Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Tschurtschenthaler, Franzi
Master of Applied Science - MASc
University of British Columbia
In certain jurisdictions worldwide, such as Alberta, the energy market is deregulated, and electricity prices fluctuate every hour. Utility scale distributed generation, such as solar photovoltaic (PV) parks, are paid these prices for the amount of energy they generate every hour. Traditionally PV systems have been designed to maximize the total energy they produce. However, when maximizing the total value of the solar park other factors become important, such as servicing peak electricity loads and maximizing return on investment. This study aims to increase the value of PV by maximizing the annual revenue as opposed to the annual electricity generation by determining the optimum orientation parameters, such as azimuth and tilt. This optimization was accomplished by taking the standard physics model of a PV cell and determining its total energy output based on the relevant environmental data. This output was then adjusted for all the losses typically encountered in a real-world PV system, such as inter-row shading, temperature, inverters, etc. Then the historical hourly electricity rates of the region were used to calculate the annual financial performance of the system. To find out the optimum parameters, a multi-variable simulation was performed which simulated 200 different orientation parameters for each system. This simulation was then run for 32 different real-world PV system configurations. The results indicate that for PV systems in northern Alberta the optimum tilt is similar to traditionally accepted values, however the optimum azimuth is generally 15° westward of what is typically installed. The average overall gain using these optimized parameters is about 1% of total revenue, which is realized with a zero increase in cost. In conclusion, this study provides a tool to harness the most value out of PV solar parks in deregulated energy markets.
OPTIMIZATION OF UTILITY SCALE PHOTOVOLTAIC ORIENTATION PARAMETERS FOR DEREGULATED ENERGY MARKETS IN NORTHERN LATITUDES by Franzi Tschurtschenthaler B.A.Sc., The University of British Columbia, 2003 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Electrical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) May 2020 © Franzi Tschurtschenthaler, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled: OPTIMIZATION OF UTILITY SCALE PHOTOVOLTAIC ORIENTATION PARAMETERS FOR DEREGULATED ENERGY MARKETS IN NORTHERN LATITUDES Submitted by Franzi Tschurtschenthaler in partial fulfillment of the requirements for the degree of Master of Applied Science in Electrical Engineering Examining Committee: Dr. Stephen K. O’Leary, School of Engineering Supervisor Dr. Jahangir Hossain, School of Engineering Supervisory Committee Member Dr. Wilson Eberle, School of Engineering Supervisory Committee Member Dr. John Madden, UBC AMPEL Director University Examiner Dr. Kevin Golovin, School of Engineering Neutral Chair iii Abstract In certain jurisdictions worldwide, such as Alberta, the energy market is deregulated, and electricity prices fluctuate every hour. Utility scale distributed generation, such as solar photovoltaic (PV) parks, are paid these prices for the amount of energy they generate every hour. Traditionally PV systems have been designed to maximize the total energy they produce. However, when maximizing the total value of the solar park other factors become important, such as servicing peak electricity loads and maximizing return on investment. This study aims to increase the value of PV by maximizing the annual revenue as opposed to the annual electricity generation by determining the optimum orientation parameters, such as azimuth and tilt. This optimization was accomplished by taking the standard physics model of a PV cell and determining its total energy output based on the relevant environmental data. This output was then adjusted for all the losses typically encountered in a real-world PV system, such as inter-row shading, temperature, inverters, etc. Then the historical hourly electricity rates of the region were used to calculate the annual financial performance of the system. To find out the optimum parameters, a multi-variable simulation was performed which simulated 200 different orientation parameters for each system. This simulation was then run for 32 different real-world PV system configurations. The results indicate that for PV systems in northern Alberta the optimum tilt is similar to traditionally accepted values, however the optimum azimuth is generally 15° westward of what is typically installed. The average overall gain using these optimized parameters is about 1% of total revenue, which is realized with a zero increase in cost. In conclusion, this study provides a tool to harness the most value out of PV solar parks in deregulated energy markets. iv Lay Summary Solar panels take incoming energy from the Sun and convert it to electricity. This electricity can be sold at market prices. These prices fluctuate hourly in many locations worldwide, and, in general, the electricity is worth more in the evening time when demand is high. Solar panels are most effective at producing electricity when they are facing the sun directly. This research discovers that solar panels will produce more energy when the electricity is worth more money in the early evening if they are facing the sun in the western sky. The study has shown that this increases the yearly revenue of the solar park by an average of 1% and does not cost anything to implement. This gain is