Master Level Thesis

Techno-economic study for a 50 MW PV plant in Nigeria

Master thesis 15 credits, 2020 Solar Energy Engineering

Author: Jacob Kelly

Supervisors: Frank Fiedler Michel Battikh

Examiner: Ewa Wäckelgård

Course Code: EG3011

Examination date: 18th December 2020

K

Dalarna University

Solar Energy

Engineering

European Solar Engineering School

No.276, December 2020

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Abstract

As part of Nigeria’s drive to increase electricity production capacity and shift to renewable sources, a new 50 MW photovoltaic (PV) plant is proposed for a town in north-west Nigeria. Rather than using conventional monofacial modules and fixed mounting, it is of interest to consider a selection of new technologies which are attracting growing attention in the global utility PV market. These can increase energy output, and could be used to advantage in this 50 MW plant. However, the technologies, namely bifacial modules and solar tracking, are more expensive than their conventional counterparts, while their relative performance depends on the latitude and climate of the plant location. Thus their economic benefit cannot be taken for granted. The aim of this study is to propose multiple designs for the 50 MW plant using different combinations of module and mounting technologies, finding their economic order of merit by estimating their respective levelised costs of electricity (LCOEs). Using the simulation software PVsyst, the electricity production of different plant layouts and component configurations was estimated. Key parameters such as tilt angle and pitch distance were varied in order to optimise each configuration of technologies. Having sourced economic data from the industry and literature, lifetime plant costs were calculated, which in combination with lifetime electricity production, were used to estimate the LCOE. As expected, results indicated that the optimum configuration was bifacial modules mounted on horizontal single-axis tracking (SAT), followed by monofacial modules on horizontal SAT. Fixed installations had greater LCOEs by a reasonable margin, while the LCOE difference between monofacial and bifacial modules on fixed mounting was within the error of the calculation, meaning this choice relies on more accurate input data. A sensitivity analysis allowed uncertainty in the results to be gauged, and highlighted the factors which most influence LCOE, so that efforts to increase profitability can be focussed in the right places. Finally, suggestions are offered to help optimise bifacial and tracking installations by comparison with conventional plants. The conclusions drawn herein will be specifically relevant to the Swedish developer and EPC contractor Svenska Solenergigruppen which, in due course, will submit a plant design proposal to the project developer of the 50 MW plant. However, it is hoped that this work will act as a guide for any EPC contractor or developer working on a utility PV plant in sub-Saharan Africa, allowing efficient design of an optimal system.

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Acknowledgements

I would like to express my gratitude to Frank Fiedler for his clear guidance throughout this thesis, and to Michel Battikh for advising me where the literature could not, on industrial matters. In particular I would like to thank both Frank and Desiree Kroner for their understanding and accommodating attitudes throughout the project which have allowed me to complete this work, and to enjoy it as I did so. Thank you Desiree for your support and friendship which guided me through my time in Dalarna.

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Contents

1 Introduction ................................................................................................................................... 7 Aims ......................................................................................................................................... 2 Overview of method ............................................................................................................. 2 Previous work ......................................................................................................................... 2 Theoretical background ........................................................................................................ 5

2 Detailed methodology and input parameters .......................................................................... 10 Selection of simulation software ........................................................................................ 10 Site and horizon ................................................................................................................... 11 Source analysis ...................................................................................................................... 12 Albedo ................................................................................................................................... 14 Choice of equipment ........................................................................................................... 15 Plant design ........................................................................................................................... 16 Soiling .................................................................................................................................... 21 Other losses .......................................................................................................................... 21 Degradation .......................................................................................................................... 22 System lifetime ................................................................................................................... 22 Performance ratio calculation .......................................................................................... 22 Cost tables........................................................................................................................... 23 Incentives ............................................................................................................................ 26 Cost of capital .................................................................................................................... 26 Lifetime cost calculations ................................................................................................. 28

3 Results and Discussion ............................................................................................................... 30 Monofacial fixed .................................................................................................................. 30 Bifacial fixed ......................................................................................................................... 33 Monofacial tracking ............................................................................................................. 37 Bifacial tracking .................................................................................................................... 40 Configuration comparison .................................................................................................. 43 Sensitivity analysis ................................................................................................................ 45 Effect of further limitations on results ............................................................................. 47 Choice of configuration ...................................................................................................... 50

4 Conclusions .................................................................................................................................. 51

5 References .................................................................................................................................... 52

6 Appendices ................................................................................................................................... 56

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Abbreviations

Abbreviation Description

BOS Balance of system

CAPEX Capital expenditure

EPC Engineering, procurement and construction

EW East-west

HJT Heterojunction

IAM Incidence angle modifier

IBC Interdigitated back contact

ILR Inverter loading ratio

IRR Internal rate of return

LCOE Levelised cost of electricity

MPP Maximum power point

NS North-south

OPEX Operating expenditure

PPA Power purchase agreement

PR Performance ratio

PV Photovoltaic

SAT Single-axis tracking

STC Standard test conditions

TMY Typical meteorological year

WACC Weighted average cost of capital

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Nomenclature

Symbol Description Unit

𝑏 Economic levered beta %

𝐶bank,amor Amortisation paid on the bank loan US dollar

𝐶bank,int Interest paid on the bank loan US dollar

𝐶equity Cost of initial investment through equity US dollar

𝐶ini,inv Cost of initial investment US dollar

𝐶OPEX Lifetime operating expenditure US dollar

𝑐OPEX(𝑦)

Operating expenditure of the year in question US dollar

𝐶PV Lifetime cost of the project US dollar

𝐷 Fraction of the project financed by debt %

𝑑𝑡bank Debt tenor Years

𝐸grid Energy fed to the grid over the period of interest kWh

𝐸(𝑦) Energy output of plant in year 𝑦 kWh

𝐺ref Irradiance under standard test conditions W/m2

𝐺T Irradiance on the module surface W/m2

𝐼DC max,inverter Inverter maximum operating input current A

𝐼MPP,string,73 °C Current of a module string at maximum power point at 73 °C

A

𝐼POA Irradiation in the plane of array over the period of interest

kWh/m2

𝐼𝐿𝑅 Inverter loading ratio Dimensionless

𝐼𝑅 Inflation rate %

𝐼𝑅bank Interest rate on the bank loan %

𝐿𝐶𝑂𝐸 Levelised cost of electricity USD/MWh

𝑀𝑅𝑃 Market risk premium %

𝑛𝑖nputs/inverter Number of inputs used per inverter Dimensionless

𝑛inverters Number of inverters Dimensionless

𝑛mod,max Maximum number of modules per string Dimensionless

𝑛mod,min Minimum number of modules per string Dimensionless

𝑛strings/input Number of module strings per input Dimensionless

𝑛strings/inverter Number of module strings per inverter Dimensionless

𝑛strings/inverter,max Maximum number of module strings per inverter Dimensionless

𝑃AC,inv Total output AC power of the inverters kW

𝑃DC,array Nameplate DC capacity of the array kW

𝑃string Power of each module string kW

𝑃𝑅 Performance ratio %

𝑅𝐹𝑅 Mean risk-free equity rate %

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Symbol Description Unit

𝑇air Ambient air temperature °C

𝑇cell Cell temperature °C

𝑇NOCT Normal operating cell temperature °C

𝑇𝑅 Corporate tax rate %

𝑉 inv,max Inverter maximum input voltage V

𝑉MPP Voltage at module maximum power point under standard test conditions

V

𝑉MPP inv,min Voltage at lower bound of inverter maximum power point operating range

V

𝑉MPP mod,73 °C Voltage at module maximum power point, at maximum predicted operating temperature 73 °C

V

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1 Introduction As the world takes steps to tackle climate change following the Paris Climate Agreement of 2015, renewable energies such as solar and wind are key players in the shift to greener energy. Not only are the technologies beneficial for the environment, they are also becoming economically favourable. According to a report this year [1], electricity produced from new solar and wind farms is now cheaper than from new coal power stations in all major markets, and this will likely be the case worldwide by 2030. With prices falling, global solar PV capacity has increased almost exponentially since the year 2000 [2]. While Africa receives the most solar radiation of any continent [3], the resource has so far been underexploited. As of 2019, less than 1 % of the world’s solar power generation was found in Africa with only 5 GW in operation [3]. With PV technology potentially offering the cheapest electricity for 40 % of Africa’s population, it is beginning to see more investment [4]. In West Africa, Nigeria is still in the very early stages of PV development, with only 30 MW installed by the end of 2018 [4]. Despite its large economy [5], the growth of industry and development of healthcare are limited by a weak electricity supply [6], [7], which is mainly available in the south of the country and suffers many outages [5]. Both standalone PV systems and utility scale PV can help to ease this problem and bring electricity to the north of the country [8]. Solar energy suits Nigeria well for a number of reasons. Firstly, the region has very high annual solar irradiation [9]. Furthermore, due to the proximity to the equator, the solar energy is more evenly distributed throughout the year: the winter solstice can provide 70 % of the energy available on a day in June, compared to 12 % in London, UK [9]. With respect to Nigeria’s weak grid, grid-connected PV can offer the possibility of delaying upgrades to the transmission network [10]. Since 2016, 14 power purchase agreement (PPA) schemes have been agreed between the government and private investors, to provide 1,075 MW of centralised solar energy [8]. None of the plants has yet been completed due to a number of issues including a poor transmission network [11]. Grid investment has been promised by the government [11] so it is hoped this bottleneck will soon be overcome. This research project focuses on a new 50 MW PV plant to be built near to the town of Kabogi, western Nigeria. The Swedish company Svenska Solenergigruppen has been tasked with submitting designs for the plant. The work herein aims to aid Solenergigruppen by designing the PV plant, identifying the optimum configuration and choice of components to maximise the economic favourability of the project. The levelised cost of electricity (LCOE) is a key figure when designing solar power plants. This is the cost per kilowatt-hour of electricity generated by the plant, which is a ratio of the lifetime cost of the plant over the total electricity produced throughout its operation. This energy cost must be paid by the grid to the plant owners to allow it to break-even. During a call for tenders, the engineering, procurement and construction (EPC) company whose proposed project design has the lowest predicted LCOE, in other words will generate the greatest profit, is most likely to be chosen by the developer. Thus to increase the appeal of investment in Solenergigruppen’s forthcoming proposal, this project will explore how to best minimise the LCOE of the 50 MW plant. Up to the present, monofacial modules upon fixed mounting have dominated the utility-scale PV plant industry. However, a number of new technologies are beginning to reshape the market. While cell efficiency itself is increasing [12], methods of harvesting more energy

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from given cells are being developed. One example of this is bifacial modules, which can collect solar energy from both their front and rear sides, thus increasing yield. A second example is PV mounting with sun-tracking capability, allowing modules to collect greater irradiation. With prices drawing closer to those of conventional equipment [13][14], these technologies are becoming more widespread and in the future will be the mainstay in utility scale PV plants [12]. Using, and potentially combining, bifacial modules and tracking is expected to increase the electricity production of this project. However, the relative improvement provided by these technologies depends heavily on the latitude and local climatic conditions amongst other factors [15]–[18]. The key question is thus whether the extra electricity produced outweighs the additional cost in this location.

Aims

This thesis focussed on the following aims:

Identify specific components for simulation from a variety of sources

Propose multiple designs for a 50 MW solar plant for Kabogi, Nigeria, utilising a

number of current technologies

Analyse energy output of each system and calculate lifetime cost in order to find the

LCOE of each case

Study the impact on LCOE of uncertainty in input variables by running a sensitivity

analysis

Overview of method

First, components were selected and modelling parameters found by consulting the literature, industry and climate databases. The plant layouts were then designed, considering different parameters including tilt, pitch distance and inverter loading ratio (DC-to-AC ratio). Four key plant configurations were selected for study and comparison:

1. Monofacial modules on fixed mounting 2. Bifacial modules on fixed mounting 3. Monofacial modules on tracking mounting 4. Bifacial modules on tracking mounting

A number of designs for each configuration were input into PVsyst simulation software to run performance simulations. Using cost figures from the literature and industry, the lifetime cost of each design was estimated. Now with estimations of both electricity production and lifetime cost, the LCOE of each design could be calculated. In this way, the optimal design for each configuration was found, which were subsequently compared to find the best configuration in this location. A sensitivity analysis was run on input parameters to gauge the level of uncertainty in the conclusions.

Previous work

To put the project into context, this section will discuss the previous work which the thesis expands upon. Some of the concepts referred to, such as types of tracking, are explained in detail in the subsequent section, Theoretical background. It should be mentioned that past techno-economic studies have been carried out on commercial and industrial PV in Nigeria. In 2014, Adaramola published a report on the viability of an 80 kW PV system in north-eastern Nigeria [10]. However, both system design and costs used are now quite outdated. Elsewhere, a study was published by Ogunnubi et al.

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in 2015 predicting the LCOE of Nigerian utility PV, however these costs are also now outdated [19]. Therefore wider studies were consulted to provide the foundations for this thesis. Rather than aiming to maximise energy output without considering economic impact, all papers discussed here focus on reducing the LCOE, reflecting the aim of this thesis. In 2017, Bahrami and co-workers published a study of the relative merit of fixed mounted, single-axis tracking and dual-axis tracking plants at nine locations in Nigeria [20]. Regarding single-axis tracking (SAT), they compared vertical axis, tilted axis and horizontal axis systems, with both azimuth tracking and elevation tracking (see Theoretical background for illustrations of some of these technologies). In Magama, located in Niger state like Kabogi, simulations showed that dual-axis tracking produced the most electricity (26 % greater than fixed), followed by tilted azimuth SAT (22 % greater than fixed), with horizontal azimuth SAT in fourth place (21 % greater than fixed). Elevation tracking performed poorly producing only 6 % more electricity than fixed. With regard to the cost of the trackers, horizontal azimuth and elevation SATs were reported to be the simplest and cheapest. Considering its high electricity production and low cost, horizontal azimuth SAT was found to have the lowest LCOE, while the next best system, tilted azimuth SAT, had a 4 % higher LCOE. Due to its large installation costs, dual-axis tracking was unfeasible having an LCOE 35 % higher than the optimum. The LCOEs cannot be taken as realistic figures, since the calculation contained many simplifications, and was on the 1 kW scale, thus incomparable with this 50 MW project. Furthermore, the LCOE of a fixed plant was not calculated, which is a key aim of this thesis to allow a comparison with tracking systems. However, the relative order of merit of the tracking technologies is of great interest and indicates that horizontal azimuth SAT (hereafter referred to as horizontal SAT) is the best tracking option to consider in this thesis. Publishing in 2018, Rodríguez-Gallegos and co-workers carried out a worldwide economic comparison of monofacial and bifacial modules [15]. The authors developed their own irradiance and power generation models based on a series of equations, rather than using commercial simulation software. Compared to the work of Bahrami et al. [20], the economic model was more rigorous, considering detailed component costs, as well as local labour and capital costs. It was found that bifacial modules have a larger optimum tilt angle than monofacial, at 22° and 12° respectively for the studied location in Nigeria. This makes sense as it increases the view factor of the rear-face (it sees a greater area of ground and therefore more reflected light), reduces module self-shading on the ground, and increases view of the sky for diffuse radiation [21]. Since modules have greater optimal tilt with increasing latitude, bifacial gain, the additional electricity due to the rear face over energy from the front face, increased further from the equator in an exponential manner. LCOE reflected this: only above a latitude of 60° did bifacial technology become favourable, considering the 11 % cost premium assumed for bifacial modules. In Nigeria, a 6 % increase in electricity production was observed using bifacial modules, but LCOE was 3 % greater. The report predicted that bifacial technology would become favourable at this location if the cost premium is reduced to less than 5 % over monofacial modules. It is therefore worth exploring if bifacial modules are now within this margin, and to see if this thesis calculates the same maximum acceptable premium, at a different location in Nigeria, with different methodology and input parameters. The authors assumed a 20 % increase in inverter capacity for bifacial systems, coupled with a 20 % inverter cost increase. This thesis will focus on finding the optimum ILR for each system so that it can be seen if this is a good rule of thumb. In a sensitivity analysis, the paper explored the most influential factors on LCOE which included the cost of PV modules, system lifetime and cost of capital. This will be compared with a sensitivity analysis in this thesis. While a reasonably detailed economic model was used, there were still limitations such as the lack of grid connection fees and land costs, thus meaning LCOEs were somewhat underestimated. However, the model was easily adaptable to increase its realism, and was chosen for use in this thesis, as outlined in the Detailed methodology chapter.

