Hybrid Power System for Green Telecom Sites

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Nordic Master’s Programme in Innovative and Sustainable Energy Engineering

Hybrid Power System for Green Telecom Sites

Giovanni Screpanti

Master’s Thesis 2021

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Author Giovanni Screpanti

Title of thesis Hybrid Power System for Green Telecom Sites Programme Nordic Master’s Programme in

Innovative Sustainable Energy Engineering – Energy Systems track

Major Master of Science

Thesis supervisor Dr. Annukka Santasalo-Aarnio Thesis advisor(s) Furat Shamurad

Collaborative partner Ericsson AB

Date 19.10.2021 Number of pages 73 Language English

Abstract

Solar Photovoltaic can offer a clean and cheap alternative to the Diesel Generator for telecom sites that are in remote locations with no grid coverage or where the grid is not fully reliable. Ericsson Enclosure & Power is looking into the opportunity to integrate this technology, together with battery storage, into their network sites around the globe, in view of a constantly growing network demand and the diffusion of the 5G technology. One of the main upfront cost is the civil works for the PV structure foundations and the company is interested in lighter weight PV chemistries, such as CIGS. Besides, they also noted how the thin-film technology harvest light in a different way than the standard crystalline silicon, which would represent an additional benefit of replacing c-Si with CIGS for certain locations.

The aim of this thesis work is to investigate into the effect that a different radiation spectrum has for several PV chemistries, mainly c-Si and CIGS, to improve the Excel model, already started by the group, with more accurate information and algorithm for all the components of a hybrid diesel-solar-battery power system, to develop a dispatch strategy among the different sources and analyse the best mix that increases the share of renewables and gives the lowest payback period. It was found that, when the spectral effects are included in the model, there is no significant difference between c-Si and CIGS and their comparison can be done simply in terms of efficiency values, cost per unit of watt peak and weight-to-power ratio, for cheaper types of structures. Besides, the best mix is found for both a grid-connected and an off-grid sites, where a combination of PV, batteries and genset or grid is used. Utilizing renewable energy only, would require a significant oversizing of the system to cover the annual load, making the investment unattractive for the cost and the land needed. A pure solar system could represent an opportunity for sites that can accept planned shutdowns for a small fraction of the year.

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3 Författare Giovanni Screpanti

Titel Hybrid Power System for Green Telecom Sites.

Utbildningsprogram Nordic Master’s Programme in Innovative Sustainable Energy Engineering – Energy Systems track

Huvudämne Master of Science

Ansvarslärare Dr. Annukka Santasalo-Aarnio Handledare Furat Shamurad

Samarbetspartner Ericsson AB

Datum 19.10.2021 Sidantal 73 Språk Engelska

Sammandrag

Solar Photovoltaic kan erbjuda ett rent och billigt alternativ till dieselgeneratorn för telekom- sajter som ligger på avlägsna platser utan elnätstäckning eller där elnätet inte är helt

tillförlitligt. Ericsson Enclosure & Power undersöker möjligheten att integrera denna teknik, tillsammans med batterilagring i sina nätverkssajter runt om i världen, och detta är en konsekvens av ständigt växande nätbehov och 5G -teknik distributionen. En av de främsta kostnaderna är arbetskraften för PV -grundstrukturen och därför företagen är intresserade av lättare PV -kemier, som CIGS. Dessutom, noterade företagen hur tunnfilmstekniken skördar ljus på ett annat sätt än det vanliga kristallina kislet, vilket skulle representera en ytterligare fördel med att ersätta c-Si med CIGS för vissa platser. Syftet med detta avhandlingsarbete är att undersöka vilken effekt ett annat strålningsspektrum har för flera PV-kemier, främst c-Si och CIGS, för att förbättra Excel-modellen. Ett arbete redan startats av gruppen, med mer exakt information och algoritm för alla komponenterna i ett hybrid diesel-sol-batteri

kraftsystem, för att utveckla en utnyttjande strategi bland de olika källorna och analysera den bästa blandningen som ökar andelen förnybar energi och som ger den lägsta

återbetalningsperioden. Det visade sig att när spektraleffekterna ingår i modellen då fanns det ingen signifikant skillnad mellan c-Si och CIGS. Jämförelsen mellan c-Si och CIGS kan göras baserat på effektivitetsvärden, kostnad per watt-peak-enhet och vikt-effektförhållande, för billigare typer av konstruktioner. Dessutom är den bästa mixen för både elnätanslutna och off-grid-platser, är en kombination av PV, batterier och generator eller elnät. Att bara använda förnybar energi skulle kräva en betydande överdimensionering av systemet för att täcka den årliga belastningen, vilket gör investeringen oattraktiv för kostnaden och marken som behövs. Ett rent solsystem kan vara en möjlighet för sajter som kan acceptera planerade elnätsavbrott under en liten bråkdel av året.

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Contents

1 Introduction ... 5

1.1 Research Objectives ... 7

2 Literature Review ... 7

2.1 Solar Photovoltaic ... 7

2.2 Ericsson background and their Power System Model ... 14

2.2.1 Solar and Wind production ... 15

2.2.2 Load ... 18

2.2.3 Battery Storage System ... 18

2.2.4 Economics ... 19

2.3 Spectral Effect ... 19

2.3.1 Radiation Spectrum ... 19

2.3.2 APE, SR and MM ... 20

2.3.3 Previous studies on the spectral effect ... 22

2.4 Optimal Tilt Angle ... 25

2.5 Dispatch strategy ... 26

3 Methodology ... 27

3.1 Model Development ... 27

3.1.1 MPPT ... 27

3.1.2 Battery ... 29

3.1.3 Diesel Generator ... 30

3.1.4 Hybrid Modes ... 31

3.1.5 Direct and diffuse components ... 32

3.2 Python ... 35

3.2.1 API ... 35

3.3 Spectral Effect ... 36

3.4 Optimal Tilt Angle ... 42

3.5 Dispatch strategy ... 44

3.5.1 Dittenheim Site ... 44

3.5.2 Mexico site ... 49

4 Results ... 49

4.1 Spectral Effect ... 49

4.2 Optimal Tilt Angle ... 52

4.3 Dittenheim site ... 54

4.4 Mexico Site ... 56

5 Conclusion ... 57

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5

5.1 Future work ... 59

6 References ... 59

7 Appendix A... 65

8 Appendix B ... 72

1 Introduction

The evolution of the society has led to a steep rise in manmade carbon emissions in the last century [1], reaching a value of 34 billion metric tons in 2020 [2]. National lockdowns around the world, caused by the Covid-19 pandemic, have prevented people from moving and declined the economic activities, resulting in a reduction of air pollution and emissions for the first time in four decades in India [3]. Despite this positive aspect, Covid-19 is causing the worst health and economic crisis in the last century [4] and it will be crucial the way the world will drive away from this crisis. The International Energy Agency (IEA) has identified the opportunities and methods that governments around the globe might follow to achieve a sustainable and clean post-pandemic development, these are presented in the Sustainable Recovery Plan [5]. In order to prevent the detrimental consequences of climate change and to avoid further depletion of the global carbon budget, it is fundamental to invest in climate mitigation and adaptation strategies. This can be achieved by fostering the development of innovative and sustainable technologies with the aim of reducing the carbon emissions and maintaining global warming well below 2°C, as recommended by the Paris Agreement [6]. Moreover, avoiding irreversible environmental damage also entails that the effect of human activity should remain within the Earth’s planetary boundaries [7]. In the same year, the United Nations published the “2030 agenda for sustainable development”, which is a collection of 17 objectives, called “sustainable development goals” or “SDGs”, that the global society should achieve by 2030. The general aim of these goals is to create a better future for everyone. They are related to the improvement of several aspects of the society and human life quality such as, economy, infrastructure, education, equality, well-being and the elimination of poverty and hunger from the world. The SDG 7 calls for ensuring access to affordable, reliable, sustainable and modern energy [8].

