Profitability and future scanning of solar photovoltaic for fur farms in Kaustinen

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LAPPEENRANTA UNIVERSITY OF TECHNOLOGY LUT School of Energy System

Degree Programme in Electrical Engineering

Adenike Morolake Ajayi

PROFITABILITY AND FUTURE SCANNING OF SOLAR PHOTOVOLTAIC FOR FUR FARMS IN KAUSTINEN

Examiner: Professor Jero Ahola

Supervisor: Associate Professor Antti Kosonen

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ABSTRACT

Lappeenranta University of Technology LUT School of Energy Systems

Degree Programme in Electrical Engineering

Adenike Morolake Ajayi

Profitability and future scanning of solar photovoltaic for fur farms in Kaustinen Master’s Thesis

2018

129 Pages, 53 Figures and 56 Tables

Examiners Professor Jero Ahola

Associate Professor Antti Kosonen

Keywords: Solar photovoltaic, viable, electricity consumption

The use of fossil fuel in the fur farms agricultural sector is one of the major contributors to environmental pollution. Due to excessive health challenges caused by it, the need to exploit renewable energy sources to build a sustainable environment is essential. Building a sustainable environment in this area emanate from using (solar Photovoltaic) solar energy sources to rebrand the energy outlook in this sector.

One vital element that will ensure that solar photovoltaic is feasible and profitable to replace fossil fuel is in the dimensioning of the solar photovoltaic (PV) system. Once this phase is correctly analyzed, the solar photovoltaic system size viable to meet the electricity consumption can be employed.

The three essential criteria described in this study are: annual zero energy production, dimensioning solar sizes with and without battery. The criteria were identified so as to determine (the exact and lower) system size that is viable on an annual basis to meet electricity consumption with and without storage, and at the same time earn some income savings profitability from the investment.

To determine the viable system sizes for electricity consumption and income savings a calculation model was developed and this was established using Microsoft Word Excel for easy assessment and modification in the future. The mentioned calculation model is indeed suitable in dimensioning solar photovoltaic technology in meeting electricity demand. The calculation model was tried several time to ensure it worked efficiently, and this model will play a vital role analysing solar PV technology in the fur farms.

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ACKNOLEGEMENTS

My heartfelt thanks goes to the Almighty God who has been my helper and deliverer in life. I return all the glory to Him.

My profound gratitude goes to my supervisor, Associate Professor Antti Kosonen for his meekness and guidance at every phase of this work, without your inestimable support this work would not have been a success despite your busy schedules. I sincerely appreciate Professor Jero Ahola for accepting me and for giving me the privilege to work with his reputable research team.

I will like to thank my parents Prince and Princess Adedayo Ajayi and my brother Ademola Ajayi for their tireless support and love at all times which has kept me going in my career pursuits. To my Aunty Mrs Ilori Oluwabunmi for her support. I am grateful to my best friend Oyewo Ayobami Solomon for his love and encouragement, and to all my friends for their support all through.

Finally, I will like to acknowledge the prestigious educational environment of Lappeenranta University of Technology. Also, I want to express my gratitude to Finnish Fur Breeders Association for the grant given to me for this research.

Lappeenranta, 2018 Adenike Morolake Ajayi

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LIST OF TABLES

Table 1. Benefits of rooftops for solar PV production [17]. ... 23

Table 2. Characteristics of solar system size [20]. ... 24

Table 3.Vehicle drive power. ... 29

Table 4. Monthly time use of vehicles. ... 30

Table 5. Suitable rooftop area for solar PV in Farm 1. ... 31

Table 6. Farm 1 current sensitivity analysis result without battery. ... 34

Table 7. Farm 1 current energy production distribution without battery. ... 35

Table 8. Farm 1 current sensitivity analysis result with battery. ... 37

Table 9. Farm 1 future sensitivity analysis result without battery. ... 44

Table 10. Farm 1 future energy production distribution without battery. ... 45

Table 11. Farm 1 future sensitivity analysis result with battery. ... 46

Table 14. Farm 2 current energy production distribution without battery. ... 52

Table 17. Farm 2 future energy production distribution without battery. ... 59

Table 21. Farm 3 future current energy production distribution. ... 66

Table 24. Farm 3 future energy production distribution. ... 71

Table 28. Farm 4 current energy production distribution. ... 77

Table 31. Farm 4 future energy production distribution. ... 83

Table 33. Suitable rooftop area for solar PV in food factory. ... 86

Table 34. Food factory sensitivity analysis result without battery. ... 88

Table 35. Food factory energy production distribution. ... 89

Table 36. Food factory sensitivity analysis result with battery. ... 90

Table 37. Current community electricity consumption. ... 94

Table 38. Total number of panels and maximum capacity for community consumption. ... 95

Table 39. Southwest current sensitivity analysis result without battery. ... 98

Table 40. Southwest current energy production distribution without battery. ... 98

Table 41. Southeast current sensitivity analysis result without battery. ... 99

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Table 42. Southeast current energy production distribution without battery. ... 99

Table 43. Southwest current sensitivity analysis result with battery. ... 100

Table 44. Southeast current sensitivity analysis result with battery. ... 100

Table 45. Future community electricity consumption. ... 105

Table 46. Southwest future sensitivity analysis without battery. ... 108

Table 47. Southwest future energy production distribution without battery. ... 109

Table 48. Southeast future sensitivity analysis result without battery. ... 109

Table 49. Southeast future energy production distribution without battery. ... 110

Table 50. Yearly income saving profitability for Farm 1. ... 117

Table 53. Yearly income incoming saving profitability for Farm 4. ... 120

Table 54. Yearly income saving profitability for the food factory. ... 121

Table 55. Yearly income saving profitability for southwest direction energy community. ... 122

Table 56. Yearly income saving profitability for southeast direction energy community. .... 123

LIST OF FIGURES

Figure 1. Kaustinen study area in Western Finland [13]. ... 21

Figure 2. The proposed solar plant area at Kaustinen. (a) Southeast direction. (b) Southwest direction. ... 22

Figure 3. Farm 1 solar rooftop area. ... 31

Figure 4. Effect of solar production output in Farm 1 current electricity consumption. (a) Effect of a 1 kWp system in current electricity consumption. (b) Effect of a 27 kWp system in current electricity consumption. ... 32