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In 2020, Rodríguez-Gallegos and co-workers published an extension to this work, considering SAT and dual-axis tracking technologies in combination with monofacial and bifacial modules. While dual-axis tracking brought a large energy gain, the cost of 0.40 USD/W compared to 0.12 USD/W for SAT elevated its LCOE such that it was only economically favourable beyond a latitude of 70°. Horizontal SAT was shown to be more economical than tilted SAT up to 10° N using bifacial modules and 15° N with monofacial modules. The extra cost of tilted trackers was not considered meaning, in reality, horizontal SATs will be favourable over tilted SATs further north than this, as predicted by Bahrami et al. [20]. This work thus provides further justification of the use of horizontal SAT over tilted SAT. As in the 2018 work, the choice of bifacial modules was borderline in Nigeria, but the use of trackers increased the bifacial gain to the point that bifacial modules with horizontal SAT was the optimum configuration in Nigeria. Indeed this was the case for 93 % of the analysed world area. After running a sensitivity analysis for a number of countries other than Nigeria, the authors found that bifacial and monofacial SAT LCOEs were within one standard deviation of each other, meaning their relative order of merit is variable depending on the location and cost parameters. Like their 2018 report, this work contained many limitations, including no consideration towards row-to-row shading or land costs, both crucial for optimising plant design and calculating a realistic LCOE. Further omissions in CAPEX means the LCOEs are likely underestimated. However, this should not have majorly affected the order of merit of technologies relative to each other. Thus the paper has been used for the purpose it was intended: a good guide for technologies to explore, before the optimum choice is found depending on local conditions. In their 2019 report, Vartiainen et al. [18] estimated the LCOEs of PV plants in multiple European locations for the construction year 2019, as a base from which to forecast the future costs of capital expenditure (CAPEX) and operating expenditure (OPEX) as far as 2050. The work highlighted the difficulty of sourcing up-to-date equipment costs, stating that even major energy research institutions use outdated information to predict erroneous cost trends. Thus an assortment of sources had to be used: an Indian cost structure, combined with global average module costs and European inverter costs from a single recent quote. A sensitivity analysis was run to find the input factors with the highest impact on LCOE. Similar results to Rodríguez-Gallegos [15] were reported: the cost of capital had the highest influence, while system lifetime and CAPEX were also crucial. OPEX had a more minor impact within the range studied, but the uncertainty range is much wider in this thesis, meaning it will be a major factor on LCOE. It was found that the inverter loading ratio (ILR) only has a marginal impact on LCOE, due in part to the low cost of inverters assumed. A comparison will be made with results found in this thesis. While the previous reports showed which technologies to consider and the cost components to be most aware of, Chudinzow et al. [17] focused on ways to maximise bifacial output on fixed and tracking mounting. According to the paper, simulation of bifacial modules carries greater uncertainty than monofacial modules, considering the extra factors affecting module power production and present lack of research on this topic. It should thus be kept in mind that the accuracy of PVsyst to predict the performance of bifacial technology will be lower than for conventional plants. In accord with the findings of Rodríguez-Gallegos et al., this work reports a lessening bifacial gain closer to the equator, since the modules are flatter to the ground. This disfavours their use in Nigeria. Furthermore, bifacial gain was found to be larger in areas with a higher proportion of diffuse light, since diffuse light can reach and reflect from ground areas which are shaded to beam radiation by the modules, thus allowing greater rear irradiance. The paper reported that in a given location, bifacial gain can be improved by increasing inter-row spacing, elevating modules further from the ground and increasing ground reflectivity by using white gravel or foil. In relation to the final point, it is well known that higher albedo improves the performance of bifacial modules which was also observed by Rodríguez-Gallegos [15]. These are all factors to be considered in this optimisation work. Bifacial gain was also increased using a larger tilt angle, however overall production was decreased due to greater row-to-row shading. Therefore, increasing the tilt

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angle was not recommended. Rodríguez-Gallegos [15] had not considered row-to-row shading, hence their use of a larger tilt for bifacial. Using 2018 US capital expenditure (CAPEX) and operating expenditure (OPEX) figures from NREL [13], as used in part by Rodríguez-Gallegos [16] and applied in this thesis, Chudinzow et al. predicted that bifacial horizontal SAT would be the optimum configuration economically in sunny locations. This would likely apply to Kabogi, whose ratio of diffuse to global irradiation is similar to Rome [9], where bifacial tracking was predicted to be optimum. Aside from the work of Chudinzow et al., which focussed on a limited number of European locations, there seems to be a gap in research on the energetic improvement of bifacial modules compared to monofacial within a large array, rather than a standalone module. The work of Rodríguez-Gallegos et al. contains limitations such as a lack of row-to-row shading, making it less applicable to the real world. Hopefully significant further work will be carried out to provide reliable guidance on the use of bifacial modules on a global scale. In summary, the research published to date has indicated the best technologies to explore, the configurations most likely to be favourable in Kabogi and the most important parameters to vary for different technologies. Based on the locations studied in the literature, and the limitations of the work, none of the articles above can provide safe recommendations for Kabogi, hence the necessity of this thesis.

Theoretical background

Today, the PV market is dominated by crystalline silicon technology, with monocrystalline having a firm lead over polycrystalline cells on the global scale [12]. To further improve the output of high performance cells, such as PERC cells, leading manufacturers are now producing modules containing half-cut cells. Each half-cell generates approximately the same voltage as a full cell, but half the current [22]. Since power loss scales with the square of current, resistive losses are reduced by ca. 75 %. Rather than all cells being in series, the module contains two parallel strings, which divide and recombine at the module terminals, illustrated in Figure 1.1. Each of the two cell strings produces similar voltage to a conventional module but half the current, so that when the parallel strings combine, the full current is output. Overall the module has a slightly elevated power compared to a conventional module. Furthermore, with two strings in parallel, the effect of shading on one half of the module will not affect the other half. In this thesis, half-cut technology is used, since it is predicted to take over the market share in the next 10 years [12].

Figure 1.1 Circuit layout in a) conventional module and b) half-cell module. Reprinted from Trina Solar webinar presentation [23].

Another trend in the global PV market is the increasing use of bifacial modules [12]. These modules can collect irradiation from both their front and rear faces, thus increasing power output. As seen in Figure 1.2a below, the cells in a monofacial module are set upon an encapsulant, which prevents the ingress of moisture, and a Tedlar back sheet. In a bifacial

a) b)

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c)

a) b)

e)

d)

module, the Tedlar back sheet is replaced with a rear glass sheet, shown in Figure 1.2b. The cells of a bifacial module have busbars on both sides [22]. Bifacial gain is the additional electricity produced by the rear face over electricity from the front face, and has been reported to reach 30 % [24]. However, bifacial technology is more expensive than monofacial, meaning its use is explored in this thesis, but not taken for granted. Cost and energy values per watt reported herein are calculated per watt of front face power.

Figure 1.2 Cross-sections of crystalline silicon modules: a) monofacial, b) bifacial. Reprinted from [25] with kind permission from R. Kopecek.

A further technology quickly becoming widespread is tracking mounting, which rotates the plane of the modules so that they follow the path of the sun. By reducing the angle of incidence, power production is increased, allowing a given module to produce more electricity. There are different types of tracking, some of which are presented in Figure 1.3. Dual-axis trackers can rotate the modules around two axes, allowing the modules to directly face the sun at almost any point in the sky (see Figure 1.3b). It is not surprising that this tracking allows modules to produce the most electricity, as referred to in the Previous work section above. However, these trackers need two drive motors [26], increasing their cost and maintenance, and they need significantly more land area per watt of PV power [27]. Azimuth single-axis tracking (SAT) has a north-south axis, which allows the modules to follow the sun’s azimuth angle from east to west across the sky. The elevation angle does not vary, but the mounting can either be set horizontally, as shown in Figure 1.3c, or tilted, seen in Figure 1.3e. Elevation tracking (Figure 1.3d) has an east-west rotation axis, allowing modules to track the sun’s height in the sky, although the technology was not explored in this thesis, as justified in the Previous work section. Figure 1.3 Tracking technologies: a) fixed tilt, b) dual-axis, c) horizontal azimuth SAT, d) elevation SAT, e) tilted azimuth SAT. Reprinted from [27] with kind permission from C. Rodríguez-Gallegos.

a) b)

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Early and late in the day, a large tilt angle is required to face the sun, but this can cause great row-to-row shading losses in tracking systems. In response to this issue, a tilt control algorithm has been developed called backtracking [28]. Using this system, the trackers follow the sun until row-to-row shading begins, at which point the tilt angle is reduced (the modules ‘backtrack’), to prevent mutual shading. While the angle of incidence is increased in the mornings and evenings, the resulting losses are outweighed by the gains from avoiding row-to-row shading [29]. Further detail can be found in [27]. Considering its benefits, the backtracking program will be used in simulations. This will introduce a further difference from the literature: backtracking was not used by Rodríguez-Gallegos [16] due to the inconsideration of row-to-row shading, nor by Chudinzow [17]. Not only does tracking increase electricity production, but by following the sun, power output is more evenly distributed throughout the day, which is favourable for the grid. This is illustrated in Figure 1.4.

Figure 1.4 Hourly power distribution for fixed and tracking technologies.

Traditionally, PV systems have run at a maximum voltage of 1000 V. Now the voltage rating of many components has increased to 1500 V, which is advantageous for a number of reasons. Fewer module strings are needed since they can operate at a higher voltage, thus saving cabling, and reducing the number of trenches, combiner boxes and inverters necessary [30]. By running at higher voltage and lower current, cable width can be reduced, allowing further savings. In 2019, 40 % of PV systems globally had a maximum voltage of 1500 V, but this is predicted to increase to 90 % by 2030 [12]. All systems in this thesis have a maximum voltage of 1500 V. Large scale plant topology is also witnessing a new trend. In the past, module strings have generally been fed into DC combiner boxes before entering central inverters, illustrated in Figure 1.5a. Today, the cost per watt of string inverters is drawing closer to central solutions [31], making a string inverter to AC combiner box topology more feasible, shown in Figure 1.5b. String inverters can be repaired by a local electrician rather than a dedicated technician, and spare inverters can be kept on-site if a complete failure occurs [32]. Conversely, it may be months before a central inverter is repaired [32]. Furthermore, string inverters have multiple maximum power point trackers, which is beneficial in plants with different module orientations or complex shading [32]. In this project, the regularity of module arrangement means that this would not bring much benefit. Considering the lower CAPEX, central inverters were chosen for this plant, although it could be worth giving thought to string inverters in further work.

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Figure 1.5 Utility PV plant topologies: a) central inverter, b) string inverters.

The inverter loading ratio (ILR), also called the DC-to-AC ratio, is the ratio of the PV array’s nominal DC power to the inverter’s nominal AC power output. This ratio is often greater than 1, meaning the inverter is undersized [33]. The justification for this stems from the fact that inverter efficiency generally improves the closer the inverter runs to capacity. Since a PV array rarely runs at nominal power, the inverter operates at lower efficiency most of the time, causing energy losses. By undersizing the inverter, it runs closer to capacity, thus more efficiently [33]. At times of high power production, the inverter will cap output power at its nameplate capacity and clip any extra power produced. An extreme case is shown in Figure 1.6. This energy loss is usually outweighed by the gain from running at higher efficiency most of the time [33]. Furthermore, a larger ILR results in a more even energy distribution throughout the day. In this thesis, cost and energy values per watt are reported per watt of array nominal DC power rather than plant nominal AC power, unless stated otherwise.

Figure 1.6 Hourly power distribution for a standard (1.2) ILR and a high (1.5) ILR.

A fundamental shortcoming of PV technology is its decreasing power production with increasing cell temperature [27]. As the temperature rises, the open-circuit voltage decreases and with it, the maximum power point voltage. This is not compensated for by the small increase in current. In hot locations, temperature loss can have a significant impact on energy output [32]. It is thus important to select a module with a low power temperature coefficient. Advanced cell technologies with lower temperature coefficients exist such as interdigitated back contact (IBC) cells (SunPower, LG) and heterojunction (HJT or HIT) cells (REC, Panasonic), however these are significantly more expensive [34]. Their use will be discussed in the Results and discussion chapter. Another significant loss to consider in the studied location is module soiling. Module performance depends on light transmission through the front glass, which worsens with

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a) b)

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increasing dust density on the surface [35]. During the harmattan season, large quantities of dust are transported south from the Sahara and deposited in Nigeria [36] clearly presenting an issue for PV plants. Transmission, and electricity production, may be reduced by more than 15 % after a month without cleaning, as reported in several African studies [35], [37], [38]. It is important to quantify soiling rates to allow cleaning frequency to be determined [35]. However, the soiling in a particular location depends hugely on plant design and local conditions, including the module tilt angle and orientation, natural and industrial sources of dust and the frequency of rain [35]. Without a year’s worth of measurements at or close by the site, it is unreasonable to estimate soiling loss. Thus a simplified method for quantifying soiling loss is proposed in the Detailed methodology chapter. When determining tilt angle, thought was also given towards reducing soiling.