The energy sector accounts for over 70% of global greenhouse gas emissions [9], therefore, reducing the environmental footprint of the energy system is a major step to halt global warming and reverse climate change.

So far, humanity has been reliant on fossil fuel to produce energy through their combustion, which generates carbon dioxide as a product. The solution entails the large-scale utilization of alternative, renewable energy sources, such as solar and wind. However, these clean resources depend on the weather condition and it is challenging to rely on them for baseload generation. The sun is not always shining and the wind is not always blowing, making their output variable and challenging to be predicted. This implies that energy coming from renewables cannot be dispatched. In the energy market this means that when solar and wind utilities are producing, they want to sell their product no matter what the price is. Therefore, they cannot set their own price on the market but just take any value. The more the penetration of renewables in the electricity market, the less the overall price because of the merit-order effect. This phenomenon, known as “cannibalisation”, hinders the opportunity of renewables supplier to gain revenues and to make low carbon energy sources profitable [10]. The effect is even more significant for solar. A clear example is given by the “Duck Curve” effect. Electricity prices are usually higher in the morning and in the evening, when people are at home, using appliances and the total residential demand is higher. However, the sun energy is mostly harvested at midday and then, drops in the late afternoon, just before the peak demand at around 5-7 pm. In this case, the solar energy reduces the need of conventional fossil fuel generation during the day but when the demand rises again the baseload generation has to catch up quickly, making it necessary to start up more expensive and polluting gas turbines, which have a fast response.

Ericsson AB is one of the largest network providers and developer of the 5G technology. The company itself embraces the value of sustainability and considers the environmental responsibility as part of the corporate’s Code of Business Ethics. The diffusion of the 5G broadband would allow the deployment of Artificial Intelligence and

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6 Internet of Things technologies, which would represent important tools to help improving the efficiency of systems and reduce costs of operations. For instance, drones could be utilized to perform maintenance activities in remote infrastructure such as an antenna or an offshore wind turbine. Furthermore, the new network technology provides a better internet connection that would increase the quality of video conferencing and remote operations, decreasing safety risk. For example, rescue operations or medical surgeries could be performed by drones controlled remotely. This would revolutionize the working world in a time of pandemic and post pandemic.

Besides, even after the covid-19 pandemic, increasing the fraction of employees working from home will reduce the emissions related to the people movements, which can have a significant impact of the global carbon emissions, as documented during the spring of 2020. Decarbonisation, Digitalisation and Decentralisation are the three main enablers of change in the power system [11] and Ericsson stands for the second one.

Ericsson promotes the research on how the Information and Communication Technology (ICT) affects the environment, the society and the economy. In 2018, the carbon footprint of the ICT sector accounted for 730 Mt CO2-equivalents, equal to 1,4% of overall global emissions and its electricity consumption accounted for 800 TWh, equal to 3,6% of the global value. Furthermore, most of the emission comes from user devices, followed by Networks and Data centres. For the first one, the emissions come equally from the usage and the rest of the life cycle, whereas, for the second and third, most of the carbon footprint is related to the operation [12]. Moreover, the 5G technology has a much higher energy consumption than previous generation ones. Therefore, decarbonising their power systems is crucial to decrease the environmental impact of ICT. As well as efficiency improvements, introducing renewables in the Ericsson portfolio, would dramatically reduce their carbon footprint.

For this reason, the company has started introducing solar photovoltaic as a power source for their Radio sites.

Ericsson Telecom site can support three different configurations with solar power: Pure Solar Sites, Genset Solar Hybrid Sites, and Solar Added Grid connected Telecom sites. Depending on the operator’s conductivity needs in a particular location, the telecom load on a site can vary between 170W – 35kW. In cities, add on solar is interesting for customers to save electricity costs and for rural sites solar can be the primary source of power. Polycrystalline and Monocrystalline type PV panels are commonly used as solar power sources for Ericsson telecom sites. The crystalline PV panels are relatively low in cost and widely available in the market. However, support structures for crystalline PV panels require foundation, due to their weight, even for a low load sites that need fewer panels, which increase Total Cost of Ownership (TCO) of a telecom site. The TCO includes both CAPEX and OPEX (including civil works cost) at a site. Consequently, Ericsson has investigated alternative PV panel chemistries for higher efficiency cells and lighter weight in order to reduce the TCO of a solar telecom site. For instance, thin PV panel chemistry of Copper Indium Gallium Diselenide (CIGS) shows to be the most promising alternative PV chemistry available widely on the market. CIGS PV panels are approximately 25% of the weight of a Poly/Monocrystalline PV panel with the same energy output. The CIGS PV panels are more expensive than Crystalline PV panels, however, in site cases with low load, few solar panels are required and thus a simpler light- weight support can be utilized, reducing the TCO for a solar telecom site. In the literature, it was noted that Solar telecom sites in Europe and North America, which have lower direct solar irradiance compared to South America or Africa, benefit from CIGS chemistry PV panel, due to the panels properties to absorb diffused solar irradiance more effectively than Crystalline PV panels. Other types of thin PV technologies, such as Cadmium Telluride (CdTe), were not favorable because it is a banned substance for Ericsson due to its toxic properties. Amorphous Silicon is another Thin PV chemistry which has a lower energy efficiency (5-10%) and considerably more expensive compared to CIGS type, was also deemed unfavorable by Ericsson at this time. In order to better understand the benefits of CIGS PV panel technology for solar telecom sites, a deeper study was requested to do a quantitative analysis on the spectral effect on different PV chemistries.

The main objective of this report is to carry out a literature investigation to understand how different PV chemistries are affected by the radiation spectrum and to quantify the performance variation in the PV power output modelling when including the spectral effect, in order to evaluate a potential technical benefit of utilizing CIGS instead of the crystalline technology for Telecom site. In addition, a general improvement work on the Ericsson solar tool was made, adding a more detailed modelling of other components of the system, such as the Diesel Generator and the MPPT, improving the logic of resources priority to know when to switch from PV to battery, to grid or diesel generator. Moreover, the accuracy of the radiation data extraction was enhanced with the

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7 implementation of state-of-the-art knowledge on solar angles and a financial calculation was implemented to investigate the payback period of the investment based on the savings that it produces. Furthermore, starting from the literature on the best tilt angle for production optimization of solar systems, this study searched into the possibility of predicting the tilt angle that would instead maximize the self-sufficiency of Telecom sites on pure solar mode, in terms of minimum number of hours of backup needed, when combining solar PV with batteries storage.

1.1 Research Objectives

The thesis aims at achieving the following research objectives:

 Demonstrate how the spectral effect depends on atmospheric condition on an hourly time-resolution in order to include it into photovoltaic based energy systems models.

 Analyse how differently would thin film technologies like CIGS perform in comparison to the crystalline silicon cells when the spectral effect is taken into account

 Develop an algorithm that predicts the best tilt angle, for self-sufficiency maximization of pure solar stand- alone system, for any site, depending only on the latitude

 Analyse the optimal dispatch strategy among the different power sources and calculate the best mix of storage, PV panels, Diesel and grid in terms of renewable energy penetration and payback period

To eventually answer three main research questions:

1. How differently would thin-film CIGS solar cells perform, compared to standard crystalline Silicon, under low-light conditions?

2. Which is the best tilt angle for the solar panels to maximize the site availability, given the latitude?

3. What is the best mix of battery, PV panels, Diesel Generator and Grid in terms of penetration of renewables and payback period?