Figure 5. Distribution of the 27 kWp production output. (a) Self-use and grid sales production. (b) Grid purchase and grid sales production. ... 33

Figure 6. Farm 1 current sensitivity analysis result without battery. (a) Excessive production sold to the grid. (b) Production taken in for self-use. (c) Current electricity consumption purchased from the grid. (d) PV production in current electricity consumption. ... 36

Figure 7. Farm 1 current sensitivity analysis results with battery. (a) % Battery potential. (b) % Solar grid sales. ... 38

Figure 8. Farm 1current battery sizing analysis (a) Battery size for 1 kWp. (b) Battery for 27 kWp. ... 39

Figure 9. Farm 1 current average hour analysis results. (a) Daily analysis result for July. (b) Daily analysis result for September. (c) Result of average day hour for July. (d) Result of average day hour for September. ... 40

Figure 10. Farm 1 future electricity consumption curve. ... 41

Figure 11. Effect of 40 kWp production on Farm 1 future electricity demand. (a) Production output of 40 kWp and future electricity demand. (b) Electricity grid purchase and excess production sales to the grid. (c). Self-use production and grid sale. ... 42

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Figure 12. Farm 1 future sensitivity analysis result without battery. (a) Excess production sold to the grid. (b) Production taken for self-use. (c) Current electricity consumption

purchased from the grid. (d) PV production in current electricity consumption. ... 43 (b) Figure 13. Farm 1 future daily battery sizing analysis. (a) Battery size for 1 kWp. (b)

Battery size for 40 kWp. ... 47 Figure 14. Farm 1 future average hour analysis results. (a) Daily analysis result for July. (b)

Daily analysis result for September. (c) Result of average day hour for July. (d) Result of average day hour for September. ... 48 Figure 15. Farm 2 solar rooftop area. ... 49 Figure 16. Dispensation output of 39.8 kWp in Farm 2. (a) 39.8 kWp Production output with

Farm 2 current electricity consumption. (b) Self-use production and grid sales. (c) Grid purchase and the excess production sales to the grid. ... 50 Figure 17. Farm 2 current sensitivity analysis result without battery. (a) Self- use production.

(b) Solar grid sales. ... 53 Figure 18. Farm 2 current daily battery sizing analysis. (a) Battery size for 1 kWp. (b) Battery

size for 39.8 kWp. ... 55 Figure 19. Farm 2 current daily and average hour analysis results. (a) Daily analysis result for

July. (b) Daily analysis results for September. (c) Average day hour analysis for July. (d) Average day hour analysis for September. ... 56 Figure 20. Farm 2 future electricity consumption curve. ... 57 Figure 21. Distribution output of 51.9 kWp. (a) 51.9 kWp Production output with Farm 2

future electricity consumption. (b) Self-use production and grid sales. (c) Grid purchase and the excessive production sales to the grid. ... 58 Figure 22. Farm 2 future daily battery sizing analysis. (a) Battery size for 1 kWp. (b) Battery

size for 51.9 kWp. ... 61 Figure 23. Farm 2 future average hour analysis results. (a) Average hour analysis for July. (b)

Average hour analysis result for September. ... 62 Figure 24. Farm 3 solar rooftop area. ... 62 Figure 25. Dispensation output of 81.8 kWp. (a) 81.8 kWp Production output with Farm 3

current electricity consumption. (b) Self-use production and grid sales. (c) Grid purchase and the excessive production sales to the grid. ... 64 Figure 26. Farm 3 current daily battery sizing analysis. (a) Battery size for 1 kWp. (b) Battery

size for 81.8 kWp. ... 68 Figure 27. Farm 3 current average hour analysis. (a) Average hour analysis for July. (b)

Average hour analysis for September. ... 68 Figure 28. Farm 3 future electricity consumption curve. ... 69 Figure 29. Distribution output of 103.3 kWp. (a) 103.3 kWp Production output with Farm 3

future electricity consumption. (b) Self-use production and grid sales. (c) Grid purchase and the excessive production sales to the grid. ... 70 Figure 30. Farm 3 future daily battery size. (a) Battery size for 1 kWp. (b) Battery size for

103.3 kWp. ... 73

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Figure 31. Farm 3 future average hour analysis. (a) Average hour analysis for July. (b)

Average hour analysis for September. ... 74 Figure 32. Farm 4 solar rooftop area. ... 74 Figure 33. Distribution output of Farm 4 14.4 kWp. (a) 14.4 kWp Production output with

Farm 4 current electricity consumption. (b) Self-use production and grid sales. (c) Grid purchase and the excessive production sales to the grid. ... 76 Figure 34. Farm 4 current battery sizing analysis. (a) Battery size for 1 kWp. (b) Battery size

for 14.4 kWp. ... 79 Figure 35. Farm 4 current average hour analysis. (a) Average hour analysis for July. (b)

Average hour analysis for September. ... 80 Figure 36. Farm 4 future electricity consumption curve. ... 80 Figure 37. Distribution production output of 26.3 kWp. (a) 26.3 kWp Production output with

Farm 4 future electricity consumption. (b) Self-use production and grid sales. (c) Grid purchase and the excessive production sales to the grid. ... 81 Figure 38. Farm 4 battery sizing analysis (a) Battery size for 1 kWp. (b) Battery size for 26.3

kWp. ... 85 Figure 39. Farm 4 future average hour analysis results. (a) Average hour analysis for July. (b)

Average hour analysis result for September. ... 85 Figure 40. Food factory solar rooftop area. ... 86 Figure 41. Distribution output of food factory 7664 kWp. (a) 7664 kWp Production output

with food factory electricity consumption. (b) Self-use production and grid sales. (c) Grid purchase and the excessive production sales to the grid. ... 88 Figure 42. Food factory battery sizing analysis. (a) Battery size for 598.5 kWp. (b) Battery

size for 7664 kWp. ... 92 Figure 43. Food factory average hour analysis results. (a) Average hour analysis for July. (b)

Average hour analysis result for September. ... 92 Figure 44. Current electricity consumption as energy community. ... 94 Figure 45. Distribution output of maximum capacity 4.3 MWp, southwest 7.8 MWp and

southeast.7.6 MWp. ... 97 Figure 46. Energy community current battery sizing analysis. (a) Battery size for southwest