10

2 Detailed methodology and input parameters This chapter will focus on the research procedure in detail, with description and justification of input data. The choice of components will be discussed, before engineering and financial calculations are laid out. Consideration will also be given towards limitations, assumptions and simplifications of the method.

Selection of simulation software

The software PVsyst was used for simulations. Essentially, PVsyst is capable of simulating bifacial modules in combination with tracking technology. Helioscope currently does not have bifacial capability and tracking technology is only in the beta stages. PVSol Premium can simulate both technologies however the software is very expensive. Thus PVsyst came out as the top choice for simulation tool. In a comparison of the most popular PV simulation software, PVsyst is found to be competitive in its accuracy of predicting energy output of fixed-tilt monofacial systems [39], within 2 % of real data from two utility scale fixed plants. This value increases to 5 % for the two monofacial tracking plants considered in the study [40]. While monofacial module simulation is well established, bifacial modelling is a challenge to develop due to many additional sensitive parameters [41]. PVsyst uses a 2D view-factor approach for estimating additional irradiance [42], illustrated in Figure 2.1a. A number of simplifications are introduced for light reaching the rear-face, such as the assumption that diffuse sky irradiance and ground reflection are both isotropic. Another factor to consider on the rear-face is shading from mounting which can lead to large mismatch losses, seen in Figure 2.1b. This is not calculated and is left to the user to decide whether to vary the default value.

Figure 2.1 a) PVsyst 2D bifacial modelling, showing beam irradiance reaching the ground (yellow) and isotropic ground reflection, b) torque tube on tracker which can cause rear mismatch losses, reprinted from Array Technologies [43].

Bifacial tracking is even more complex, as the diffuse irradiance and view factor must change as a function of time. As for bifacial fixed simulation, a simple 2D cross-sectional model is used to calculate rear incident irradiance. Considering all these simplifications, the question of the software’s accuracy with bifacial technology may be asked. Its prediction of bifacial gain on a fixed system was within 5 % of the measured value, and within 24 % of the measured value for a tracking system [44]. This validation, the only one which could be found, was carried out on a single plant, so may not represent the standard accuracy, however it is clear that significant uncertainty is involved. In the study, no other software performed notably better than PVsyst. It should be remembered that this is only uncertainty in the rear-face contribution, which is small in comparison with the front electricity production.

a) b)

11

PVsyst’s unlimited sheds and trackers models were used, which assume infinitely long PV rows, thus neglecting row edge effects. This is a reasonable assumption for large PV plants [44], but to verify this, 3D shading models were created for the optimised plants in this thesis.

Site and horizon

When choosing a location for a PV plant, some major considerations include the topology of the land, ease of access to the site, proximity to a grid connection point and water availability for cleaning. The town of Kabogi is situated in a region with shallow hills. Using topology information from Google Earth, it was apparent that very few plots of land large enough for a 50 MW solar plant within the area were not significantly undulating, which would cause extra land preparation costs. A plot of land ca. 10 km to the west of the town was identified as having only minor undulations, whilst being situated beside a secondary road, allowing easy site access. The location relative to the town is shown in Figure 2.2. Local detail can be seen in Figure 2.3, with the minor road visible at the top of the image and the potential for extension to the south and east. Although no information was known about the availability of the land, the site was selected for use, solely for the purposes of this study. The location of the land was 10.1035° N, 5.3111° E.

Figure 2.2 Location of the site relative to Kabogi and the primary road through the town. Map data from Google, Maxar Technologies, CNES/Airbus.

Figure 2.3 Close-up of the plot of land, showing the secondary road at the top of the image. Map data from Google, Maxar Technologies, CNES/Airbus.

9.6 km

N

1 km

1 km

N

12

Horizon data were gathered from PVGIS [9], and is overlaid on the sun path diagram shown in Figure 2.4. It was clear that far shading would not majorly impact plant performance.

Figure 2.4 The sun path diagram for the location of interest with horizon profile in red, generated by PVGIS [9].

According to the Rural Electrification Agency of Nigeria, the town of Kabogi is grid-connected [45]. It is found on the distribution network which was assumed to run at the highest distribution voltage of 33 kV [46]. However, a second database [47] indicates that the transmission lines follow the A1 road north, until they join the high-voltage transmission network 40 km to the north. Thus a 10 km transmission line would have to be installed to connect the solar plant to the grid. Outside of the towns, water points are scarce, so thought would also have to be given to either constructing a water pipe, digging a bore hole or transporting water for module cleaning.

Source analysis

Solar resource analysis is vitally important for forecasting project performance, thus the data source should be chosen carefully. Ground source data is generally the most accurate and thus most favourable. Provided they were well-maintained, ground weather stations would certainly be desirable in this location due to the high levels of dust in the air [36] during the harmattan season, which may reduce the accuracy of satellite data. A NIMET [48] weather station is found only 30 km to the north-east of the site. Efforts were made to retrieve data from the organisation however the access fee was beyond the budget of this study. A list of the freely available irradiation datasets considered for use is presented in Table 2.1. The records all follow similar trends, which can be seen in Figure 2.5, with an obvious decrease in irradiation during the rainy season in the middle of the year. However, there is significant variation in the magnitude of annual irradiation, with the PVGIS TMY (typical meteorological year) [9] reporting 9 % greater insolation than Meteonorm [49]. The PVGIS TMY is based on the CMSAF’s SARAH dataset. This set was selected for simulation due to its good spatial resolution, more recent data collection and its hourly temporal resolution [50], meaning the distribution of the solar energy used in simulations would be more realistic, rather than using synthetic hourly data. By comparison with the PVGIS-CMSAF, which shares many of these advantages, the SARAH data are more accurate to real-life conditions according to [51]. Furthermore the SARAH dataset provides more information (ambient

13

temperature and wind speed) than the NASA [52] and freely-available SolarGIS [53] datasets. To provide a lower bound for expected performance, the Meteonorm dataset was chosen for use in the sensitivity analysis. Meteonorm’s monthly irradiation data were converted to hourly values through PVsyst’s hourly synthetic data generation feature. Table 2.1 Weather database comparison.

Database Annual irradiation (kWh/m2)

Spatial resolution

Years Temporal resolution

PVGIS TMY

2154 5 km 2005–2016

Hourly

PVGIS CMSAF

2049 2.5 km 2007–2016

Hourly

Meteonorm 1968 5 km 1993–2012

Monthly

NASA 2042 111 km 1983–2005

Monthly

SolarGIS 1974 4 km 1994–2018

Monthly

Figure 2.5 Comparison of annual irradiation distribution of weather databases.

Uncertainty is an important factor in database selection, however this value is not clearly stated for all datasets, including the PVGIS TMY set. Further statistical analysis and comparison with ground-based data should be undertaken before selection for formal energetic projections. The best way to improve the accuracy of the satellite data would be to take 12 months of measurements, which could then be used to calibrate existing multi-year datasets [32]. Key design parameters to consider are the minimum and maximum system operating temperatures. If modules run colder than predicted, array voltage will rise above the maximum design value which may damage the inverters. While the lowest reported ambient temperature was 14.6 °C [9], wind could further cool modules, or extreme weather could occur; thus a significant safety margin was ensured, assuming a lowest operating temperature of 5 °C.

0

50

100

150

200

250

Irra

dia

tio

n (

kWh

/m2)

Month

PVGIS TMY PVGIS CMSAF Meteonorm NASA SolarGIS

14

On the other hand, if modules run hotter than predicted, the voltage at their maximum power point (MPP) may be lower than the inverter’s minimum required operating voltage. In this case, the modules must run above MPP voltage, which can lead to major power loss. For this reason, strings were designed considering the hottest likely cell temperature, which was calculated according to Equation 2.1, based on that used in [15]:

𝑇cell = 𝑇air +𝑇NOCT − 20

800𝐺T Equation 2.1

where 𝑇cell is the cell temperature (°C), 𝑇air is the ambient air temperature (°C), 𝑇NOCT is

the normal operating cell temperature found on the module’s datasheet (°C) and 𝐺T is the irradiance on the module surface (W/m2). The equation was used to predict the cell temperature at every hour throughout the PVGIS

12 year dataset. 𝑇cell very rarely exceeded 70 °C, but peaked at 72.5 °C, so the maximum operating temperature was set to 73 °C. With regard to bifacial modules, despite their increased absorption of light, the rear side glass does not absorb infrared radiation like a Tedlar back sheet, and releases heat more readily. The module runs no hotter than a monofacial module, under moderate albedo [54].

Albedo

Monthly albedo values were collected from the SolarGIS database [53]. The database provides a good resolution of 1 km × 1 km in an area with minimal variation in surface type, meaning the figures should be accurate for the plant location. For comparison, data from NASA [55] was also sourced, by averaging the four data points surrounding the site location. Key features of both datasets are tabulated in Table 2.2 which justify the choice of the SolarGIS database, while albedo values are plotted in Figure 2.6. The figures fit the range expected for savannah land [56]. A comparison of the plants’ specific energies calculated using the two databases is given in the sensitivity analysis. Table 2.2 Comparison of key features of two albedo data sources.

SolarGIS Albedo NASA Earth Observations Albedo

Time coverage 10 years 2006–2015 3 years 2014–2016 Spatial resolution 1 km 11 km

Figure 2.6 Monthly albedo values from the two data sources.

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

Alb

edo

val

ue

Month

SolarGIS NASA

15

Choice of equipment

The plant equipment which was specifically chosen comprised the modules, inverters and trackers. Details are given in the following section. Datasheets for each component are found in Appendices A–C.

2.5.1 Modules

The largest share of the cost of a utility PV plant is the modules. Not only should these be of high build quality, with suitable performance characteristics (low light, temperature performance), but the manufacturer should also be economically stable. If the firm collapsed during the project’s lifetime, the warranty would be void. Thus, only economically Tier 1 suppliers [57] were considered. One such company is Longi Solar which was a world top 5 module provider in 2019 [58]. They produce a monofacial and bifacial module, seen in Table 2.3, with very similar performance characteristics, allowing a fair comparison between the technologies. With regard to temperature coefficient, datasheets had been studied for 16 Tier 1 module manufacturers [57] which revealed that the best power temperature

coefficient from each manufacturer ranged from – 0.35 %/°C to – 0.42 %/°C, with an

average of – 0.39 %/°C. The temperature coefficient of the selected modules is at the better end of this range at – 0.37 %/°C, rendering them suitable for this climate. These modules were used in the study by Rodríguez-Gallegos [16]. Despite a slight premium in cost per watt to use high efficiency modules, the reduced array area leads to decreased land costs, shorter cables, less mounting and lower O&M costs [32]. The selected modules have very high performance, due in part to the use of half-cut cells.

Table 2.3 Specifications of the modules selected for study.

Technology Monofacial Bifacial Manufacturer LONGi Solar LONGi Solar Model LR6–72HPH 390M LR6–72HBD 390M Cells 144 half-cut monocrystalline PERC Maximum power at STC, PMPP 390 W 390 W (front face) Module efficiency 19.5 % 19.4 % Open-circuit voltage, VOC 49.5 V 49.1 V Max. power point voltage, VMPP

41.0 V 40.8 V

Short-circuit current, ISC 10.12 A 10.07 A Max. power point current, IMPP 9.51 A 9.56 A Bifaciality factor N/A 0.7 PMPP temperature coefficient – 0.370 %/°C – 0.370 %/°C VOC temperature coefficient – 0.286 %/°C – 0.300 %/°C ISC temperature coefficient 0.057 %/°C 0.060 %/°C

2.5.2 Inverters

With inverter type not being a focus of this study, a common, generic inverter was sought to represent a typical large scale PV project. The inverters chosen were selected from the SMA Sunny Central range. SMA were in the top 5 inverter manufacturers in terms of MW shipments in 2019 [59]. Furthermore, their large variety of models in the 2.2–3 MW range allowed all ILRs to easily be configured. It transpired that only the 2500-EV and 2750-EV models needed to be used in the study, shown in Table 2.4. The 3000-EV inverter had too narrow an MPP voltage range.

16

Table 2.4 Specifications of the inverters used in the study.

Manufacturer SMA SMA Model Sunny Central 2500-EV Sunny Central 2750-EV Power output (AC) 2500 kW 2750 kW MPP voltage range (at 25 °C) 850–1425 V 875–1425 V European efficiency 98.3 % 98.5 %

2.5.3 Trackers

Horizontal SAT was favoured over tilted SAT, as justified in Previous work. The two trackers recommended by the industry came from the Nextracker and Array Technologies brands. Practically, the former tracker can self-power with its own solar module, while the latter is capable of running up to 32 rows with a single motor via grid AC power. Thus each had its own advantage, while many other features were shared. The Nextracker tracker, whose details are tabulated in Table 2.5 and is illustrated in Figure 2.7, was selected somewhat arbitrarily, as the characteristics of each tracker meant they would perform the same in simulations. Table 2.5 Specifications of the trackers chosen for study.

Manufacturer Nextracker Model NX Horizon Type Horizontal single-axis tracker, independent

row Row length 78–90 modules Features Backtracking capability

Night-time stow Self-powered with module or AC supply

Figure 2.7 Nextracker Horizon model showing a) drive mechanism, b) torque tube and clamps. Reprinted from Nextracker [60].

Plant design

In this project, the plant design comprised two major aspects. First, the electrical design will be discussed, before the explanation of physical design, including module tilt and plant layout. The first step in the electrical design was to identify a suitable number of modules in series per string. The minimum number of modules per string was found by rounding up the output of Equation 2.2 to the nearest module.

𝑛mod,min =𝑉MPP inv,min

𝑉MPP mod,73 °C Equation 2.2

17

where 𝑛mod,min is the minimum number of modules per string, 𝑉MPP inv,min is the lower

bound of the inverter’s MPP operating range (V) and 𝑉MPP mod,73 °C is the module MPP

voltage (V) at the maximum predicted operating temperature of 73 °C. Next the maximum number of modules per string was found according to Equation 2.3, whose output was rounded down to the nearest module.

𝑛mod,max =𝑉 inv,max

𝑉OC mod,5 °C Equation 2.3

where 𝑛mod,max is the maximum number of modules per string, 𝑉 inv,max is the inverter

maximum input voltage (V) and 𝑉OC mod,5 °C is the module open-circuit voltage (V) at 5 °C.

Generally strings of length 26–28 modules were feasible. The SMA Sunny Central inverters run more efficiently at lower input voltage (see efficiency

curves in Appendix D), thus favouring a string length of 26 modules. However, 𝑉𝑀𝑃𝑃 at regular operating temperatures became close to the inverter’s minimum MPP input voltage,

𝑉MPP inv,min. As voltage degrades over system lifetime [61], the MPP would likely fall below

minimum input voltage in the future, causing power loss. It was assumed that this effect would outweigh the inverter efficiency loss at the higher voltage, which was found to cause less than 1 % energy loss. Accordingly, a string length of 28 modules was always used. It should be noted that the Sungrow (SG250HX) and Huawei (SUN2000-185KTL) string inverters have a much wider MPP range of 600–1500 V and 500–1500 V respectively, and generally increasing efficiency with voltage, meaning these are worth exploring in future work. In order to obtain the desired ILRs, Equation 2.4 could be used to find the necessary total inverter power and thus the type and quantity of inverters required:

𝐼𝐿𝑅 =𝑃DC,array

𝑃AC,inv Equation 2.4

where 𝐼𝐿𝑅 is the inverter loading ratio, 𝑃DC,array is the nameplate DC capacity of the array

(kW) and 𝑃AC,inv is the total output AC power of the inverters (kW).

