to

have a converter in the system as standard load are AC applicants.

4.1 Solar photovoltaic panels

Sunlight is the most abundant renewable energy source on the planet and is converted directly into

electricity through the photovoltaic (PV) process (Bostan et al., 2013). The basis in the PV technology is

the so called “photovoltaic effect” in cells that convert light directly into electricity. The solar cells are

composed of semiconductor materials that absorb the light and its photons can transfer their energy to

electrons, so they flow through the material as electrical current. These cells are linked together through

electrical connections and they are encapsulated and framed to form a module, also known as PV panel.

Several modules in series and parallel can form a PV array and be installed on a building or at ground-

level, producing usually electricity in the form of DC at 12 or 24 volts (U.S. Energy Information

Administration, 2017b).

This project will include system proposals using either multicrystalline PV panels with the standard cell

architecture named Back Surface Field (BSF) and monocrystalline PV panels manufactured using an

innovative architecture called PERC.

BSF technology has been used during three decades and it features the vast majority of commercial PV

panels produced with efficiencies between 16% to 18% (Woodhouse et al., 2015) . They have the

following structure from the top to the rear layer (see Figure 10):

− Contacts formed with screen printed silver paste

− Antireflective coating

− Boron doped silicon wafers with P-N junction

− Aluminum Back Surface Field (BSF)

− Screen printed aluminum paste

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Figure 10. The left figure represents a standard solar cell structure and the right one is the PERC solar cell structure (Vogt et al., 2017)

In order to obtain the maximum amount of electrons out of solar cells, Martin A. Green in 1989

published the first paper in University of New South Wales describing the PERC cells (Green, 2015).

PERC stands for Passivated Emitter and Rear Cell as the main features compared to BSF are the

reduction of recombination in the rear surface including a dielectric surface passivation and reducing the

contact area between metal and semiconductor, and it also increases the rear surface reflection with a rear

metal reflector.

All the features mentioned before enable to achieve higher efficiencies than with BSF solar cells that are

currently reaching their physical limits. The research paper in that time showed efficiencies up to 19.1%

but until recent years industry has not been able to achieve this level of efficiency in mass production.

Trina Solar achieved an efficiency record of 20.8% for multicrystalline PERC cells and Schott Solar

reported an efficiency of 21% for monocrystalline PERC cells (Green, 2015). According to the

International Technology Roadmap for Photovoltaic (ITRPV), PERC solar cells are now considered one

of the best potential solar cells structure to produce at a competitive price with high efficiencies.

What is more, Figure 11 shows that the world market share for PERC technology is more than 30%,

being the second highest production capacity in the market. The main outcomes confirm that PERC cells

will gain more share along the near future compared to BSF cells and from 2020 on, they will become the

main PV solar cell in the market (ITRPV, 2018).

Figure 11. Worldwide market shares for different solar cell technologies (ITRPV, 2018)

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4.1.1 Manufacturing process

Regarding the type of silicon cell, both monocrystalline and multicrystalline silicon modules will be

included in this project and it should be noted that they have different procedures in the wafer

production. Hereafter, the unit process is described briefly:

− Polysilicon production: metallurgical grade silicon is transformed into polysilicon, which is the

feedstock of the silicon PV. It is usually performed by Siemens process.

− Wafer production: thin slices of pure semiconductor material, which is the base of silicon PV, are

produced from the polysilicon feedstock.

o Monocrystalline: the wafer is sliced from an ingot obtained by the Czochralski process

and it is already doped with the adequate dopant.

o Multicrystalline: the wafer is sliced from an ingot obtained from a simpler process, which

consists in melting both polysilicon and dopant in a cast with the future shape of the

ingot (Ferrazza, 2012).

− Cell production: starting from the silicon wafer, a solar cell is obtained after suffering several

elements diffusions, depositions and screen printings (Seigneur et al., 2016).

− Module production: cells are normally grouped in modules of 60 or 72 solar cells. Electrical

connections and a junction box are added, as well as encapsulate to protect solar cells from

moisture. Finally, a polymeric back sheet, a glass cover and an aluminum frame are added to the

module (Mulvaney, 2015).

In this case, looking into cell production, BSF and PERC are the technologies that will be used along this

project, so their corresponding production process will be explained hereunder (the simplified process

flow is included in Figure 12).

The innovative manufacturing process of PERC technology does not differ too much compared to the

established BSF technology. The first two steps are common and consist of wafer damage removal etch

and texturing, followed by emitter diffusion and etch. In case of PERC solar cells, they often include a

low-cost step rear side polish etch to enhance rear reflection and reduce rear combination, with an

increase of energy conversion efficiency from 0.4% to 1.5% (Green, 2015).

In the BSF sequence, a simple silicon nitride antireflective (AR) coating deposition is done whereas in

PERC an AR and rear dielectric coating with either aluminum or silicon oxide, and silicon nitride stack is

required. Then, the contact holes through the rear dielectric are done by laser ablation.

Finally, contact screening and cell testing are in both BSF and PERC processes but in this last technology

sequence, different paste compositions would be used to avoid damage to rear dielectrics (Green, 2015).

Figure 12. Simplified process flow for BSF (upper) and PERC (lower) (Green, 2015)

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4.1.2 Solar cell performance

PV systems are already widely implemented worldwide and successfully provide power to grid-connected

or stand-alone applications. The efficiency of a PV cell is the ratio between the electrical power produced

in the solar cell divided by the sunlight energy coming in. The electricity produced in a PV cell depends on

multiple factors that are described below:

− Wavelength: light is composed of photons with a wide range of wavelengths and energies. Some photons are reflected when light reaches the solar cell surface whereas others pass through it. Taking into consideration the absorbed photons, some of the energy is turned into heat and the remainder is in charge of separating electrons from their atomic bonds and produce charge carriers and electric current (U.S. Department of Energy, 2017).

− Recombination: it is one of the factors that limits efficiency of solar cells. There are two types of charge carriers in a semiconductor, the negatively-charged electrons that flow through the material and the positive charge carriers that are holes caused by the absence of an electron. A recombination occurs when an electron encounters a hole, they may recombine and cancel their contributions to the electrical current (U.S. Department of Energy, 2017).

− Temperature: solar cells have a better performance at low temperatures. Semiconductor properties shift with high temperatures, resulting in a slight increase in current but a larger decrease in voltage. Extreme temperatures can lead to shorter operating lifetimes and can also damage the cell (U.S. Department of Energy, 2017).

− Reflection: the efficiency of a cell can be increased if the amount of light reflected is minimized. Untreated silicon reflects 30% of the incident light. So, antireflection coating and textured surfaces are applied to reduce reflection. Moreover, dark blue or black are the colours in which cells have a high efficiency performance (U.S. Department of Energy, 2017).

Therefore, PV cells are significantly affected by temperature and solar radiation. For instance, at lower

solar irradiation than 1000 W/m2 and/or higher temperatures than 25ºC, PV cells efficiency decreases,

therefore, they provide less power than their rating suggests (Bostan et al., 2013).

Microgrids systems can take advantage of implementing an HES to provide a better service and reduce

production costs compared to single-source systems. The cost of solar PV panels has been falling all over

the year, making them more favourable to deploy PV hybrid systems (see Figure 13 below).

Figure 13. Evolution of the solar panel price and global solar panel installations (Zachary Shahan, 2018)

-26-

Solar PV operation & maintenance (O&M) costs have not been considered a huge contributor to the total

costs when installing a solar PV system. However, the rapid decrease in solar PV module and installed

costs along these last five years has led to an increase in the share of O&M costs in the LCOE of solar PV

projects (Renewable Energy Agency, 2017).

The PV power output is estimated from the solar radiation and the ambient temperature:

𝑃𝑃𝑉 = 𝜂𝑃𝑉 · 𝐴𝑃𝑉 · 𝐼𝛼

With 𝜂𝑃𝑉 being the Solar PV efficiency, 𝐴𝑃𝑉 the area of the module (m2) and 𝐼𝛼 the solar radiation on a

tilted plane (W/m2).

𝜂𝑃𝑉 = 𝜂𝑃𝐶 · 𝜂𝑟 · [1 − 𝛽(𝑇𝑐 − 𝑇𝑐,𝑟)]

The efficiency is expressed in terms of 𝜂𝑟 which is the reference module efficiency, 𝜂𝑃𝐶 the power

conditioning efficiency (which is 1 with perfect MPPT), 𝛽 the temperature coefficient (1/ºC), 𝑇𝑐 the cell

temperature (ºC) and 𝑇𝑐,𝑟 the reference one (Notton, Diaf and Stoyanov, 2011).

4.2 Energy storage – Batteries

Most of remote locations are powered by diesel generation which is oversized to meet peak demand and

not operating below 30% of their capacity. The lack of energy infrastructures in the area means constant

diesel imports which are costly and a risk of security of supply. The integration of batteries in off-grid

system can help to integrate renewable energy in the system and reduce dependency on diesel generators

(Renewable Energy Agency, 2015).

However, there are some challenges when including batteries for energy storage in remote locations. First

of all, the cycle life of the battery should be a crucial parameter to consider when choosing the type of

battery in order to reduce the need of replacing batteries frequently. In addition, external factors such as

ambient conditions and temperature affect directly the performance of the batteries. Then, in these critical

locations shipping and transportation of such equipment may be more difficult due to government

restrictions (Renewable Energy Agency, 2015).

In Zimbabwe CO, as it is grid-connected, there is no need to include batteries as the grid is reliable. In

case a battery would be included, it would cover critical loads during an specific amount of time but the

investment costs would be very high, however an analysis will be performed to see if it is feasible to

include Li-Ion batteries to cover critical loads.

4.2.1 Battery performance

The amount of current that can be stored and withdrawn from a battery is the measure of capacity or

nominal capacity. Battery capacity is a measure of the charge stored in the battery which represents the

maximum amount of energy that can be extracted from the battery under certain conditions. Varying

conditions can significantly change the energy storage from the rated nominal capacity. These conditions

are explained hereunder (Renewable Energy Agency, 2015):

− Temperature: This has a strong impact on the battery’s operation life. The most optimal conditions for batteries are at 20-25 ºC; both higher and lower temperatures reduce its capacity. When the temperature is low, the reactions in the battery slow down so the performance goes down. On the other side, high temperatures over the optimal range will enhance the corrosion of the electrodes and reduce the battery lifetime.

− Discharging rate: It is measured in number of cycles and basically depends on the depth of discharge reached in every cycle: lifespan shortens when the battery is discharged at a lower level each cycle.

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− Age of battery: the battery capacity will only stay close to its rated capacity for a limited number of charges and discharges cycles. Even following manufacturing specifications and operating the batteries under recommended conditions still results in replacement of the batteries after up to ten years. The end of the battery’s life is defined as the point where the capacity has declined 80% to its nominal value.

Operational conditions have a strong impact on battery lifespan and they need to be considered when

installing batteries. An optimal battery sizing is such that it cycles within a discharge depth that allow

enough cycles for the battery to last at least six years, and ideally eight to ten years (Energy Ageny, 2014).

In order calculate the battery bank required in a specific project, it can be estimated with the following

expression (Malmquist, 2017):

𝐸𝑏𝑎𝑡 =𝐸𝑑𝑎𝑖𝑙𝑦 · 𝑎𝑢𝑡𝑜𝑛𝑜𝑚𝑦

𝐷𝑂𝐷

Where 𝐸𝑏𝑎𝑡 is the capacity required of the battery, 𝐸𝑑𝑎𝑖𝑙𝑦 the desired energy to be stored (Wh), the

𝑎𝑢𝑡𝑜𝑛𝑜𝑚𝑦 in days and 𝐷𝑂𝐷 the depth of discharge (%).

As the capacity is usually expressed in Ah, it is divided by the system voltage as it can be seen in the

following equation:

𝐸𝑏𝑎𝑡(𝐴ℎ) =𝐸𝑏𝑎𝑡(𝑊ℎ)

𝑆𝑦𝑠𝑡𝑒𝑚 𝑣𝑜𝑙𝑡𝑎𝑔𝑒 (𝑉)

4.2.2 Lead Acid vs Lithium-Ion Batteries

4.2.2.1 Life Cycle

Lithium-ion has a higher cycle life than lead acid batteries with deep discharge applications. When the

ambient temperature is taken into account, the disparity of their performance becomes more pronounced.

As it can be seen if Figure 14 lead acid (referred as AGM in the graphs) is more sensitive to the

aforementioned factors.

In Zimbabwe, with extreme climate conditions, the cycle life for lead acid batteries drops dramatically

50% compared to moderate climate whereas Li-ion cycle life remains stable with ambient temperatures up

to 49ºC (Mobbs, 2016).

Figure 14. Hot Climate, Cycle Life comparison between Lead Acid (AGM) and Li-Ion (Lithium) (Mobbs, 2016)

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4.2.2.2 Energy Density

Another advantage Li-Ion battery features is its energy density which is highly distinguishable compared to

the other most common type of batteries (see Figure 15). This lead to a higher energy capacity storage

with lower volume and weight required which reduces the freighting costs to the Country Office.

Figure 15. Battery Density Comparison (Joe O’Connor, 2017)

4.2.2.3 Environmental Impact

Lead acid batteries need more raw materials to obtain the same levels of energy storage which results in a

higher impact on the environment during the extraction of the minerals required. Apart from the mining

process, the manufacturing is also very energy intensive, however even lead is hazardous to human health,

the processing process and battery packaging has no risk for human health.

On the other hand, Lithium-ion cells need the mining of lithium carbonate, copper, aluminum and iron.

The most polluting process during the mining is the extraction of lithium but it is a minor part of the

battery cell, whereas aluminum and copper extraction have lower impact to the environment. Moreover,

the potential to recover and recycle lithium-ion cells foster their usage compared to lead acid batteries

nowadays (Mobbs, 2016).

4.2.2.4 Lifecycle Cost

The initial cost of Lithium-ion batteries is more expensive compared to other technologies as it is a newer

technology, so the tendency is that the current gap of costs will be reduced in the near future.

The initial cost can be an important factor when it is required the total budget of the system. However,

batteries with cheaper initial cost may have higher expenses in the long run (see Figure 16). It should be

noted that the total cost of batteries along their lifecycle may be affected due to maintenance, depth of

discharge (DOD) and the number of cycles each battery has, which results in a very low total lifecycle cost

for Lithium-Ion batteries compared to Lead-Acid as their properties and performance are better.