4308 kWp. (b) Battery size for southeast 4308 kWp. (c) Battery size for southwest 7831 kWp. (d) Battery size for southeast 7670 kWp. ... 103 Figure 47. Southwest current average hour analysis for community consumption. (a) Average

hour analysis for July. (b) Average hour analysis for September. ... 104 Figure 48. Southeast current average hour analysis for community consumption. (a) Average

hour analysis for July. (b) Average hour analysis for September. ... 104 Figure 49. Future community electricity consumption curve. ... 105 Figure 50. Distribution output of 4.3 MWp, southwest 7.9 MWp and southeast.7.7 MWp. . 108 Figure 51. Energy community future battery sizing analysis. (a) Battery size for southwest

4308 kWp. (b) Battery size for southeast 4308 kWp. (c) Battery size for southwest 7890 kWp. (d) Battery size for southeast 7728 kWp. ... 112

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Figure 52. Southwest average hour analysis for community consumption. (a) Average hour analysis for July. (b) Average hour analysis for September. ... 113 Figure 53. Southeast average hour analysis for community consumption. (a) Average hour

analysis for July. (b) Average hour analysis for September. ... 114

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LIST OF ABBREVIATIONS

Aroof Total Rooftops Area

Ebuying Electricity Buying Price

Ecost Electricity Cost

Edemand Electricity Demand

Eselling Electricity Selling Price

EV Electric Vehicle

Gsales Grid Sale

IFF International Fur Federation

IRR Internal Rate of Return

kW Kilowatt

kWh/year Kilowatt hour per year

LUT Lappeenranta University of Technology

Mcapacity Maximum System Capacity

MWp Megawatt peak power

MWh/year Megawatt hour per year

NASA National Aeronautics and Space Administration

Npanel Number of Panels

NPV Net Present Value

P Grid Purchase

Parea Solar Panel Area

Pcost Grid PurchaseElectricity Cost

Poutput Solar Panel Output Power

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PPA Power Purchase Agreement

PV Photovoltaic

Wp Watt Peak

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LIST OF SYMBOLS

Im Maximum power current Pm Maximum power

Vm Maximum Power Voltage

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1. INTRODUCTION

The idea of profitability and future scanning of solar photovoltaic (PV), is an approach that does not only imply energy generation using solar as renewable energy sources, but as a larger view of improving economy profitability. The economy profitability does not only mean saving cost as a result of producing parts of the energy needed for the fur farms activities but also includes the income savings derived by selling to the grid. This concept also supports the need to eradicate the use of fossil fuel and promoting renewable energy source in creating a sustainable energy environment with the use of solar photovoltaic. An approach to achieving a sustainable energy environment is to create more awareness in stimulating the use of renewable energy as a clean, reliable and cost efficient energy source than conventional fossil fuels [1].

In this study, an energy simulation tool is used to carry out the feasibility and profitability studies for the case of fur farms in Kaustinen, Finland. The simulation is conducted based on the solar energy production of the area and the result is integrated to analyze the electricity demand and energy cost savings for fur farms located at Kaustinen. However, the main concept of this study focuses on the recipient needs, activities and environment. Also, generating an amiable solution based on the recipient energy needs is of a priority in this study. Thus, achieving a sustainable energy environment that is profitable for all forms of policies in producing self-use electricity for fur farm activities sustainably with the use of solar energy source is essential. Due to variability of solar energy source, storage solution can be used to balance generation and to increase solar self-use, in particular, battery storage. This research will focus on integrating the solar electricity production to meet the electricity consumption. Also, a sensitivity analysis will be carried out with different PV and battery system sizes to know, which system size is a viable solution for both the current and future electricity.

This chapter further gives details on the research background, research aim and objectives, research questions, literature review and organization of the thesis.

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1.1. Research Background

In previous and current fashion outlook, the fur skin from animals such as mink, fox, cat, dog, bear, raccoon to mention a few are worn for thermal comfort and traded as business commodity between countries [2]. According to the International Fur Federation (IFF) association in corporation with vogue Italian, on a global scale, fur has now been considered for all weather and all season clothing and not warmth only [3]. For this reason, the fur sectors has been working towards a high standard continually and this is not only limited to rebranding the outlook of the fur fashion and bringing nature closer to human, but also to ensure the energy use during the production stages are also environmentally sustainable [2].

It is a well-known fact that fossil fuel is the main fuel use for farms activities. It is used mostly for the feed processing, storage, transport and production. On a global scale, the electricity consumption of a farm for cooling, building facilities, transport and storage is highly dependent on the farm location and weather condition of the seasons [4]. To support the world in eradicating the hazardous effects of fossil fuel such as global warming which is diminishing the live span of the planet and also to reduce the depletion of natural resources for energy generation, there is need to amplify the use of renewable energy sources such as solar energy, wind, biomass to mention a few for sustainable energy production. For this reason, at Kaustinen, Finland, there is an amassed awareness of fur farmers who are interested in investing in renewable energy, in particular, solar energy, for self-use electricity production for their farms, so as to support the global targets of eliminating emissions and also to reduce their electricity purchase cost.

Apart from increased electricity price that could be presume to be a reason of investing in renewables, other vibrant reasons which could have triggered the investment is the fact that there are a lot of roofs with high solar potential, which has not been utilized and electricity consumption in the farming sector is expected to experience a linear growth due to new technology development. Also, solar PV is expected to be one the viable renewable energy technology that could be use in agricultural sector in the future to deal with greenhouse gas emission [5]. Hence, having a good awareness coupled with good political system will contribute largely to the lower costs and higher performance of solar photovoltaic in the future.

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1.2 Research Aim

The aim of this study is to carry out simulation based analysis of solar PV technology for each of the fur farm at Kaustinen, Finland. The main purpose of this research is to determine the required solar PV system size suitable to meet the current and future electricity demand of each fur farm. To get a good analysis result there is need to choose the best places of the roof for solar PV installation, since the main energy source of the solar PV technology is the sun.

Another aim of this research is to determine battery potential of solar PV production to increase the self-use production so as to minimize excess production sold to the grid, and also to know the income savings when excess production is sold to the grid without the use of battery in simulating the PV size.