2 Literature Review 2.1 Solar Photovoltaic

Solar PV is a power generation technology that utilizes the photovoltaic effect to convert the sunlight into electricity. The smallest component of a solar PV system is the cell. A cell is about 15 cm long and 15 cm wide and it is typically built by placing two layers of heavily doped semiconductor material, p++ and n++, like the p-n junction in a diode, with an absorber layer in the middle. When the photons strike the PV surface, they enter in contact with the semiconductors, transferring their energy to the free carriers, electrons and holes, which can then

“jump” from the valence band to the conduction band. The electrons are then free to flow through an external circuit, pushed by an induced electric field, to go back to the original membrane of the cell, thus generating a current. Depending on the material used for building the cell, the difference between the energetic levels of the valence and conduction bands varies. This bandgap, measured in electronvolt (eV), is a fundamental feature of a solar cell because it represents the amount of energy that an electron should receive from the photons in order to be freed from its atomic bond and create a current. [13]

The radiation coming from the sun can be thought as a flux of particles or as a wave because of the wave-particle duality of light [14], therefore, as any other wave, the light has a spectrum and a range of wavelength. Depending on the wavelength, each photon will contain a specific amount of energy, following the Planck-Einstein relation [15]:

𝐸 = ℎ ∗𝑐 𝜆

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8 where E is the energy in Joule, h is the Planck’s constant equal to 6,62 ∗ 10 𝑚 ∗ , c is the speed of light and λ is the photon’s wavelength in meter. The longer the wavelength, the lower the energy carried by the photon.

Thus, there will be a maximum value for the wavelength, above which the photons will not contain enough energy to move the electrons across the bandgap and pull them off the atom. Furthermore, even if some photons will contain a much higher energy than the bandgap one, the energy that the solar cell can extract is no larger than the bandgap itself, the rest of it will be dissipated by the electrons in the form of heat. These two phenomena represent two important types of losses in the photovoltaic conversion process. The former is the non-absorption, the latter is called thermalization. Due to these losses, the efficiency of a single junction cell can only reach a theoretical maximum equal to 33,16 %, known as Schockley-Queisser limit [16]. Figure 1 illustrates well the information discussed above. In the two graphs, the spectrum of the solar radiation for the standard AM1.5 is shown in grey.

Then, on the graph on the left, the area in red represents the energy actually captured by a Silicon cell. This technology has a bandgap equal to 1,12 eV which, according to the equation , is the energy content of photons with a wavelength equal to 1130 nm. Therefore, no power is harvested from the radiation spectrum beyond that value. Whereas, for wavelengths smaller than 1130 nm, the energy absorbed remains almost constant despite the increasing higher irradiance at short wavelengths. On the right-hand side of the figure, the same insights are shown for a multijunction cell. A multijunction cell is a type of technology that attempts to overcome the Schockley- Queisser limit by overlaying semiconductors with different bandgap value in order to optimize the harvest of the light. The graph nicely illustrates how a larger bandgap material harvests more effectively a part of the spectrum with shorter wavelengths.

Figure 1 - Standard AM1.5 radiation spectrum and the parts of the spectrum that can be harvested by Silicon cells (a) and GaInP/GaInAs/Ge multijunction cells (b) [17]

An I-V curve can be used to describe the relation between the voltage applied to the cell and the current flowing through it. The I-V curve for a typical solar cell is represented by the red graph shown in Figure 2, where several important parameters are included. The Short circuit current (𝐼 ), is the current generated when the open circuit voltage is equal to zero. It is the maximum amount of current that can be generated under certain irradiance and temperature condition. The Open Circuit Voltage (𝑉 ). Voltage of the cell when the circuit is open and no current is flowing. The Maximum Power Point (MPP) is the working point where the multiplication between the value of the current and voltage reaches the highest values for a certain atmospheric condition. The Maximum Power Current (𝑰𝑴𝑷𝑷) and the Maximum Power Voltage (𝑽𝑴𝑷𝑷) are the value of current and voltage relative to the MPP. The Fill Factor (ff) is the ratio between the areas of the square included below the I-V graph and the square built from the short circuit current and the open circuit voltage and it tells how much squared is the I- V graph of a solar cells:

𝑓𝑓 = (𝐼 ∗ 𝑉 )/(𝐼 ∗ 𝑉 )

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Figure 2 - IV curve of a solar cell [18].

The working principle of a solar cell can be modelled by a single diode equivalent circuit, see Figure 3. The main components of the circuit are:

 Photocurrent (𝐼 ), the current generated by the sunlight

 Diode Dark Current (𝐼 ), opposite to the photocurrent, it increases with the voltage:

𝐼 = 𝐼 ∗ 𝑒 − 1

 The total current (I), is the current utilized by the external load, it is equal to:

𝐼 = 𝐼 − 𝐼 − 𝐼

 The series resistance (𝑅 ), representing the resistance of the metal electrodes and cables, the higher it is, the worse is the solar cell performance

 The shunt resistance (𝑅 ), represents the losses related to manufacturing defects, which creates an alternative path to the external circuit for the current to flow, between the top and the bottom of the cell.

A large shunt resistance is desirable.

Figure 3 - SIngle diode equivalent circuit [19]

When the voltage is zero, the total current is equal to the photocurrent. Open circuit voltage and short circuit current can be calculated as:

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10 𝑉 = 𝑘 ∗𝑇

𝑒∗ ln 𝐼 𝐼 + 1 𝐼 = 𝐼

The I-V curve of a solar cell can change when the solar irradiance and the ambient temperature vary. Figure 4 illustrates that, as the radiation increases, the photocurrent, and so the short circuit current, increases by the same factor, while the logarithmic growth of the open circuit voltage as a function of the photocurrent is noticeable only at lower intensities.

Figure 4 - Effect of solar irradiance on the photocurrent [20]

On the other hand, if the temperature increases, the open circuit voltage decreases its value, because 𝐼 increases rapidly with the temperature [21], offsetting the linear dependence between 𝑉 and the temperature, see Figure 5.

Figure 5 - impact of the temperature on the I-V characteristic in a PV module [20]

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11 Panels can be connected in series or in parallel to form an array. When they are connected in series, the total voltage is the sum of all the panels’ voltages and the total current stays the same, whereas when they are connected in parallel, the currents are summed and the voltage is constant [22]. The output of a PV array is not always constant and weather conditions such as solar irradiance and ambient temperature change the maximum power point (MPP) [23]. A rise in temperature affect the output open circuit voltage following the equation [24]:

𝑉 = 𝑉 ∗ 1 − 𝛼 ∗ (𝑇 − 𝑇)

On the other hand, loads and battery banks operate at a fixed voltage and the power delivered to them depends on the amount of current flowing from the array. if the PV voltage output is much higher than the one of the load, we would deliver a small current and lose power. Therefore, it is important to match the voltages of the array and the battery in order to maximize the power transmission, this can be done by using a solar charge controller. there are several types of solar charge controllers but the Maximum Power Point Tracker (MPPT) is the most popular, due to his high efficiency. Moreover, a solar charge controller helps protecting the batteries from being over charged or discharged too quickly, enhancing their lifetime [25].

The following paragraphs explain the solar angle theory [26].