independent of the type of solar panel or electrical setup of the system. v Preface This dissertation is original, unpublished, and represents independent work performed by the author, Franzi Tschurtschenthaler. Highly valuable direction and guidance has been provided throughout the process by my supervisor, Dr. Stephen O’Leary. In addition, real-world industrial inspiration was taken from conversations with PV system designers regarding their currently active utility-scale projects at multiple solar conferences over the past several years. vi Table of Contents Abstract ............................................................................................................................. iii Lay Summary ................................................................................................................... iv Preface .................................................................................................................................v Table of Contents ............................................................................................................. vi List of Tables .................................................................................................................. viii List of Figures ................................................................................................................... ix List of Abbreviations ...................................................................................................... xii Acknowledgements ........................................................................................................ xiii Dedication ....................................................................................................................... xiv Chapter 1: Introduction ....................................................................................................1 1.1 Solar energy basics ............................................................................................. 5 1.2 Motivation and objectives ................................................................................... 6 1.3 Thesis organization ............................................................................................. 9 Chapter 2: Background ...................................................................................................10 2.1 The photovoltaic effect ..................................................................................... 10 2.2 PV technology and innovation .......................................................................... 11 2.3 The classical PV system.................................................................................... 13 2.4 PV energy market ............................................................................................. 15 2.5 Current literature on PV system orientation optimization ................................ 18 Chapter 3: Simulating the PV System ...........................................................................22 3.1 PV system optimization .................................................................................... 24 vii 3.1.1 Modules......................................................................................................... 26 3.1.2 Inverters ........................................................................................................ 26 3.1.3 Civil Layout .................................................................................................. 29 3.1.4 Electrical Design ........................................................................................... 31 3.1.5 Orientation & Solar geometry ....................................................................... 34 3.2 Simulation workflow ........................................................................................ 35 3.3 Simulation parameters ...................................................................................... 37 3.3.1 Software ........................................................................................................ 37 3.3.2 Meteorological data ...................................................................................... 38 3.3.3 Specifics to the Alberta electricity market .................................................... 39 3.3.4 Detailed variable selections .......................................................................... 39 Chapter 4: Results & Discussion ....................................................................................45 4.1 General approach .............................................................................................. 45 4.2 Statistical Variance ........................................................................................... 47 4.3 Individual Results ............................................................................................. 47 4.4 Aggregate Results ............................................................................................. 89 Chapter 5: Conclusions & Recommendations for Further Study ...............................94 5.1 Advantages and Limitations ............................................................................. 94 5.2 Possible Future Work ........................................................................................ 95 References .........................................................................................................................96 viii List of Tables Table 2-1 Classification of PV Technologies .......................................................................... 