ILRs of 1.15, 1.20, 1.25, 1.30, 1.40 and 1.50 were sought. Using the 2500-EV and 2750-EV inverters, the final plant ILRs were always within 0.01 of these desired ratios. Next, the maximum number of strings tolerated per inverter to ensure MPP operation was found according to Equation 2.5, based on maximum current input.

𝑛strings/inverter,max =𝐼DC max,inverter

𝐼MPP,string,73 °C Equation 2.5

where 𝑛strings/inverter,max is the maximum number of strings per inverter, 𝐼DC max,inverter

is the maximum operating inverter input current (A) and 𝐼MPP,string,73 °C is the MPP current

of a string at 73 °C (A).

Using the stated value for 𝐼DC max,inverter at 50 °C, each inverter could accept 302 strings,

or 276 bifacial strings assuming 10 % current increase from the rear face. However, at high ILRs, more than 302 strings were required per inverter. Since this current limit is an operating limit but not a safety limit, this was not a major concern, provided the arrays were incapable of nearing the safety limit of 6400 A, the short circuit current rating of the inverter.

18

This was not possible under the explored conditions. Under high irradiance, high temperature conditions, the input current may exceed the operating range, meaning inverters would have to increase the input voltage to reduce input current, thus losing power. The results show that energy loss due to this process was negligible. Subsequently, the number of strings required per inverter was calculated by varying the respective term in Equation 2.6 until the DC power of the array was closest to 50 MW.

𝑃DC,array = 𝑛inverters ∙ 𝑛strings/inverter ∙ 𝑃string Equation 2.6

where 𝑛inverters is the number of inverters, 𝑛strings/inverter is the number of strings

connected per inverter and 𝑃string is the power of each string (kW).

For clarity of analysis, all inverter fields in a given plant were identical. Finally, the number of inputs per inverter and number of strings per input could be calculated. This was achieved by varying each term in Equation 2.7 until the pre-determined number of strings per inverter were found.

𝑛strings/inverter = 𝑛inputs/inverter ∙ 𝑛strings/input Equation 2.7

where 𝑛inputs/inverter is the number of inputs per inverter and 𝑛strings/input is the number

of strings per input.

The default value for 𝑛inputs/inverter was 24, the number of double pole fused DC inputs

available. This was reduced until both terms as integers could multiply to 𝑛strings/inverter.

If this was not possible, the closest value to the desired 𝑛strings/inverter was found, and the

equations above were run in reverse to calculate the altered total plant power. The electrical configuration was now complete, having calculated the number of modules per string, strings per input, inputs per inverter and number of inverters. Due to the arrangement of bypass diodes in the modules, landscape orientation was used for fixed installations to minimise shading losses [62]. Modules were mounted in rows of four, as shown in Figure 2.8.

Figure 2.8 Module mounting configuration for fixed plants.

With a clear horizon to the south, naturally all arrays were south-facing.

19

PVsyst was used to find the optimum module tilt. For the location under study this was 15°. As opposed to a single shed of modules, a table of sheds may have a decreased optimum tilt angle, to reduce row-to-row shading at a given pitch distance. Considering this effect, the optimum angle was 10°. This only afforded a 0.1 % yield increase, which was probably outweighed by the increased soiling due to reduced tilt [63]. Thus 15° was chosen as tilt

angle. Incidentally, a greater tilt angle of 20° led to less than 1 % greater loss, so in future it

may be worth exploring whether the reduced soiling at 20° outweighs the incident energy loss. For the tracking plants, the backtracking algorithm was enabled. Since this control program works to prevent row-to-row beam irradiance shading, modules could be mounted in portrait orientation without the concern of major shading losses. Each tracker was fitted with a single row of modules in portrait, shown in Figure 2.9, as recommended by a major tracker manufacturer to minimise LCOE [64]. The trackers could accommodate 78–90 modules, so three strings were fitted to each, giving 84 modules.

Figure 2.9 Module mounting configuration for tracking plants.

Next the plant layout could be designed. Dimensions discussed in the following section are visualised in Figure 2.10. Arrays were designed such that the plant shape would be as close to square as possible. For a given land area, this would minimise cable lengths and fencing. A computer tool was designed to allow quick variation of the number of sheds until the smallest difference between the NS and EW lengths was found. A key input was shed pitch distance, the spacing between the front of a shed of modules and the front of the next shed. Sometimes the pitch distance is determined by using the simple winter solstice rule [65]. In this case, the distance is set such that no row-to-row shading occurs between the times of 10 am and 2 pm (or 9 am and 3 pm) on the winter solstice. This is not a rigorous method since the optimum spacing will depend hugely on the cost of land, and unnecessary increases in LCOE could be suffered. Therefore in this project, optimisation scans were run to determine the optimum pitch distance, as a balance between land preparation and lease costs, and energetic production. For each distance, the optimum plant shape was recalculated. The lower limit on distancing was set such that inter-row spacing exceeded 1.7 m, allowing vehicular access to all modules. This was the minimum spacing observed during a satellite study of several of the world’s largest PV plants. As a limitation, cable costs were not considered since calculating the number of cables and lengths was beyond the scope of this research. This will lead to slight overestimation of optimal pitch distance, but the effect should not be significant.

20

Figure 2.10 Key plant layout dimensions.

A further input was the number and width of access routes. Access routes were provided between each inverter array. The width was set to 5 m, based on the satellite study of existing PV plants. If only two arrays were found in either the NS or EW direction, an additional access route was incorporated at the halfway point of each array. Regarding the tracking plants, 5 m was incorporated between each tracker in the NS direction, as seen in Figure 2.11.

Figure 2.11 NS spacing between each tracker.

The inverter location depended on the number of strings per inverter. If these could be arranged such that each PV array could be rectangular, the inverter was incorporated in an access route (illustrated in Figure 2.12a), with additional space given to the inverter. If one or two sheds of an array were shorter than the rest, the inverter was installed in this space (illustrated in Figure 2.12b).

Figure 2.12 Inverter positioning options. Both images of the Solar Star 750 MW plant, California. Map data from Google.

A 10 m perimeter of land was set on all edges of the PV field. As the plant was joining a medium voltage network, it was assumed that a single step-up transformer would be required.

a) b)

Perimeter 10 m

Access route 5 m m

Inverter space 7.6 m

Inverter

Minimum inter-row 1.7 m

Tracker NS spacing 5 m

21

Soiling

When considering dust accumulation on modules, Kabogi has two contrasting seasons to consider: the dry and dusty harmattan season, and the rainy season. To determine the average start and end month of the rainy season, 10 years of rainfall records were consulted [66]. The rainy season was assumed to occur during months with greater than 4 mm rainfall. On average, the rainy season was found to start at the beginning of April and continue until the end of September. Records from this data source closely align with a second source [67], from a town 40 km north. As seen in Previous work, soiling could be up to or greater than 15 % during the harmattan season. However, rather than trying to predict soiling, which without a year’s worth of measurements would have great error, the recommendation from the industry [68] was to set a maximum soiling value as a commitment. The frequency of cleaning would then be set for this to be achieved. A maximum soiling loss of 3 % was recommended, which was applied for the harmattan season. A sensitivity analysis was carried out to gauge the impact of increased soiling on LCOE. While it could be argued that a soiling figure less than 1 % could be applied for the rainy season since rainwater can clean modules [69], it has also been reported that light rain can worsen soiling losses, in some cases [70]. Therefore a conservative estimate of 2 % was set for this period. A simplification made here was the abrupt change of soiling value. Since rainfall gradually increases and decreases, soiling variation will do the same. Thus monthly outputs will contain error, and furthermore, will differ each year as the rainy season start and end dates vary. However, if the soiling values chosen are indeed set as a pledge, the performance can be no worse than predicted, only better. There should not be a significant impact on results since the magnitude of the loss is low in both seasons. It is worth considering that soiling losses on tracking installations will be different. However, research couldn't be found comparing soiling on tilted fixed mounting with tracking. Therefore the same soiling figures were used. Logically, the use of trackers would lead to less soiling as the modules are predominantly tilted greater than the fixed installations (15° in this location), and are assumed by PVsyst to be tilted at 60° overnight. For this reason, tracking may be slightly more favourable than results show. The soiling losses can be calculated using 12 months of data collected after construction.

Other losses

Of the remaining detailed loss inputs, all non-default loss values are tabulated in Table 2.6. Ohmic loss percentages are shown for operation at standard test conditions (STC) power. Both components of the Field Thermal Loss Factor, the constant loss factor and wind dependent loss factor, were sourced from a 2013 industry presentation [71]. A limitation of predicting the cable ohmic losses was that it wasn’t clear how long the cables would be. Therefore generic values were used [71] for DC and AC (inverter to transformer) ohmic losses. It was assumed that a single transformer would be required to step the voltage up from 600 V to 33 kV. Default losses proposed by PVsyst were used. Regarding the transmission line, a length of 10 km at 33 kV using a wire cross-section of 1200 mm2 gave a further loss of 0.70 %. The light-induced degradation (LID) module loss was set to 1.4 %, as recommended by a document from the industry for monocrystalline modules [72]. All other losses were left as default values.

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Table 2.6 Non-default loss parameters assumed in the study.

Constant thermal loss factor (Uc)

25.0 W⋅m-2⋅K-1

Wind loss factor (Uv) 1.2 W⋅m-2⋅K-1⋅(m/s)-1 DC ohmic losses 2.00 % AC ohmic losses (inverter to transformer)

0.50 %

Transformer iron loss 0.10 % Transformer copper loss 1.00 % Transmission line losses 0.70 % Light-induced degradation loss

1.4 %

Degradation

As the modules degrade, their energy output reduces over time. A degradation rate must be assumed to calculate lifetime electricity production, which is commonly taken as 0.5 % per year [73] to reflect module degradation. However, module degradation is only one component of overall system degradation, which is estimated at 1.3 % per year, according to a study of 411 utility scale PV plants in the US [73]. The authors explain that this additional degradation stems from inverter and tracker aging, amongst other factors. For a more realistic energetic and financial projection, system degradation should be considered, although 50 % of PPAs studied in the report still chose 0.5 %. The paper finds that degradation rate is reduced in newer and larger projects, while it is increased in warmer and sunnier sites. Therefore the value in Nigeria is quite uncertain. In this thesis, degradation was set at 1 % since the authors of [73] suggest their valuation of 1.3 % may be slightly overestimated, and previous studies report 0.8–1 % system degradation [74], [75]. A sensitivity analysis on degradation rate was run and can be seen under Results and discussion.

System lifetime

Traditionally the system lifetime was set to 25 years corresponding with the module warranty period. Today, developers are assuming lifetimes of 30 years, 35 years or more [76]. This will reduce the LCOE, making investments more favourable. The default lifetime used in this thesis was 30 years, as recommended by IEA-PVPS Task 12 [77]. A system lifetime sensitivity analysis was run.

Performance ratio calculation

One of the main indicators of good plant design is a high performance ratio. This is the ratio of energy fed into the grid over theoretical energy the plant would have output with the given in-plane irradiation if it were running at STC conditions. In other words, it gauges the efficiency of the whole system relative to ideal production. This is generally presented as a monthly or annual value and is calculated thus [78]:

𝑃𝑅 =𝐸grid

𝑃DC,array ∙1000 ∙ 𝐼POA

𝐺ref

Equation 2.8

where 𝑃𝑅 is the performance ratio (%), 𝐸grid is the energy fed to the grid over the period

of interest (kWh), 𝑃DC,array is the nameplate DC capacity of the array (kW), 𝐼POA is the

23

irradiation in the plane of array over the period of interest (kWh/m2) and 𝐺ref is the irradiance at STC (W/m2). Modern utility PV plants would be expected to have a performance ratio (PR) of 80–90 % [79], although this may be reduced in hot environments.

Cost tables

With such rapidly changing technology prices, it was essential to find up-to-date cost information. This is very difficult to source, since the industry usually does not disclose financial data. Even major institutions in Europe and the US use inaccurate figures [18]. African costs are even more tightly guarded, since confidentiality is limited with so few plants under construction [80]. For this reason, financial calculations in this report are based on a CAPEX/OPEX pricing structure from NREL 2018 [13]. The source is used by multiple papers [15], [16], [18] due to a lack of more recent data. The costs are slightly outdated, and are averages for US installations so are likely to differ largely from Nigerian costs. Indeed, Bloomberg reports that costs in developing markets in sub-Saharan Africa are particularly high due to inefficient administration and extended lead times [81]. Consequently, both the structure and some of the costs were adjusted to improve their relevance. For example, Nigerian taxes and labour rate and sub-Saharan rural land cost were considered, while 2020 module and inverter costs were used. Despite the absolute uncertainty of cost figures, the analysis will still allow a fair comparison of the relative performance of each configuration. The full cost structure for the monofacial fixed design is tabulated in Table 2.7, with adjustments for the other plant configurations listed later in Table 2.8. In terms of the module costs, two PV module cost index websites were consulted. The first, PVinsights, provides a weekly global spot price for monocrystalline PERC modules. This was 0.192 USD/W in July. The second, PVxchange, offers a monthly European spot price for PERC modules, at 0.338 USD/W in July. The midpoint value between these two sources was used in simulations. Clearly this cost figure introduces great uncertainty, considering the large percentage difference between the sources. For this reason, a sensitivity analysis was run, taking the PVinsights cost as the lower bound and 0.35 USD/W as the upper bound. With respect to bifacial cost, PVxchange also provides a bifacial spot price, which was 0.349 USD/W in July. The percentage premium over monofacial of 3 % was applied throughout the project, but this premium was also varied in a sensitivity analysis. Similarly for the inverters, a wide range of potential costs was found. The most recent figure was found in [18], which reports European costs of 34 USD/W to 43 USD/W of AC power for multi-string inverters. This is at the lower end of all costs seen, and suggests inverter costs have fallen rapidly recently. Central inverters may be even lower cost. The midpoint of the cost range suggested by the referenced paper of 38 USD/W of AC power was taken as the default inverter cost for this project. Naturally a sensitivity analysis was run for inverter cost.

24

Table 2.7 Cost structure for monofacial fixed designs.