Figure 16. Total Lifecycle Cost of Batteries (Joe O’Connor, 2017)

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To sum up, Table 4 includes the main properties of both type of batteries and one can conclude that

nowadays the most suitable storage technology for hybrid systems in rural electrification and stand-alone

systems is Lithium Ion batteries.

Lead Acid Lithium-Ion

Initial Cost per Capacity ($/kWh) 221 530

Cost per Life Cycle ($/kWh) 0.71 0.19

Specific Energy (Wh/kg) 40 150

Regular Maintenance Yes No

Number of Cycles to 80% DOD 200-650 1000-4000

High Temperature Sensitivity Degrades above 25ºC Degrades above 49ºC

Table 4. Comparison table with main properties of Lead acid and Li-ion Batteries (Joe O’Connor, 2017)

For all the aforementioned properties, Li-Ion battery will be used for the investigation in this project.

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5 Methodology

5.1 Data Collection – Power Consumption Measuring and

Monitoring

A Power Consumption Measuring and Monitoring (PCMM) device has been installed in Zimbabwe

Country Office to analyze the load consumption in the premises. As it can be seen in Figure 17, it consists

of a set of current sensors that are so-called clamps and they are placed around the cables to measure the

electrical induction and to precisely quantify the current going through a wire without modifying the

circuits. There are different size of clamps depending on the selected circuit to be measured, the most

common sensor sizes are 200A for individual electrical circuits such as server rooms, air conditioning

equipment or lighting, and 600A sensors are installed in the main line distribution board. Either

monophasic or three-phase circuits can be monitored as each sensor module is equipped with 3 ports to

connect the corresponding current sensors used. In order to gather all the data monitored, a gateway is

plugged in a socket close to the current sensors so that the data can be transferred and collected in the

online dashboard from Eyedro (UNDP, 2017) the provider offers.

Most of the Country Offices have electrical circuits based on a three-phase system, the most common

method of generating, transmission and distribution AC. In this symmetric three-phase power system, the

three AC’s have the same frequency and voltage amplitude relative to a common reference, but with a

phase difference of one third the period. This phase delay results in a more balanced linear load than

single-phase systems.

Figure 17. A standard Power Consumption and Monitoring (PCMM) device from the UNDP LTA vendor Eyedro (Eyedro, 2017)

Through the online dashboard, UNDP can remotely access to load consumption data and it is extracted

for the purpose of this project. The collected data is essential to plan and design an efficient, customized

hybrid solution for the Country Office. As it is represented below in Figure 18, the online dashboard from

the provider allows the user to visualize the load consumption data in hourly, daily and monthly views

over customized periods. This illustrates the many options and possibilities for managing the load

consumption data measured by the PCMM.

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Figure 18. UNDP Eyedro Portal illustrating the total daily power consumption in Zimbabwe CO. (UNDP, 2017)

It illustrates the total daily power consumption in UNDP Zimbabwe Country Office measured in the

main line distribution board. The three-phase electrical system is visualized by different colors: orange,

blue and green, each representing a phase (UNDP, 2017).

The measured data can be reviewed by using the web page and credentials specified in Annex D.

5.2 PCMM Data Management

In order to manage the load consumption data as input to analyze the optimal Hybrid System to be

implemented in UNDP Zimbabwe CO, energy consumption data has been extracted from the online

portal in the following way:

− Period: Data has been downloaded in the full period from the July 1st 2017 to March 26th 2018.

− Electrical Power Phase: PCMM has measured electrical power with three-phases; A, B and C,

illustrated in Figure 18 have been extracted and summed up.

− Time Format: Data extracted on an hourly basis as it is required as input for HOMER.

− Time zone: load consumption data have been extracted with the corresponding time zone in

Zimbabwe (GMT +2).

− Measured Energy in units of power (Wh)

Once the available load consumption data is extracted from the Eyedro Portal, HOMER Software

requires a complete year of consumption pattern to optimize the Hybrid Solar System. As there are nine

months of data, a MATLAB code has been created to estimate the remaining months. The idea behind is

to select which months are considered Summer, Winter or Intermediate based on the average temperature

available of the location. Then, with the available data, the other months can be extrapolated and weighted

accordingly. The code is included in Annex E.

-32-

It is recommended to have at least a month of data to increase reliability of results and ensure a properly

sized hybrid system. For detailed PCMM data collection, check Annex F.

Figure 19. Average weekdays hourly data extracted from the PCMM load consumption in UDNP Zimbabwe CO, Harare. (HOMER Energy, 2018)

5.3 Tools selection

5.3.1 PV*SOL

The maximum PV installation capacity has been evaluated considering the available roof space in the

UNDP compound.

Two PV panel models have been analyzed to see the performance and power output obtained with a

monocrystalline and a multicrystalline PV panel from recognized Chinese manufacturers, JA Solar and

JINKO Solar. Both modules have the same area but as explained in Section 4.1 monocrystalline PV

modules using PERC technology have higher efficiencies than standard BSF technologies. This let include

less modules obtaining the same power output, as each monocrystalline panel offers 4.7 % more

production per unit area. Moreover, the manufacturer of the monocrystalline PV module, JA Solar offers

an efficiency higher than 21% on average mass production which means their manufacturing process is

more energy efficient (JA Solar, 2017).

Datasheets of the specific PV modules used in the simulation are available in Annex G and Annex H,

respectively.

The shading created by the trees surrounding the building and the limited space reduce the possible areas

to be included in the system. A detailed shading analysis will be performed with PV*SOL to check the

most feasible solution and PV panels with shading higher than 10% will not be considered. Moreover, the

overall performance of the PV array will be studied and a minimum of 75% will be requested in the

system.

-33-

5.3.2 Green Team Costs Database

UNDP has developed several projects in other Country Offices installing Hybrid Energy Systems with PV

panels and batteries. A database with all the costs from the projects developed is available to include the

fixed and variable costs of both PV panels and batteries in HOMER software. The size of both the PV

panels and the battery affect the final cost of the project, as the higher the system the lower the specific

cost ($/kW).

In the Database Spreadsheet, it is recommended to select the Hybrid Solar Systems that have similar PV

size and in case the system should include batteries, select the projects with batteries as their high price

may affect the overall Hybrid Solar System.

Once the PV total capacity that can be installed in the premises and the optimal battery storage are

known, these values are introduced in the Project Cost Estimation. This spreadsheet also includes the cost

of power electronics & equipment, installation, design & civil works, technical room, maintenance and

freight.

All the system costs are then grouped in the three main inputs requested in HOMER which are:

- Total solar PV variable costs ($/kWp without batteries)

- Total solar PV maintenance O&M costs ($/year/kWp)

- Total battery variable costs ($/kWh)

5.3.3 HOMER System Simulation Software

The Hybrid Optimization of Multiple Energy Resources (HOMER) tool is a microgrid software that

navigates the complexities of building cost effective and reliable hybrid solutions by combining

traditionally generated and renewable power, storage and load management (HOMER Energy, 2018).

Designing the customized hybrid solution for UNDP Zimbabwe CO has been done through the hybrid

system simulation software HOMER for several reasons. To start with, it is the most popular and

comprehensive software tool for the purpose of Hybrid Energy Systems (Sinha and Chandel, 2014).

HOMER has shown to be well suited for denser and more advanced simulations, as it is the most accurate

software when it comes to time series data import analysis, graphical representation and additional

features such as temperatures effect on solar PV.

Secondly, HOMER has access to a climate and weather database from NASA which is easily implemented

in the simulations. The database directly links with the geolocation of the site and it makes easier the

assessment based on the local natural resources such as irradiation, temperature and wind velocities.

Thirdly, HOMER has a great economic assessment and can design various systems based on the

economic parameters (Sinha and Chandel, 2014) to decide the feasibility of the energy system from the

economic point of view. In addition, it offers the possibility to develop sensitivity analysis to see how a

hybrid energy system can vary subject to a parameter, e.g. variation of diesel price.

HOMER simulates the operation of a system by making hourly energy balance calculations over a year.

For each hour, HOMER compares the electric and thermal hourly demand to the energy that the system

can supply in that hour, then calculates the energy flow to and from each component of the system. The

energy balance calculations are executed for each system configuration that is considered and it determines

whether a configuration is feasible in terms of meeting the electric demand. Finally, it estimates the cost of

the system over the lifetime of the project (HOMER Energy, 2017).

-34-

5.3.3.1 Election of the Components

The software enables customization of the system per specific requirements so that a tailored energy

system can be designed following the same specifications as in Zimbabwe CO.

In this case, these premises are limited by available roof space for PV panels. Thanks to HOMER

software, the most cost-effective solution will be analyzed to meet the energy requirements derived from

the data measured by the PCMM in Zimbabwe. The aim of this project is to reduce green-house gases

emissions derived from the electricity production from the grid and switching it to greener options such as

solar PV panels. An analysis including Li-Ion batteries as an energy storage will be performed to study its

feasibility in this Country Office.

The different components and inputs included in HOMER software are listed below:

− Load Consumption Data: Energy consumption from Zimbabwe CO that it is being collected

since the installation of the PCMM that was held in between May and June 2017. The available

data since July has been extracted from the Eyedro Portal as it is the month since the PCMM

installation was correct and data collected was coherent. Then, a code has been developed with

Matlab to extrapolate and obtain a whole year data to include in HOMER.

− Generator: There is a backup generator but as the grid is quite reliable, it is assumed in HOMER

that Zimbabwe CO is only grid-connected.

− PV panels: The PV panel model that PV*SOL suggests fostering higher power capacity in the

Country Office will be considered in HOMER.

− Batteries: The battery type is a Li-Ion and the standard model offered by HOMER has been

chosen. The battery capacity will be optimized by the software.

− Converter: The converter type is likewise a standard from HOMER, automatically sizing to

optimize the individual system in the simulation. Allowing for variations only in the converter

capacity.

5.4 Levelized Cost of Energy

The Levelized Cost of Energy (LCOE) is used to describe and compare the underlying economics of

energy-related projects. In case of wind and solar power projects, the LCOE represents the lifetime sum

of all costs of a fully operational energy system with financial flows discounted to a common year. In

order to assess the cost of a renewable energy system, it should be included capital costs, operation and

maintenance (O&M) costs and the expected annual energy production (AEP) over the life of the project

(Renewable Energy Agency, 2012). Most renewable power generation technologies are capital intensive

but they have no fuel costs.

LCOE is a prediction of the energy production cost of the specific energy system developed as a constant

value of savings over the lifetime. The general LCOE formula is (Predescu, 2016):

𝐿𝐶𝑂𝐸 =𝐼0 + ∑

𝐴𝑡

(1 + 𝑖)𝑡𝑛𝑡=1

∑𝑀𝑡,𝑒𝑙 · (1 + 𝑟)−1

(1 + 𝑖)𝑡𝑛𝑡=1

𝐼0 Investment expenditure in $

𝐴𝑡 Annual total costs of the system operation (fuel and O&M costs) in $ in year t

𝑀𝑡,𝑒𝑙 Electricity produced in the respective year in kWh

𝑟 Yearly degradation rate of the system

𝑖 Real discount rate which is 8% as default in HOMER

𝑛 Economic operational lifetime in years

𝑡 Year of lifetime (1, 2, …n)

-35-

6 Baseline scenario

6.1 Energy supply

Zimbabwe CO is grid-connected and the reliability in Harare is moderate-high which means they can suffer an outage maximum once every two weeks. For this reason, they own a diesel generator as a backup to satisfy its energy needs in case a blackout occurs and a technical operator oversees and ensures its functioning in the premises.

The current price of electricity in Zimbabwe is cheap with a value of 0.145$/kWh (RECP, 2017) and the

diesel fuel costs 1.41 $/L (UNDP, 2017). For this reason, it is very probable that the most cost-effective

Hybrid energy system would only include Solar PV panels due to their current low price, however, the

final LCOE of the energy system proposal might have higher value that the current electricity price as the

installation cost and the implementation of the whole system would mean a considerable initial

investment.

Regarding energy storage as Li-Ion batteries, they will probably not appear as an effective solution in this

Country Office as the system is grid-connected with quite high reliability and this type of batteries are

nowadays expensive.

6.2 Energy consumption

Power Consumption Measuring and Monitoring (PCMM) sensors were installed in the main circuit line

distribution board in June 2017. The hourly power consumption measured by the installed sensors is

depicted below in Figure 20, thus a daily profile for the Country Office is obtained. The measured data can

be reviewed by using the web page and credentials specified in Annex D.

Figure 20. Hourly energy consumption in UNDP Zimbabwe CO (May 21st, 2018). (UNDP, 2017)

-36-

The electrical load input in HOMER is based on the total consumption with a daily average energy

consumption of 390.80 kWh/day. As it can be seen in Figure 20, the average night time consumption

does not exceed 15 kW, and during working hours (from 7 am to 6 pm) the average energy consumption

is 26.25 kW with a peak load of 43.40 kW.

More precisely, during weekdays 448.30 kWh/day are consumed on average whereas 246.90 kWh/day

during the weekends. A summary with the load characteristics is depicted below in Table 5.

Daily average energy consumption 390.80 kWh/day

Weekdays average energy consumption 448.30 kWh/day

Weekends average energy consumption 246.90 kWh/day

Peak load 43.40 kW

Table 5. UNDP Zimbabwe CO Load consumption characteristics (Own source)

Finally, when analysing the season profile in Figure 21, the highest energy consumption months are June

and July whereas the lowest energy consumption month is December.

Figure 21. Seasonal profile of UNDP Zimbabwe CO, Harare. (HOMER Energy, 2018)

Once the yearly load profile is obtained with Matlab, a tailored renewable solution will be offered to

UNDP Zimbabwe CO so that their current levels of green-house gases emissions and energy dependency

on the grid can be reduced substantially.

Even the consumption is quite efficient in the premises, a complete set of recommendations for energy savings in UNDP Premises has been detailed in Annex I. The Green Energy Team is also ready to support Zimbabwe CO in implementing an awareness campaign at the premises and to promote best practises for energy consumption.

