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 14. Generic flat plate PV production (HOMER Energy, 2018) ...............................................................47
Table 15. Generic flat plate PV performance (HOMER Energy, 2018) ............................................................47
Table 16. Electrical production (HOMER Energy, 2018) ....................................................................................48
Table 17. Electrical consumption (HOMER Energy, 2018) ................................................................................48
Table 18. Generic flat plate PV production (HOMER Energy, 2018) ...............................................................49
Table 19. Generic flat plate PV performance (HOMER Energy, 2018) ............................................................49
Table 20. Cost summary of HOMER simulation (HOMER Energy, 2018) .....................................................50
Table 21. Cost summary of HOMER simulation (HOMER Energy, 2018) .....................................................51
Table 22. Cost summary of HOMER simulation (HOMER Energy, 2018) .....................................................52
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 34. Monthly average electric production (HOMER Energy, 2018) ........................................................47
Figure 35. Consumption and production of the required electricity in week 34 (HOMER Energy, 2018) .48
Figure 36. Monthly average electric production (HOMER Energy, 2018) ........................................................49
Figure 37. Consumption and production of the required electricity in week 34 (HOMER Energy, 2018) .49
Figure 38. Cumulative cash flow comparison between PV system proposal and current system (HOMER
Energy, 2018) ...............................................................................................................................................................50
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Figure 39. Cumulative cash flow comparison between PV system proposal and current system (HOMER
Energy, 2018) ...............................................................................................................................................................51
Figure 40. Cumulative cash flow comparison between PV system proposal and current system (HOMER
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
Figure 58. PV power output (HOMER Energy, 2018) ...................................................................................... 116
Figure 59. Consumption and production in week 26 at UNDP Zimbabwe CO (HOMER Energy, 2018)
..................................................................................................................................................................................... 116
Figure 60. PV power output (HOMER Energy, 2018) ...................................................................................... 117
Figure 61. Consumption and production in week 26 at UNDP Zimbabwe CO (HOMER Energy, 2018)
..................................................................................................................................................................................... 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
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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)
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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.
-31-
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
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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.
Production kWh/year %
Generic flat plate PV 42,950 28.7
Grid purchases 106,913 71.3
Total 149,863 100
Table 12. Electrical production (HOMER Energy, 2018)
Consumption kWh/year %
AC primary load 142,708 100
Table 13. Electrical consumption (HOMER Energy, 2018)
Table 14 and Table 15 summarize PV production and annual performance. Its production corresponds to
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
Mean power output 4.90 kW
Mean energy output 118 kWh/day
Capacity factor 17.5 %
Total production 42,950 kWh/year
Table 14. Generic flat plate PV production (HOMER Energy, 2018)
Minimum output 0 kW
Maximum output 23.6 kW
PV penetration 30.1 %
Hours of operation 4,418 hours/year
LCOE 0.171 $/kWh
Table 15. Generic flat plate PV performance (HOMER Energy, 2018)
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.
Figure 34. Monthly average electric production (HOMER Energy, 2018)
-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.
Figure 35. Consumption and production of the required electricity in week 34 (HOMER Energy, 2018)
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.
Production kWh/year %
Generic flat plate PV 46,248 30.7
Grid purchases 104,604 69.3
Total 150,852 100
Table 16. Electrical production (HOMER Energy, 2018)
Consumption kWh/year %
AC primary load 142,708 100
Table 17. Electrical consumption (HOMER Energy, 2018)
Table 18 and Table 19 summarize PV production and annual performance. Its production corresponds to
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
Mean power output 5.28 kW
Mean energy output 127 kWh/day
Capacity factor 17.5 %
Total production 46,248 kWh/year
Table 18. Generic flat plate PV production (HOMER Energy, 2018)
Minimum output 0 kW
Maximum output 25.4 kW
PV penetration 32.4 %
Hours of operation 4,418 hours/year
LCOE 0.174 $/kWh
Table 19. Generic flat plate PV performance (HOMER Energy, 2018)
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).
Figure 36. Monthly average electric production (HOMER Energy, 2018)
Figure 37. Consumption and production of the required electricity in week 34 (HOMER Energy, 2018)
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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)
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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
Table 21. Cost summary of HOMER simulation (HOMER Energy, 2018)
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.
Figure 39. Cumulative cash flow comparison between PV system proposal and current system (HOMER Energy, 2018)
-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
Table 22. Cost summary of HOMER simulation (HOMER Energy, 2018)
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.
Figure 40. Cumulative cash flow comparison between PV system proposal and current system (HOMER Energy, 2018)
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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)
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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.
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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)
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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).
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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)
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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-
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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
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,' 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
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,' 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]
,'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,
'XTick',[0:3:24],'XTickLabel',{'0','3am','6am','9am','12pm','3pm','6pm','9p
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);
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]); 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);
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set(gca,
'XTick',[0:3:24],'XTickLabel',{'0','3am','6am','9am','12pm','3pm','6pm','9p
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]); 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);
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
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,' 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); 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); 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]); 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);
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
Weekends','Maximum Load') set(gca,
'XTick',[0:3:24],'XTickLabel',{'0','3am','6am','9am','12pm','3pm','6pm','9p
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]
,'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,
'XTick',[0:3:24],'XTickLabel',{'0','3am','6am','9am','12pm','3pm','6pm','9p
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]); subplot(1,tot,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); 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]); 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
Power Electronics & Equipment
Installation, Design & Civil Works
Technical Room
Maintenance
Freight
Miscelleneous Costs
UNDP Service Costs
Total Project Costs
Amount ($)Millares
Acti
vity
Project Costs
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Annex Q Costs estimation of PV system with
monocrystalline PV panel using PERC technology
Figure 55. Projects Costs of UNDP Zimbabwe CO, without Li-Ion batteries and monocrystalline PV panels with PERC technology in the system
(UNDP, 2017)
0 200 400 600 800
Solar panels
Battery Storage
Power Electronics & Equipment
Installation, Design & Civil Works
Technical Room
Maintenance
Freight
Miscelleneous Costs
UNDP Service Costs
Total Project Costs
Amount ($)Millares
Acti
vity
Project Costs
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Annex R 44 kWp solar PV system results HOMER
Figure 56. PV power output (HOMER Energy, 2018)
Figure 57. Consumption and production in week 26 at UNDP Zimbabwe CO (HOMER Energy, 2018)
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Annex S 28 kWp solar PV system results HOMER
Figure 58. PV power output (HOMER Energy, 2018)
Figure 59. Consumption and production in week 26 at UNDP Zimbabwe CO (HOMER Energy, 2018)
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Annex T 30.15 kWp solar PV system results HOMER
Figure 60. PV power output (HOMER Energy, 2018)
Figure 61. Consumption and production in week 26 at UNDP Zimbabwe CO (HOMER Energy, 2018)
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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)