% of CAPEX

Cost (USD/W)

Cost including taxes (USD/W)

VAT [82]

Import duty [82]

Source and notes

Initial costs

Module 31 % 0.265 0.298 7.5% 5% Midpoint between PVinsights [83] and PVxchange [84] monofacial cost

Inverter 3.5 % 0.032 0.035

5% Midpoint in recent offers to European plant [18]. Calculated in USD/W of AC power, converted to USD/W of DC power for illustration using ILR, here 1.21

Structural balance of system (BOS) 10 % 0.087 0.091

5% Average fixed mounting cost from NREL 2018 [13], Rodríguez-Gallegos [16], Industry contacts [85]

Electrical BOS, equipment and plant installation labour

25 % 0.22 0.238 8.4%

Calculated from Rodríguez-Gallegos [15], [16] and verified against NREL 2018 [13]

EPC overhead 7.4 % 0.07 0.07

NREL 2018 [13]

Land preparation 1.3 % 0.012 0.012

NREL 2012 [86]. Calculated using 5000 USD/acre

Permitting fee + interconnection fee 6.3 % 0.06 0.06

NREL 2018 [13]

Transmission line 2.5 % 0.024 0.024

NREL 2018 [13] adjusted for 10 km transmission line

Developer overhead 3.2 % 0.03 0.03

NREL 2018 [13]

Contingency 3.0 % 0.028 0.028

NREL 2018 [13]. Set to 3 % of CAPEX

EPC + developer net profit 6.5 % 0.062 0.062

NREL 2018 [13]. Set to 6.5 % of CAPEX

Total 0.890 0.947

Annual costs % of OPEX

Insurance 80 % 0.0190 0.0190

Set such that OPEX = 2.5 % CAPEX

O&M materials

O&M labour

Other OPEX costs (excl. inverter)

Land lease 2 % 0.00048 0.00048

African mini-grid contact [87]. Calculated using 200 USD/acre

Inverter warranty 18 % 0.0042 0.0042

Rodríguez-Gallegos [15]. Calculated in USD/W of AC power, converted to USD/W of DC power using ILR, here 1.21

Total 0.0237 0.0237

25

Rather than purchasing land, it was assumed that the plot would be leased for an annual sum. Land preparation costs still had to be considered in CAPEX. The cost figure of 5000 USD/acre was the lower bound of the range reported by NREL 2012 [86], which was deemed reasonable considering the low cost of labour in Nigeria, the plant’s position by a road thus requiring only a very short access route, and the need for minimal vegetation clearance. A further source of uncertainty is the OPEX costs, which have been reported in the range of 1 % of CAPEX for the European market [18] to 2 % of CAPEX for Pakistan [88]. It is expected that OPEX costs will be high in Nigeria, considering the inexperienced market, high delivery costs and heavy soiling requiring regular cleaning. Therefore the cost was set at 2.5 % of CAPEX for monofacial fixed plants. Conversely, the low cost of labour in Nigeria would justify a much smaller OPEX, so a sensitivity analysis was run on this input. Compared to monofacial fixed plants, OPEX is greater for bifacial and tracking plants [13], [16]. To take this into account, for each bifacial and tracking design, the OPEX (at 2.5 % CAPEX) was calculated for a monofacial fixed plant with the same electrical and spatial parameters, which was then increased proportionally for the plant type in question. The factors used for the increase are explained below, with reference to Table 2.8. While most of the OPEX costs were combined into a single value, land cost and inverter warranty costs were kept separate. This was necessary to allow the effect of pitch distance, and therefore land area, and inverter loading ratio on LCOE to be observed. As these values were varied, the combined OPEX cost value was adjusted to set the overall cost of OPEX to 2.5 % of CAPEX (for the equivalent monofacial fixed plant if bifacial modules or trackers were used). Of course, this meant that the effect of separating land and inverter warranty costs was lost. The impact of this on results will be discussed in the Results and discussion. It was assumed that the OPEX cost would stay constant over time. Due to equipment degradation, the cost will likely increase, while the development of the market will drive the price down. Considering the complexity of these factors, the cost was kept constant. The inverter warranty was calculated using the approach and values proposed by Rodríguez-Gallegos 2018 [15]. The warranty is extended every five years. It was assumed that the cost of renewal is a particular percentage of the PV inverter cost at the time of renewal (see [15]). The cost is listed as an annual cost in Table 2.8 solely for clarity. In the cited work, the cost is reported in USD/W of DC power. Correspondence was made with the author to request the DC-to-AC ratio assumed, however he could only advise that the value would have been in the range 1 to 1.3. Therefore the ratio of 1.25 was taken to convert the values to USD/W of AC power. With regard to the bifacial and tracking systems, many of the costs in Table 2.7 had to be updated, listed in Table 2.8. Aside from the modules, all costs for bifacial systems listed in the table were increased considering the additional length of the bifacial modules. An estimation of the percentage of each cost component to be affected by module length was made, and this percentage of the component was increased proportionally with module length. A part of the electrical BOS cost was increased proportionally with the additional current produced by the bifacial modules. The EPC overhead and O&M (assumed to be 50 % of OPEX [18]) were increased for tracking systems according to NREL 2018 [13]. The bifacial tracking OPEX (excluding land and warranty) is lower than that for monofacial tracking. This is due to OPEX scaling as a percentage of CAPEX and is the same limitation as discussed previously. With greater land costs, OPEX increased, so the cost in Table 2.7 was reduced accordingly to give the desired OPEX percentage of CAPEX. Again, the effect of this limitation will be discussed in the Results and discussion.

26

Table 2.8 Cost adjustments for each system configuration, before tax.

Cost (USD/W)

Configuration Monofacial fixed

Bifacial fixed

Monofacial tracking

Bifacial tracking

Initial

Module 0.265 0.274 0.265 0.274

Structural BOS 0.087 0.088 0.149 0.149

Electrical BOS, Installation labour and Equipment

0.22 0.231 0.23 0.241

EPC overhead 0.07 0.0703 0.08 0.0803

Annual

Insurance 0.0190 0.0190 0.0198 0.0195

O&M materials

O&M labour

Other OPEX costs (excl. inverter)

Using the structures presented above, a CAPEX of ca. 0.95 USD/W for monofacial fixed was calculated. This is a step above the global 2020 cost predicted by Bloomberg of 0.77 USD/W [89], but sits below the Nigerian range reported in 2019 of 1.10–1.60 USD/W for commercial and industrial PV [90]. With regard to monofacial tracking, the calculated CAPEX was ca. 1.05 USD/W, somewhat higher than the global prediction of 0.82 USD/W in 2020, but still shy of Nigerian prices, although a specific tracking price could not be found for Nigeria. The values calculated herein may be underestimates, although with decreasing prices and the scale of the plant, they may not be so wide of the true figures.

Incentives

There are few incentives relevant to utility PV plants in Nigeria. The Nigerian Renewable Energy and Energy Efficiency Policy (NREEEP) set out planned incentives in 2015 but only some have come to fruition [91], [92]. The most prominent is a “five year tax holiday on dividend incomes from investments on domestic renewable energy sources”. Since the effect of corporate tax on income has not been applied in this thesis, neither this incentive nor any others affect this work directly.

Cost of capital

The weighted average cost of capital (WACC), also known as the nominal discount rate, was evaluated according to Equation 2.9 adapted from [93]. The unclear terms are explained below.

𝑊𝐴𝐶𝐶 = (1 − 𝐷) ∙ (𝑅𝐹𝑅 + 𝑏 ∙ 𝑀𝑅𝑃) + 𝐷 ∙ (𝐼𝑅bank) ∙ (1− 𝑇𝑅)

Equation 2.9

where 𝑊𝐴𝐶𝐶 is the weighted average cost of capital (%), 𝐷 is the fraction of the project

financed by debt (%), 𝑅𝐹𝑅 is the mean risk-free equity rate reported in Nigeria (%), 𝑏 is

levered beta (%), 𝑀𝑅𝑃 is the market risk premium (%), 𝐼𝑅bank is the interest rate on the

bank loan (%) and 𝑇𝑅 is the corporate tax rate (%).

27

The risk free rate is the base cost of equity, before risk is considered. It is increased by the market risk premium, or in other words, the average percentage risk premium applied across the whole market. This is finally adjusted to consider the particular market in question, by multiplying by levered beta. In this case, the value taken for levered beta applied to the green and renewable energy market (see below). Economic values used for Nigeria are given in Table 2.9. Real values are not generally disclosed so the figures must be estimated using literature sources. There can be large variation depending on the project and the market, which introduces significant uncertainty in the financial analysis. Table 2.9 Economic values assumed for Nigeria.

Component Value Source

Share of equity capital (1 − 𝐷) 20 %

Share of borrowed capital (𝐷) 80 %

Interest on bank loan (𝐼𝑅bank) 15.03 % Trading Economics [94]

Corporate tax (𝑇𝑅) 30 % Trading Economics [95]

Risk-free equity rate (𝑅𝐹𝑅) 20.2 % IESE Business School [96]

Market risk premium (𝑀𝑅𝑃) 10.7 % IESE Business School [96]

Beta (levered) (𝑏) 89 % A. Damodaran [97]

WACC 14 %

Inflation rate 13 % Trading Economics [98]

The value taken for the Interest on bank loan was the mean loan rate in Nigeria between September 2019 and July 2020 [94]. The risk-free equity rate is sourced from the 2020 edition of the paper used by Rodríguez-Gallegos, which compiled rates from academics and professionals in Nigeria. The rate has a standard deviation of 9 percentage points, meaning there is very large uncertainty in this value. The market risk premium is from the same source and has a much lower standard deviation of 1.8 percentage points. The levered beta value was sourced from A. Damodaran, and is the 2020-published value for Green and Renewable Energy in Emerging Markets. Being a worldwide value, it carries great uncertainty for Nigeria. The inflation rate was set considering Trading Economics’ forecast for 2021 and 2022. The WACC was calculated to be 14 %, as compared to a value of 16.4 % proposed by Rodríguez-Gallegos in 2018 [15]. This is a very high cost of capital and would be unlikely to allow reasonable profit to be made. Indeed, the cost of capital could be significantly higher still. This is a major problem for developers in Nigeria, who often cannot afford the local interest rates [99]. The LCOE will be seen in the results with a sensitivity analysis comparing WACCs. An alternative method to financing is via international bodies which can provide capital in USD, rather than NGN, at much reduced interest rates. This is common in Nigeria [100]. Some such initiatives include the IRENA/ADFD Project Facility, IFC Blended Finance and the International Development Association (IDA) from the World Bank. The rates seen in Table 2.10 were used to calculate the cost of capital using the IDA initiative.

28

A suitable source for the cost of equity from international investors could not be found. It was therefore assumed equal to the value given by the World Bank for a 50 MW solar plant in Pakistan [88], another developing market. Table 2.10 Economic values assumed using the IDA initiative for debt financing.

Component Value Source

Share of equity capital

(1 − 𝐷)

20 %

Share of borrowed

capital (𝐷)

80 %

Interest on bank loan

(𝐼𝑅bank)

2 % The World Bank [101]

Corporate tax (𝑇𝑅) 30 % Trading Economics [95]

Cost of Equity rate 15 % The World Bank [88]

WACC 4 %

Inflation rate 2 % Trading Economics [98]

Evidently, the WACC is significantly lower when using foreign initiatives, at 4 %. For this reason, this financing route was taken to be the default in calculations and is compared with the Nigerian route in the Results and discussion.

Lifetime cost calculations

Having established a cost structure, long-term costs could now be calculated. A key factor in financial indicators is the lifetime cost of the project. This is formed of several components, which are outlined below, adapted from Rodríguez-Gallegos [15]. First the initial investment is split into its equity and debt components.

𝐶equity = 𝐶ini,inv ∙ (1 − 𝐷) Equation 2.10

where 𝐶equity is the cost of initial investment through equity (USD), 𝐶ini,inv is the cost of

the initial investment (USD) and 𝐷 is the fraction of the project financed by debt (%). Subsequently, the interest paid on the bank loan can be calculated for each year, then summed over the debt tenor period.

𝐶bank,int = ∑𝐶ini,inv ∙ 𝐷 ∙ 𝐼𝑅bank ∙ ((1 + 𝐼𝑅bank)𝑑𝑡bank+1 − (1 + 𝐼𝑅bank)𝑦)

(1 + 𝐼𝑅bank) ∙ ((1 + 𝐼𝑅bank)𝑑𝑡bank − 1) ∙ (1 + 𝑊𝐴𝐶𝐶)𝑦

𝑑𝑡bank

𝑦=1

Equation 2.11

where 𝐶bank,int is the interest paid on the bank loan (USD), 𝑑𝑡bank is the debt tenor (years),

𝑦 refers to the year in question, 𝐼𝑅bank is the interest rate on the bank loan (%) and 𝑊𝐴𝐶𝐶 is the weighted average cost of capital (%). Next the bank amortisation costs can be calculated over the debt tenor period.

𝐶bank,amor = ∑ [𝐼𝑅bank ∙ (𝐶ini,inv ∙ 𝐷 ∙ (1 + 𝐼𝑅bank)𝑑𝑡bank)

((1 + 𝐼𝑅bank)𝑑𝑡bank − 1) ∙ (1 + 𝑊𝐴𝐶𝐶)𝑦]

𝑑𝑡bank

𝑦=1− 𝐶bank,int

Equation 2.12

where 𝐶bank,amor is the amortisation paid on the bank loan (USD).

The final component is the lifetime OPEX which can be calculated by summing the yearly OPEX, adjusted by the inflation rate and WACC.

29

𝐶OPEX = ∑ 𝑐OPEX(𝑦)

∙(1 + 𝐼𝑅)𝑦

(1 + 𝑊𝐴𝐶𝐶)𝑦

𝑙

𝑦=1 Equation 2.13

where 𝐶OPEX is the lifetime OPEX (USD), 𝑐OPEX(𝑦)

is the OPEX of the year in question (USD)

and 𝐼𝑅 is the inflation rate (%). The lifetime cost is the sum of all of these components.