To achieve the aim of this research, some of the features of this research that will be addressed objectively are: examining the current electricity consumption rate of the fur farms, measuring the total rooftops area, estimating solar potential area of each rooftops, estimating the number of panels needed to cover the proposed solar rooftop area, identification of criteria suitable in dimensioning the solar PV performance and identifying the software reliable for executing the simulation.

1.3. Research Questions

The main research questions that will be treated in this study includes:

i. What is the maximum and sensitive roof area required for solar PV capacity for the current and future electricity consumption for the fur farms and food factory?

ii. What are the criteria for dimensioning the solar system sizes?

iii. How possible is it to meet the future electricity consumption of the farms with the use of solar PV system?

iv. How feasible would it be to consider all the fur farms as a single energy community?

v. How profitable is it to sell to solar energy to the grid?

1.4. Literature Review

Several renewable energy sources and the production technologies required for harnessing them are been exploited to generate the green and electrical energy needed for use. This exploitation is motivated due to detrimental health issues such as cancer which is claiming several lives as a

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result of the use of conventional fuels. The pressing problem has increased the search for an alternative form energy to stimulate energy use. This chapter gives a comprehensive literature review which has been carried out within this research area. This chapter is divided into two sections. The first section gives a detailed literature review carried out regarding the use of solar energy in agriculture and the second section presents a literature review concerning the importance of policies, incentives or subsidies for solar PV investment in Finland.

1.4.1 Solar Energy Use and Agriculture

The day to day activities executed on the farm such as driving of the machines, cooling, feeding to mention a few requires the dire need of stable and reliable energy supply. Agricultural production requires substantial energy input. Currently, most of the fuel used to meet this energy demand is fossil fuel but the rising cost of fossil fuel and also the damaging effects of burning fossil fuel as promote the use of renewables to stimulate production, generate income, creates empowerment, social and economic development [6].

A lot of research has been conducted regarding the use of solar energy source for farm electricity use because it is; energy efficient, sustainable, of low investment risk and environmentally friendly amongst many others [6-8]. According to the reviewed journal articles, the application of solar energy for agriculture has a high potential in executing farm operation. They also emphasized on the advantages the farmers and the international community would get to benefit from using solar energy as the main energy source to reduce emissions in the atmosphere and combat with the effect of climate change. Apart from this environmental benefit and energy cost saving realized from the use of solar energy in agriculture, it will also help in rebranding the outlook of the agriculture platform from the well-known local and traditional practice to a modern prospect [8]. Also, Chel and Kaushik 2011 buttress on solar energy as an alternative form of energy for farm operation, as it requires low maintenance, reliable, long-life span and cost efficient to enhance agricultural productivity globally [9].

1.4.2. Role of Policy for Solar Photovoltaic in Finland

The solar PV market has experienced a remarkable growth in Europe, and most of the capacity is installed in Germany, which happens to be one of the leading countries in the solar PV market.

It is also part of the top 10 PV markets installation in 2016 after China, Unite State of America

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and Japan. This is achieved as result of good supportive policy and incentive instruments. The feed-in tariffs has been a very good effective mechanism in promoting solar PV market. The country has a market installation of about 1.48 GW and it also a share of about 2 % in the global PV market in 2016 [10].

In Finland the market installed capacity is about 80.4 MW, which is still lower compared to the market installation in Germany. However, the PV market is anticipated to increase as Finland has an European Union target of reducing greenhouse gas emissions by 40% by 2030 and 80%

to 95% by 2050 [11]. Although, there have not been any governmental schemes to support PV systems in Finland. However, this story is changing as Finland plans to introduce a premium- based Power Purchase Agreement (PPA) auction, which will commence from autumn 2018 to foster the use of renewable electricity and this includes of solar PV system. The purpose of this auction is to achieve an electricity production target of 1.4 TWh/a from the renewable energy sources. Furthermore, the use of financial instruments to promote PV investment is also implemented by the Ministry of Economic Affairs and Employment and the Agency for Rural Affairs, and tax breaks to support people with investment of PV installation. Also, several municipalities have aims to install PV system on public buildings. In addition, in 2016, the grid- grid-connected PV capacity experienced an increase of about 150%, which is expected to double in 2018 [12]. Thus, from the literature review it can be drawn that governmental and financial support instruments will play a vital role in promoting PV market growth and making it a more viable technology in Finland.

1.5. Organization of the Thesis

This master’s thesis is made up of six chapters and it has follow the final thesis instructions guidance of Lappeenranta University of Technology, LUT. A brief overview of what each chapter entails is stated below and how it is arranged.

Chapter 1: This chapter presents the general overview of the study, background information of the study and relevant reasons for embarking on the research. Also, the aim and objectives, key research questions and literature review is presented in this section.

Chapter 2: This chapter gives detailed information on the methodological approaches employed to develop a calculation model in this research and the reasons. It provides

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information on how the data used in this study were collected and furthermore gives a description of the study area, the solar PV plant area. It also explains the rooftop use for solar PV and how it is measured, the characteristics of the system use in this study and the software selected for the simulation.

Chapter 3: A descriptive review of each fur farm including the food factory was given in this chapter. After the collection of the data and when all necessary measurements had been taken for all the rooftops considered, some sensitivity analysis results was carried out for both the current and future scenarios for each farm including the food factory based on the selected dimensioning criteria.

Chapter 4: This chapter analyzed the electricity consumption data and solar PV production output for the entire energy community for both southwest and southeast direction and the result is presented for current and future scenarios.

Chapter 5: This chapter expresses the profitability key parameters used in this study and analyses the income saving results for each farm, the food factory and community energy state for both current and future cases.

Chapter 6: This is the final chapter and it gives a summary of the research and also a concluding statement. It also review the research aim and questions to ensure the thesis was able to achieve and deliver the main cause of embarking on the research.