-37-

6.3 Drone pictures of the compound

In order to properly do the Solar potential assessment, a drone was shipped to Zimbabwe CO to take

aerial pictures. The technical operator took some pictures and Figure 22 and Figure 49 from the Annex J

are the results. Even they are not perpendicular to precisely have the same measures, blueprints of the

compound were available and distances from it were used to define the building in PV*SOL with the

same area (see blueprints in Annex K).

Figure 22. UNDP Zimbabwe Country Office Building (UNDP, 2017)

As it can be seen in the picture above, there are many trees surrounding the UNDP Block, for this reason

they are included in the Solar PV assessment to check the shading they can produce over the roof, but

only trees in the North, East and West side were considered as no PV panels will be placed facing the

South.

The technical operator of the Country Office gave us the difference of height between the highest roof

point. Trees on the east, north and west sides are 5 meters higher and the ones from the south are 10

meters higher that the roof.

-38-

7 Solar PV panels configuration

In order to assess the solar potential of UNDP Zimbabwe CO, PV*SOL is used to determine the optimal

configuration. The following factors are considered while performing the solar potential analysis of the

compound:

− Usable area: even the total roof area is 720 m2, the final usable area is 270 m2 because there are

many obstacles in the roof such as pointed dormers, there is shading from trees surrounding the

compound and not all the sides from the roof can be used, which reduces substantially the

placement area for PV panels.

− Optimal orientation: the optimal roof side to obtain the maximum power output is the one

facing the North (the optimal orientation of the panels is facing the North as Zimbabwe is in

South Hemisphere) but East and West oriented roofs can also be included to increase the power

output from PV panels.

− Tilt angle: the optimum tilt angle for PV panels in Harare is 15.50º (Landau, 2017), however, as

they should be placed over the roof, the best way is to install them in the same inclination the

roof has so that the installation cost is lower it is safer than including additional structures. In this

case, the roof inclination is assumed to be 24º checking the drone pictures and the blueprints

available information, so there is 10º of difference compared to the optimum tilt angle.

7.1 Option 1 – 44 kWp

The aim of this scenario was to obtain the maximum PV power output from the available room in the

compound. In this case, a 320Wp multicrystalline module from Jinko Solar was used (see datasheet from

the PV panel in Annex H). Each panel has 1.94 m2 and 139 panels can fit over the available roof, giving a

final average PV output of 44 kWp (see Figure 23) which is ideal for this project as the peak load is 44

kW.

The configuration of the inverters, the PV system layout in the roof and the PV power output of each

group of modules is depicted in Annex L.

Figure 23. 44 kWp PV multicrystalline panels layout (Own source)

-39-

7.2 Option 2 – 28 kWp

As the goal for UNDP Zimbabwe CO is to reduce the carbon footprint in their offices and the price of

electricity is currently very cheap, none of the options seem to be feasible with a payback time lower than

20 years. However, they want to promote the importance of renewable energy and be a showcase for the

country. For this reason, this option was offered with a lower PV power output which would have a lower

investment cost and they can achieve a considerable reduction of CO2 emissions.

In this case, 3 roof sides have been used to place multicrystalline PV panels and a total PV power output

of 28 kWp is obtained (see Figure 24). In Annex M, the system configuration and the inverters properties

are included.

Figure 24. 28kWp multicrystalline PV panels layout (Own source)

7.3 Option 3 – 30.15 kWp

Finally, a last option with the same configuration as the previous one has been analyzed but using

monocrystalline PV panels with PERC technology and it offers a PV peak power of 30.15 kW (see Figure

25). Following the same structure as previous options, the system configuration and the inverters

properties can be found in Annex N.

Figure 25. 30.15kWp monocrystalline PV panels layout (Own source)

-40-

8 Model overview

8.1 Cost estimation

8.1.1 System with Li-Ion batteries and multicrystalline PV panels

Once the PV configurations are obtained, the technical and economic performance of each system will be

analyzed with a PV capacity of 44 kWp, 28 kWp and 30.15 kWp, respectively.

A 42 kWh Li-Ion will be included in HOMER to see if it is cost-effective to have a battery in the system

even it is a moderate reliable grid connected system. The value from the battery capacity is obtained

assuming an outage of 2 hours of duration with the aim to cover the critical load which is around 15 kW

and considering a round trip efficiency of 90% and a Depth of Discharge (DOD) of 80%.

As mentioned in Section 5.3.2 Green Team Costs Database, once the PV and battery size are known for

the desired system, these values should be included in the available database from the Green Team and

other projects developed with similar size and the same type of PV panel will be considered to obtain an

estimation of the costs. All the costs breakdown is depicted below in Figure 26 in which installation,

design & civil works, Li-Ion batteries and power electronics & equipment represent almost 60% of the

total cost of the system.

Figure 26. Costs breakdown of Hybrid System in UNDP Zimbabwe, including 44 kWp PV and 42 kWh Li-Ion batteries (UNDP, 2017)

In Annex O, it is included a table that summarizes the overall costs of the system and Figure 27 depicted

below serves to verify that the estimation for this project is in accordance with the rest of the project

developed, it can be seen in that our project (red square) follows the tendency line from the other projects

available in the database.

13%

16%

15%

29%

03%

06%

10%

05%05%

Solar Panels

Battery Storage

Power Electronics &Equipment

Installation, Design & CivilWorks

Technical Room

Maintenance

Freight

Contingency (MiscelleanousCosts)

UNDP Service Charge

-41-

Figure 27. Total project costs of installations with lower PV capacity than 90kWp (UNDP, 2017)

8.1.2 System without Li-Ion batteries and multicrystalline PV panels

The same procedure from the previous section is followed to obtain the costs breakdown shown in Figure

28, but without Lithium Ion batteries. The same system will be analyzed without batteries and it can be

noted that installation, design & civil works, solar panels and freight represent more than 75% of the total

costs of the project.

Figure 28. Costs breakdown of a 44 kWp PV solar system in UNDP Zimbabwe, Harare.(UNDP, 2017)

In this case, a verification of the total costs of the system is performed considering the projects developed

in UNDP with only PV system and no batteries included, and it really fits the tendency line from the

projects (see Figure 29 below). Check Annex P for further details.

Liberia GGuinea G

Sierra Leone G

SaoTome E

SaoTome J

SaoTome G

SaoTome EE

SaoTome T

Niger 50kW ENiger 50kW J

Niger 50kW G

Niger 50kW EE

Niger 40kW E

Niger 40kW G

Niger 40kW J

Niger 40kW EE

Djibouti FAO G

Djibouti FAO EE

Rajaf 35kW EE

Rajaf 35kW J

Rajaf 35kW G

Rajaf 43kW J

Mozambique

Sierra Leone 2

Rajaf

UNFPA Accommod.

Eritrea Djibouti FAO

Brazzaville

$0

$50.000

$100.000

$150.000

$200.000

$250.000

$300.000

$350.000

0 10 20 30 40 50 60 70PV System Size (kW)

21,9%

0,0%

8,2%

36,1%

0,0%

7,3%

17,5%

4,5%4,5%

Solar Panels

Battery Storage

Power Electronics &Equipment

Installation, Design & CivilWorks

Technical Room

Maintenance

Freight

Contingency (MiscelleanousCosts)

UNDP Service Charge

-42-

Figure 29. Total project costs for PV only installations (UNDP, 2017)

8.1.3 System without Li-Ion batteries and monocrystalline PV panels

A last case has been analyzed considering monocrystalline PV panels using PERC technology. Their price

is currently 27% higher than standard BSF PV panels, more precisely, the average price for polycrystalline

silicon solar modules is 0.288 $/W whereas monocrystalline PERC modules have a cost of 0.364 $/W

(PVinsights, 2018). With the continuous improvement and standardization of PERC mass production, the

processing cost is expected to reduce compared to BSF technologies (Green, 2015).

With a PV power output of 30.15 kW and using monocrystalline solar panels, the costs breakdown

obtained are the following ones (see Figure 30).

Figure 30. Costs breakdown of a 30.15 kWp PV solar system using monocrystalline PV panels in UNDP Zimbabwe, Harare.(UNDP, 2017)

SaoTome E

SaoTome J

SaoTome G

SaoTome EE

SaoTome T

Djibouti FAO G

Djibouti FAO EE

Mozambique

E…

Djibouti FAO

$0

$20.000

$40.000

$60.000

$80.000

$100.000

$120.000

$140.000

0 10 20 30 40 50 60PV System Size (kW)

24,6%

0,0%

6,8%

29,9%

0,0%

8,8%

20,9%

4,5%4,5%

Solar Panels

Battery Storage

Power Electronics &EquipmentInstallation, Design & CivilWorksTechnical Room

Maintenance

Freight

Contingency (MiscelleanousCosts)UNDP Service Charge

-43-

Moreover, in the graph below it can be seen that the red point is located over the tendency line as the other projects developed in UNDP have used multicrystalline PV panels which are more inexpensive. However, as the only difference in price compared to the other options is the type of PV panels, the total costs of the project still are really close to projects with standard PV panels. Check Annex Q for further details.

Figure 31. Total project costs for PV-Only installations (UNDP, 2017)

8.2 HOMER inputs

It is defined a project lifetime of 20 years and default values are left for discount and inflation rate at 8%

and 2%, respectively.

Regarding the grid characteristics, a grid price of 0.145 $/kWh is considered (RECP, 2017) with no

demand rates as there is no feed-in tariff available in the country. For this reason, HOMER should be

prevented from selling any electricity back to the grid, by setting the sellback rate to 0.01 $/kWh, the

optimization model will never sell electricity back to the grid. This way, it is easy to assess and control the

excess energy production from renewables in the results.

Moreover, it is not desired to charge the battery using electricity from the grid. To ensure this, a very low-

price threshold is set to 0.01 $/kWh.

Solar PV panels costs result from the Green Team Excel Database (fixed and O&M) and replacement

cost are set to zero. PV panels lifetime is set to 20 years, considering temperature effects and ground

reflectance of 20% and a derating factor of 70% which includes all PV system losses (module mismatch,

soiling, wiring losses, etc). To convert the DC generated power from PV to AC an inverter is included

with an efficiency of 90%.

Finally, a set of Li-Ion batteries is included as energy storage and their cost estimation comes from the

Green Team Excel Database. Replacement and O&M is set to zero as these costs are combined with PV

O&M cost and are already included in the PV component. It is considered a lifetime of 10 years with a

throughput of 4,040 kWh, which gives the maximum limit for battery regardless how much energy can

pass through the battery within a year.

SaoTome E

SaoTome J

SaoTome G

SaoTome EE

SaoTome T

Djibouti FAO G

Djibouti FAO EE

Mozambique

E…

Djibouti FAO

$0

$20.000

$40.000

$60.000

$80.000

$100.000

$120.000

$140.000

0 10 20 30 40 50 60PV System Size (kW)

-44-

44 kWp Hybrid System

with Li-Ion batteries

System without Li-Ion batteries

44 kWp 28 kWp 30.15 kWp

PERC

Total variable costs ($/kWp) 2,891.7 2,273.2 2,627.2 2,739.2

Battery variable costs

($/kWh) 591.6 0 0 0

Total maintenance O&M

costs ($/year/kWp) 75.6 58.0 91.2 83.7

Table 6. Project costs summary in UNDP Zimbabwe CO (Own Source)

Table 6 summarizes the projects costs used when doing all the simulations in HOMER which vary

depending on the PV power output and whether a Li-Ion battery is included or not. It can be noted that

the higher the PV power output, the lower the specific costs per kWp due to installation works and freight

are almost constant, and they get a lower percentage when the PV system size is high.

-45-

9 Optimization results

Once the simulation results are obtained in HOMER, there is no cost-effective solution including Li-Ion

batteries in the system as they are costly with a value of 54,530$ and they are not required in the Country

Office because they suffer very little outages nowadays. For this reason, simulations without Li-Ion

batteries are carried out with the cost estimation from Section 8.1.2 to see the performance of each

particular system that will be offered to UNDP Zimbabwe CO.

As it can be seen below, Table 7 summarizes the main figures of each simulation.

Option PV

[kWp]

Required

Investment

[$]

Renewable

Fraction

[%]

Solar

Production

[kWh/year]

Grid

Purchase

Savings

[$/year]

Annual CO2

emissions

savings

[kg/year]

1 44 100,021 37.0 67,500 7,600 30,000

2 28 73,562 25.0 42,950 5,200 20,616

3 30.15 82,587 26.7 46,248 5,525 21,946

Table 7. System proposals for UNDP Zimbabwe CO, Harare. (HOMER Energy, 2018)

9.1 Technical results

This section aims to show the relevant results of the optimized Solar PV system in UNDP Zimbabwe

Country Office in Harare.

9.1.1 Option 1 – 44 kW

Electrical production and consumption are summarized in Table 8 and Table 9, respectively. The

renewable fraction in this case is 37% and as both tables show there is a mismatch between electricity

generated and required which represents a 6.08 % of electricity excess.

Production kWh/year %

Generic flat plate PV 67,493 42.7

Grid purchases 90,623 57.3

Total 158,116 100

Table 8. Electrical production (HOMER Energy, 2018)

Consumption kWh/year %

AC primary load 142,708 100

Table 9. Electrical consumption (HOMER Energy, 2018)

Table 10 and Table 11 summarize PV production and annual performance. Its production corresponds to

42.7% of the total, with 17.5% capacity factor. The maximum power output is obtained in August, as

shown in Figure 56 in Annex R.

Rated capacity 44 kW

Mean power output 7.70 kW

-46-

Mean energy output 185 kWh/day

Capacity factor 17.5 %

Total production 67,493 kWh/year

Table 10. Generic flat plate PV production (HOMER Energy, 2018)

Minimum output 0 kW

Maximum output 37.1 kW

PV penetration 47.3 %

Hours of operation 4,418 hours/year

LCOE 0.170 $/kWh

Table 11. Generic flat plate PV performance (HOMER Energy, 2018)

As it is depicted below in Figure 32, the monthly average electric production is split into the energy

coming from the grid and the one obtained from the set of PV panels.

Figure 32. Monthly average electric production (HOMER Energy, 2018)

To finish the technical assessment of this option, a week performance of the system is shown in Figure

33, where the PV array covers the peak demand in the premises and as there is no battery in the system,

the remaining required energy is obtained from grid purchases. Due to season variability in the demand-

side and the difference GHI obtained along the year, each week shows a different performance of the

system, so another week is included in Annex R to reflect the variability of the system.