𝐶PV = 𝐶equity + 𝐶bank,int + 𝐶bank,amor + 𝐶OPEX Equation 2.14

where 𝐶PV is the lifetime cost of the project (USD). It may be noted that residual value is not included in the lifetime cost of the plant. It is assumed that the plant will function until many of the components retain minimal value. There will of course be decommissioning costs, but these are very uncertain as decommissioning is currently not widespread, but will be very common in 30 years’ time. The costs will be largely reduced by the discount rate, so that the present value of the cost is assumed minimal. Both the EPC contractor and developer require a profit margin on the project. From the viewpoint of a developer, the EPC contractor profit will have been accounted for in the EPC cost, but the developer’s profit still must be considered. This can be achieved by setting an internal rate of return (IRR) which the project is expected to generate to cover the desired profit margin. The IRR is set assuming a mark-up on the relevant cost of capital and may be 15 %, for example. This IRR of 15 % can then be set as the discount rate and substituted for the WACC to calculate the grid payback rate required to generate this profit, i.e. the LCOE. However, in this study, costs are considered from the viewpoint of the whole project, so both the EPC and developer profits have been incorporated into the CAPEX. This allows the discount rate to be varied considering only the cost of capital, independently of profit, so that the effect of WACC on LCOE can be seen. LCOE is a standard economic indicator in the utility PV industry [102]. It shows the revenue per unit of electricity required to break-even, but does not indicate profit. This still allows it to be a good comparison tool between projects as the lower the LCOE, the larger the benefit-to-cost ratio will be. The LCOE is calculated thus:

𝐿𝐶𝑂𝐸 =𝐶PV

∑𝐸(𝑦)

(1 + 𝑊𝐴𝐶𝐶)𝑦𝑙𝑦=1

Equation 2.15

where 𝐸(𝑦) is the energy output in year 𝑦 (kWh). One disadvantage of LCOE is that it does not indicate how much electricity is being produced so a different configuration may have marginally higher LCOE but produce significantly more electricity. The calculation of project net present value overcomes this issue but this and Profit index require the PPA tariff to be known. This will be discussed further in the Results and discussion. All methods and input data used during the research project have now been described. The following chapter will discuss the findings, having put these procedures into practice.

30

3 Results and Discussion In this chapter, results for each of the four configurations will be presented and discussed separately before a comparison and general discussion about the findings. For each configuration, details of the performance and losses will first be given, based on the optimised plant, before results of the optimisation process and images of the proposed plant layout. The simulation reports can be found in Appendices E–H.

Monofacial fixed

3.1.1 Plant performance

The optimised design had a specific production of 1720 kWh kW-1 year-1 and a performance ratio (PR) of 77 %. As seen in Figure 3.1, the PR was highest in August, at 80 %, due to lower temperatures and lower soiling loss and lowest in March, at 75 %, when temperatures are high and conditions are dusty. Unfortunately, a meaningful comparison with the literature cannot be made since real-world PR data are rarely made publically available by PV plant operators [103], and the limited number of academic studies report a wide range of PRs [104]. Nonetheless, compared to modern PV plants in colder climates, this PR could be considered surprisingly low, but it is the price to pay in locations with high temperatures. Indeed the most major loss was due to temperature, at 10 %. This highlights the importance of selecting modules with a reasonable temperature coefficient. Other significant losses included soiling at 2 % and inverter efficiency loss also at 2 %. Full losses are listed in Appendix E.

Figure 3.1 Monthly performance ratio.

3.1.2 Plant optimisation

Firstly, the plant pitch distance was optimised, which is illustrated by the LCOE curve in Figure 3.2. The normalised specific energy curve shows the value of specific energy relative to its value at the optimum LCOE, in percent. The same applies for the normalised lifetime

cost per watt (𝐶PV/𝑊) curve. These normalised energy and cost curves show how these two components of LCOE vary away from the optimum point so that the reasons for the LCOE increase can be determined. It can be seen from the figure that the optimum pitch distance was found at 6.5 m. As the pitch distance decreased from 10 m to 6.5 m, row-to-row shading increased, however, as the graph shows, land cost reduced more rapidly than energy loss percentagewise, so the LCOE was improved. Installing modules at a pitch distance less than 6.5 m began to affect

60%

65%

70%

75%

80%

85%

90%

Per

form

ance

rat

io

Month

31

energy output more rapidly, so LCOE increased. Even so, 6.5 m was the minimum pitch distance to allow sufficient space for O&M (inter-row spacing of 2.6 m). It is important to note how small the variation of energy and cost, and therefore LCOE, was across this wide range of pitch distances. Specific energy increased by only 0.4 % and cost by less than 1 % across the range. Thus any of these distances could be chosen with little effect on performance or cost. Furthermore, the optimum distance could change largely if any of the input parameters were varied within their error margins. If land cost increased for example, a smaller distance could be used (provided O&M could be carried out in a narrow space) since the effect on specific energy output is so small percentagewise.

Figure 3.2 LCOE as a function of pitch distance at ILR 1.26.

With regard to the ILR, as the DC-to-AC ratio increases, energy is lost more and more rapidly, as seen in Figure 3.3. This means that LCOE rises rapidly at higher ILRs. However, from an ILR of 1.14 to 1.31, neither cost nor performance was hardly affected percentagewise. In this range clipping losses increased by less than 0.5 %, showing how infrequently the plant runs close to nameplate power. Although ILR 1.21 had the lowest LCOE, any ILR between 1.14 and 1.31 could be selected, with LCOEs found close to 51 USD/MWh. Indeed, any of these systems could be optimal, considering the error in the input parameters. A discussion of the choice of higher or lower ILR, when the LCOE is not majorly affected, is given under Choice of configuration. It may be noticed that the specific energy point for ILR 1.26 is marginally lower than the trend would suggest. This is due to the use of 2500 kW inverters which have a lower efficiency. If 2750 kW inverters could be used, ILR 1.26 would have the lowest LCOE. Therefore, when using inverters other than SMA, this may be the optimum point.

99.8%

100.0%

100.2%

100.4%

100.6%

100.8%

50.90

50.95

51.00

51.05

51.10

51.15

6 7 8 9 10

Spec

ific

en

ergy

or

CP

V/W

rel

ativ

e to

o

pti

mu

m

LCO

E (U

SD/M

Wh

)

Pitch (m)

LCOE Normalised specific energy Normalised CPV/W

32

Figure 3.3 LCOE as a function of ILR at pitch distance 6.5 m.

3.1.3 Optimum plant design and layout

The optimum plant had a pitch distance of 6.5 m and ILR of 1.21. The string and inverter design parameters are listed in Table 3.1. Table 3.1 String and inverter specification for optimum monofacial fixed design.

Modules per string 28 Strings per inverter input 16 Inputs used per inverter 19 Number of inverters 15 Total number of strings 4560

An image of the proposed plant can be found in Figure 3.4. As seen in the figure, there were 15 inverters in total: 5 inverter arrays ran in the NS direction, with 3 inverter arrays in the EW direction. Each inverter array had 16 strings per shed running from east to west and 19 sheds installed north to south. Since the module arrays were rectangular, inverters were placed in EW running access routes. Full details of the plant layout are tabulated in Table 3.2.

Figure 3.4 Optimum plant layout for monofacial fixed.

95.5%

96.0%

96.5%

97.0%

97.5%

98.0%

98.5%

99.0%

99.5%

100.0%

100.5%

50.50

51.00

51.50

52.00

52.50

53.00

1.00 1.10 1.20 1.30 1.40 1.50 1.60

Spec

ific

en

ergy

or

CP

V/W

rel

ativ

e to

o

pti

mu

m

LCO

E (U

SD/M

Wh

)

ILR

LCOE Normalised specific energy Normalised CPV/W

N

16 strings per shed 19 sheds per inverter array

685 m 710 m

33

Table 3.2 Plant layout parameters.

Total sheds NS 95 Inverter arrays NS 5 Inverter arrays EW 3 Access routes NS running 2 Access routes EW running 4 Length NS (including 10 m perimeter) 685 m Length EW (including 10 m perimeter) 710 m Total area 0.49 km2

The 3D shading scene model showed very similar performance to the 2D model. Shading loss was slightly lower, due to the possibility of light entering at either end of the rows. The two model outputs were within 0.1 %, meaning the 2D model was sufficiently accurate. In summary, the optimised monofacial tracking design has a pitch distance of 6.5 m and an ILR of 1.21, giving an LCOE of ca. 51 USD/MWh. However, the design has wide scope for pitch distance and ILR alterations with minor effect on LCOE, depending on desired plant characteristics. Indeed the optimum pitch distance and ILR may change significantly due to the error in input parameters, considering the very narrow range of LCOEs.

Bifacial fixed

3.2.1 Performance and losses

Compared to monofacial modules, the bifacial modules received 5 % greater irradiation, providing 3 % greater electricity production when the bifaciality factor and slightly greater low irradiance losses were considered. With reference to the literature, this improvement is shy of the 5.8 % production increase reported by Rodríguez-Gallegos [15]. The main reason

for this was their use of a much greater tilt of 22° for bifacial modules, which allowed a large incident irradiation increase, since they had not considered row-to-row shading. Other work such as [24] focuses on single bifacial modules, rather than an array with multiple rows, so this cannot directly be compared with this thesis either. As mentioned in Previous work, there seems to be a gap in research on PV array bifacial improvement. Regarding module and system losses, these were very similar to the monofacial designs, partly due to the similarity of the modules’ performance characteristics. The performance ratio, seen in Figure 3.5, was naturally higher compared to that of the monofacial system since more of the light reaching the array could be converted to electricity. The same annual trends were observed for bifacial modules.

Figure 3.5 Monthly performance ratio.

60%

65%

70%

75%

80%

85%

90%

Per

form

ance

rat

io

Month

34

1750

1760

1770

1780

1790

1800

0.5 0.75 1 1.25 1.5 1.75 2

Spec

ific

en

ergy

(kW

h/k

W)

Height (m)

3.2.2 Plant optimisation

Now with a second module face to consider, it was decided to re-optimise the tilt angle. As shown in Figure 3.6a, the specific energy decreased marginally with steeper tilt, contrary to the trend reported by Rodríguez-Gallegos [15], described in Previous work. As explained in [17], the bifacial gain may have increased, due to the rear-face’s increased view of reflected light and diffuse sky irradiance, but the overall production decreased due to row-to-row

shading. This effect was not considered in [15], hence their contrary conclusion. Thus, 15° remained optimal.

The next additional consideration for bifacial modules was mounting height. As seen in Figure 3.6b, specific energy increased with height. This was due to a greater view factor and less self-shading, as reported by [17], [24]. The improvement was marginal with only 1 % energy increase between the heights of 0.75 m and 1.5 m. As mounting is ca. 10 % of initial cost, the extra cost of higher mounting and larger O&M costs may outweigh the energetic benefit. Thus 0.75 m was chosen as mounting height.

Figure 3.6 Specific energy a) as a function of tilt angle at pitch distance 8.5 m, ILR 1.26, height 0.75 m, and b) as a function of height at pitch distance 8.5 m, ILR 1.26.

The optimum pitch distance was found at 8 m, shown in Figure 3.7. As seen in the literature [17], the bifacial modules benefitted from greater inter-row spacing than monofacial, since this allowed more ground reflection and therefore improved rear-face power production. Like monofacial, energy and lifetime cost were only marginally affected across a wide range of pitch distances, 6.5 m to 12.5 m, meaning LCOE had less than 0.5 % variation. Therefore the optimal design will be largely affected by the error in input parameters and other real life factors not considered here.

1756

1758

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1766

15 16 17 18 19 20

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h/k

W)

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35

Figure 3.7 LCOE as a function of pitch distance at ILR 1.26, height 0.75 m.

Similarly to monofacial, LCOE was lowest at ILR of 1.21, as seen in Figure 3.8. However, with these modules producing more energy per W of front power, energy was clipped more rapidly as ILR increased, so that LCOE increased at a greater rate than for monofacial. Under the studied conditions, the ILR of 1.21 or perhaps 1.26 should be chosen, but the ratios of 1.14 up to 1.31 were still found in a narrow range of LCOE. Thus anywhere in this range may become most favourable under more accurate modelling conditions, and indeed any may be chosen with minimal impact on LCOE.

Figure 3.8 LCOE as a function of ILR at pitch distance 6.5 m, height 0.75 m.

3.2.3 Optimum plant design and layout

The optimal bifacial fixed plant had a pitch distance of 8 m and ILR of 1.21. Due to the similar module characteristics and identical ILR to the monofacial fixed plant, the bifacial plant’s string and inverter design parameters match those for monofacial fixed. These values are listed in Table 3.3.

99.2%

99.4%

99.6%

99.8%

100.0%

100.2%

100.4%

100.6%

100.8%

50.65

50.70

50.75

50.80

50.85

50.90

6.5 7.5 8.5 9.5 10.5 11.5 12.5

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LCOE Normalised specific energy Normalised CPV/W

97%

98%

99%

100%

101%

102%

50.50

50.60

50.70

50.80

50.90

51.00

51.10

51.20

51.30

51.40

51.50

1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45

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rel

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ILR

LCOE Normalised specific energy Normalised CPV/W

36

Table 3.3 String and inverter specification for optimum monofacial fixed design.

Modules per string 28 Strings per inverter input 16 Inputs used per inverter 19 Number of inverters 15 Total number of strings 4560

The plant design is visualised in Figure 3.9. Many similarities can be drawn with the monofacial fixed design: a total of 15 inverters, with 5 inverter arrays north to south and 3 east to west. Considering the larger pitch distance, each inverter array had only 16 sheds in the NS direction but 19 strings per shed in the EW direction. Again, inverters were placed in the EW running access routes. Full details of the plant layout can be found in Table 3.4.

Figure 3.9 Optimum plant layout for bifacial fixed.

Table 3.4 Plant layout parameters.

Total sheds NS 80 Inverter arrays NS 5 Inverter arrays EW 3 Access routes NS running 2 Access routes EW running 4 Length NS (including 10 m perimeter) 700 m Length EW (including 10 m perimeter) 845 m Total area 0.59 km2

Again, the 3D shading scene performed very similarly to the 2D model. This was not surprising since bifacial shading scenes are processed using the 2D unlimited sheds model [105]. The marginal energy increase can be attributed to the EW access routes, reducing shading on a few rows. Since the production increase was negligible, it can be considered reasonable to use the 2D unlimited sheds model rather than the 3D model. To summarise, the optimum plant had tilt 15°, height 0.75 m, pitch distance 8 m and ILR of 1.21, giving an LCOE of ca. 51 USD/MWh. The bifacial energetic gain was less than simulated by Rodríguez-Gallegos due in part to more realistic simulation parameters. As for the monofacial fixed configuration, very little variation in LCOE values was found, meaning there is great scope for design adaptation depending on desired characteristics and accurate costs.

N

19 strings per shed 16 sheds per inverter array

700 m 845 m

37

Monofacial tracking

3.3.1 Performance and losses

Compared with the monofacial fixed array, the tracking array received a further 23 % irradiation. Extra near shading occurred due to adjacent trackers reducing diffuse irradiance, but conversely incidence angle modifier losses were decreased due to smaller incidence angles. Low irradiance losses decreased, while temperature losses increased due to greater direct irradiation but by less than 1 percentage point. With all these considered, the tracking array provided a further 21 % energy. This is concordant with the findings of Rodríguez-Gallegos [16], Afanasyeva [79] and Bahrami [20], who independently reported energetic increases of 20–25 % using horizontal single-axis trackers in Nigeria. The performance ratio, shown in Figure 3.10, followed the familiar trend, although for most of the year it was slightly lower than the monofacial fixed plant. The PR calculation assumes the extra in plane energy can be used, so it would not be expected to be higher than the fixed plant. It is reduced due to greater shading and higher array temperature.