The global horizontal radiation and the diffuse horizontal radiation for every hour of the year can be extracted from a software to then compute the direct horizontal radiation, following the equation:

𝐺 , = 𝐺 , + 𝐺 ,

by subtracting the diffuse horizontal irradiation from the global horizontal one. Next, on top of what was already present in the model, other parameters were needed, such as:

 The Solar Azimuth angle 𝛾 , representing the angular displacement from the south direction of the projection of the sun beam on the horizontal plane, it has negative values to the east and positive values to the west. It is a function of the declination angle, the zenith angle and the hour angle:

sin(𝛾 ) =cos(𝛿) ∗ sin(𝜔) sin(θ )

Figure 6 - a) Zenith angle,Solar angle, solar azimuth angle, tilt angle and surface azimuth angle. b) Plant view showing solar azimuth angle [26]

 The angle of incidence between the sun beams and the tilted surface, it is equal to one when the sun rays are perpendicular to the PV panel while it goes below zero when the sun is behind the surface, meaning that there is no incoming direct radiation. It measures how much aligned the vector of the sun and the

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12 vector normal to the plane of array are. The former depends on the zenith and solar azimuth angle whereas the latter on the tilt and azimuth angle of the surface as shown in Figure 7:

𝑮 = sin(𝜃 ) ∗ cos(𝛾 ) ∗ 𝒊 − sin(𝜃 ) ∗ 𝑠𝑖𝑛(𝛾 ) ∗ 𝒋 + cos(𝜃 ) ∗ 𝒌 𝑮 = sin(𝑇𝐴) ∗ cos(𝛾) ∗ 𝒊 − sin(𝑇𝐴) ∗ sin (𝛾) ∗ 𝒋 + cos(𝑇𝐴) ∗ 𝒌 cos(𝜃 ) = 𝑮 ∗ 𝑮

|𝐆 | ∗ |𝐆 |= sin(𝑇𝐴) ∗ sin(𝜃 ) ∗ cos(𝛾 − 𝛾 ) + cos(𝑇𝐴) ∗ cos(𝜃 )

Figure 7 - Incidence angle between the position vectors of the sun and the surface [26]

 The geometrical factor R, which is used to converts the direct irradiance on the horizontal plane to any other tilted plane, as illustrated in Figure 8:

𝑅 = 𝑐𝑜𝑠(𝜃 ) 𝑐𝑜𝑠(𝜃 ) 𝐺 , = 𝐺 , ∗ cos(𝜃 )

𝐺 , = 𝐺 , cos(𝜃 )

𝐺 , = 𝐺 , ∗ 𝑐𝑜𝑠(𝜃 )

𝑐𝑜𝑠(𝜃 ) = 𝐺 , ∗ 𝑅

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13 Figure 8 – Trigonometric relation among the horizontal direct irradiance GbH, normal direct irradiance Gbn and direct irradiance on the tilted surface GbT [26]

 The diffuse view factor 𝐹 , based on the HDKR model [27], assuming the diffuse radiation to be only isotropic, it will come from all angles [28]. The view factor tells how much fraction of the sky is seen by the surface, see Figure 9, it is equal to 1 when the surface is horizontally oriented:

𝐹 =1 + cos(𝑇𝐴) and the diffuse radiation on the tilted surface will be given by: 2

𝐺 , = 𝐹 ∗ 𝐺 ,

Figure 9 -a) Clouds scattering the radiation in an omogenous way (isotropic model) generating a diffuse radiation. b) How the tilt angle affects fraction of the sky seen by the surface [26]

 The reflected view factor 𝐹 , it tells how large share of the ground the surface sees:

𝐹 =1 − cos(𝑇𝐴)

The reflected radiation takes into account the amount of irradiation that is reflected from the ground to a 2 tilted surface, as described in Figure 10. It is found by:

𝐺 , = 𝜌 ∗ 𝐺 , + 𝐺 , ∗ 𝐹

where 𝜌 is the composite ground reflectance, which depends on the type of surrounding area.

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Figure 10 - the beam and diffuse radiations hitting the ground are reflected onto a tilted surface [29]

Next, the total solar irradiance in on the tilted surface is obtained by summing the direct, diffuse and reflected components, shown in Figure 11:

𝐺 , = 𝐺 , + 𝐺 , + 𝐺 ,

Figure 11 - Direct radiation, diffuse radiation and reflected radiation on a tilted surface [30]

2.2 Ericsson background and their Power System Model

Ericsson is a Swedish multinational networking and telecommunications company headquartered in Stockholm.

The company sells software, infrastructure and network services, such as 3G, 4G and 5G equipment to telecommunications providers. Besides, Ericsson is ranked highest in the 5G infrastructure market, making the Swedish firm a leader in the deployment of the new technology around the globe. Ericsson Enclosure & Power is a sub-division, based in the company headquarter in Stockholm, that develops a wide portfolio of cost-efficient power products for Telecom Sites, see Figure 12, such as enclosure, cabinets, inverter, genset, batteries and recently also solar panels, MPPT and wind turbines and wind inverters. Their main purpose is to provide reliable power to Telecom Sites and recently they have shown a growing interest for low-carbon alternatives, to reduce the telecommunications sector environmental footprint and to reduce the operating costs deriving from the use of fossil fuel based units like the genset in remote locations.

5g radio sites are expected to require three times as much energy as 4G but at the same time, software improvements are constantly improving the efficiency, decrease the power consumption of new antennas in term of kWh per gigabyte transmitted. 5G might eventually be consuming less than 4G by 2030. However, much more data will be required because traffic and network demand will steadily increase. Therefore, for the Jevon’s paradox, the overall energy requirements of 5G site will be higher, although it is difficult to understand to what extent.

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15 Figure 12 - View of an Ericsson power enclosure at the bottome of a Telecom Site

Ericsson Enclosure & Power department is building a model tool on Excel to size and design hybrid energy systems to power their Radio sites such as antennas. The first phase of this thesis work consisted in contributing to add and improve some parts of it and this section will illustrate its structure and purpose. The electricity is generated from a combination of Solar PV and wind turbines in combination with a battery bank as storage system.

An optional secondary power is offered and the costumer can then decide among three different configuration:

stand-alone (or pure solar), diesel generator backup or grid connected system. As a result, the model produces a techno-economic evaluation of the chosen system, in terms of availability of renewable energy throughout the year, cost savings compared to an equivalent system connected only to the grid or to the gen-set and the payback period.

2.2.1 Solar and Wind production

The solar irradiance and wind data are collected from Soda [31] by just entering latitude and longitude of the location. The website will deliver meteorological data in terms of global horizontal irradiance (GHI) and windspeed for each hour of the time span chosen, usually one year. In the model, for every hour of the year, several parameters used to describe the angular position of the sun with respect to an oriented surface are calculated [26]. These are the declination angle (δ):

δ = 23.34 ∗ sin 360 ∗284 + 𝑛 365

Figure 13-declination angle [32]

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16 with n the number of the day in the year, it describes the angular position of the sun respect to the plane of the equator at noon, it is the same in any location in the world, Figure 13. North positive; -23.45≤ δ ≥23.45. The hour angle (ω):

ω = 15 ∗ (h − 12)

where h is the solar time in hours, measures the deviation from south of the sun’s position, it is negative in the morning and positive in the afternoon and it changes 15 degrees every hour.

Figure 14 - hour angle [33]

The solar angle (SA), defined as the angular position of the sun compared to the horizon, is the complementary of the Zenith angle, as shown in Figure 15, and can be found in this way:

𝑐𝑜𝑠(𝜃 ) = sin(δ) ∗ sin(φ) + cos(δ) ∗ cos (ω)∗ cos(δ) 𝑆𝐴 = arcsin (cos(𝜃 ))

Figure 15-Zenith and solar angles [34]

The user can input the inclination of the PV surface as Tilt Angle (TA), based on which other factors can be computed. The Tilt Factor (TF), defined as:

𝑇𝐹 = cos|90 – SA – TA|

describe how much aligned the beam radiation and the tilted surface are, the more they are, the closer to 1 TF is.