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2. APPROACH TO DEVELOPING CALCULATION MODEL FOR FUR FARMS

2.1. Research Approach

To achieve the aim of the research, a comprehensive and analytical quantitative approach with a literature review was conducted throughout the course of this study. There are several reasons of carrying out literature review before executing the project. In this study, the literature review was conducted to achieve an extensive understanding of this research area. This is because there might have been a relapse in the awareness about the project researched at hand, which a new research study might be able to improve and propagate more research outcomes to provide more opportunities for continuous work in the research area. In this study, the information, knowledge and understanding considered were examined from various sources, which includes but not necessarily limited to academic journals articles, reports, university library and databases.

However, due to language limitation English and Finnish academic materials were examined in this study. All this helps to give profound insight in accomplishing the aim of the research and to know the relevance of the solar energy use in fur farms.

In this study, the design is based on the simulation tool (Homer) and the output results are integrated using a Microsoft Excel tool. The following subsection in this chapter discusses explicitly other research approaches adopted and good reasons for considering them.

2.1.1. Description of Study Area

The fur farms are located in Jylhäntie and Kettutarhantie in Kaustinen, Finland. Kaustinen is located in Western Finland in the region of Central Osthrobonia. The municipality of Kaustinen has a total population of 4,302 with a population density of 12.15 person per square kilometer and a total land area of 361.12 square kilometer. Also, about 7.09 square kilometer of the total area is covered with water. The study area domain covers a coordinate of 63ô 33Ń 023ô 41.5É.

It is important to know the sun energy potential of the project location before embarking on the installation of solar PV. Apart from the literature review, there was also visit to Kaustinen to have more knowledge about the area of study. It was found out the study area has a good solar production potential, which is one of the motivated source of the investment since Finland is

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progressing in implementing renewable energy solutions for the future. Figure 1 illustrates the municipality of Finland and the red highlighted portion of the graph is the study area.

Figure 1. Kaustinen study area in Western Finland [13].

2.1.2. Solar Power Plant Area

Solar panels are technology devices that capture solar radiation and converts it directly into electricity. The area where the proposed solar power plant is expected to be built at fur farms and food factory needs to be verified so as to know the roof direction suitability for solar PV, roof types and the type of solar PV feasible for the project. There are four fur farms and one food factory to be analyzed in this study and the solar panels will be installed on a gable type of roofs. Also, all the panels will be a fixed systems and they will be facing two different directions.

Categorically, the panels will be facing the southeast and the southwest directions, as these are the two possible directions to build the solar plant in this research location. Figure 2 shows the highlighted rooftops of fur farms and the food factory where the solar power plant will be simulated. To the left, Figure 2(a) shows the highlighted roofs of a fur farm and food factory facing the southwest direction; and to the right Figure 2(b) shows the roofs of three fur farms facing the southeast direction.

Furthermore, there four different types of solar panels, which can be used for this type of study;

the crystalline silicon, polycrystalline silicon, amorphous silicon and thin film crystalline

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silicon. However, in this study the polycrystalline system will be used, since it is the cheapest in the market. Although, it has low heat tolerance, which affects its performance when temperature is high and also a low efficiency compared to the other types. More knowledge on solar panel types and characteristics can explored in [14].

(a) (b)

Figure 2. The proposed solar plant area at Kaustinen. (a) Southwest direction. (b) Southeast direction.

2.1.3. Rooftop Solar PV Use

The use of rooftops for solar energy production plays an important role in renewable and sustainable energy use [15]. It has a high prospects of reducing land use for solar installation and also reduces the additional cost of transmission. Although, the use of rooftops for solar energy production could be challenging in some project locations due to bird poops, snow and ices effects, which could be causing shading effects and this may lead to an inefficient output of the panels, more knowledge can be explore in [16]. However, all these effects can be avoided by cleaning the panels to enhance a good production output of the panel. Table 1 presents the diverse benefits of using rooftops for solar energy production.

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Table 1. Benefits of rooftops for solar PV production [17].

Construction

Site access Photovoltaic (PV) systems are at the point of consumption, thus do not require additional investment for access during construction or for operation and maintenance.

Modularity They can be designed for easy expansion if power demand increases Operation and Maintenance

Primary energy supply Solar energy is freely available, and the PV system does not entail environmental costs for conversion to electricity.

Maintenance PV rooftop systems require minimal inspection and maintenance for example safety of the system needs to be checked.

Peak generation These systems offset the need for grid electricity generation to meet expensive peak demand during the day.

Mature technology PV systems nowadays are based on proven technology that has operated for over 25 years.

Impact

Investments Rooftop PV system costs help offset part of the investment needed for new power generation, transmission, and distribution in the power grid.

Cost Fuel savings from PV systems typically offset their relatively high initial cost.

Environment PV systems create no pollution or waste products while operating, and production impacts are far outweighed by environmental benefits.

2.1.4. Rooftop Analysis

The evaluation of the rooftops is carried out to know the area of the rooftops suitable for solar energy production and to know the capacity potential of each rooftops facing both directions. In order to achieve an accurate measurement, “Sun energia” solar test application is used to measure the rooftops and to know the possible areas with good solar energy outputs [18].

However, in this study, only the total area values of the roof measured by “Sun energia” will be considered but the estimated area values suitable for solar use according to the “Sun energia”

will not be considered. The suitable area for solar PV production in this study is estimated using (1) below, where Aroof represents the total roof area obtained from “Sun energia”, and it is divided by two to get the suitable solar roof area (Proof) for the research project location.

𝑃_roof =𝐴_roof

2 (1)

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2.1.5. System Characteristics

The solar photovoltaic system of 250 Wp is simulated to estimate the number of panels that can contain the solar PV potential rooftop and to determine the maximum capacity potential for the fur farms and food factory in Kaustinen. The number of panels Npanels which the rooftops can contain and the maximum system capacity Mcapacity is estimated using (2) and (3) below, where Parea and Poutput represents the solar panel area and solar panel power output respectively.

𝑁_panels = 𝑃_roof (𝑚²)

𝑃_area (𝑚²) (2)

𝑀_capacity = 𝑁_panels× 𝑃_output (3)

The estimated maximum capacity is further used in dimensioning the exact solar system size for each farm case based on the selected criteria. The module efficiency ranges from 15% to 18%

but for this study an efficiency of 15.74% is used to carry out this analysis. The panel efficiency used in this study was objectively chosen, because efficiency is one of the determining factors for panel output [19]. Table 2 shows the characteristics of a 250 Wp system used in this study.