Figure 3.10 Monthly performance ratio.

3.3.2 Plant optimisation

With a step down in LCOE due to increased specific energy, the optimal EW pitch distance was found at 8.5 m, as seen in Figure 3.11. However, the variation in LCOE was almost negligible between 7 and 9 m distances. These distances are similar to fixed installations, yet each shed here only held one module across, meaning land area was much greater. It is clear that tracking plants are suited to areas with low land cost, if their full energetic benefit is to be harnessed. Even so, it was found that a tracking plant built within the land area of a fixed plant can still have a lower LCOE than the fixed plant, meaning they are worth exploring even at greater land cost. Considering the large area of the plant, and associated greater cost of cables, a pitch distance of 7 m or lower might in fact be optimal. For the ILR studies, a pitch distance of 8 m was used. This was because 8 m had been the optimum distance before minor changes were made. It is clear that the effect on LCOE is negligible.

60%

65%

70%

75%

80%

85%

90%

Per

form

ance

rat

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38

Figure 3.11 LCOE as a function of pitch distance at ILR 1.25.

Regarding ILR, the optimal ratio was found at 1.31 as seen in Figure 3.12. The unexpectedly high LCOE for ILR 1.26 stemmed from the use of the less efficient 2500 kW inverter. With matching efficiencies, ILR 1.26 would have been the DC-to-AC ratio of choice. Again, the variation in energy and cost between ILR 1.14 and 1.31 were within the error margin of the calculations, so a wide range of ILRs could be chosen.

Figure 3.12 LCOE as a function of ILR at pitch distance 8 m.

It is surprising to note that the tracking plant seems to call for a larger ILR than the fixed plants (1.31 vs 1.21 respectively). By tracking the sun, one would expect the system to run at high power for longer periods and thus favour greater inverter capacity. Due to being horizontal, rather than tilted, the tracker’s energetic advantage comes into play during the middle of the year, rather than either end of the year. In the middle of the year, irradiance is lower so the modules run at lower power, while they collect their additional energy compared to fixed. At either end of the year, when irradiance is greater, the trackers are not optimally tilted so the power is reduced and again, larger inverter capacity is not required. This shows that assumptions cannot always be trusted for choosing the ILR, which depends on latitude and climate.

90%

92%

94%

96%

98%

100%

102%

45.50

46.00

46.50

47.00

47.50

48.00

48.50

3 4 5 6 7 8 9

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LCOE Normalised specific energy Normalised CPV/W

98.0%

98.5%

99.0%

99.5%

100.0%

100.5%

101.0%

45.90

46.00

46.10

46.20

46.30

46.40

46.50

1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45

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ILR

39

3.3.3 Optimum plant design and layout

The optimum monofacial tracking plant had a pitch distance of 8 m and ILR of 1.31. The associated string and inverter design parameters are listed in Table 3.5. Reflecting the higher ILR of the plant, a greater number of strings were used, connected to only 14 inverters. Table 3.5 String and inverter specification for optimum monofacial fixed design.

Modules per string 28 Strings per inverter input 15 Inputs used per inverter 22 Number of inverters 14 Total number of strings 4620

The proposed plant is visualised in Figure 3.13. Two inverter arrays were found in the NS direction and 7 in the EW direction. Each array had 5 trackers north to south, and 22 trackers east to west. Dedicated access routes were not necessary to incorporate in the dimensioning, since each tracker had at least a 5 m gap in all directions around it, allowing full access. Inverters were installed in the east to west gap dividing the northern and southern arrays. This gap was therefore widened from 5 m to 10.6 m. Full layout parameters are tabulated in Table 3.6.

Figure 3.13 Optimum plant layout for monofacial tracking.

Table 3.6 Plant layout parameters.

Total trackers NS 10 Inverter arrays NS 2 Inverter arrays EW 7 Length NS (including 10 m perimeter) 925 m Length EW (including 10 m perimeter) 1250 m Total area 1.16 km2

The 3D model predicted 0.2 % greater energy output than the 2D model, mainly due to the differing modelling technique, rather than the additional light entering via the NS gaps between trackers. The 2D model was deemed sufficiently accurate for the optimisations. To summarise, despite its greater land use, horizontal single-axis tracking is favourable at this location. As before, there is little effect on LCOE across a wide range of pitch distances

N

22 trackers EW per inverter array

5 trackers NS per inverter

array

925 m 1250 m

40

and ILRs. The optimal plant had a pitch distance of 8 m and ILR of 1.31, giving an LCOE of ca. 46 USD/MWh.

Bifacial tracking

3.4.1 Performance and losses

Using bifacial modules on the trackers allowed not only the collection of extra ground-reflected light, but also the addition of significant diffuse radiation, received on the rear-face. Due to the large tilt (phi) angles used by the trackers, the rear-face collected 15 times as much diffuse sky radiation as the rear-face of a fixed mounted bifacial installation. Overall bifacial gain was 5 %, compared to 3 % bifacial gain on fixed mounting, showing their advantage is augmented with trackers in this location. The system losses were very similar to monofacial tracking. Good agreement was made with the literature. Rodríguez-Gallegos found that bifacial tracking had a ca. 32 % energetic improvement over monofacial fixed in the geographical region of interest. In this thesis an improvement of 28 % was found, which increased to 32 % if the bifaciality factor was enlarged to that used in the literature. With respect to the performance ratio, seen in Figure 3.14, this system followed the same trend as monofacial tracking, but was increased by an average of 4 percentage points due to the additional energy collected on the rear-face.

Figure 3.14 Monthly performance ratio.

3.4.2 Plant optimisation

The effect of tracker height on specific energy is shown in Figure 3.15. The height of the Nextracker Horizon, from the ground to the central rotating axis, can be set in the range 1.3–1.8 m. Across this range, specific energy increased but by less than 1 %. It was therefore unclear if the additional revenue would outweigh the extra cost of higher mounting and elevated cost of O&M. Therefore a midrange height of 1.5 m was selected, as this most likely reflected the average tracker cost used in calculations.

60%

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75%

80%

85%

90%

Per

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41

Figure 3.15 Specific energy as a function of height at pitch distance 9.5 m, ILR 1.26.

The LCOE, lower than monofacial tracking, was optimised at a large pitch distance of 9.5 m, as seen in Figure 3.16. As with all other configurations, different designs could be optimal within the error of the input parameters. The trend shows that a larger pitch distance should be used than with monofacial modules, since the bifacial modules benefit from increased view of the ground as distancing increases. Clearly, this configuration favours great land use, however it was found to be the most economically favourable configuration at all explored pitch distances, showing it should still be considered if less land is available or it is more expensive.

Figure 3.16 LCOE as a function of pitch distance at ILR 1.26, height 1.5 m.

Regarding the ILR, a ratio of 1.22 was optimal, as seen in Figure 3.17. Unsurprisingly this was lower than for monofacial tracking, considering the higher operating power. An ILR anywhere between 1.14 and 1.25 could be selected.

2190

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2198

2200

2202

2204

2206

2208

1.3 1.4 1.5 1.6 1.7 1.8Sp

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gy (

kWh

/kW

)

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95%

96%

97%

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99%

100%

101%

44.60

44.80

45.00

45.20

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45.60

45.80

5.00 6.00 7.00 8.00 9.00 10.00

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LCOE Normalised specific energy Normalised CPV/W

42

Figure 3.17 LCOE as a function of ILR at pitch distance 9.5 m, height 1.5 m.

3.4.3 Optimum plant design and layout

The optimum bifacial tracking plant had a pitch distance of 9.5 m and an ILR of 1.22. The string and inverter parameters are more closely aligned with the fixed plants than the monofacial tracking plant and are given in Table 3.7. Table 3.7 String and inverter specification for optimum monofacial fixed design.

Modules per string 28 Strings per inverter input 17 Inputs used per inverter 18 Number of inverters 15 Total number of strings 4590

The layout of the proposed bifacial plant can be seen in Figure 3.18. 10 inverter arrays were placed north to south and 3 east to west. Individual arrays had 3 trackers in the NS direction and 34 in the EW direction. Inverters were placed in the NS running gaps between the 3 arrays east to west. These gaps were thus increased from 7.5 m to 15.0 m. Full details of the plant layout are tabulated in Table 3.8.

Figure 3.18 Optimum plant layout for bifacial tracking.

97.0%

97.5%

98.0%

98.5%

99.0%

99.5%

100.0%

100.5%

44.40

44.60

44.80

45.00

45.20

45.40

45.60

45.80

1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45

Spec

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an

d C

PV/W

rel

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LCO

E (U

SD/M

Wh

)

ILR

N

34 trackers EW per inverter array

3 trackers NS per inverter

array

1370 m

1005 m

43

Table 3.8 Plant layout parameters.

Total trackers NS 10 Inverter arrays NS 5 Inverter arrays EW 3 Length NS (including 10 m perimeter) 1370 m Length EW (including 10 m perimeter) 1005 m Total area 1.38 km2

Using the 3D shading scene, the electricity production increased by 0.3 %. This was partly due to the increased irradiation through the access routes, but also due to the use of the 3D shading model. The 2D unlimited model was again deemed accurate enough for the optimisation studies. In summary, it was found that bifacial modules with trackers is an effective combination in this location. The optimal plant had a height of 1.5 m, pitch distance 9.5 m and ILR 1.22, giving an LCOE of ca. 45 USD/MWh, with large potential for design variation.

Configuration comparison

The optimised systems are listed in Table 3.9, allowing a comparison of their key features. There is an apparent trend showing that a larger pitch distance should be used when installing bifacial and tracking technology. Subsequently, land use will be greater, which is particularly the case for bifacial tracking. Regarding the optimal ILRs, for fixed installations, bifacial modules did not call for a reduced ILR and in the tracking systems, a 7 % reduction was optimum. This is in contrast with the assumption made by Rodríguez-Gallegos [15], [16] that bifacial modules require 20 % greater inverter capacity than monofacial. While this may be a reasonable first approximation on the global scale, it is clearly not a safe rule-of-thumb in all locations. Thus, if LCOE is to be minimised, simulations should be run. To compare the best system with the base configuration, the initial investment for bifacial tracking was 15 % higher than monofacial fixed, but this allowed the generation of 28 % more electricity per watt of peak power. In combination, this provided a 12 % reduction in LCOE. Figure 3.19 shows that the biggest LCOE improvement stems from the use of trackers, and bifacial gain increases when trackers are used, at this location. Table 3.9 Comparison of optimum plants for each configuration.

Configuration Monofacial fixed

Bifacial fixed Monofacial tracking

Bifacial tracking

Pitch distance (m) 6.5 8 8 9.5

ILR 1.21 1.21 1.31 1.22

Power DC (MW) 49.8 49.8 50.5 50.1

Land (km2) 0.49 0.59 1.16 1.38

CAPEX/W (USD) 0.95 0.97 1.06 1.09

Annual cost OPEX/W (USD) 0.019 0.020 0.021 0.021

Performance ratio 78 % 80 % 77 % 81 %

Specific energy (kWh/kW) 1720 1770 2080 2200

LCOE (USD/MWh) 51 51 46 45

44

Figure 3.19 Optimal LCOE for each configuration.

Compared with most sub-Saharan LCOEs found in the literature, the LCOEs calculated in this thesis are an underestimate, although with the help of the World Bank’s initiatives (as assumed in this report), very low LCOEs have been reported of ca. 40 USD/MWh in Senegal and ca. 35 USD/MWh in Zambia [81]. This suggests the prices estimated herein may be realistic. Although the LCOEs are incomparable with those calculated by Rodríguez-Gallegos [15], [16] due to a more rigorous pricing structure in this thesis, the trends can be compared. Their 2018 paper predicted that bifacial fixed would be favourable over monofacial fixed when the bifacial modules had less than a 5 % premium cost. In this study, a 3 % premium was assumed and indeed, bifacial was marginally better than monofacial, although the improvement was within the error of the calculation. Regarding the monofacial tracking configuration, this thesis found a 10 % reduction in LCOE compared to monofacial fixed, in reasonable agreement with Rodríguez-Gallegos’ prediction of 12 %. Finally, bifacial tracking led to a 13 % LCOE reduction in this thesis, compared to 15 % reported by Rodríguez-Gallegos. The sensitivity analysis will explore how these findings are affected by uncertain input values and limitations.

42

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52

Monofacial fixed Bifacial fixed Monofacial tracking Bifacial tracking

LCO

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Configuration

45

Sensitivity analysis

While clear conclusions could be drawn from calculation results, the findings contained error due to the uncertainty of the input parameters. This section looks at the impact of the uncertainty in the most important parameters on the LCOE and the relative order of merit of the plants. This will allow an appreciation of the magnitude of uncertainty to assume in the final conclusions. Module cost, which was varied between the two online price indexes, PVinsights [83] and PVxchange [84], plus 0.012 USD/W as an extra margin for shipping, had a significant effect on LCOE, seen in Figure 3.20a. This is due to the fact that the modules make up a large percentage of lifetime cost. Across this cost range, LCOE can increase by up to 22 %. Another major influence on LCOE was the weather source, as seen in Figure 3.20c, with Meteonorm reporting 9 % less annual irradiation than PV-GIS. The production of the fixed plants decreased by 9 %, so that LCOE increased by 10 %. Curiously, the tracking installations suffered greater losses of 13 % using the Meteonorm dataset, meaning their economic advantage over fixed mounting was slightly reduced. This was due to Meteonorm reporting a lower ratio of direct beam irradiation to diffuse irradiation than PV-GIS, so that the benefit of following the sun was reduced. It is clear that the choice of weather file is instrumental in economic projections. A further significant contributor to LCOE was OPEX shown in Figure 3.20g, due to its necessity throughout the project's lifetime. With the large uncertainty in OPEX cost, the LCOE may vary hugely, with a potential increase of 40 % or greater according to this analysis. Incidentally, the graph can also be used to find the tolerance on the level of increase of OPEX costs for tracking systems before fixed systems become most favourable. The largest influence on LCOE was the cost of capital, illustrated in Figure 3.20h. Increasing the bank interest rate from 2 % to 10 %, which is easily possible depending on financing options available, led to a 55–57 % increase in LCOE. It is ironic that after careful design work to achieve small improvements in plant cost, it is the cost of money whose minor variations far outweigh the engineering's influence on LCOE. However, the cost of money is a concern for the developer, not the EPC contractor which should still focus on engineering optimisation, in order to win the contract. In addition, this analysis confirmed the unfeasibility of sourcing capital from Nigerian banks and investors, whose estimated WACC of 14 % would lead to a sky high LCOE: 105 USD/MWh for monofacial fixed or 93 USD/MWh for bifacial tracking. Due to the inverter being a small component of lifetime cost, variation in its cost had a minor impact on LCOE. The remaining parameters, soiling rate, degradation rate and system lifetime, played smaller roles, but they are certainly not negligible. Each should be carefully considered in more advanced stages of LCOE projection, since combined they could cause a reasonable percentage error in LCOE. These findings reflect those discussed in Previous work, that module cost and cost of capital are critical to the project cost. OPEX had a greater influence here than in the literature, due to its variation across a wider range, while weather source had not been investigated in the publications. By contrast with the literature, this analysis shows that system lifetime has a lesser effect on LCOE, although it is unclear why this discrepancy arose.