Figure 33. Consumption and production of the required electricity in week 34 (HOMER Energy, 2018)

-47-

9.1.2 Option 2 – 28 kW

Following the same scheme as the previous case, electrical production and consumption are summarized

in Table 12 and Table 13, respectively. The renewable fraction in this case is 25% and there is an excess of

electricity of 2.12 %, as it can be seen in both tables below showing a mismatch between electricity

generated and required.




Total 149,863 100






28.7% of the total, with 17.5% capacity factor. As the maximum power output depends on the GHI of

the location, the same month is obtained as in the previous case, so August offers the maximum PV

power, as shown in Figure 58 in Annex S.

Rated capacity 28 kW






Minimum output 0 kW




LCOE 0.171 $/kWh


In this case, it can be seen in Figure 34, that energy coming from the PV panels has a lower share in the

overall energy demand as this PV power output has been reduced 36% from the previous case.


-48-

Finally, week 34 is depicted below in Figure 35 where an excess of PV energy is produced only during the

weekends, and during office hours it reduces the amount of electricity required from the grid. The same as

mentioned in the previous section, in Annex S another week graphic consumption is included to see the

different performance.


9.1.3 Option 3 – 30.15 kW

Finally, this last system proposal shows its electrical production and consumption in Table 16 and Table

17, respectively. The renewable fraction in this case is 26.7 % and there is an excess of electricity of 2.59%,

as it can be seen in both tables below showing a mismatch between electricity generated and required.




Total 150,852 100






30.7 % of the total, with 17.5% capacity factor. The maximum power output is obtained in August, as

shown in Figure 60 in Annex T.

-49-

Rated capacity 30.2 kW






Minimum output 0 kW




LCOE 0.174 $/kWh


The option of installing monocrystalline PV panels offers 6.8 % more share of renewable energy and a

similar system performance as the previous case (see Figure 36 and Figure 37).



-50-

9.2 Economic results

9.2.1 Option 1 – 44 kW

Cost summary of the electrical system designed is presented in Table 20, resulting LCOE of 0.170 $/kWh

is slightly higher than the current price from the grid (0.145 $/kWh). However, the grid price may differ in

the near future and it can increase which results in a lower LCOE as it is shown in Section 11. Sensitivity

Analysis.

Capital ($) Yearly operating costs ($)

PV solar system 100,021 2,552

Grid 0 13,140

System 100,021 15,692

Table 20. Cost summary of HOMER simulation (HOMER Energy, 2018)

As it is shown in Figure 38, the payback time is 19 years with a grid tariff price of 0.145 $/kWh but as the

price of electricity is sensitive and may vary the results could be more attractive with a lower payback time

as it can be seen in Section 11. Sensitivity Analysis.

Figure 38. Cumulative cash flow comparison between PV system proposal and current system (HOMER Energy, 2018)

-51-

9.2.2 Option 2 – 28 kW

Cost summary of the second electrical system designed is presented in Table 21, the corresponding LCOE

is 0.171 $/kWh which has a higher value compared to the current price from the grid (0.145 $/kWh) but

as mentioned previously, the grid price may increase along the years which would result in a lower LCOE

as it is shown in Section 11. Sensitivity Analysis. It should be noted that fix costs involve installation of

the system and freight cost and they are independent from the PV size of the system, that is why the lower

the PV system the higher percentage they represent in the total costs.

Capital ($) Yearly operating costs ($)

PV solar system 73,562 2552

Grid 0 15,502

System 73,562 18,054


Operating costs are more expensive compared to the first option as the renewable fraction is reduced by

36%, so the grid purchases have a higher impact in the cost along the lifetime of the project.

As it is shown in Figure 39, the payback time is more than 20 years which is the project lifetime set (21

years to be precise) with a grid tariff price of 0.145 $/kWh.


-52-

9.2.3 Option 3 – 30.15 kW

Cost summary of the last electrical system designed with 30.15 kWp Solar PV system with monocrystalline

PV panels with PERC technology is presented in Table 22 with a LCOE of 0.174 $/kWh is also higher

than the current price from the grid (0.145 $/kWh).

Capital ($) Operating ($)

PV solar system 82,587 2552

Grid 0 15,168

System 82,587 17,720


As it is shown in Figure 40, the payback time is more than 20 years (21 years to be precise) with a grid

tariff price of 0.145 $/kWh.


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10 Planning

Once the business cases are developed and accepted by the Country Office, the following steps are

structured in the Seven-Step Solution Process. First, the Request for Quotation (RfQ) is written to choose

the vendor that will provide the Solar System to Zimbabwe and they will proceed with the following steps

shown in Figure 41.

Figure 41. Planning of UNDP Zimbabwe CO (Own source)

10.1 Site Survey

The vendor and its local partner would perform a site survey at UNDP Zimbabwe CO in Harare to verify

the possible issues they could encounter along the installation. Moreover, they will determine where the

equipment would be installed.

They would also check the buried cables and pipes to be careful during the works. They would take

advantage of being onsite to discuss with the head of the UNDP Office in Harare to schedule the

installation and minimize its impact to ensure proper working conditions for the people in the office.

10.2 Procurement

When the Bill of Material (BOM) is finished, the vendor will procure all the material and equipment

required for the project. All the material would be shipped to Beira, Mozambique as it is the nearest

harbor to Harare. The procurement is expected to last up to 12 weeks, being the largest process along the

installation, followed by 8 weeks to transport the 20ft container to Beira.

Then, the local partner would be in charge of the transportation of the container and the workers at once

to the Country Office with their own trucks. This way, the installation time and cost are reduced doing

only one trip and they reduce the risk of theft and degradation of material.

10.3 Installation and commissioning

The installation would be carried out by the local partner with close and constant support from the

vendor remotely and a vendor engineer would be onsite during the installation to supervise the progress

and the quality of the works.

Local labor from Zimbabwe will be included in the workforce to involve the community in developing

renewable projects and to improve the image of the UN locally. Moreover, they will ensure safety on the

site and try to minimize the impact of the works during working hours, using noisy and dusty machines

during break hours, and before or after working hours.

The project will be coordinated with the head of UNDP Zimbabwe CO, he will be informed of the whole

plan, the progress and any problem they could face along the process. It would be useful to have two

people from the UN in charge of general maintenance at the UN to attend to the works, this way local

workers will improve their skills to fix any possible issue once the system is commissioned.

When the project is finalized and the equipment is tested ensuring the system is working properly,

commissioning will be proceeded, keeping the heat of UNDP CO aware of the progress. Warranty

periods of all the equipment are also included and Solar PV panels, inverters and the rest of equipment

have 10 years, 5 years and 2 years of warranty.

Site SurveyProcurement

and shipment of the equipment

Installation and commissioning

Training Maintenance

-54-

10.4 Training

A minimum of two people should be trained on the functioning and basic maintenance of the system. It is

preferably to train the same UN workers that have been involved along the installation of the system as

they are already aware of the location and connection of the system.

The training will be done in English and in the local language, if possible. In addition, a manual

summarizing the main points of the training will be provided by the vendor to the end-user to make sure

they have on hand the important parts and safety recommendations of the training. Safety instructions and

preventive maintenance is detailed in Annex U, respectively.

After finishing of the installation, user acceptance testing (UAT) and training, a completion letter will be

signed by a representative of the UNDP as well as the vendor engineer onsite to certify the installation

and training and verifying the well-functioning of the system.

The overall tasks and Gantt chart of the project are depicted below in Table 23 and Figure 42,

respectively.

Task

Duration (weeks)

Preparation Signature of the contract 1

Site Survey 1

Logistics

Procurement of material 12

Transportation to Beira 8

Custom clearance (UNDP) 4

Transportation to Harare 1

Installation

Civil works 1

Installation at UNDP Zimbabwe CO

3

Commissioning, UAT, Training 1

Maintenance Maintenance of the solar system 3 years Table 23. Tasks duration of UNDP Zimbabwe CO in Harare (Own source)

Figure 42. Delivery and installation schedule of UNDP Zimbabwe CO (Own source)

15-ago 04-oct 23-nov 12-ene 03-mar 22-abr

Signature of the contract

Site Survey

Procurement of material

Transportation to Beira

Custom clearance (UNDP)

Transportation to Harare

Civil works

Installation at UNDP Zimbabwe CO

Commissioning, UAT, Training

Maintenance of the solar system 3 years

-55-

11 Sensitivity analysis

A sensitivity analysis has been carried out to see how the grid electricity price fluctuation can affect the

results of the renewable energy system proposal. The idea is to discover which value should be the grid

price tariff to ensure the same or lower LCOE of the system proposal. From this value on, the higher the

grid electricity price, the lower LCOE will be obtained which can be translated into a reduction of the

payback time, which would offer a system even more attractive in economic terms.

Figure 43. Sensitivity analysis of grid electricity price in UNDP Zimbabwe CO, Harare (Own source)

The grid electricity price threshold from which the LCOE of the system gets lower than the grid price is

0.19 $/kWh. Over this value, it can be seen in Figure 43 that LCOE decreases accordingly its value and

this means the payback time (PBT) would result in periods lower than 20 years.

When analysing the payback time for each case, if the grid electricity price would rise up to 0.22 $/kWh,

the PBT would be 11 years whereas if the grid price increases until 0.3 $/kWh the corresponding PBT

would be 7.7 years. In Annex V, cumulative cash flow graphs are included with a hypothetical grid price of

0.22 and 0.30 $/kWh.

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

Ele

ctri

city

Pri

ce (

$/kW

h)

Grid electricity price ($/kWh) LCOE ($/kWh)

-56-

12 Scenarios comparison

In this chapter, a comparison between the technical, economic and environmental results will be carried

out to see which option would offer major benefits to UNDP Zimbabwe CO.

First of all, analyzing the technical outcomes from all three options shown in Figure 44, it can be seen that

the higher the PV installed capacity, the higher the renewable fraction (RF). However, the gap between

PV capacity and RF is higher in option 1 as there is more excess produced in the system, 6.08% to be

precise. In case of option 2 and 3, they both have very little excess along the system performance, 2.12%

and 2.59%, respectively, offering more attractiveness as these two systems exploit their full potential.

Figure 44. Technical results comparison for UNDP Zimbabwe CO (Own source)

When it comes to the main figures that concern the Country Office, both economic and environmental

results are shown in Figure 45. The investment required in each of the cases is proportional to the PV

installed capacity, but option 3 includes innovative monocrystalline PV panels with cells developed with

PERC technology, which make its investment costs much higher as the current price of such PV panels

exceed almost 30% the standard BSF PV panels. For this reason, the LCOE in option 3 with PERC PV

panels is a bit higher than with standard BSF PV panels.

Figure 45. Economic and environmental results comparison for UNDP Zimbabwe CO (Own source)

-57-

Finally, annual CO2 emissions abated through each Solar system proposal is the main figure for the

Country Office as their goal is to reduce carbon footprint by shifting the energy from the Grid with the

Solar system. Looking into each option, option 1 has 30,000 kg CO2/year abated because its PV size is

higher, whereas both option 2 and 3 offer almost the same CO2 emissions savings which are

approximately 30% lower than in option 1, but option 3 has higher investment cost for using an

innovative PV panel in the system.

Taking into account the overall results commented previously and that the Country Office does not

require the Solar system to ensure security of supply as they are connected in a quite reliable grid suffering

very little outages, option 1 shows the maximum PV power output they can obtain with the available roof

in the building, but it has higher costs. As a matter of fact, I would recommend choosing either option 2

or 3 as they also cover UNDP Zimbabwe CO requirements with a lower capital investment.

-58-

13 Sustainability assessment

Sustainability concerns constitute a red thread that connects all the

parts of the project, in fact sustainability is the most important

characteristic that such a project requires to be successful.

This section will analyze the sustainability of the proposal dividing

the analysis in the three pillars of sustainability: economic, social and

environmental (see Figure 46). Only a project that embraces all these

three categories can be defined sustainable and can bring the

maximum advantage to the country office of Zimbabwe.

13.1 Economic

Economic sustainability is a key requirement to ensure the project could actually be implemented bringing

economic welfare to the organization. Thus, three economic parameters need be analyzed: capital cost,

operating cost and levelized cost of energy.

Capital cost is probably the most delicate of the three costs because it is the biggest obstacle to the

realization of the project and the one that incorporates the risk of the investment. In fact, a high capital

cost often creates an obstacle that blocks the implementation of a project because of difficulties in

gathering all the necessary funds. In this project, the high capital cost in all the proposals constitutes

indeed a big challenge, however its risk is smoothened by different aspects. First, the project is dedicated

to a Country Office which is willing to invest in a renewable energy project to reduce its carbon footprint

and be a showcase to the country implementing renewable energy projects. Therefore, it is reasonable

assuming that there are many financing schemes that Zimbabwe CO could follow in order to avoid a big

payment upfront. The last characteristic that decreases the risk of the investment is the fact the proposal is

based on rather proven technologies and that the project is in accordance with the United Nations SDGs.

Keeping operating costs low is important to grant that the hybrid energy system will be used in the future

years without adding cost constraints along its lifetime. The total operating costs of all system proposals

are 2552 $ per year which are based on the global maintenance of the renewable energy system and the

available backup diesel generator in the CO. In addition to these operating costs, it should be noted that

the remaining energy is provided by the grid, so yearly grid purchases are also included in the total

operating costs. These total operating costs of all the options offered are lower compared to the previous

annual expenses of the Country Office to cover the energy needs with grid purchases which were 20,695 $

per year.

Finally, the comparison between the LCOE and the electricity obtained from the grid shows that the

project would need approximately 20 years to recover the total investment because the grid tariff costs are

very low. However, from literature review in the country situation, it seems grid electricity price will

increase in the near future and this project will be even more attractive for the Country Office and will let

recover the money much faster. Implementing these solar PV systems in on-grid location can benefit the

organization, offering less dependence on grid based on fossil fuels energy sources and a reduction of

carbon footprint.

Figure 46. Sustainability triangle. (Kurry, 2011)

-59-

13.2 Social

The social aspect, especially in this project, is extremely important considering the wide impact that this

project could have on the local society. Social impact in this case can be summarized in three categories:

improved health and services, jobs opportunity and community empowerment.

The first explicit impact of improving the energy supply to the Country Office is the increased reliability

of energy to meet the load. In fact, access to energy is a security concern in these locations to ensure

constant communication in case there is crisis in the country and the United Nations need to react

immediately. For this reason, there areas with communication equipment in the compound require energy

24/7.