Table 2. Characteristics of solar system size [20].

Characteristics Units Values

Maximum power Pm (W) 250

Maximum power voltage Vm (V) 30.4

Maximum power current Im (A) 8.26

Module efficiency % 15.74

Operating temperature range ôC −40ôC ~ +85ôC

Dimension (L*W*H) mm 1620*980*40

Weight Kg 18

2.1.6. Simulation Software

A large number of technology, seasonal variations, economical cost and different periods of the availability of various renewable energy sources such as solar, wind, biomass and hydro-power

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are the main reason for simulating the performance of the renewable energy systems. These factors accounts to the difficulties in making a decision on the energy system and the size of each components needed to be operated with the renewable system for successful outcome of the design. There are large number of simulation tools that could be used for modeling the different renewable energy system configuration such as Homer, RETScreen, and Pvsyst for an efficient output of the technology. However, amongst the mentioned simulation software the Homer software is the software chosen in this study to simulate the outputs of the renewable energy systems. For the purpose of this study Homer is used for simulating only one renewable energy system which is the solar photovoltaic and a Microsoft Excel calculation tool is used to integrate the simulation output result. Homer is a micro-power optimization model for designing an analyzing hybrid power system. It consists of wind turbine, solar photovoltaic, conventional generators, batteries and other equipment. It is used for evaluating designs that are off-grid or grid connected power systems, more information on Homer can exploited from [21].

In this study, the solar PV system was simulated with a system size of 1 kWp on hourly basis and incorporated with the hourly electricity consumption of the fur farms and food factory. The incident solar radiation data used for the estimation of the solar PV was provided by the Homer software which gather the data from National Aeronautics and Space Administration (NASA).

The solar irradiation and weather condition used for this simulation were gathered from the city of Kaustinen, which is situated in the western part of Finland. However, the simulation result for the production output of solar PV for the month of January, February and December were assumed to be zero due to snow and lack of sunlight availability. In addition, different slope angle can be used for this simulation but in this study a single slope of 35ôwas simulated all through the course of the simulation and the azimuth angle used is dependent on the direction of the roofs. For the southeast direction an azimuth of −45ô is used while 45ô is used for the southwest direction. Although a lifetime cost is not calculated in this study but a lifetime of 30 years is assumed for solar PV in this simulation.

2.1.7. Data Collection

The data used in this study are collected in two different parts. The first parts of data is the hourly electricity consumption, and it was collected from the fur farms and the food factory considered in this study to know the present situation of the electricity use for operation. The

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second part of the data was derived from measuring of the rooftops potential suitable for the use of solar photovoltaic, and this is conducted to know the estimated amount of solar photovoltaic that could be installed on the roofs and to know the direction at which the rooftops are facing for an optimal production output of the solar PV.

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3. DESCRIPTIVE ANALYSIS RESULTS OF KAUSTINEN FUR FARMS

The aim of this study is to simulate the solar energy system for each of the fur farm and the food factory as mentioned earlier, so as to know the feasibility of using solar PV technology in generating part of the electricity needed for operations. In this chapter, the results would be analyzed for both the current and future case. The solar PV sizes would be dimensioned based on three selected criteria to know the required solar PV capacity and production needed to meet the electricity demand of each farm and the food factory. Firstly, the system sizes would be dimensioned based on an annual zero energy production, which means producing solar energy sufficient enough to cover the electricity consumption on annual basis. Secondly, to determine the system size that is viable for the consumption without battery, and thirdly to estimate the system size efficiently viable enough with the use of battery. As mentioned earlier, there are four fur farms and a food factory to be analyzed in this study. The four fur farms are named as Farm 1, Farm 2, Farm 3 and Farm 4 for better understanding.

As discussed in the previous chapter that the homer software is the energy simulated tool used in this study and was firstly simulated with 1 kWp. The output result of the 1 kWp system is then integrated analytically to derive the exact system needed to cover the electricity consumption of both the current and the future scenarios in each of the farm case. Also, sensitivity analysis is carried out to determine the impact of solar PV and battery on the annual consumption demand of each farm.

Furthermore, the battery potential would be analyzed on an annual and daily basis. It will be analyzed on both bases so as to determine how much of the excess production output going into grid sales could be curtail into self-use. To derive the annual percentage battery potential the battery potential of the PV was firstly analyzed on a monthly basis as follows: if the grid purchase is lower than the grid sale, then the grid purchase value is taken as the battery potential value and vice versa as presented as follows

𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 = IF (𝑃 > 𝐺_sales; 𝐺_sales; 𝑃) (4)

Where the grid purchase is P and the grid sale is 𝐺_sales, after implementing (4), the excess production going to grid sale got reduced. Then summation of all the battery potential of the

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months in a year is divided by the total production output in a year to get the percentage battery potential on annual basis. It is estimated as follows

% 𝑎𝑛𝑛𝑢𝑎𝑙 𝑏𝑎𝑡𝑡𝑒𝑟𝑦 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝑏𝑎𝑡𝑡𝑒𝑟𝑦 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙

𝐴𝑛𝑛𝑢𝑎𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑜𝑢𝑡𝑝𝑢𝑡 (5)

Furthermore, the annual grid sales analysis after implementing the use of battery into the calculation model is calculated using (6), where Gintial is the annual initial grid sales without battery use and annual battery potential.

𝐴𝑛𝑛𝑢𝑎𝑙 𝐺_sales= 𝐺_intial− 𝑎𝑛𝑛𝑎𝑢𝑙 𝑏𝑎𝑡𝑡𝑒𝑟𝑦 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 (6)

After the use of the (6) the annual percentage grid sales with use of battery is estimated using (7).

% 𝑎𝑛𝑛𝑢𝑎𝑙 𝐺_sales = 𝐴𝑛𝑛𝑢𝑎𝑙 𝐺_sales

𝐴𝑛𝑛𝑢𝑎𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑜𝑢𝑡𝑝𝑢𝑡 (7)

The battery size is estimated on a daily basis to determine how many days in a year it will fully back up with each capacity. In this chapter, the days that requires no battery storage will be differentiated with a yellow color while the other days are the days that requires battery for better understanding. It is also worth mentioning that there is a point whereby increasing the battery size is of no use, and this is the point where the battery size suddenly experience an inclined peak after been reduced. This is not profitable due to high cost of battery storage, hence, investing on a larger battery size at this point in order to store excess production is not viable.