46

Figure 3.20 Results of sensitivity analysis (default values in brackets): a) module cost (0.265 USD/W), b) inverter cost (0.038 USD/W of AC power), c) weather source (PVGIS), d) soiling loss (3 %), e) degradation rate (1 %), f) system lifetime (30 years), g) OPEX percentage of CAPEX (2.5 %), h) bank interest rate (2 %).

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Average monthly soiling harmattan period

a) b)

c) d)

e) f)

g) h)

47

A sensitivity analysis was also run on the cost premium of bifacial modules. According to the trend seen in Figure 3.21a, if bifacial modules are greater than 6 % more expensive than monofacial, monofacial modules should be chosen for fixed mounting. Considering the uncertainty in the input values, thesis calculations and PVsyst modelling of bifacial modules, there is undoubtedly some leeway around this. Good agreement was made with Rodríguez-Gallegos 2018 [15], who reported a maximum 5 % premium should be accepted for bifacial modules. With regard to tracking plants, which allow greater bifacial gain, Figure 3.21a shows that bifacial modules should always be used under all reasonable premium costs. A similar test was run for the tracking cost premium, with tracker cost varied across the full range reported by members of the industry. Figure 3.21b shows that the technology retains its economic advantage across all reasonable price premiums, thanks to its significant energetic advantage, and thus it should be considered in all cases.

Figure 3.21 Cost premium analysis for a) bifacial modules (default 103 %) and b) tracking (default 171 %).

Albedo databases were also compared. The NASA data were substituted for the SolarGIS data, and simulations were rerun. With little variation in the average albedo from each source, at 18 % and 19 % respectively, there was minimal difference between results. Judging by the similarity of the databases, the albedo values can be considered sufficiently reliable for preliminary studies, although onsite measurements could be taken to check for minor local variations.

Effect of further limitations on results

The sensitivity analysis has shown that the LCOEs of the proposed plants can vary hugely, although they remain within the LCOE range seen in the literature [81]. Throughout this report, further limitations have been identified, whose impact on results will be discussed in this section. The magnitude of the uncertainty introduced by PVsyst is unclear, and PVsyst itself admits that very little validation work has been carried out [106]. As mentioned in the Detailed methodology chapter, a limited number of studies have been realised. The reported errors for the different configurations and their effect on this project’s LCOE values are found in Table 3.10. For the bifacial plants, only error in bifacial gain was reported, so the error in front-face production was assumed to equal the error of the monofacial plants. Applying these error values to the results of this thesis allowed the minimum LCOE improvement of each configuration relative to monofacial fixed to be found (due to PVsyst error). The LCOE improvement of bifacial fixed was barely affected and remains within the error of the calculation. Clearly more accurate input figures should be sourced before a decision is

40

45

50

55

150% 175% 200% 225% 250%

LCO

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)

Tracker cost relative to fixed mounting

40

45

50

55

102.5% 105.0% 107.5% 110.0%

LCO

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Bifacial cost relative to monofacial

a) b)

48

made on the use of bifacial modules on fixed mounting. For monofacial tracking plants, the LCOE remains 3 % better than monofacial fixed, so the investment in trackers is appealing. Bifacial tracking retains the lowest LCOE relative to monofacial fixed by some margin, meaning it is a wise and safe choice to invest in trackers with bifacial modules. Of course, the validation studies in the literature are not wide or rigorous enough to draw definite conclusions about the certainty of these thesis results, with the validation reports studying only one or two examples of each configuration. However, the thesis conclusions remain the same under the limited software error information available. Table 3.10 PVsyst error according to literature and effect on LCOEs.

Configuration Energetic uncertainty [10,11]

Bifacial gain uncertainty

LCOE reduction relative to monofacial fixed

Using PVsyst output

With max. PVsyst error

Monofacial fixed 2 % - - -

Bifacial fixed 2 % (front face)

5 % < 1 % < 1 %

Monofacial tracking 5 % - 10 % 3 %

Bifacial tracking 5 % (front face)

24 % 12 % 5 %

It has been mentioned above that in this location, the extra investment into bifacial modules on fixed mounting is questionable. However, their performance could be further improved by using bifacial-specific mounting, with increased row transparency and reduced rear shading from the structure. The cost of this specialist mounting would be greater, but the improvement would likely outweigh the cost. Thus the bifacial fixed LCOE may reduce further, so it should certainly be considered as a feasible option. In terms of specific plant design, the ILR of 1.25–1.26 was disfavoured in all configurations due to the use of the less efficient 2500 kW inverter. Furthermore, using 2500 kW rather than 2750 kW inverters would in reality be more expensive because more inverters are needed per watt. This price difference wasn’t considered but would further disfavour the ILR of 1.25–1.26. If a different inverter brand were used, this ILR would likely be optimal, so it should be considered. Regarding plant pitch distance, the values proposed in this report may be overestimates, given the lack of cable cost variation. The LCOEs reported should still offer a reasonable approximation, having used average cable cost per watt of the plant. According to advice from the industry, cabling only contributes ca. 2 % to lifetime cost so LCOEs will not be majorly affected by marginal length variations. Plant design may have also been affected by a limitation of the financial model. This was the independence of OPEX from land cost and ILR. OPEX was set such that it remained as 2.5 % of CAPEX (for the equivalent monofacial fixed plant, before the additional OPEX for bifacial and tracking were added as necessary). In reality, OPEX would have increased with land cost and inversely with ILR, meaning LCOE would be underestimated in many cases. As a final test in this project, the calculation was improved by allowing the OPEX to vary with land and ILR. The LCOE improvements of each configuration relative to monofacial fixed are shown in Table 3.11, using the old and new models. Although the LCOEs increased relative to monofacial fixed, the effect was so marginal that it is generally not seen in the table. Thus the conclusions of the research remain valid.

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Table 3.11 LCOE reductions relative to monofacial fixed with the original and updated OPEX models.

Configuration LCOE reduction with original model

LCOE reduction with updated model

Bifacial fixed < 1 % < 1 % Monofacial tracking 10 % 9 % Bifacial tracking 12 % 12 %

A limitation which would affect LCOEs equally was the assumption of 100 % grid availability. In reality, regular grid outages are suffered [5]. If this loss is considered as an energetic loss, the LCOE values predicted in this report would increase. Grid compensation should be available from the distribution company, so outages may not be a serious disincentive to investment. In any case, the order of configurations relative to each other would be unchanged, so the conclusions remain valid. As in much of the literature [15], [16], [18], [19], [99], the detailed study of corporate tax effects was beyond the scope of this work, due to its complexity and lack of clear input parameters for the Nigerian market. Tax on income would reduce profit and increase the LCOE, potentially quite significantly. On the other hand, corporate tax can also be used to reclaim some of the initial plant costs through tax reductions, which would decrease the LCOE [107]. However, it is unclear if advantage could be taken of this benefit in Nigeria. Evidently, detailed economic studies by the developer would be necessary for calculating a final LCOE. Nonetheless, the order of merit of plant configurations would be unchanged. A further reason to believe the LCOEs predicted may be underestimated was the use of US prices in the financial model, due to a lack of information relevant to Africa. Grid connection fees, land cost, equipment supply cost and OPEX cost may all be higher due to the undeveloped market. Overhead costs and required profit may also be elevated considering the rurality of the location and project risks involved. This work therefore can provide initial cost indicators before in-depth research can be undertaken by the developer, by contacting the relevant Nigerian authorities and suppliers. The proposed pricing structure is adaptable as and when detailed costs become available. As seen above, the cost of capital is one of the most influential factors, but this realistically could be sought at 4 %, which narrows uncertainty in the cost. Furthermore, the majority of the uncertainty around African financial figures would not affect the relative LCOEs of the configurations, so this study can still provide a good idea of the optimum technology for the plant. All performance values reported in this thesis, whether monthly or annual values, are averages based on mean input data. These outputs reflect the most probable performance, or the average performance across the project’s lifetime. Naturally, real life performance will vary from year to year. The main sources of variation are the weather and soiling level, although the latter will not be worse than projected if the soiling losses in this work are set as a commitment. Since Kabogi is already grid-connected, this annual variation should not directly affect end-users.

50

Choice of configuration

Having analysed the economic results, explored how they vary under different conditions, and finally discussed their uncertainty considering many limitations, the key question may now be answered: which configuration is the best choice? Given the boundary conditions of this project, with a large land area and a good view of the horizon in all directions, trackers are safely a better investment option than fixed mounting under all explored conditions. The optimum investment choice is bifacial tracking, which had the lowest LCOE in all tests, reflecting conclusions of the literature [16], [17]. If on the other hand fixed mounting is chosen, bifacial modules may well lead to a more profitable investment, but this is not always the case. Forecasts should be rerun with more accurate cost figures. It is worth bearing in mind that the bifacial modules have a 30 year warranty, 5 years longer than the monofacial warranty. In the long-term, bifacial modules may therefore be a safer investment. Considering the significant temperature losses of the arrays, it is wise to select modules with a low temperature coefficient. The Longi modules studied are at the better end of Tier 1

manufacturers at – 0.37 %/°C, but this could be slightly improved to – 0.35 %/°C by choosing QCells modules. The use of advanced cell technologies such as interdigitated back contact (IBC) and heterojunction (HJT) cells could be considered. In a study of manufacturer datasheets, these technologies were found to have improved temperature

coefficients (ca. – 0.30 %/°C for IBC and – 0.26 %/°C for HJT), so would suffer reduced losses. However, earlier this year, HJT modules were on average 100 % more expensive per watt than Tier 1 monocrystalline PERC on the European market, and IBC technologies were even more expensive [34]. Thus their use is likely economically unfeasible at the current time. With regard to the choice of inverter, in many cases throughout this project the inverters ran close to their minimum input voltage. It is possible that this voltage would not be reached after years of degradation, causing significant power loss. The MPP voltage range of the array should therefore carefully be compared to the inverter’s MPP voltage window before finalising the choice. While LCOE has been used to indicate merit of each plant design, it should not be the only economic factor to gauge success in a final analysis. In some cases in this report, a plant with a marginally higher LCOE than another produced significantly more electricity than the other. While the cost per kWh was slightly lower in the first plant, the second plant would generate a much greater profit than the first due to the additional energy sold. Therefore net present value should also be considered when a PPA rate is known. Additionally, the merit of a plant design should not only be judged from an economic perspective. A further key factor to consider is the impact on the weak grid, which would likely favour even daily and yearly energy distribution. This can be achieved by using trackers or by using a higher ILR to smooth peak power production, as illustrated earlier in Figure 1.4 and Figure 1.6 respectively. Also to consider is the impact on the environment, which would favour maximising the specific energy of the plant, by lowering ILR or using trackers. These factors can easily be taken into consideration when LCOE is almost constant across a wide range of ILRs, as was the case in all studied configurations. Evidently, further calculation and consideration is required before finalising the choice of technology, but clear conclusions have been drawn about the relative economic success of the different configurations.

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4 Conclusions Using modules, inverters and trackers relevant to the current market, this thesis has drawn safe conclusions about the order of economic merit of configurations in Kabogi: bifacial tracking > monofacial tracking > bifacial and monofacial fixed. These results reflect conclusions drawn by [15]–[17], although greater economic detail was considered in this thesis. Other factors to gauge plant merit have been discussed, such as energetic distribution and environmental impact. With regard to the LCOE values themselves, there is significant uncertainty in their estimation, due to the simplifications assumed, as well as the error in input values. However the calculation was more rigorous than previous literature studies, and results were found to be feasible when compared to reported costs. The accuracy can be improved through contact with the relevant industry and local agencies, as well as collection of measurements at the location. Within each configuration, an optimal plant design was put forward. This was possible due to the simulation of row-to-row shading, use of land costs and consideration of inverter cost per AC watt, some or all of which had not been considered in the literature. However, it was found that the LCOE remained almost unchanged across a wide range of ILRs and pitch distances, the latter due in part to the low cost of land assumed. There is therefore great scope to design plant layout and inverter topology to suit the site and desired electrical behaviour without significant impact on LCOE. Certain clear trends were observed regarding the use of bifacial and tracking technologies. Bifacial modules should not have a greater tilt angle than monofacial, but they may be elevated further to allow marginal improvements in performance, if the extra mounting costs are minimal. Both bifacial and tracking should have greater pitch distances than monofacial modules and fixed mounting, although the advanced technologies remain advantageous at narrower distancing, so should still be considered at much greater land cost. Finally, the most influential factors on LCOE were identified, to inform where efforts are most efficiently directed to improve the economic potential of the plant. These were module cost, OPEX and most importantly, cost of capital. As alluded to several times in the report, string inverters have a number of advantages over central inverters and are now economically feasible in certain larger scale projects. With more time, it would be fascinating to redesign the plant topology and make further economic projections to see if their use is worthwhile in this location. Elsewhere, a study could be run to find the optimum increase in tilt, at which the reduction in soiling most offsets the slightly greater row-to-row shading losses. Other future work could explore if backtracking really is the best algorithm to use in this location or if continuous sun tracking would produce better results. Also of interest is the use of east-west module arrays, since these have the lower capital costs of fixed plants but allow more even energy distribution throughout the day. To improve the economic model proposed herein, cable costs could be incorporated and the effect of taxes could be considered. While there is scope for further exploration, clear conclusions have been drawn for the use of the most relevant technologies today. It is hoped this work will provide guidance for the design of the Kabogi 50 MW power plant, and indeed utility projects beyond this as the industry gathers pace in sub-Saharan Africa.

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6 Appendices

List of Appendices

Appendix A Module datasheets Appendix B Inverter datasheet Appendix C Tracker datasheet Appendix D Inverter efficiency curve Appendix E Simulation report: Monofacial fixed Appendix F Simulation report: Bifacial fixed Appendix G Simulation report: Monofacial tracking Appendix H Simulation report: Bifacial tracking

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Appendix A Module datasheets

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Appendix B Inverter data sheet

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Appendix C Tracker datasheet

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Appendix D Inverter efficiency curve

Efficiency curve for SMA Sunny Central 2750-EV inverter, showing increasing efficiency with operating power and decreasing voltage.

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Appendix E Simulation report: Monofacial fixed

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Appendix F Simulation report: Bifacial fixed

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Appendix G Simulation report: Monofacial tracking

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Appendix H Simulation report: Bifacial tracking

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