The proposed system could offer job opportunities for locals in a direct and indirect way. Direct job

opportunities come from the implementation of the Hybrid Energy System in the Country Office. As

people from the area will see the project and they have heard about the potential to install solar PV panels

in their country, their interest towards renewable energy will increase and they would like similar hybrid

systems in their offices.

A strong social impact of the project would be the effect on the local area. In fact, a reliable and secure

energy service is a key resource to empower a community or town, increasing the wellbeing of its citizens

thanks to the autonomy obtained from a reliable and non-fossil fuel dependent energy source. Improving

the United Nations compound in Harare could generate a domino effect in the community that could

bring other facilities to be improved. Indeed, the interest has already borne fruit and OIMT/UNDP is

starting to collaborate with other UN Agencies in Zimbabwe such as UNICEF and FAO.

13.3 Environmental

In this section, a deep environmental assessment will be assessed to analyze the impact of installing a Solar

PV system in Zimbabwe Country Office in Harare. The system proposals consist of an array of PV panels

with either 44 kWp or 28 kWp of rated capacity for multicrystalline PV panels and 30.15 kW for

monocrystalline PV panels. Therefore, Life Cycle Assessments (LCA) of the production of PV panels

both monocrystalline and multicrystalline will be analyzed to evaluate the environmental impact of this

project. Moreover, less dependency on the grid will lead to a reduction of greenhouse gases emitted.

The main process to be considered when analyzing the environmental assessment is the production of

both monocrystalline and multicrystalline silicon modules which is detailed in Section 4.1.1.

Manufacturing Process.

Checking scientific publications based on the environmental impact of the PV modules throughout their

lifetime, a study developed in China by Y. Fu, X. Liu and Z. Yuan in 2014 (Fu, Liu and Yuan, 2015)

analyzes the effects of producing both monocrystalline and multicrystalline silicon modules in China.

Since the electricity production in China is highly dependent in coal, they conclude that the production of

silicon from polysilicon, which is most energy intensive stage in the module production, accounts for a

great share of pollution generated. Moreover, manufacture monocrystalline modules causes slightly more

emissions than producing multicrystalline modules. Finally, the study also concludes that the transport to

the location selected for installing the modules does not represent a major environmental impact if

compared to the production process stages.

The cumulative energy demand (CED) to produce monocrystalline PV panels in China is 5000 MJ (1390

kWh/panel), whereas multicrystalline PV panels require 4500 MJ (1250 kWh/panel) (Yue, You and

Darling, 2014).

-60-

Table 24 shows the carbon footprint results consulted in the literature review:

Author(s) Year Characteristics Emissions of CO2 equivalent

(kg CO2-eq/kWh)

D. Yue, F. You and S.

B. Darling (Yue, You

and Darling, 2014)

2014

Monocrystalline 30-year

lifetime, efficiency 14 %

produced in China

0.0722

D. Yue, F. You and S.

B. Darling (Yue, You

and Darling, 2014)

2014

Multicrystalline 30-year

lifetime, efficiency 13.2 %

produced in China

0.0692

Table 24. Carbon footprint result per electricity generated (Own source)

An important parameter to consider is the CO2 equivalent payback time (EPBT) which is an indicator

that assesses how much time is needed to compensate the CO2 emitted during the production and

transport of the PV modules with the CO2 saved while generating electricity from the same modules

(Stoppato, 2008). In each proposal, the corresponding number of PV panels installed and the amount of

CED required to produce them in each case has been used to calculate the total amount of CO2 emitted

along the production of the PV modules. This value is compensated with the total useful yearly

production from PV panels as it offers energy savings from the grid with an equivalent annual CO2

savings to see which exact EPBT has each system proposal.

Considering the electricity generation coming from the grid in the Country Office, its corresponding

emissions of CO2 to generate electricity are 0.576 kg CO2/kWh which means that there will be a big

amount of carbon footprint savings due to the installation of PV panels in Harare as they will replace

purchases from the grid which is based on fossil fuel energy resources (UNEP, 2017). Therefore, Table 25

shows the overall EPBT of each system proposal in days as the system is big enough to compensate the

CO2 emitted along the production of the corresponding PV panels in less than a year in all three cases.

Type of PV panel PV power

output (kWp)

Nº of PV

panels

Annual CO2 savings

(kg CO2/year)

EPBT

(days)

Multicrystalline 44 139 30,000 147

Multicrystalline 28 90 20,616 138

Monocrystalline 30.15 90 21,946 151

Table 25. CO2 equivalent payback time for each system proposal in UNDP Zimbabwe Country Office (Own source)

-61-

14 Conclusions

A Solar installation presents a great opportunity for UNDP Zimbabwe CO; not only from an

environmental and sustainability point of view, but also from a social point of view. As the

geographical position of the country provides the CO with great solar potential, UNDP Zimbabwe

CO could fully benefit from a solar installation.

Once the three options offered have been assessed technically and economically, my advice is to

choose either option 2 (28kW multicrystalline BSF PV panels) in case Zimbabwe CO wants to follow

the same type of Solar System as other Country Offices, or option 3 (30.15kW monocrystalline PERC

PV panels) in case they want to be disruptive being the leaders in using an innovative technology for

PV panels in the United Nations.

From the economic point of view, none of them show profitability after 20 years with the current

price of electricity, however, the Country Office wants to reduce its carbon footprint and be a

showcase for the country to raise awareness of the importance of renewable energy.

For this reason, as the two last options have very similar technical and environmental results, my

advice is to choose option 2 as nowadays the price of monocrystalline PV panels developed with

PERC solar cells are more expensive compared to PV panels with standard BSF technology. Other

Country Offices are willing to implement Hybrid Energy Systems in the near future and they could

install PV panels with PERC solar cells as their price will be then more competitive and it will offer

more economic advantages than implementing this kind of PV panels right now.

Under that scope, this business case offers an optimal solution for a solar installation in UNDP

Zimbabwe CO in Harare. The system proposed has an estimated $73,562 initial investment, including

PV panels with 28 kWp of installed capacity. This would be sufficient to cover 25% energy

consumption at the UNDP Compound on an average day. Once the system is in place, UNDP

Zimbabwe CO will see 20,616 kg CO2 of annual CO2 savings versus maintaining the current energy

set-up and see a payback of the initial investment after 21 years. The technical, financial, and

environmental results of this study are summarized in Table 26 below.

The Green Energy Solutions team will fully support UNDP Zimbabwe CO as it moves towards a greener future and will start an official procurement process to identify a vendor which will provide the detailed design of the solar solution, aligning to the UNDP Seven-Step Process.

It should be highlighted that all the proposals were offered to UNDP Zimbabwe CO and the result was that other UN Agencies want to come on board in the project. Then, the next step is to install PCMM in all the interested buildings in the premises and create a complete Solar PV system for all UN House in Zimbabwe. The Green Energy Solutions team looks forward to work with the UN Zimbabwe CO to cooperatively contribute towards the achievement of championing the sustainable development goals.

UNDP Zimbabwe Country Office

Initial Investment 73,562 $

Solar Production 42,950 kWh/year

Installed Capacity 28 kWp

Renewable Fraction 25%

LCOE 0.171 $/kWh

Annual Savings 5,200 $/year

Annual CO2 abated 20,616 kg CO2/year

Table 26. Summary of Solar PV proposal for UNDP Zimbabwe CO. (Own source)

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Acknowledgements

First of all, I would like to thank my supervisor and manager in UNDP, Marcela Treviño and Shathiso

Nyathi, respectively, for providing constant and valuable guidance along the Master Thesis development

and internship.

Secondly, I would like to thank all the staff from UNDP Office of Information and Technology (OIMT)

for helping whenever needed and the good work environment they have created in the office.

A special thanks to my supervisor in KTH, Reza Fakhraie for helping me through the management of the

Master Thesis and solving whatever questions I might have had.

Last but not least I would like to thank my friends, my boyfriend Josep and his family for never losing

their faith in me and for always being there when I needed it the most.

-63-

Bibliography

Bostan, I., Gheorghe, A., Dulgheru, V., Sobor, I., Bostan, V. and Sochirean, A. (2013) Resilient Energy Systems. SPRINGER.

Chipika, J. T. and Kowero, G. (2000) ‘Deforestation of woodlands in communal areas of Zimbabwe: is it due to agricultural policies?’, Agriculture, Ecosystems & Environment. Elsevier, 79(2–3), pp. 175–185. doi: 10.1016/S0167-8809(99)00156-5.

Climatescope (2017) Zimbabwe — Climatescope 2017. Available at: http://global-climatescope.org/en/country/zimbabwe/#/ghg-management (Accessed: 18 March 2018).

Costantini, V., Gracceva, F., Markandya, A. and Vicini, G. (2007) ‘Security of energy supply: Comparing scenarios from a European perspective’, Energy Policy, 35(1), pp. 210–226. doi: 10.1016/j.enpol.2005.11.002.

Danish Wind Industry Association (2003) Wind Speed calculator. Available at: http://drømstørre.dk/wp-content/wind/miller/windpower web/en/tour/wres/calculat.htm (Accessed: 7 April 2018).

Energy Ageny, I. (2014) ‘Technology Roadmap Energy storage’. Available at: http://www.iea.org/termsandconditionsuseandcopyright/ (Accessed: 29 April 2018).

Eyedro (2017) Products - Eyedro. Available at: http://eyedro.com/products/ (Accessed: 13 May 2018).

Ferrazza, F. (2012) Crystalline Silicon, Practical Handbook of Photovoltaics. Elsevier Ltd. doi: 10.1016/B978-0-12-385934-1.00004-0.

Fu, Y., Liu, X. and Yuan, Z. (2015) ‘Life-cycle assessment of multi-crystalline photovoltaic (PV) systems in China’, Journal of Cleaner Production. Elsevier Ltd, 86, pp. 180–190. doi: 10.1016/j.jclepro.2014.07.057.

Green, M. A. (2015) ‘The Passivated Emitter and Rear Cell (PERC): From conception to mass production’, Solar Energy Materials and Solar Cells. Elsevier, 143, pp. 190–197. doi: 10.1016/j.solmat.2015.06.055.

HOMER Energy (2017) HOMER Pro Manual. Available at: https://www.homerenergy.com/products/pro/docs/3.11/index.html (Accessed: 13 May 2018).

HOMER Energy (2018) HOMER Pro - Microgrid Software for Designing Optimized Hybrid Microgrids. Available at: https://www.homerenergy.com/products/pro/index.html (Accessed: 13 May 2018).

International Energy Agency (2017) Renewables. Available at: https://www.iea.org/topics/renewables/ (Accessed: 28 April 2018).

ITRPV (2018) ‘International Technology Roadmap for Photovoltaic (ITRPV) - Results 2017’, International Technology Roadmap for Photovoltaic, 9(March), pp. 1–37. doi: http://www.itrs.net/Links/2013ITRS/2013Chapters/2013Litho.pdf.

JA Solar (2017) JA SOLAR. Available at: http://www.jasolar.com/html/en/ (Accessed: 17 June 2018).

Joe O’Connor (2017) Battery Showdown: Lead-Acid vs. Lithium-Ion – Solar Micro Grid. Available at: https://medium.com/solar-microgrid/battery-showdown-lead-acid-vs-lithium-ion-1d37a1998287 (Accessed: 13 May 2018).

Kurry, A. (2011) Athena Kurry | Thesis Portfolio. Available at: https://macaulay.cuny.edu/eportfolios/akurry/ (Accessed: 17 June 2018).

Landau, C. R. (2017) Optimum Tilt of Solar Panels. Available at: http://www.solarpaneltilt.com/ (Accessed: 3 May 2018).

Léna, G. (2013) Rural Electrification with PV Hybrid Systems, IEA PVPS Task 9 – CLUB-ER. Available at: http://www.iea-pvps.org/index.php?id=1&eID=dam_frontend_push&docID=1590.

Malmquist, A. (2017) ‘An Introduction to Solar PV-Integrated Microgrid Design Agenda • What is a

Microgrid ?’

-64-

Malmquist, A. (2018) ‘Small-scale windmills in Polygeneration Micro wind turbines’, pp. 1–7.

Mobbs, M. (2016) ‘Lead acid vs. lithium-ion battery comparison’. Available at: www.sustainablehouse.com.au (Accessed: 13 May 2018).

Mulvaney, D. (2015) ‘Overview of PV manufacturers, technologies, supply chains, performance standards & certifications’, p. 19.

Nandi, S. K. and Ghosh, H. R. (2010) ‘Prospect of wind–PV-battery hybrid power system as an alternative to grid extension in Bangladesh’, Energy. Pergamon, 35(7), pp. 3040–3047. doi: 10.1016/J.ENERGY.2010.03.044.

NASA Surface meteorology and Solar Energy - Available Tables (2017). Available at: https://eosweb.larc.nasa.gov/cgi-bin/sse/grid.cgi?email=skip%40larc.nasa.gov&step=2&lat=-9.571&lon=119.692&num=300081&p=grid_id&p=swvdwncook&p=swv_dwn&p=exp_dif&p=avg_dnr&p=avg_kt&p=daylight&p=dy_cos_sza&p=cos_zmt&p=ret_tlt0&p=mnavail1&p=deficit1&p=no_sun (Accessed: 5 December 2017).

Notton, G., Diaf, S. and Stoyanov, L. (2011) ‘Hybrid photovoltaic/wind energy systems for remote locations’, Energy Procedia, 6, pp. 666–677. doi: 10.1016/j.egypro.2011.05.076.

Ohrbeck, B., Gylling, N., Diaz, P., Bechmann, A., Conti, D., Davis, N., Hansen, B. O., Kelly, M. C., Mortensen, N. G., Nielsen, M., Badger, J. and Peña, A. (2018) ‘MyWindTurbine – Energy Yield Calculations’. Available at: http://orbit.dtu.dk/files/126905164/Mywindturbine_DK_Validation.pdf (Accessed: 28 April 2018).

OIMT/CIAS (2017) ‘Smart Facilities’, pp. 1–14.

OIMT, U. (2018) Green Energy - 7 step solar solution. Available at: https://intranet.undp.org/unit/oolts/oimt/CIAS/greenenergy/SitePages/7 step solar solution.aspx (Accessed: 22 April 2018).