The Delta Tilt Factor (DTF), is defined as the difference between the TF and the TF of a flat surface (for which TA=0). Since the irradiation data are given as global, it is necessary to distinguish between the components of direct and diffuse radiation. This is done through a linear approximation based on the fact that, when the sky is clear and the PV surface is perpendicular to the sun rays, the beam component is 90 % of the global irradiation and it decreases with 1/3 of the misalignment degrees

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17 𝑃𝑜𝐷𝐼 =100 ∗ 0,9 − 1

3∗|90 – SA – TA|

100

Once differentiated the direct and diffuse component, the Tilt Effect (TE) is defined as:

𝑇𝐸 = 𝑃𝑜𝐷𝐼 ∗ (1 + 𝐷𝑇𝐹) + (1 − 𝑃𝑜𝐷𝐼) ∗ 90 − 𝑇𝐴 90 Finally, the total harvested energy from the PV (PVH) is calculated:

𝑃𝑉𝐻 = 𝐺𝐻𝐼 ∗ 𝑇𝐸 ∗ 𝑃𝐸 ∗ 𝑇𝑃𝐴 ∗ (1 − 𝑇𝐿)

Where PE is the panel efficiency, TPA the total area and TL total losses. One of the weaknesses of this method lies on the fact that the input data do not distinguish between direct and diffuse radiation but they consist only of global horizontal irradiation. Furthermore, in the PV harvest equation, when the TE is zero, the whole production goes to zero. The TE goes to zero when the solar angle is negative. However, during the sunset and sunrise hours, the sun is very close to the horizon and, since the calculations are made for an entire hour span, there could be situations in which the solar angle is negative but there is some radiation registered in the software, due especially to the diffuse component. This might lead to an error in the total harvested energy throughout one year, especially for high latitude locations, where the sun stays a relatively longer time near the horizon on a winter day.

In addition, the performance of PV depends on the spectral distribution of the incoming radiation and several studies have investigated the relation between that and the panels yield. Stark and Theristis [35] study the effect of atmospheric composition on the performance of a photovoltaic panel. Besides, it is known that the diffuse radiation has a different spectral distribution than the direct radiation [36], see Figure 16, the diffuse component highlighted in blue, is more concentrated among higher wavelengths, which are more effective in producing short circuit current [37]. Hence, considering just the overall incoming solar energy might distort the total output and distinguishing between direct and diffuse radiation from the beginning, would enable to evaluate their contribution to the PV yield differently, achieving more reliable results.

Figure 16 - Clear-sky direct and diffuse irradiances. Both spectra were normalised to have unit integrals. [38]

When a stream of air is in motion, it carries kinetic energy following the formula [39]:

𝑊𝑖𝑛𝑑 𝑃𝑜𝑤𝑒𝑟 =1

2∗ 𝜌 ∗ 𝐴 ∗ 𝑣

Where 𝜌 is the density of air in Kg/m3, A is the area of the air stream (or the area of the wind turbine rotor) in 𝑚 and v the wind speed in . The turbine blades convert the kinetic energy of the wind in mechanical energy and then the alternator in electrical power. The conversion efficiency is described by the Wind Turbine Power coefficient:

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18 𝐶 =𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 𝑝𝑜𝑤𝑒𝑟 𝑜𝑢𝑡𝑝𝑢𝑡

𝑤𝑖𝑛𝑑 𝑝𝑜𝑤𝑒𝑟 𝑖𝑛𝑝𝑢𝑡

According to the Betz’s law [40], the maximum conversion factor from kinetic to mechanical energy that a wind turbine can achieve is equal to 0.593, when a 70% efficiency of the mechanical to electrical conversion is added, a value for Cp of around 0.41 is obtained. Typical values for Cp go from 0.30 to 0.45 [41], [42].

Figure 17-Example of power conversion in a wind turbine. The Betz limit says that no more than 59,3% of kinetic energy can be converted into mechanical power [41]

Then, by converting equation x with y the total electrical power harvested by the wind turbine is found:

𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 𝑃𝑜𝑤𝑒𝑟 𝑜𝑢𝑡𝑝𝑢𝑡 = 𝐶 ∗1

2∗ 𝜌 ∗ 𝐴 ∗ 𝑣

The tool offers different types of panels and wind turbines that the user can choose. There are four model of PV, among which a 455 𝑊 silicon monocrystalline and a 380 𝑊 , 355 𝑊 , 340 𝑊 silicon multicrystalline. Each of them differs for efficiency value and voltage and current parameters. With reference to the wind turbines, two models can be picked, a 5 𝑘𝑊 with a rotor diameter of 5 meters and a 9.8 𝑘𝑊 , whose rotor’s diameter is equal to 7.8 meters

2.2.2 Load

The load of the site can be added as an input, the model specifies a low load and a high load to give the option to distinguish between the load during the daytime and the one during the night time. The daytime is usually considered from 9am to 9pm. Ericsson sites requires a power input of about 120 W (simple LTE site) to 32 kW [43]. The main difference between Telecom Solar and other solar applications is that the latter are usually aimed at maximizing the energy harvest over time and that they can usually sell the excess to the grid. Telecom applications in pure solar mode require an over dimensioning of the panels, to harvest enough energy during the least sunny month and ensure the highest self-sufficiency level.

2.2.3 Battery Storage System

After modelling the power production and consumption, the energy storage system has to be designed. The tool offers several models of Lithium-ion and Lead-acid batteries, mainly differentiated by their nominal capacity, given in Ampere-hours and the charging efficiency, 95% for Lithium-ion and 85% for Lead-acid. Then, the user can input the amount needed and the total storage capacity can be simply calculated as:

𝑆𝑡𝑜𝑟𝑎𝑔𝑒 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 [𝑊ℎ] = 𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝐶𝑎𝑝𝑎𝑐𝑖𝑦[𝐴ℎ] ∗ 𝑛 𝑜𝑓 𝑏𝑎𝑡𝑡𝑒𝑟𝑖𝑒𝑠 ∗ 𝑠𝑦𝑠𝑡𝑒𝑚 𝑣𝑜𝑙𝑡𝑎𝑔𝑒[𝑉]

Based on the electric power definition [44]:

𝑃 = 𝐼 ∗ 𝑉

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19 where I is the current and V the voltage. Ericsson telecom site has 48 V as a default system voltage.

The utilization and charging scheme of the storage is controlled by a logic loop that includes a nested if. The code is used to calculate how much it is charged or discharged, to or from the battery bank, for every hour of the year.

However, instead of calculating the charge and discharge stage separately and combine them later, it directly computes the hourly storage level based on the power produced by renewables, power available from the secondary source, the load and the storage level of the previous hour.

2.2.4 Economics

In addition to the technical characteristics of the system, a financial analysis is performed by the model to evaluate the economic feasibility of investing in such a hybrid system. Firstly, a list of the capital investments of every component is made. These are:

 Photovoltaic panels;

 MPPT;

 Batteries;

 Wind Turbines;

the structure and the civil works costs are included as well. Besides, fixed operation and maintenance costs have to be addressed, together with any costs related to the replacement of some of the components, such as batteries, when they reach their limit of number of cycles. Then, two base cases are taken as references, one where the system is fully reliable on the grid and another in which it is supported only by the diesel generator. In each case, the yearly cost of generating energy is calculated, based on the retail electricity rate in USD/kWh the former and on the cost of the Diesel in USD/liters and the Diesel consumption of the system in liters/kWh for the latter. Next, the yearly generation from the renewable sources is subtracted from the total yearly energy demand and the cost of producing the remaining energy is calculated again. This cost will be lower than the previous one and their difference is considered as the savings of not using the grid or the diesel generator when the renewable energy is available. To analyse the profitability of such an investment that returns yearly savings, a discounted payback period method is used [45]. A discounted payback period is an economic tool that computes a yearly cash flow with the initial investment in the year zero, the operating costs and the savings produced every year, discounted by a certain interest rate, to keep into account the time value of the money. The investment will be considered economically feasible if the discounted cash flow will reach a positive value within the 25 years of operation considered.