11 Table 3-1 Parameters and variables for each simulation ......................................................... 41 Table 4-1 Conditions for Simulation 1 .................................................................................... 48 Table 4-2 Tabular results for Simulation 1 ............................................................................. 50 Table 4-3 Tabular results of all simulations ............................................................................ 90 ix List of Figures Figure 1-1 Estimated renewable energy share of global electricity production at the end of 2017 [2] ................................................................................................................. 1 Figure 1-2 Total installed PV capacity worldwide .................................................................. 2 Figure 1-3 Regulated and partially deregulated global energy markets [7] ............................. 3 Figure 1-4 AESO historical 10 year average pricing each month of the year in Alberta shown in hourly increments .............................................................................................. 4 Figure 1-5 PV module orientation parameters [11] ................................................................. 6 Figure 1-6 Price paid for electricity and the angle of the Sun, as a function of the hour of the day on August 3, 2017 in Edmonton, AB ............................................................. 7 Figure 2-1 PV production by technology............................................................................... 12 Figure 2-2 Best PV cell efficiencies of different technologies between 1975 and 2017 ....... 13 Figure 2-3 Overview of a solar PV power plant .................................................................... 15 Figure 2-4 Solar paths and 10 year annual average of normalized hourly cost of electricity in Edmonton ............................................................................................................ 18 Figure 3-1 Global solar PV cell manufacturing ..................................................................... 23 Figure 3-2 Characteristic PV module power curve [39]. ....................................................... 27 Figure 3-3 Typical layout of the physical position of the modules for the systems being simulated ............................................................................................................. 30 Figure 3-4 Calculation of the array GCR ............................................................................... 31 Figure 3-5 Vertical string layout (a) vs. horizontal string layout (b) ..................................... 33 Figure 3-6 Archetypal basic inverter efficiency curve [42] ................................................... 34 x Figure 3-7 Diagram showing the process taken to simulate the annual energy and revenue of multiple array orientations for each different type of system design .................. 36 Figure 3-8 Typical loss diagram for an example system ....................................................... 38 Figure 3-9 Historical meteorological data for Edmonton ...................................................... 39 Figure 4-1 Sensitivity of annual revenue and annual energy output for Simulation 1 .......... 51 Figure 4-2 Sensitivity of annual revenue and annual energy generation for Simulations 1 through 10 ............................................................................................................ 53 Figure 4-3 Sensitivity of annual revenue and annual energy output for Simulation 11 ....... 55 Figure 4-4 Sensitivity of annual revenue and annual energy output for Simulation 12 ....... 56 Figure 4-5 Sensitivity of annual revenue and annual energy output for Simulation 13 ....... 57 Figure 4-6 Sensitivity of annual revenue and annual energy output for Simulation 14 ....... 58 Figure 4-7 Sensitivity of annual revenue and annual energy output for Simulation 15 ....... 59 Figure 4-8 Sensitivity of annual revenue and annual energy output for Simulation 16 ....... 60 Figure 4-9 Sensitivity of annual revenue and annual energy output for Simulation 17 ....... 61 Figure 4-10 Sensitivity of annual revenue and annual energy output for Simulation 18 ...... 62 Figure 4-11 Sensitivity of annual revenue and annual energy output for Simulation 19 ...... 63 Figure 4-12 Sensitivity of annual revenue and annual energy output for Simulation 20 ...... 64 Figure 4-13 Sensitivity of annual revenue and annual energy output for Simulation 21 ...... 65 Figure 4-14 Sensitivity of annual revenue and annual energy output for Simulation 22 ...... 66 Figure 4-15 Sensitivity of annual revenue and annual energy output for Simulation 23 ...... 67 Figure 4-16 Sensitivity of annual revenue and annual energy output for Simulation 24 ...... 68 Figure 4-17 Sensitivity of annual revenue and annual energy output for Simulation 25 ...... 69 Figure 4-18 Sensitivity of annual revenue and annual energy output for Simulation 26 ...... 70 xi Figure 4-19 Sensitivity of annual revenue and annual energy output for Simulation 27 ...... 71 Figure 4-20 Sensitivity of annual revenue and annual energy output for Simulation 28 ...... 72 Figure 4-21 Sensitivity of annual revenue and annual energy output for Simulation 29 ...... 73 Figure 4-22 Sensitivity of annual revenue and annual energy output for Simulation 30 ...... 74 Figure 4-23 Sensitivity of annual revenue and annual energy output for Simulation 31 ...... 75 Figure 4-24 Sensitivity of annual revenue and annual energy output for Simulation 32 ...... 