Peel, M. C., Finlayson, B. L. and McMahon, T. A. (2007) ‘Updated world map of the Köppen-Geiger climate classification’, Hydrology and Earth System Sciences, 11(5), pp. 1633–1644. doi: 10.5194/hess-11-1633-2007.

Pew Research Center (2015) Religions in Zimbabwe | PEW-GRF. Available at: http://www.globalreligiousfutures.org/countries/zimbabwe#/?affiliations_religion_id=0&affiliations_year=2010&region_name=All Countries&restrictions_year=2015 (Accessed: 15 March 2018).

Predescu, M. (2016) ‘Economic evaluation of small wind turbines and hybrid systems for residential use’, Renewable Energy and Environmental Sustainability. EDP Sciences, 1, p. 33. doi: 10.1051/rees/2016023.

PVinsights (2018) PVinsights. Available at: http://pvinsights.com/ (Accessed: 15 June 2018).

RECP (2017) Energy Sector – RECP Africa-EU Renewable Energy Cooperation Programme. Available at: https://www.africa-eu-renewables.org/market-information/zimbabwe/energy-sector/ (Accessed: 15 March 2018).

Renewable Energy Agency, I. (2012) ‘Renewable Energy Cost Analysis: Wind Power’. Available at: https://www.irena.org/DocumentDownloads/Publications/RE_Technologies_Cost_Analysis-WIND_POWER.pdf (Accessed: 29 April 2018).

Renewable Energy Agency, I. (2015) ‘IRENA Battery Storage Report 2015’. Available at: https://www.irena.org/documentdownloads/publications/irena_battery_storage_report_2015.pdf (Accessed: 29 April 2018).

Renewable Energy Agency, I. (2017) ‘Renewable Power Generation Costs in 2017’, International Renewable Energy Agency. Available at: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Jan/IRENA_2017_Power_Costs_2018.pdf (Accessed: 1 May 2018).

Schinckel, A. (2016) Effect of Temperature. Available at: http://pvcdrom.pveducation.org/CELLOPER/TEMP.HTM (Accessed: 22 April 2018).

-65-

SDSN, S. D. S. N. (2018) Overview | World Happiness Report. Available at: http://worldhappiness.report/ (Accessed: 22 April 2018).

Seigneur, H., Mohajeri, N., Brooker, R. P., Davis, K. O., Schneller, E. J., Dhere, N. G., Rodgers, M. P., Wohlgemuth, J., Shiradkar, N. S., Scardera, G., Rudack, A. C. and Schoenfeld, W. V. (2016) ‘Manufacturing metrology for c-Si photovoltaic module reliability and durability, Part I: Feedstock, crystallization and wafering’, Renewable and Sustainable Energy Reviews. Elsevier, 59, pp. 84–106. doi: 10.1016/j.rser.2015.12.343.

Sinha, S. and Chandel, S. S. (2014) ‘Review of software tools for hybrid renewable energy systems’, Renewable and Sustainable Energy Reviews. Pergamon, 32, pp. 192–205. doi: 10.1016/J.RSER.2014.01.035.

Stoppato, A. (2008) ‘Life cycle assessment of photovoltaic electricity generation’, Energy, 33(2), pp. 224–232. doi: 10.1016/j.energy.2007.11.012.

The Fund for Peace (2017) Fragile States Index | The Fund for Peace. Available at: http://fundforpeace.org/fsi/ (Accessed: 22 April 2018).

Trading Economics (2015) CO2 emissions (kt) in Zimbabwe. Available at: https://tradingeconomics.com/zimbabwe/co2-emissions-kt-wb-data.html (Accessed: 14 April 2018).

U.S. Department of Energy (2017) Solar Performance and Efficiency | Department of Energy. Available at: https://www.energy.gov/eere/solar/articles/solar-performance-and-efficiency (Accessed: 29 April 2018).

U.S. Energy Information Administration (2017a) ‘International Energy Outlook 2017’. Available at: www.eia.gov/ieo (Accessed: 28 April 2018).

U.S. Energy Information Administration (2017b) Using Solar Electricity at Home | Department of Energy. Available at: https://www.energy.gov/energysaver/buying-and-making-electricity/using-solar-electricity-home (Accessed: 28 April 2018).

UN, U. N. (2018) Sustainable development goals - United Nations. Available at: https://www.un.org/sustainabledevelopment/sustainable-development-goals/ (Accessed: 22 April 2018).

UNDP (2016) ‘Undp Support To the Implementation of Sustainable Development Goal 7’.

UNDP (2017) UNDP Green Energy Solutions. Available at: https://undp.eyedro.com/ (Accessed: 13 May 2018).

UNDP, O. of I. M. and T. (2018) Office of Information Management and Technology - Green Energy. Available at: https://intranet.undp.org/unit/oolts/oimt/CIAS/greenenergy/SitePages/Home.aspx (Accessed: 22 April 2018).

UNEP (2017) UNEP Environmental Data Explorer - The Environmental Database (search | map | graph | download). Available at: http://ede.grid.unep.ch/results.php (Accessed: 15 June 2018).

United Nations (2017) Member States | United Nations. Available at: http://www.un.org/en/member-states/index.html (Accessed: 24 March 2018).

United Nations Population Division (2017) World Population Prospects - Population Division - United Nations. Available at: https://esa.un.org/unpd/wpp/DataQuery/ (Accessed: 22 April 2018).

Vogt, M. R., Schulte-Huxel, H., Offer, M., Blankemeyer, S., Witteck, R., Kontges, M., Bothe, K. and Brendel, R. (2017) ‘Increased yield of PERC compared to Al-BSF cells due to inherently lower module operating temperatures’, in 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC). IEEE, pp. 1–3. doi: 10.1109/PVSC.2017.8366278.

Woodhouse, M., Feldman, D., Fu, R., Horowitz, K., Chung, D., Jordan, D., Kurtz, S., Jones-Albertus, R. and Department of Energy, U. (2015) ‘On the Path to SunShot: The Role of Advancements in Solar Photovoltaic Efficiency, Reliability, and Costs’. Available at: https://www.nrel.gov/docs/fy16osti/65872.pdf (Accessed: 15 June 2018).

World Bank Group (2018) Global Solar Atlas. Available at: http://globalsolaratlas.info/?c=8.331083,29.047852,6&s=4.82826,31.574707&m=sg:temp (Accessed: 22 April 2018).

-66-

World Weather & Climate Information (2016) Climate and average monthly weather in: Juba, Sudan. Available at: https://weather-and-climate.com/average-monthly-Rainfall-Temperature-Sunshine,Juba,Sudan (Accessed: 22 April 2018).

World Weather and Climate Information (2016) Climate and average monthly weather in: Harare, Zimbabwe. Available at: https://weather-and-climate.com/average-monthly-Rainfall-Temperature-Sunshine,Harare,Zimbabwe (Accessed: 18 March 2018).

WWEA, W. W. E. A. (2016) World Wind Energy Association – Small Wind World Market: Back on track again. Available at: http://www.wwindea.org/small-wind-world-market-back-on-track-again/ (Accessed: 28 April 2018).

Yue, D., You, F. and Darling, S. B. (2014) ‘Domestic and overseas manufacturing scenarios of silicon-based photovoltaics: Life cycle energy and environmental comparative analysis’, Solar Energy, 105, pp. 669–678. doi: 10.1016/j.solener.2014.04.008.

Zachary Shahan (2018) Solar Panel Prices Continue Falling Quicker Than Expected (#CleanTechnica Exclusive) | CleanTechnica. Available at: https://cleantechnica.com/2018/02/11/solar-panel-prices-continue-falling-quicker-expected-cleantechnica-exclusive/ (Accessed: 13 May 2018).

Zambezi River Authority (2018) Kariba Reservoir Data | Zambezi River Authority. Available at: http://www.zaraho.org.zm/hydrology/kariba-reservoir-data (Accessed: 8 April 2018).

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Annex A Roughness Class

Roughness Class Roughness Length (m) Landscape Type

0 0.0002 Water surface

0.2 0.0005 Inlet water

0.5 0.0024 Completely open terrain with a smooth surface, e.g.

concrete runways in airports, mowed grass, etc.

1 0.03 Open agricultural area without fences and very scattered

buildings.

1.5 0.055 Agricultural land with some houses and 8-metre-tall

sheltering hedgerows with a distance of approximately 1250

metres.

2 0.1 Agricultural land with some houses and 8-metre-tall

sheltering hedgerows with a distance of approximately 500

metres.

2.5 0.2 Agricultural land with many houses, shrubs and plants, or 8-

metre-tall sheltering hedgerows with a distance of

approximately 250 metres.

3 0.4 Villages, small towns, agricultural land with many or tall

sheltering hedgerows, forests and very rough and uneven

terrain.

3.5 0.8 Larger cities with tall buildings.

4 1.6 Very large cities with tall buildings and skyscrapers.

Table 27. Roughness definition according to landscape type (Danish Wind Industry Association, 2003)

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Annex B Aeolos-H 3kW Micro-wind turbine specifications

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Annex C Micro-wind turbine main parameters

The global interest into micro-wind turbines is increasing particularly in developing countries where off-

grid installations are successful. Micro-wind turbines in areas without access to the national electricity grid

are sometimes economically competitive and could serve as a substitute for the existing expensive and

polluting diesel generator (WWEA, 2016). Accurate wind installations require several years of data

collection in order to avoid seasonal and inter-annual variability. Moreover, these analysis are expensive

and time consuming (Ohrbeck et al., 2018).

Wind turbines convert kinetic energy from the wind into mechanical power, which is further converted

into electricity. Wind is widely available throughout the world and it does not emit direct greenhouse gases

or other pollutants, so it has very little direct environmental impacts. The power extracted from a wind

turbine is estimated with the following equation (Malmquist, 2018):

𝑃𝑤𝑖𝑛𝑑 =1

2· 𝜌 · 𝐴 · 𝑣3 · 𝐶𝑝

Where 𝜌 is the air density, 𝐴 is the swept area of the blades (m2), 𝑣 the wind velocity (m/s) and 𝐶𝑝 the

capacity factor which ranges from 0.35 to 0.45 with the Betz limit of 𝐶𝑝=0.59.

The most widely used technology for small wind turbines is the Horizontal Axis Wind Turbine (HAWT).

Despite this, the interest in Vertical Axis Wind Turbine (VAWT) is increasing due to lower cut-in wind

speeds and installation costs (Renewable Energy Agency, 2012).

When comparing small wind turbines with utility-scale wind systems, small wind turbines generally achieve

lower capacity factors and have higher capital costs but they can supply unmet electricity demand in

remote off-grid areas, offering local and social benefits (Renewable Energy Agency, 2012)

The main challenges that small wind turbines face are the following (Renewable Energy Agency, 2012):

− Siting: Collecting accurate wind measurement with an anemometer for a statistically long period is costly for the time required to obtain a yearly wind distribution. It is a critical issue for small wind turbines and as a result, many systems perform poorly from bad siting, increasing the need of further investigation.

− Tower height: It is a key factor for small wind turbines as low-height wind turbines have low capacity factors and they are exposed to excessive turbulence. A solution is using tall tower but the mounting and installation costs increase significantly.

− Urban environments: It is the most used location of small wind turbines and wind speeds are often low and turbulent due to surrounding obstacles such as trees, buildings or other infrastructures. As a result, there is low wind predictability and it has negative influence on the power production (Bostan et al., 2013).

− Capital investment: The key drivers of wind power economics are linked to investment costs, O&M cost (fixed and variable), capacity factor and economic lifetime. The dominating upfront capital cost (including towers and installation) represents up to 84% of the total installed cost.

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Annex D PCMM Data Credentials for Zimbabwe CO

Portal: https://undp.eyedro.com/

Username: [email protected]

Password: p@ssw0rd

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Annex E Yearly load profile Matlab code

clear all; clc % Enter FILE NAME with Eyedro data % =============================================== Excel_name = 'South Sudan Load.csv'; % =============================================== mydata = readtable(Excel_name); % Enter country name prompt = {'Please enter the name of the country office'}; xq = inputdlg(prompt,'Country Office Name'); country = xq{1} timezonename = mydata.Properties.VariableNames(3); % display the time zone

of the country time_zone = timezonename{1} %% Define Load Data and TimeSeries start_data = find(mydata.Total,1); % finds the index of the first non-zero

value start_dayx = floor(start_data/24); % defines the first day in the dataset % set cut_days if needs to cut of first few days cut_days = 0; start_day = start_dayx + cut_days; % Define load and datetime variables load = mydata.Total(start_day*24+1:end)/1000; % Power Consumption (kWh) time_extract = mydata.(1)(start_day*24+1:end); % Timeseries value for the

corresponding load period % Create Time and Date vector depending if Excel input file has 12 or 24H

format. % TF = any(~cellfun('isempty',strfind(time_extract(1),'AM'))) %if TF == 1 timestamp = datetime(time_extract,'Format','dd/MM/yyyy h:mm a'); % elseif TF == 0 % timestamp = datetime(time_extract,'InputFormat','dd-MM-yyyy HH:mm'); %else % print('error') %end0 % remove extra zeros %timestamp2string = string(timestamp_raw); %clean_time = strip(timestamp2string,'left','0'); %timestamp = datetime(clean_time,'InputFormat','dd-MM-yyyy HH:mm');

% Creating vectors with Day numbers and Hours DayNumber = weekday(timestamp)-1; HourNumber = hour(timestamp); % subset load data to start with a new day (hour = 0) and end with a full

day (hour = 23) first_full_day = find(HourNumber==0,1); last_full_day = find(HourNumber==23,1,'last'); timestamp = timestamp(first_full_day:last_full_day); load = load(first_full_day:last_full_day);

% set a threshold under which the load value is considered an error (as %

of max load)

prompt1 = {'Please enter the minimum valid value for load (as % of max

load, try 0.07) '}; if exist('error_load_level','var') == 1 def_threshold = {num2str(error_load_level)}; else def_threshold = {'0.07'}; end

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load_in_cell = inputdlg(prompt1,'Error Load Level', 1, def_threshold); error_load_level = str2double(load_in_cell{1}); % if the metered load value is under this threshold (based on maximum

recorded load), % the record for that hour will be considered an error

% Sometimes it may happen that PCMM data readings is stack at a certain

value (error_value) for % multiple hours or even days. The following line can be uncommneted to % replace the error value with zero % error_value = 0.16; % for i = 1 : length(load) % if load(i) == error_value % load(i) = 0; % else % load(i) = load(i); % end % end