2.3 Spectral Effect

One of the main source of errors when modelling the production of a photovoltaic project is the fact that the diffuse-to-direct ratio is approximated and spectral effects are not explicitly included [38]. The solar beams hit the PV surface with an angle that varies during the day due to the different position that the sun assumes in the sky dome and the position of the surface itself, defined by its azimuth and tilt angles. Including this information and utilizing radiation data that distinguish the diffuse component to the direct one, can help increasing the accuracy of the calculations. Besides, the spectral distribution of the incoming radiation is not always similar to the standard AM1.5 considered in the models but can vary during a year and even during the same day. The next two sub- chapters will dig into these phenomena through an extensive literature review. Furthermore, a sub-chapter about the research done about the optimization of tilt angle for solar energy project will be presented.

2.3.1 Radiation Spectrum

The power coming to the Earth from the sun travels in the form of radiation and it has a spectrum ranging from a wavelength of 100 nm to 1mm. This range can be divided in three main areas: ultraviolet from 100 nm to 400 nm, visible light from 400 nm to 750 nm and infrared covering the rest. A PV panel is not able to harvest the energy at all wavelengths in the same way, some photons do not have enough energy to overcome the barrier represented by the band gap, a characteristic typical of the material used in the panel, while others carry more energy that the one needed and will be wasted in the form of heat. The spectral response is a parameter that helps

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20 us to quantify and visualize for which wavelengths a specific PV technology is more effective [46]. Nowadays, most of the radiation models and solar manufacturers refer to the Standard Test Conditions (STC) when measuring the performance of a PV module. STC means a cell temperature of 25 °C and an irradiance of 1000 W/m2 with an Air Mass 1.5 (AM1.5) spectrum. However, that is not always the case and the spectral distribution of the irradiance varies during the day and the year. The main parameters that affect the spectrum are the water vapour, the Air Mass (AM) and the aerosol. [47] [48]. The first absorbs wavelengths in the infrared region, making the irradiation more concentrated in the blue part of the spectrum, or blu-shifted. When the second increases, the sunlight crosses a larger fraction of the atmosphere, which lowers the power of the short wavelengths because of the Rayleigh scattering effect, making the radiation more concentrated in the red part of the spectrum, or red-shifted.

Since the diffuse component of the radiation mostly comes from the presence of clouds, it is usually more blue than the direct one. Anyway, both the spectra of direct and diffuse radiation can vary according to other factors.

In [47], the spectral distribution of the direct and diffuse components of the irradiation is measured for different days at different times. Figure 18 illustrates how the spectral distribution varies with the time of the day, compared to the standard conditions. It is possible to notice from both graph that direct and diffuse components are more blue at midday, while their long wavelengths are enhanced at 8 in the morning, when the AM is higher.

Figure 18 - Diffuse (left) and direct (right) irradiance solar spectra measured at various weather conditions in Ljubljana normalized to AM 1.5 spectral irradiance.

2.3.2 APE, SR and MM

Obtaining the whole spectral distribution for every wavelength for every hour would require special instruments and as well as that, the treatment of a large amount of data would be needed. It would be useful to identify a certain spectral distribution with a single parameter instead. The most common ones that have been used in the literature are the Average photons energy (APE) and the AM. The APE measures the average energy of the photons belonging to a radiation with a certain spectral distribution:

𝐴𝑃𝐸 = ∫ 𝐸(𝜆)𝑑𝜆

∫ 𝛷 ( )

where 𝐸(𝜆) [𝑊 𝑚 𝑛𝑚 ] is the spectral irradiance, 𝛷 ( ) [𝑚 𝑛𝑚 𝑠 − 1] is the spectral photon flux and a [nm] and b [nm] are the lower and upper limits of the waveband of the solar spectrum considered [49]. Several works examined the relationship of this parameter with astronomical and atmospheric characteristics. According to [50] and [51], the APE increases when the zenith angle decreases. When the zenith angle is large, the light crosses a longer fraction of the atmosphere and the Rayleigh scattering in the UV region, caused by aerosols and gases, is more important. The radiation will then be constituted more by photons with longer wavelength that carry a smaller amount of energy, thus reducing the average photons energy. On the other hand, [52] and [53] show how a higher content of water vapour in the atmosphere during cloudy days, results in a bigger APE value. Water vapour absorbs well the infrared region [54], making the spectrum blue shifted. Depending on which spectrum interval is considered, the APE value relative to the standard AM1.5 spectrum is 1.88eV or 1.58eV for the range 350-1050 nm and 350-1700 nm respectively [48].

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21 The Spectral Response (SR) is a factor that measures how effectively a certain PV technology responds to the different wavelengths of an incoming radiation, it goes from 0 to 1 and it depends on the band gap of the module.

The panel will harvest more efficiently those wavelengths slightly higher than the one carrying exactly the amount of energy similar to the one of the band gap, it will then be less and less effective with shorter wavelength, since the energy of the photons will increase and the losses due to thermalization will be larger, whereas the longer wavelength will not be utilized and the spectral response will quickly go to zero. Technologies with a larger bandgap like a-Si and Cd-Te will have a SR curve shifted more on the left than the one with a smaller bandgap (c-Si and CIGS) as illustrated in Figure 19, where the SR curves of several PV technologies are shown. The SR could be seen as a series of efficiency values relative to each part of the spectrum and can be used to calculate the short circuit current, knowing the irradiation spectrum 𝐺 [47]:

𝐽 = 𝑆𝑅(𝜆) ∗ 𝐺 ∗ 𝑑𝜆

Figure 19 - Normalized spectral response data for single junction PV technologies. Data taken from measurements at Fraunhofer ISE [55]

The mismatch factor (MM) is a measure of the spectral gains or losses between the actual and the AM1.5 reference spectrum, according to the IEC 60904-7 standard [56]:

𝑀𝑀 =∫ 𝐸(𝜆)𝑆𝑅(𝜆)𝑑𝜆 ∫ 𝐸∗( )

∫ 𝐸∗( ) ∫ 𝐸(𝜆)𝑑𝜆

Where 𝑆𝑅(𝜆) is the spectral response of the PV device, 𝐸∗( ) (𝑊 ∗ 𝑀 ∗ 𝑛𝑚 ) is the spectral irradiance of the standard spectrum AM1.5, 𝜆 (nm) and 𝜆 (nm) are the lower and upper limits of the wavelength where the PV device is active, 𝜆 (nm) and 𝜆 (nm) are the lower and upper limits of the full spectrum (300-4000 nm). A MM value of 1.02 means that the PV module presents a spectral gain of 2%. The MM is a useful parameter that can quantify how much more or less the PV panel is harvesting a radiation with a particular spectral distribution, compared to a radiation with the same total power, calculated as the integral of the spectrum, but standard spectral distribution.

The spectral factor (SF) is an alternative index to the MM used in literature that quantifies in percentage how much the performance of a PV differs between an actual spectral distribution and the standard AM1.5-G.

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22 2.3.3 Previous studies on the spectral effect

Many studies investigated the effect of a variable spectral distribution on the yield of different PV technologies.