76 Figure 4-25 Sensitivity of annual revenue and annual energy output for Simulation 33 ...... 77 Figure 4-26 Sensitivity of annual revenue and annual energy output for Simulation 34 ...... 78 Figure 4-27 Sensitivity of annual revenue and annual energy output for Simulation 35 ...... 79 Figure 4-28 Sensitivity of annual revenue and annual energy output for Simulation 36 ...... 80 Figure 4-29 Sensitivity of annual revenue and annual energy output for Simulation 37 ...... 81 Figure 4-30 Sensitivity of annual revenue and annual energy output for Simulation 38 ...... 82 Figure 4-31 Sensitivity of annual revenue and annual energy output for Simulation 39 ...... 83 Figure 4-32 Sensitivity of annual revenue and annual energy output for Simulation 40 ...... 84 Figure 4-33 Sensitivity of annual revenue and annual energy output for Simulation 41 ...... 85 Figure 4-34 Sensitivity of annual revenue and annual energy output for Simulation 42 ...... 86 Figure 4-35 Sensitivity of annual revenue and annual energy output for Simulation 43 ...... 87 Figure 4-36 Sensitivity of annual revenue and annual energy output for Simulation 44 ...... 88 Figure 4-37 Aggregated optimum orientation results of all simulations performed ............. 92 Figure 4-38 Histogram, mean, median, and standard deviation of revenue gains ................. 93 xii List of Abbreviations PV Photovoltaic AESO Alberta Electric System Operator MPPT Maximum Power Point Tracking TL Transformerless GCR Ground Coverage Ratio DC Direct Current AC Alternating Current EM Electromagnetic LCOE Levelized Cost of Electricity GWPDC Gigawatt Peak Direct Current TMY Typical Meteorological Year ROI Return on Investment BOS Balance of System KWP KiloWatt Peak STC Standard Test Conditions PTC Performance Test Conditions CIGS Copper Indium Gallium Selenide CdTe Cadmium Telluride a-Si Amorphous Silicon NREL National Renewable Energy Laboratory Wp Watt-peak SSW South-SouthWest VOC Open Circuit Voltage c-Si Crystalline Silicon SIM Simulation 3P Three Phase PCC Point of Common Connection SRC Standard Reporting Conditions APP Alberta Power Pool xiii Acknowledgements I offer my enduring gratitude to the faculty, staff and my fellow students at UBC, who have inspired me to continue my work in this field. I owe particular thanks to Dr. Stephen O’Leary, whose advice and guidance have taught me to think more deeply and effectively. I would also like to thank Dr. John Madden and Dr. Joshua Brinkerhoff for providing insight and inspiration to ‘think outside of the box’ in my chosen discipline. I would like to thank NSERC, MITACS, and UBCO; each who have supported work on this thesis through various types of funding. Special thanks to: my best friend Jordo, and housemates Kyle and Amanda, for enduring the late nights while I was performing simulations, to Darren and Jenna for inspiring me to embark on my Master’s journey, to my engineering colleagues Cam and Warren for providing continuous intellectual stimulation, and to my long-term friend Tanya and also my constant inspiration Kora for motivating me to finish the job. The most essential gratitude is owed to my parents, who have supported me throughout my many years of education, both morally and financially. I have been fortunate enough to be supported by the best family I could ever ask for. xiv Dedication For science. 1 Chapter 1: Introduction Climate change is a global issue which is being addressed in part through the development and commissioning of renewable energy sources, such as those related to photovoltaics (PV) [1]. PV technology has been constantly improving, particularly in terms of cost and efficiency, and in recent years, there have been many developments which are pushing PV towards being the most promising renewable energy resource. Figure 1-1 shows the percentage of renewable energy being produced globally. The value of 1.9% for PV represents a large increase over previous years. In fact, there has been a 5.4% growth rate of renewables on an annualized basis over the past decade [2]. This can be seen in Figure 1-2 [3], which shows the dramatic rise in total installed PV capacity worldwide over the past several years. Figure 1-1 Estimated renewable energy share of global electricity production at the end of 2017 [2]. The online version of this figure is depicted in colour. 2 Figure 1-2 Total installed PV capacity worldwide. This figure is adapted from Masson et al. [3]. The online version of this figure is depicted in colour. The continually rising level of solar penetration makes it increasingly important to ensure that the solar farms that are installed are designed in the most advantageous way possible. In order to effectively deploy solar energy projects, and in order to decide which specific PV parameters and technology is best suited for which environment, one must be able to predict their performance under real-world conditions. These predictions need to take into account both financial and technical merits. The accurate modeling and simulation of these technologies will allow for the energy industry to focus its effort on deploying the most advantageous combinations of technologies and system parameters. This thesis uses the most well-known and widely used software for PV system modeling, PVSYST, in order to find these most advantageous combinations [4, 5]. The current electricity market reality is that many jurisdictions have moved to a deregulated energy system. These markets allow for the generation of electricity from independent power 050100150200250300350400450Installed PV Capacity (GWpDC)Year3 producers while employing a system, commonly referred to as a “power pool”, in which the