% PLOT figure(1) plot(timestamp, load, 'color', 'b'); hold on; plot(timestamp(load<max(load)*error_load_level),

load(load<max(load)*error_load_level), 'r.','MarkerSize',18) counts = length(load(load<max(load)*error_load_level)); % count the

occurance of missing values %plot(idx,zeros(length(idx)),'o','MarkerSize',10); title(country,'FontSize',16,'FontWeight','Bold'); ylabel('Load (kW)','FontSize',14,'FontWeight','Bold'); xlabel('Timestamp','FontSize',14,'FontWeight','Bold'); legend('consumption',sprintf('The number of errorneous readings = %f',

counts)) set(gcf,'Color','white') prompt2 = {'Do you want to change error threshold? (Yes/No) '}; cell_answer = inputdlg(prompt2,'Threshold Level'); checkpoint = cell_answer{1} %if exist('checkpoint','var') == 1 if checkpoint == 'Yes' close all %if exist('error_load_level','var') == 1 prompt1 = {'Please enter the minimum valid value for load (as % of max

load, try 0.07) '}; def_threshold = {num2str(error_load_level)}; load_in_cell = inputdlg(prompt1,'Error Load Level', 1, def_threshold); error_load_level = str2double(load_in_cell{1}); % if the metered load value is under this threshold (based on maximum

recorded load), % the record for that hour will be considered an error

% Sometimes it may happen that PCMM data readings is stack at a certain

value (error_value) for % multiple hours or even days. The following line can be uncommneted to % replace the error value with zero % error_value = 0.16; % for i = 1 : length(load) % if load(i) == error_value % load(i) = 0; % else % load(i) = load(i); % end

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% end

% PLOT figure(1) plot(timestamp, load, 'color', 'b'); hold on; plot(timestamp(load<max(load)*error_load_level),

load(load<max(load)*error_load_level), 'r.','MarkerSize',18) counts = length(load(load<max(load)*error_load_level)); % count the

occurance of missing values %plot(idx,zeros(length(idx)),'o','MarkerSize',10); title(country,'FontSize',16,'FontWeight','Bold'); ylabel('Load (kW)','FontSize',14,'FontWeight','Bold'); xlabel('Timestamp','FontSize',14,'FontWeight','Bold'); legend('consumption',sprintf('The number of errorneous readings = %f',

counts)) set(gcf,'Color','white') end

%% Select to which season each month belongs monthss = timestamp.Month; % creates a vector with the number of month for

each load value prompt_month = {'Please enter the SUMMER months as space-seperated

numbers',... 'Enter WINTER months','Enter the months which can be considered as

transition period'}; %x = input(prompt) %prompt = {'Enter matrix size:','Enter colormap name:'}; %dlg_title = 'Input'; num_lines = 1; defaultans = {'6 7 8','1 11','2 3 4 5 9 10 12'}; xx = inputdlg(prompt_month,'Seasons', num_lines, defaultans); summer_months = str2num(xx{1}); winter_months = str2num(xx{2}); transition_months = str2num(xx{3}); %transition_months = [6 7 8]; %Jul Aug Sep %transition_months = [5 9]; % Apr May jun %summer_months = [10 11 12 1 2 3 4]; % Dec Jan Feb summer = ismember(monthss, summer_months); % Logical Variable trans = ismember(monthss, transition_months); % Logical Variable winter = ismember(monthss, winter_months); % Logical Variable % Sellect working days and weekends (could be different in Arab countries) DayNumber = weekday(timestamp)-1; DayNumber( DayNumber(:,1)==0, 1 ) = 7; prompt = {'Please enter the office working days as space-seperated

numbers',... 'Enter the office non-working days'}; num_lines = 1; defaultans = {'1 2 3 4 5','6 7'}; xy = inputdlg(prompt,'Office Working Days', num_lines, defaultans); wdays = str2num(xy{1}); wends = str2num(xy{2}); weekdays = ismember(DayNumber, wdays); % Logical Variable weekends = ismember(DayNumber, wends); % Logical Variable %% MONTHLY VALUES load_M = mydata.Total/1000; % Power Consumption (kWh) time_M = mydata.(1); % Timeseries value for the corresponding load period % Create Time and Date vector depending if Excel input file has 12 or 24H

format. % TF = any(~cellfun('isempty',strfind(time_extrmonthssact(1),'AM'))) %if TF == 1

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timestamp_M = datetime(time_M,'Format','dd/MM/yyyy h:mm a'); mont = timestamp_M.Month; months_vec = []; for i = 1:12 months_vec = [months_vec sum(load_M(mont==i))]; end months_vec = months_vec'; bar(months_vec) %% Defining the boundaryies of the existing dataset %first_Monday = find(DayNumber==1,1); %load = load(first_Monday:end); fulldays = floor(length(load)/24); rng(0,'twister'); % initialize the random number generator to make the

results in this example repeatable load_for_sim = load(1:24*fulldays); DayNumber_for_sim = DayNumber(1:24*fulldays); firstday = day(timestamp(1), 'dayofyear'); % the date of the first day in

the recorded data firstweek = ceil(firstday/7); % the week number of the first week in the

dataset lastday = day(timestamp(length(timestamp)), 'dayofyear'); % the day number

of the last day in the dataset lastweek = floor(lastday/7); % the week number of the last week in the

dataset %% LOAD PROFILES FOR HOMER sum_subset_WD = (load(summer==1 & weekdays==1)); sum_subset_WE = (load(summer==1 & weekends==1)); tran_subset_WD = (load(trans==1 & weekdays==1)); %tran_subset_WD = [tran_subset_WD(7:end,:); zeros(6,1)] ; tran_subset_WE = (load(trans==1 & weekends==1)); win_subset_WD = (load(winter==1 & weekdays==1)); win_subset_WE = (load(winter==1 & weekends==1)); % Excluding error values from timeseries by replacing them with 0 load(load == 0) = NaN; sum_subset_WD(sum_subset_WD < max(load)*error_load_level) = NaN; sum_subset_WE(sum_subset_WE < max(load)*error_load_level) = NaN; tran_subset_WD(tran_subset_WD < max(load)*error_load_level) = NaN; tran_subset_WE(tran_subset_WE < max(load)*error_load_level) = NaN; win_subset_WD(win_subset_WD < max(load)*error_load_level) = NaN; win_subset_WE(win_subset_WE < max(load)*error_load_level) = NaN; % The average hourly load for k = 1:24 allyear_load(k,:) = nanmean(load(k:24:length(load),:)); allyear_max(k,:) = max(load(k:24:length(load),:)); end % The average hourly summer load for m = 1:24 summer_weekday(m,:) = nanmean(sum_subset_WD(m:24:length(sum_subset_WD),:)); summer_weekend(m,:) = nanmean(sum_subset_WE(m:24:length(sum_subset_WE),:)); summer_maximum(m,:) = max(sum_subset_WD(m:24:length(sum_subset_WD),:)); %

maximum load recorded in each hour STD_summer_weekday(m,:) =

nanstd(sum_subset_WD(m:24:length(sum_subset_WD),:)); % Standard Deviation STD_summer_weekend(m,:) =

nanstd(sum_subset_WE(m:24:length(sum_subset_WE),:)); % Standard Deviation

end % The average hourly transition-period load for j = 1:24 TransPer_weekday(j,:) =

nanmean(tran_subset_WD(j:24:length(tran_subset_WD),:));

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TransPer_weekend(j,:) =

nanmean(tran_subset_WE(j:24:length(tran_subset_WE),:)); TransPer_maximum(j,:) = max(tran_subset_WD(j:24:length(tran_subset_WD),:));

% maximum load recorded in each hour STD_TransPer_weekday(j,:) =

nanstd(tran_subset_WD(j:24:length(tran_subset_WD),:)); % Standard Deviation STD_Transper_weekend(j,:) =

nanstd(tran_subset_WE(j:24:length(tran_subset_WE),:)); % Standard Deviation

end % the average hourly winter load for n = 1:24 winter_weekday(n,:) = nanmean(win_subset_WD(n:24:length(win_subset_WD),:)); winter_weekend(n,:) = nanmean(win_subset_WE(n:24:length(win_subset_WE),:)); winter_maximum(n,:) = max(win_subset_WD(n:24:length(win_subset_WD),:)); %

maximum load recorded in each hour STD_winter_weekday(n,:) =

nanstd(win_subset_WD(n:24:length(win_subset_WD),:)); % Standard Deviation STD_winter_weekend(n,:) =

nanstd(win_subset_WE(n:24:length(win_subset_WE),:)); % Standard Deviation end %% LOAD PROFILE PLOTS if isempty(sum_subset_WD) & isempty(tran_subset_WD) tot = 1; figure(3); c1 = plot(1:24, winter_weekday,'--','Color',[0 .5 0] ,'LineWidth',1.4);

hold on; c2 = plot(1:24, winter_weekend,'--','Color','r','LineWidth',1.4); hold

on; c3 = plot(1:24, winter_maximum, 'o', 'Color','k','LineWidth',1.4); leg_var = legend([c1 c2 c3],'Average Load on Weekdays','Average Load on

Weekends','Maximum Load') set(gca,

'XTick',[0:3:24],'XTickLabel',{'0','3am','6am','9am','12pm','3pm','6pm','9p

m','12am'},'FontSize',14) title(horzcat(country,' Winter

Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',16,'FontWeight','Bold'); xlabel('Hour','FontSize',16,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); elseif isempty(win_subset_WD) & isempty(tran_subset_WD) tot = 1; figure(3); c1 = plot(1:24, summer_weekday,'--','Color',[0 .5 0] ,'LineWidth',1.4);

hold on; c2 = plot(1:24, summer_weekend,'--','Color','r','LineWidth',1.4); hold

on; c3 = plot(1:24, summer_maximum, 'o', 'Color','k','LineWidth',1.4); leg_var = legend([c1 c2 c3],'Average Load on Weekdays','Average Load on



m','12am'},'FontSize',14) title(horzcat(country,' Summer

Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',16,'FontWeight','Bold'); xlabel('Hour','FontSize',16,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); elseif isempty(win_subset_WD)

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tot = 2; % number of graphs figure(3); subplot(1,tot,1); a1 = plot(1:24, summer_weekday,'--','Color',[0 .5 0] ,'LineWidth',1.4);

hold on; a2 = plot(1:24, summer_weekend,'--','Color','r','LineWidth',1.4); hold

on; a3 = plot(1:24, summer_maximum, 'o', 'Color','k','LineWidth',1.4); leg_var = legend([a1 a2 a3],'Average Load on Weekdays','Average Load on




Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',14,'FontWeight','Bold'); xlabel('Hour','FontSize',14,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); set(gcf,'Color','white') subplot(1,tot,2); b1 = plot(1:24, TransPer_weekday,'--','Color',[0 .5 0]

,'LineWidth',1.4); hold on; b2 = plot(1:24, TransPer_weekend,'--','Color','r','LineWidth',1.4);

hold on; b3 = plot(1:24, TransPer_maximum, 'o', 'Color','k','LineWidth',1.4); set(gca,


m','12am'},'FontSize',14) title(horzcat(country,' Transition Period

Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',16,'FontWeight','Bold'); xlabel('Hour','FontSize',16,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); elseif isempty(sum_subset_WD) tot = 2; subplot(1,tot,1); c1 = plot(1:24, winter_weekday,'--','Color',[0 .5 0] ,'LineWidth',1.4);


on; c3 = plot(1:24, winter_maximum, 'o', 'Color','k','LineWidth',1.4); leg_var = legend([c1 c2 c3],'Average Load on Weekdays','Average Load on




Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',16,'FontWeight','Bold'); xlabel('Hour','FontSize',16,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); subplot(1,tot,2); b1 = plot(1:24, TransPer_weekday,'--','Color',[0 .5 0]


hold on; b3 = plot(1:24, TransPer_maximum, 'o', 'Color','k','LineWidth',1.4);

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set(gca,



Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',16,'FontWeight','Bold'); xlabel('Hour','FontSize',16,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); set(gcf,'Color','white') elseif isempty(tran_subset_WD) tot = 2; figure(3); subplot(1,tot,1); a1 = plot(1:24, summer_weekday,'--','Color',[0 .5 0] ,'LineWidth',1.4);






Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',14,'FontWeight','Bold'); xlabel('Hour','FontSize',14,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); set(gcf,'Color','white') subplot(1,tot,2); c1 = plot(1:24, winter_weekday,'--','Color',[0 .5 0] ,'LineWidth',1.4);


on; c3 = plot(1:24, winter_maximum, 'o', 'Color','k','LineWidth',1.4); set(gca,



Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',16,'FontWeight','Bold'); xlabel('Hour','FontSize',16,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); set(gcf,'Color','white') else tot = 3; figure(3); subplot(1,tot,1); a1 = plot(1:24, summer_weekday,'--','Color',[0 .5 0] ,'LineWidth',1.4);





m','12am'},'FontSize',14)

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title(horzcat(country,' Summer

Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',14,'FontWeight','Bold'); xlabel('Hour','FontSize',14,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); set(gcf,'Color','white') subplot(1,tot,2); b1 = plot(1:24, TransPer_weekday,'--','Color',[0 .5 0]


hold on; b3 = plot(1:24, TransPer_maximum, 'o', 'Color','k','LineWidth',1.4); set(gca,



Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',16,'FontWeight','Bold'); xlabel('Hour','FontSize',16,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); subplot(1,tot,3); c1 = plot(1:24, winter_weekday,'--','Color',[0 .5 0] ,'LineWidth',1.4);


on; c3 = plot(1:24, winter_maximum, 'o', 'Color','k','LineWidth',1.4); set(gca,



Load'),'FontSize',14,'FontWeight','Bold') ylabel('Average Load (kW)','FontSize',16,'FontWeight','Bold'); xlabel('Hour','FontSize',16,'FontWeight','Bold'); xlim([1 24]); ylim([0 max(load)+5]); set(gcf,'Color','white') end % Save the graph as PNG file file_name_graph = horzcat('Load_graph','_',country); % creates txt file

name print(file_name_graph,'-dpng') %% LOAD EXPORT TO EXCEL to_Excel_weekdays = []; to_Excel_weekends = []; % creates a matrix HOURS x MONTHS, where the loop selects the correct month for i = 1 : 12 if ismember(i,winter_months) to_Excel_weekdays = [to_Excel_weekdays, round(winter_weekday,1)]; elseif ismember(i, transition_months) to_Excel_weekdays = [to_Excel_weekdays, round(TransPer_weekday,1)]; elseif ismember(i, summer_months) to_Excel_weekdays = [to_Excel_weekdays, round(summer_weekday,1)]; else print('error') end end % the same for weekends for i = 1 : 12 if ismember(i,winter_months)