All these criteria and analysis dimensioning assumptions is implemented for each case in the calculation model and the results obtained would be discussed in this chapter.

3.1. Future Electricity Demand Commodities 3.1.1 Electric Vehicle

The future electricity use of all the fur farms is expected to increase due to integration of the current electricity consumption with cooling system and electric vehicles (EV) loads. There are various types of vehicles used in these fur farms, examples of such vehicles are forklift, feeding

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truck, tractor, manure packer, compact loader to mention a few. However, to operate these vehicles the use of fossil fuels is required but since the world is moving towards a green energy to reduce pollution, electricity is expected to replace the use of fossil fuels in the future for these vehicles. To replace the fossil fuel with electricity, the monthly hour consumption data of these vehicles was collected from these farms so as to estimate the power consumption, the charging time of the vehicles and the charge power. However, in this study, a charge power of 10 kW is used for all the vehicles in this analysis. Table 3 and 4 presents the consumption time use data of these vehicles on a monthly basis and the driving power of each vehicle used in this study.

From Table 3 it can be observed that the driving power of the forklift ranges from 7.6 kW to 30 kW, but in this study an average drive power of 16 kW is used. In addition, the driving time of all the vehicles are the same for all the months and for all the other farms except for the forklift that varies for this farm and the other fur farms. It is also important to mention that the charging time of each vehicles varies due to different drive power and the vehicle time of use.

Table 3.Vehicle drive power.

Vehicles Driving power (Pdrive) (kW)

Forklift 7.6–30

Feeding truck 20.0

Tractors 24.6

Manure 20.0

Compact loader 24.6

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Table 4. Monthly time use of vehicles.

Months Forklift Feeding truck

Manure truck

Compact loader

Tractor Time use (Hour/day in a month)

January 1.65 0.50 0.50 0.32 0.14

February 1.65 0.50 0.50 0.32 0.14

March 1.65 0.50 0.50 0.32 0.14

April 1.65 0.50 0.50 0.32 0.14

May 1.65 1.00 0.50 0.32 0.14

June 1.65 1.00 0.50 0.32 0.14

July 1.65 1.00 0.50 0.32 0.14

August 1.65 3.00 0.50 0.32 0.14

September 1.65 3.00 0.50 0.32 0.14

October 1.65 3.00 0.50 0.32 0.14

November 1.65 3.00 0.50 0.32 0.14

December 1.65 3.00 0.50 0.32 0.14

3.1.2. Cooling System

The cooling system is anticipated to be an important technology that will facilitate the fur farms activities and enhance the wellbeing of the animals. It is a well-known fact that during the summer season of the year the atmosphere is always very hot and sometimes it could be itchy even for some beings and results to skin irritations at times. For this reason many individuals prefer to keep the rooms constantly cool to reduce the hotness. This same reason has triggered the fur farmers to give more attentions to the animals more closely and found out that during the summer period the animals act itchy by giving unpleasant sounds which indicates the sign of lack of comfort as a result of warmness. However, the cooling system is analyzed to be used for a stipulated period of three hours per day during summer months (May, June and July) in all the farms. To calculate the power consumption needed for cooling per animal, the total number of the animals that needs cooling was collected from each of the fur farm and the power consumption of the cooling system of 11 kW is used in this study.

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3.2. Fur Farm 1

The fur Farm 1 consists of fox animal only. There are thirty-eight roofs in total in this farm location and they are all facing the southwest direction. Figure 3 shows the picture of the rooftops area considered for Farm 1. Although, the roofs varies in length from each other, however, the total roof area of the farm is about 10675.9 m².

Figure 3. Farm 1 solar rooftop area.

Figure 3 illustrates that the rooftops are grouped into roof A and roof B, with 18 roofs and 20 roofs respectively. The analysis of rooftop area suitable for solar PV installation is presented in Table 5. From Table 5, it can be seen that the roof cannot contain more than the estimated amount of panels except the roofs are extended in the future.

Table 5. Suitable rooftop area for solar PV in Farm 1.

Roofs Total

roof area (m²)

Solar roof area (m²)

No. of panels

Maximum capacity (kW)

A 4962.0 2481 1563 390.7

B 5713.9 2857 1800 449.9

Total 10675.9 5338 3363 840.6

3.1.1. Farm 1 Current Electricity Use

The current electricity consumption of this farm is about 24.92 MWh/year. To deploy the use of solar PV in generating parts of the current electricity consumption, there is a need to

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dimension the solar PV system size based on the defined criteria in order to achieve a system size that is viable enough to meet the electricity consumption.

Dimensioning System Size Based on Annual Zero Energy Production

The exact system size required to meet the present electricity consumption is about 27 kWp based on an annual zero energy production level. The total production output of 27 kWp is about 24.91 MWh/year. Figure 4 depicts the effect of the production output of 1 kWp on the current consumption and of 27 kWp needed to cover the current electricity consumption. From Figure 4, it can be noticed that the farm has the highest production during the summer months (May, June and July) and this is due to solar resource availability and low electricity consumption. The other months with high electricity consumption and low PV production are winter months, due low solar resource availability and the cold weather condition which requires more electricity for lighting and heating of the buildings.

(a) (b)

Figure 4. Effect of solar production output in Farm 1 current electricity consumption. (a) Effect of a 1 kWp system in current electricity consumption. (b) Effect of a 27 kWp system in current electricity consumption.

As a result of excess PV production and low electricity consumption during the summer, and low PV production and high electricity consumption during the winter. Further analysis is carried out to determine electricity purchased from the grid during low PV production and grid sales during high PV production as shown in Figure 5.

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

1 2 3 4 5 6 7 8 9 10 11 12

Energy(MWh/month)

Months

Current analysis result

Consumption 1 kWp

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

1 2 3 4 5 6 7 8 9 10 11 12

Energy(MWh/month)

Months

Current analysis result

Consumption 27 kWp

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(a) (b)

Figure 5. Distribution of the 27 kWp production output. (a) Self-use and grid sales production.

(b) Grid purchase and grid sales production.