Two studies, [57] and [58], measured the spectral distribution of the irradiation and the actual production of the PV modules in Lima and south-eastern Brazil respectively. In both cases, the annual APE is blue shifted with a small seasonality variation, due to the low latitude and high solar angles, and so low values of AM, throughout all year. They both conclude that mostly large bandgap technology benefit from higher APE values like a-Si, Cd-Te and Perovskite whereas, small bandgap modules like CIGS, are less affected by the spectral distribution and present an opposite trend as illustrated in Figure 20, where the mismatch factor of several PV technologies is plotted against the APE. Both papers also underline how important it is not to neglect spectral irradiance in low latitude sites, where the annual output variation can be up to 10 %, while, in mid-latitude sites, where the sun height changes significantly during the year, the seasonality tends to result in an overall small annual variation, since the effect in summer can be the opposite than the one in winter, partially cancelling each other.

Figure 20 - Monthly weighted MM vs monthly weighted APE for various PV technologies in Sao Paulo. Here 1.88eV is the APE of the AM1.5 standard spectrum. [58]

A similar study is conducted in India and it can be seen how the monthly variation of the APE value follows atmospheric and seasonal factors [48]. The autumn, characterised by low AM values and the monsoons and so cloudy weather, presents a high APE. Whereas in winter, where the AM becomes higher and the humidity lower, the APE goes down. Figure 21 illustrates the APE trend over the course of a year, in this study, 1.58 eV represents the average photons energy of the standard AM1.5 radiation.

Figure 21 - Monthly variation of average photon energy (APE) in India. [48]

The same work shows how the AM affect the useful fraction of radiation for some PV technologies with a large bandgap, a-Si included. The useful fraction is an alternative way to address the variation in performance due to

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23 different spectral distributions. Figure 22 describes this relation, large bandgap technologies decrease their performance with higher AM values, which make the radiation more red.

Figure 22 - Relation between AM and normalized useful fraction (UF) for three PV technologies. [48]

Nofuentes et al [59] analyse the spectral gains for some PV technologies in two cities in Spain, considered as a good example of mid-latitude sunny locations, with no extreme weather conditions like storms and persistent cloudy sky. The summer is mostly blue-shifted while the winter has a red-rich spectrum, because of the AM. Figure 23 shows how large bandgap technologies, like amorphous silicon on the left-hand side, are more affected, increasing their output during the summer and decreasing it in the winter, while CIGS, on the right-hand side, presents a flatter curve. Besides, they are also characterized by an opposite trend, a-Si gains in summer and CIGS gains from the red-rich spectra in winter.

Figure 23 - Experimental and modelled values of the monthly spectral mismatch factor in Jaen for the modules considered: a-Si (left), CIGS (right)

Chantana et al [60], demonstrate how red-shifted spectra with an APE value below 1.88 eV result in spectral gains for small bandgap PV technologies, which have a SR curve more shifted on the right. The more the SR is on the right, the larger the gains for spectra richer in longer wavelengths regions. Figure 24 shows the SR on the left-hand side and the MM trends on the graph on the right-hand side, for several technologies considered in the experiment.

It can be noticed that there is a clear relation between the SR curve position respect to the wavelength region and the slope of the MM vs APE curves. As the SR peaks go from left to right, the slopes of the relation between MM and APE goes from higher positive values to larger negative ones.

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24 Figure 24 - Relative spectral response of the different type PV modules and pyranometer (left). MM for the a-Si, perov, Cd-Te, CIS, BC, mc-Si, and HIT PV technologies as a function of APE. [53]

Dirnberger et al [61], perform a similar investigation for five different PV technologies in Freiburg, Germany, a mid-high latitude location with cloudy weather. The results are given in terms of monthly and annual average spectral impact in percentage for four different PV technologies, as can be seen in Table 1. Once again, a-Si, a large bandgap material, is the one showing higher annual average effect compared to the crystalline silicon and CIGS technologies. However, even if the overall annual average impact of small bandgap materials is only 1.1%

and 0.6% for c-Si and CIGS, it is important to notice that during winter months, their average gains reach values above 3 %. Figure 25, shows the spectral impact for the largest and smallest bandgap technology examined in the paper for a two years period, they have opposite seasonal trends and CIGS gains in the winter and stays the same during summer, since the winter season in Germany is characterized by low solar angles that shift the spectrum to the infra-red region.

Table 1 - Monthly spectral impact based on the monthly sums of irradiance of a reference year and the determined average monthly spectral impact [55]

FREIBURG

monthly spectral impact (%)

month a-Si c-Si high-eff c-Si CIGS

1 -2 1,9 2,4 2,6

2 -1,3 1 1,4 1,6

3 0,1 0,7 0,8 0,9

4 3,5 1,2 0,9 0,4

5 4,2 1,5 0,9 0,3

6 5,1 1,4 0,8 0

7 5,3 1,5 0,8 0

8 5,3 1,6 0,9 0,1

9 4,3 1,5 1 0,4

10 2,8 1,9 1,7 1,3

11 0,8 2,1 2,2 2,1

12 -2,2 2,4 3 3,3

Annual (%) 3,4 1,4 1,1 0,6

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25 Figure 25 - Comparison of monthly spectral impact calculated from data as measured and with a calibration correction function, for a-Si, which was most affected, and CIGS, which was least affected. [62]

2.4 Optimal Tilt Angle

The optimum tilt angle is the slope that yields the highest amount of energy during a chosen time span, it is usually a function of the latitude and the day of the year. According to [63], during winter, the most harvesting slope is about (latitude + 15), whereas in summer, (latitude – 15). Ahmad and Tiwari [64] investigate the optimal tilt angle for a flat-plate collector in several locations around the world and found that the average optimum slope for the winter season at New Delhi is equal to the latitude +19 and for the summer is (latitude – 16); whereas, the best tilt angle for the whole year is equal to the latitude. Elminir et al [65] compute the best angle for a surface in Helwan, Egypt and performs an extensive research on previous similar works. The results are in line with what many other studies found, around (Latitude + 15) in winter and (latitude – 15) in summer, while the optimum angle throughout the year is approximately the same as the site’s latitude. Kern and Harris [66] found that the optimum value also depends on the climate and the demand characteristics. Jacobson and Jadhav [67], underline how two locations at the same latitude (Calgary, Canada and Beek, Netherlands) have a different optimal tilt (45 for the former ad 34 for the latter), mainly due to the fact that the European city has a greater cloudiness and aerosol pollution, resulting in a more significant diffuse radiation component, which is isotropic and can be better harvested with lower tilt angles. The same paper, gathers data from a large number of cities in the world and estimate a polynomial fit of optimal tilt as a function of the latitude, shown in Figure 26. In the graph, the red curve bends for higher latitudes, due to the cloudier conditions of Nordic locations.

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26 Figure 26 - Estimated optimal tilt angles and 3-rd order polynomial fits through them of fixed-tilt solar collectors for countries in the northern hemisphere [67]

2.5 Dispatch strategy

In a hybrid Power System, elaborating a robust strategy to determine when and how each source is used is of extreme importance, in order to manage the system in a smart way, optimize the use of renewables, minimize the use of fossil-fuel and avoid waste of installed capacity and capital. Especially when variable sources like solar and wind belong to the picture, it is fundamental to plan in advance the behaviour that the system should follow in any given situations. A literature review on previous works that investigated into the dispatch strategies of Hybrid Power System was made to understand what is the state-of-the-art regarding this topic. Many works present their own optimized strategy for PV-diesel hybrid system [68] [69], microgrid [70] or Trigeneration Power Systems [71].