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to_Excel_weekends = [to_Excel_weekends, round(winter_weekend,1)]; elseif ismember(i, transition_months) to_Excel_weekends = [to_Excel_weekends, round(TransPer_weekend,1)]; elseif ismember(i, summer_months) to_Excel_weekends = [to_Excel_weekends, round(summer_weekend,1)]; else print('error') end end % Average Daily Load for Summer and Winter & Highest Load record from

Eyedro data avg_summer_day = (sum(summer_weekday).*5 + sum(summer_weekend).*2)/7; avg_winter_day = (sum(winter_weekday).*5 + sum(winter_weekend).*2)/7; max_load = max(load); loads = {'Average summer weekday (kWh/day)', avg_summer_day; 'Average

winter weekday (kWh/day)', avg_winter_day; 'Max Load (kW)', max_load}; % to Excel file file_name_out = horzcat('Daily_Load_Profile','_',country); % creates txt

file name xlswrite(file_name_out,to_Excel_weekdays,1,'A1:L24') xlswrite(file_name_out,to_Excel_weekends,2,'A1:L24') xlswrite(file_name_out,loads,3,'A1:B3') %% % format bank % leaves two digits for decimals % outputs = table(round(summer_weekday,1), round(summer_weekend,1),

round(summer_maximum,1), round(TransPer_weekday,1),... % round(TransPer_weekend,1), round(TransPer_maximum,1),

round(winter_weekday,1), round(winter_weekend,1), round(winter_maximum,1));

% save load profile values in a table % outputs_weekday =

table(round(summer_weekday,1),round(summer_weekday,1),round(summer_weekday,

1),round(summer_weekday,1),... %

round(winter_weekday,1),round(winter_weekday,1),round(winter_weekday,1),rou

nd(winter_weekday,1),round(winter_weekday,1),... %

round(winter_weekday,1),round(summer_weekday,1),round(summer_weekday,1)); % outputs_weekend =

table(round(summer_weekend,1),round(summer_weekend,1),round(summer_weekend,

1),round(summer_weekend,1),... %

round(winter_weekend,1),round(winter_weekend,1),round(winter_weekend,1),rou

nd(winter_weekend,1),round(winter_weekend,1),... %

round(winter_weekend,1),round(summer_weekend,1),round(summer_weekend,1)); % load_profile_weekday = table2array(outputs_weekday); % a matrix for

export to Homer % load_profile_weekend = table2array(outputs_weekend); % a matrix for

export to Homer % %file_name_load = horzcat('Daily_Load_Profile','_',country,'.txt'); %

creates txt file name % %fileID = fopen(file_name_load,'w'); % %fprintf(fileID,'%6s\r\n','Load'); % this shows the column name % %fprintf(fileID,'%4.3f %4.2f\r\n',load_profile_matrix); % % %fclose(fileID); % avg_summer_day = sum(summer_weekday); % avg_winter_day = sum(winter_weekday); % max_load = max(load); % loads = {'Average summer weekday', avg_summer_day; 'Average winter

weekday', avg_winter_day; 'Max Load', max_load}; % % to Excel file

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% file_name_out = horzcat('Daily_Load_Profile','_',country); % creates txt

file name % xlswrite(file_name_out,load_profile_weekday,1,'A1:L24') % xlswrite(file_name_out,load_profile_weekend,2,'A1:L24') % xlswrite(file_name_out,loads,3,'A1:B3')

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Annex F PCMM Data Collection

Figure 47. Weekly energy consumption in UNDP Zimbabwe from July 1st 2017 to March 26th 2018 (Eyedro, 2017)

Figure 48. Monthly average consumption of UNDP Zimbabwe CO in Harare (Eyedro, 2017)

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Annex G JA Solar PV panel datasheet

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Annex H JINKO Solar PV panel datasheet

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Annex I Energy Efficiency measures

Lighting

− Turn off lights when not in use or when natural daylight is sufficient. This can reduce lighting expenses by 10-40%.

− Replacing incandescent light bulbs with energy efficient LEDs saves 30-80% on energy bills.

− Maximize daylighting. Open or close blinds to make the best use of natural daylight and take advantage of skylights or other natural daylight sources to reduce lighting during daytime hours.

Cooling

Active

− Regularly change or clean HVAC filters every month during peak cooling or heating season. Dirty filters cause overwork in the equipment, resulting in lower indoor air quality.

− Set the thermostat of ACs to minimum 21ºC or higher; setting it colder will not cool the room faster and it will use 3-5% electricity more per degree cooler.

− Keep exterior doors and windows closed while running your HVAC. It will help avoiding wasteful loss of heated or cooled air.

− Install a fan lowers the felt temperature by 5%.

Passive

− Use shades and blinds to control direct sun irradiation through windows in both summer and winter to prevent or encourage heat gain.

− Control direct sun irradiation through windows depending on the season and local climate. During cooling season, it is recommended to block direct heat gained from the sun irradiation through glass on the east and especially west sides of the facility. Depending on the facility, options such as solar screens, solar films, awnings, and vegetation can offer the desired shade. Over time, trees can attractively shade the facility and help cleaning the air. Interior curtains or drapes can benefit, but it is better to prevent the solar heat flux getting through the glass and inside the building. During heating season, with the Sun’s low position in the south, unobstructed southern windows can contribute solar heat gain during the day (outside blinds are the best option).

− Repair damaged insulation and replace missing insulation with thicknesses calculated for the operating and ambient conditions of the mechanical system.

− A reflective roof can reduce the roof surface temperature by up to 15.5ºC, preventing the sun’s heat being transferred into the building, depending on the region’s climate.

− Ventilate the office thoroughly in the morning; this will require less cooling for the rest of the day.

− If ventilation is required later during the day, all windows should be opened at the same time for a few minutes to maximize the effect and then be closed again. Windows should not be kept open during longer periods. It could be useful to measure the air quality in the room through CO2 sensors.

− Plants in the office can increase humidity, filter CO2, pollutants from air and even lessen stress.

Office of Information Management & Technology, Country Office ICT Advisory Services Unit Prepared by: Cathrin Stadler and Montserrat Pitarch, Green Energy Team, OIMT Copenhagen Created on: 06 Feb 2018 Last Updated: 07 Feb 2018 ISO 9001 Approved for Release by: Gerald Demeules, Global ICT Advisor

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Appliances

− Using a laptop instead of a desktop reduces electricity consumption by a factor of five. If you must have a desktop, be sure to get an LCD screen instead of an outdated CRT screen. Enable the power management function on your computer: contrary to popular belief, screen savers do not save energy.

− Use a power strip for your office equipment to easily turn multiple devices on and off at the wall, all at once.

− Consolidate standalone office equipment to achieve a ratio of one device (typically a networked multifunction device) per 10 or more users. Typical cost savings can reach 30-40 % including electricity, hardware, consumables (paper, ink, and toner), and maintenance.

Employee behaviour and commitment

− Installing energy efficient products is only a small first step of the way towards a more sustainable office space – employee’s behaviour and the correct and sustainable usage of equipment is the most important contribution.

− Reward energy-efficient behaviours and habits to engage employees in helping your organization save energy.

− Educate staff about how their behaviours affect energy use. Some teams have created energy patrols to monitor and inform others when energy is wasted.

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Annex J Compound picture

Figure 49. Building 9 and 10 of UN Zimbabwe Country Office in Harare ((UNDP, 2017)

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Annex K UNDP Zimbabwe CO Blueprint

Figure 50. UNDP Zimbabwe Office Blueprints. (UNDP, 2017)

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Annex L 44 kWp PV*SOL results

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Annex M 28 kWp PV*SOL results

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Annex N 30.15 kWp PV*SOL results

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Annex O Costs estimation with Li-Ion battery

Figure 51. Projects Costs of UNDP Zimbabwe CO, including Li-Ion batteries and multicrystalline PV panels in the system (UNDP, 2017)

Figure 52. Project Cost Estimation from Database (UNDP, 2017)

0 200 400 600 800

Solar panels

Battery Storage

Power Electronics & Equipment

Installation, Design & Civil Works

Technical Room

Maintenance

Freight

Miscelleneous Costs

UNDP Service Costs

Total Project Costs

Amount ($)Millares

Acti

vity

Project Costs

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Annex P Costs estimation without Li-Ion battery

Figure 53. Projects Costs of UNDP Zimbabwe CO, without Li-Ion batteries and multicrystalline PV panels in the system (UNDP, 2017)

Figure 54. Project costs estimation from Database (UNDP, 2017)

0 200 400 600 800

Solar panels

Battery Storage



Technical Room

Maintenance

Freight

Miscelleneous Costs

UNDP Service Costs

Total Project Costs

Amount ($)Millares

Acti

vity

Project Costs

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Annex Q Costs estimation of PV system with

monocrystalline PV panel using PERC technology

Figure 55. Projects Costs of UNDP Zimbabwe CO, without Li-Ion batteries and monocrystalline PV panels with PERC technology in the system

(UNDP, 2017)

0 200 400 600 800

Solar panels

Battery Storage



Technical Room

Maintenance

Freight

Miscelleneous Costs

UNDP Service Costs

Total Project Costs

Amount ($)Millares

Acti

vity

Project Costs

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Annex R 44 kWp solar PV system results HOMER

Figure 56. PV power output (HOMER Energy, 2018)


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Annex S 28 kWp solar PV system results HOMER



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Annex T 30.15 kWp solar PV system results HOMER



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Annex U Preventive Maintenance of a Solar PV System

It consists on a series of period tasks that aim at minimizing the likelihood of unplanned equipment

failure.

Regular Preventive Maintenance

1) The person in charge should be asked about the operation of the system to identify if there are

any problems.

If changes in the operation are noticed, a careful check-up of the system must be made.

Changes must always be noted, so that if a problem develops it is possible to track how did it

start.

2) Every part of the solar system must be checked for good connections, proper operation and

cleanliness.

Problems with any part of the system will interfere with the operation of the rest of the

system, causing it to work poorly. Especially, problems with wiring, panels or the controller

can greatly damage the battery and dirt and corrosion can damage electrical equipment.

3) All components that are not in good condition must be repaired or replaced.

Do not make changes to the system without authorization.

Bi-Monthly Maintenance

A bi-monthly check must be performed.

All defects must be reported in the maintenance log book. Even if it cannot be fixed immediately, it allows

to keep track of problems.

Please remember that before any interventions inside the protection boxes all breakers must be

turned off.

AC Distribution Board

1) Open the connection box and try the distribution breakers and main breaker. Make sure they

open the different circuits correctly.

2) Open all breakers (Off- position) and the transfer switch so that all the system is turned off.

3) Turn off the generator and the solar system by turning off the AC breaker inside the AC

Distribution Board.

4) Check the connection inside the AC distribution board to make sure there is no corrosion, lost

connection or any damage to the connection box and the electrical wiring.

5) Tie up any lost connection.

6) Clean the AC Distribution board with a soft cloth.

Solar Panels

1) Check the panel mounting structure is strong and well attached

2) Make sure the panels are not broken

3) Check there are no shading problems due to vegetation. If there are, trim the vegetation

accordingly.

4) Clean the solar panels with clean water and dry them with a soft cloth

5) Check individual string voltage and current for any disruption

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Charge Controller and Inverter

1) Check all equipment is securely mounted

2) Perform a visual inspection for corrosion and general damage

3) Clean the fans of the charge controller and the inverter with a dry cloth

4) Ensure there is no humidity or corrosion in the connection of the inverter and the charge

controller or inside of the AC distribution board.

5) Check charge controller charging currents for any disruption

Battery Bank

1) Ensure there is no object preventing air flows; if there is, remove it

2) Perform a visual check of all screwed connectors as well as the battery racks

3) Clean the battery with a dry cloth

4) Check the state of charge of the battery

5) Note the run time of the battery when during its first uses when it is fully-charged and use as a

reference of the battery run time.

6) Consider replacing the battery if:

a. The battery run time drops below 80% of the original run time

b. The battery charge time increases significantly

Generator

1) Check the genset for any alarms or messages

2) Perform an inspection for leakages and/ or loose connections

3) Check oil level, refill if necessary

4) Check coolant and fuel level, refill if necessary

5) Clean / change the air filter, if necessary

6) Drain Fuel Tank Water and Sediment. Collect water and sediments in a suitable container and

follow local regulations for their disposal.

Bi-Annual Maintenance

A bi-monthly check is highly recommended.

Please remember that before any interventions inside the protection boxes all breakers must be turned off.

Fuse Replacement

The state of the fuses must be checked. In order to do so, follow the steps indicated below:

1) Open the junction boxes of the solar panels, located on the main pillars of the mounting

structure.

2) Open the breaker of the solar panels (Off-position)

3) Measure the continuity of the fuses with a multimeter, making sure all of them are working

correctly.

4) If no continuity is observed, that means the fuse is broken and must be replaced with a fuse

with the same characteristics.

5) Test the fuses again

6) Close the junction box.

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Generator

1) Check alarms or messages

2) Inspect any leakages and loose connections

3) Check oil level. Refill if necessary

4) Check coolant and fuel level. Do not fill above maximum

5) Check air filters. Clean and replace if necessary. Air filters should be replaced at least once per

year, regardless of the number of cleanings.

6) Clean oil filters and replace if necessary. Replacement should be made after 500 service hours

or 1 year latest

7) Revise fuel filters and replace if necessary. Replacement should be made after 500 service

hours or 1 year latest

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Annex V Sensitivity analysis

Figure 62. 44 kWp Solar PV system cashflow comparison with a grid price of 0.22 $/kWh (HOMER Energy, 2018)

Figure 63. 44 kWp Solar PV system cashflow comparison with a grid price of 0.30 $/kWh (HOMER Energy, 2018)

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