Dimensioning System Size without Battery

The system size is further dimensioned by carrying out a sensitivity analysis. To determine the system sizes lower than the annual zero energy dimensioning of 27 kWp that is needed to cover the current electricity consumption, so as to derive a system size viable without any sales to the grid. In addition, system sizes higher than the capacity of 27 kWp is also analyzed so as to determine the income saving profitability due to the recipient requirement of selling excessive production to the grid. The result of the sensitivity analysis carried out is presented in Table 6.

The exact system sizes required in all scenarios will be highlighted with in green color to distinguish.

From Table 6 it can be observed that the higher the system size the more the electricity consumption is covered, while grid purchase reduces, grid sales increases while the self-use is reduced. In addition, it can also be noticed from Table 6 that increasing the system size above 27 kWp has no impact on the grid purchase as the percentage of the electricity consumption covered remains the same for 27 kWp to 30 kWp and 35 kWp to 40 kWp, while the grid purchase also remain the same for these system capacities. Also, increasing the system size above 27 kWp for the purpose of selling to the grid may not be feasible and viable for investment as the percentage difference of 1 % is observed for the following installed capacities 27 kWp, 30 kWp, 35 kWp and 40 kWp, while it increased by 2 % for 30 kWp to 35 kWp. The percentage

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

1 2 3 4 5 6 7 8 9 10 11 12

Energy(MWh/month)

Months

Current analysis results

Self use Grid sales

0.0 1.0 2.0 3.0 4.0 5.0

1 2 3 4 5 6 7 8 9 10 11 12

Energy(MWh/month)

Months

Current analysis result

Grid purchase Grid sales

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sensitivity analysis presented in Table 6 is further detailed in Table 7 to show the production distribution in energy unit for each system size.

Table 6. Farm 1 current sensitivity analysis result without battery.

System sizes (kWp)

PV in consumption

covered

Grid purchase

Self-use production

Grid sales

1 3% 97% 79% 21%

2 5% 95% 63% 37%

3 6% 94% 55% 45%

4 7% 93% 49% 51%

5 8% 92% 44% 56%

6 9% 91% 41% 59%

7 10% 90% 38% 62%

8 10% 90% 35% 65%

9 11% 89% 33% 67%

10 11% 89% 30% 70%

11 12% 88% 29% 71%

12 12% 88% 27% 73%

13 12% 88% 25% 75%

14 13% 87% 24% 76%

15 13% 87% 23% 77%

20 14% 86% 19% 81%

27 15% 85% 15% 85%

30 15% 85% 14% 86%

35 16% 84% 12% 88%

40 16% 84% 11% 89%

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Table 7. Farm 1 current energy production distribution without battery.

System sizes (kWp)

Annual production (MWh/year)

Consumption covered (MWh/year)

Grid Purchase (MWh/year)

Self-use Production (MWh/year)

Grid Sales (MWh/year)

1 0.92 24.92 24.19 0.73 0.19

2 1.85 24.92 23.75 1.17 0.68

3 2.77 24.92 23.40 1.52 1.25

4 3.69 24.92 23.11 1.81 1.88

5 4.61 24.92 22.87 2.05 2.56

6 5.53 24.92 22.67 2.25 3.28

7 6.45 24.92 22.50 2.42 4.03

8 7.38 24.92 22.35 2.57 4.81

9 8.30 24.92 22.23 2.69 5.61

10 9.22 24.92 22.13 2.79 6.43

11 10.15 24.92 22.03 2.89 7.25

12 11,07 24.92 21.95 2.97 8.09

13 11.99 24.92 21.87 3.05 8.94

14 12.92 24.92 21.79 3.13 9.78

15 13.83 24.92 21.73 3.19 10.64

20 18.45 24.92 21. 46 3.46 14.99

27 24.91 24.92 21.19 3.73 21.18

30 27.67 24.92 21.10 3.82 23.85

35 32.29 24.92 20.97 3.95 28.34

40 36.90 24.92 20.86 4.06 32.84

Graphical illustration of the results, particularly for the lower capacities is shown in Figure 6 to give more insightful understanding of the result obtained. The plausible reason for illustrating the graphical result of the capacity of 10 kWp 40 kWp as shown in Figure 6 and 7, is to show the behavior of higher capacity in response to the four analytical factors that is; the grid purchase, grid sales, self-use and consumption covered by PV, to show how self-use is lower than grid sales compared to the lower of 1 kWp to 3kWp which makes these not affordable due to more sales than self-use.

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(a) (b)

(c) (d)

Figure 6. Farm 1 current sensitivity analysis result without battery. (a) Excessive production sold to the grid. (b) Production taken in for self-use. (c) Current electricity consumption purchased from the grid. (d) PV production in current electricity consumption.

Dimensioning System Size with Battery

The battery storage potential analysis is integrated into the calculation model to curtail part of the excess PV production for self-use while the remaining part is sold to the grid. However, it is worth mentioning that self-use is the main determinant for estimating the battery potential.

This analysis is applied for the various system sizes presented in Table 6 of subsection 3.1.1.

The output result of the analysis is shown in Table 8, and the graphical illustration of the capacities of 10 kWp to 40 kWp is shown in Figure 7. Table 8 presents the annual sensitivity analysis result and energy production distribution with the battery use. It is observed that the lower capacities ranging from 1 kWp to 8 kWp has a good battery potential that is, the energy

0%

10%

20%

30%

40%

10 15 20 25 30 35 40

System size(kWp)

Self use production

0%

20%

40%

60%

80%

100%

10 15 20 25 30 35 40

Solar grid sales

80%

82%

84%

86%

88%

90%

10 15 20 25 30 35 40

Consumption purchased

0%

5%

10%

15%

20%

10 15 20 25 30 35 40

Profitability and future scanning of solar photovoltaic for fur farms in Kaustinen

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF ABBREVIATIONS

LIST OF SYMBOLS

1. INTRODUCTION

2. APPROACH TO DEVELOPING CALCULATION MODEL FOR FUR FARMS

3. DESCRIPTIVE ANALYSIS RESULTS OF KAUSTINEN FUR FARMS

Current analysis result

Current analysis result

Current analysis results

Current analysis result

Self use production

Solar grid sales

Consumption purchased

PV in consumption