Soudan and Darya [72] go even beyond, developing a smart switching control among the components of a PV- Battery-Diesel system based on the day-ahead weather forecast, to pre-determine how much renewable energy will be available to decide whether to start-up the diesel generator or rely more on the storage. The main difference between the two is that the former requires some time to kick in, whereas batteries have a fast response. Therefore, when the PV is expected to produce a decent amount of power during the day, the batteries are used to quickly fill in the gaps if there will be any, instead of switch on and off the generator continuously. On the other hand, if a cloudy day is expected, the diesel generator is turned on and kept running for several hours to cover the load and recharge the battery too. This paper proposes three different algorithms based on what is the main priority for the developer. The first algorithm aims to minimize the number of hours when the DG is used, without any regards for the cycling frequency. The second minimizes the generator cycling, which is done by just keeping the DG running once it is started-up during night-time. The objective of the third is to use the battery in an optimal way.

It is interesting because it calculates the ability of the battery to be charged again by the PV before night-time and based on that it decides whether to discharge them or to use the DG to cover the load at a certain time. This algorithm is illustrated by the flowchart in Figure 27.

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27 Figure 27 - Decision flowchart to make optimal use of the battery according to [73]

3 Methodology

In order to address the research objectives, the excel tool developed by Ericsson Energy System has been utilized as a reference. The hybrid pv-diesel power system and any effect of the topics addressed in the research will be developed, implemented and analysed with the model.

3.1 Model Development

During the thesis work, the Excel Tool was implemented and additional components of the system were added in the calculation, to make the model more similar to a real case and to increase the accuracy of the results obtained from it.

3.1.1 MPPT

An MPPT is used to extract the maximum possible amount of power from the PV array, by making it work at its maximum power point voltage and then converting it to the best voltage to get the maximum current into the battery bank. To size an MPPT, it is firstly needed to identify the battery nominal voltage 𝑉 and the PV array peak

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28 power 𝑊 , from which the maximum current that the controller should be able to handle is found. Following the Ohm’s law:

𝐼 ≥𝑊

𝑉

After selecting an MPPT that can cover that amperage, it has to be checked that the voltage of maximum power point 𝑉 of the array lies within the operating voltage range that the MPPT can handle. In fact, an MPPT is characterized by un upper and lower voltage limits and if the PV output is beyond those safety values the controller might be damaged. Moreover, the open circuit voltage 𝑉 (which is usually higher than the 𝑉 ) has to be below the controller’s upper limit. Since the 𝑉 and 𝑉 vary with the cell temperature, it has to be verified that the adjusted values, related to the operating temperature of the cells, are within the safety range. Moreover, the current and voltage output of the array depends on the number of panels and whether they are connected in strings or in parallel.

Hence, a control on the voltage output of the PV system, depending on the disposition of the panels and the ambient temperature for every hour, was added to the Excel model, since it previously lacked of this information and was assuming a direct connection between the PV array and the batteries without any voltage requirements or power losses, which is not possible in a real case. The MPPT operating range is given by the supplier, as well as the variation of the PV performance according to the voltage output,Table 2. The MPPT can absorb a 12 A current and a voltage between 120 V and 425 V. In addition, the efficiency of the PV is maximum when the voltage output is between 200 V and 425 V, while it linearly decreases for voltages below 200 V by a factor of 0,5 %/V.

Table 2 - Voltage operaing range and maximum amperage of the MPPT Operating Limits

Upper Operating Limit

400 VDC

12 ADC

Lower Operating Limit

120 VDC

Table 3 - Variation of efficiency as a function of the input voltage from the PV array to the MPPT s48-2000e3 Performance Considerations

Power efficiency Voltage Input

100% from 200 to 400 V

90% 180 V

80% 160 V

70% 140 V

60% 120 V

For every hour of the year, the model checks how many strings in parallel the controller can handle depending on the amperage limit.

𝑛 𝑜𝑓 𝑠𝑡𝑟𝑖𝑛𝑔𝑠 = 𝑅𝑜𝑢𝑛𝑑 𝐷𝑜𝑤𝑛 𝐼 𝐼

In this case, the 𝐼 = 12 𝐴 and the 𝐼 ~11 𝐴, so only one string can be connected to each MPPT.

(29)

29 Then it computes the voltage output of the PV array as a function of the number of panels connected in series in a single string and the ambient temperature gathered from the meteorological data source, it checks that it stays within the MPPT range and eventually, it calculates the temperature-related losses.

𝑉 , = 𝑉 ∗ 1 − 𝛼 ∗ (𝑇 − 𝑇 ) ∗ 𝑁

with 𝑁 the number of panels in a string and 𝑇 the ambient temperature, since the open circuit voltage refers to the panel condition at cold start, the cell temperature is the same as the ambient one. For the maximum power point voltage, the cell is in operating conditions and its temperature is roughly 30 °C more than the ambient one.

𝑉 , = 𝑉 ∗ [1 − 𝛼 ∗ (𝑇 − 𝑇 + 30)] ∗ 𝑁

The output current produced by the single string will be given by:

𝐼 = 𝐼 ∗ [1 − 𝛼 ∗ (𝑇 − 𝑇 + 30)]

The power actually delivered will be given by the multiplication of the voltage and current of the array. However, it cannot be higher than the MPPT nominal power

𝑝𝑜𝑤𝑒𝑟 𝑑𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 = min (𝑉 , ∗ 𝐼 ∗ 𝑁 ; 𝑊 ∗ 𝑁 )

With 𝑁 the number of the strings in parallel and 𝑁 the number of MPPT.

Lastly, the temperature losses are calculated

𝑇 = 1 − min 1; 1 − 0,005 ∗ 200 − 𝑉 , ; 𝑠. 𝑡. {𝑉 , < 400 𝑉 ; 𝑉 , > 120 𝑉}

In conclusion, with this additional sheet, the model is able to account for the power reduction related to the temperature, by adjusting the true value of the voltage and current of the maximum point for every hour, resulting in a lower or higher maximum deliverable power. Besides, the maximum power that can be transmitted by the MPPT is added as an upper limit.

3.1.2 Battery

The model allows the user to choose among three different configurations:

 Stand alone or pure solar

 Diesel generator backup

 Grid connected system

When a secondary power source is available, either genset or grid, the system is not allowed to bring the state of charge of the battery below 20 % in order to prolong their lifetime, as it is known that the higher the dept-of- discharge is, the less number of cycles the battery will be able to perform. The dept-of-discharge is the complement of the state of charge [74].

𝑆𝑜𝐶 = 𝑎𝑐𝑡𝑢𝑎𝑙𝑙𝑦 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑐ℎ𝑎𝑟𝑔𝑒 (𝑄)

𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑐ℎ𝑎𝑟𝑔𝑒 (𝐶)∗ 100%

𝐷𝑜𝐷 = 𝑟𝑒𝑚𝑜𝑣𝑒𝑑 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑐ℎ𝑎𝑟𝑔𝑒 (𝑄 )

𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑐ℎ𝑎𝑟𝑔𝑒 (𝐶)∗ 100%

𝐷𝑜𝐷 = 100% − 𝑆𝑜𝐶

Batteries manufacturer often suggest a maximum DOD, going beyond that will shorten battery life. Watanabe and Kinoshita [75] show how discharging completely the battery increases the rate of cell deterioration.

On the other hand, when a stand-alone (pure solar) mode is chosen, the option to fully discharge the batteries was added. The previous version of the model did not allow to utilize the whole available capacity preventing the system from achieving the highest possible load coverage during the year. Furthermore, it neglected both self- discharging and charging losses, whereas the ones occurring during the charging stage are now considered. The information was given by the suppliers and are equal to 5% for Li-ion and 15 % for Lead-acid [76]. Thus, the battery behaviour can be modelled in a more realistic way.

Figur

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Referenser

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