price paid for energy produced or consumed fluctuates based on demand [6]. This price is publicly available and typically changes every hour. This differs from a regulated electrical market in which a vertically integrated utility produces, transmits, and distributes its own the electricity. These markets do not have power pools with publicly available information on hourly fluctuating energy costs. Instead, set value tiered pricing is typically imposed by a central utility and is regulated by a government agency. Figure 1-3 shows the increasing list of countries which have partially deregulated electrical power markets, in either the whole country or some of its constituent jurisdictions [7]. Figure 1-3 Regulated and partially deregulated global energy markets [7]. The online version of this image is in colour. The price of electricity in these deregulated energy markets fluctuates throughout the day and follows the basic premise of supply and demand. As the demand goes up, so does the cost. 4 This means that in the evenings when the general population comes home from work and starts using appliances, the price of electricity rises. It also rises in the late afternoons and early evenings in warm climates when air conditioning units start being used. It is more prominent in northern cities where the Sun has longer hours in the summer. The 10-year average monthly price for Edmonton, Alberta, taken from Alberta Electric System Operator (AESO) historical data, is presented in Figure 1-4 [8]. It shows a maximum hourly pricing in summer early evenings. It has also been shown that climate change will have a larger impact on peak electricity demand than monthly average electricity demand [9]. These factors make these peak electricity costs very important to mitigate, if at all possible, both now and in the future. Figure 1-4 AESO historical 10 year average pricing each month of the year in Alberta shown in hourly increments. Original figure created from publicly available data [8]. The online version of this figure is depicted in colour. 5 1.1 Solar energy basics Solar modules convert incoming solar radiation directly into electricity through the photovoltaic effect in a clean and silent process which has no moving parts. The PV effect is a semiconductor effect whereby solar radiation falling onto the semiconductor PV cell generates charge carrier movement, which in an electric circuit, becomes usable electricity. A solar module is typically comprised of generally 60 to 72 individual cells connected together and laminated to a piece of glass and an outer frame. These modules are installed with an inverter to change the power from DC to AC for use with grid loads. The main design parameter which influences the power produced by the module throughout the day is the angle of the solar radiation hitting the module. More direct angles produce more power. This is why some installations are installed with solar trackers, which allow the modules to follow the Sun throughout the day. However, the cost of solar trackers is prohibitive and therefore, they are only used for very specific installations [10]. Typically, large scale PV systems are locked at a fixed angle, with the angle being optimized using software to maximize the annual energy production. The calculated tilt/slope, β, is usually several degrees below the latitude of the location, and the calculated azimuthal angle, γ, is 0°, or due south. The angle orientation parameters are depicted graphically in Figure 1-5 [11]. 6 Figure 1-5 PV module orientation parameters [11]. The online version of this figure is depicted in colour. 1.2 Motivation and objectives To optimize cost and customer financial return on a PV system, the economics need to be evaluated at the system level. Due to the rapid rate of progress in this field, there have been many unique parameters for which no system level simulation models have been developed. All available modeling software surveyed did not take into account the fluctuating price paid for utility scale solar electricity in some jurisdictions. This means that the overall system financial performance, which is judged by the annual revenue, is based on a fixed solar angle adjusted for maximum annual output. Figure 1-6 shows the angle of the Sun on the same time-scale as the price of electricity in Edmonton, Alberta for a typical summer day [12]. It can be seen that the largest prices paid per hour for utility-scale electricity are when the Sun is going down. Therefore, 7 it would make sense to fix modules at a more westerly facing angle so that they can produce more power in the evenings when it is worth the most. Figure 1-6 Price paid for electricity and the angle of the Sun, as a function of the hour of the day on August 3, 2017 in Edmonton, AB. Original figure created from publicly available data [8, 12]. The online version of this figure is depicted in colour. Most solar farm designs have focused on determining the angles required in order to accrue the maximum amount of solar energy generated on an annualized basis. For a regular market, with a set price for electricity, maximizing the amount of solar energy generated directly corresponds to maximum revenue. For an unregulated market, however, or for a market over which the price 01020304050601 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24020406080100120Angle of Sun above the Horizon (°)Hour of the DayAESO Pool Price Paid for Electricity Generation ($/MWh)8 varies over the day, this does not necessarily follow, and a re-evaluation of the situation is called for. Accordingly, in this thesis, an examination of what solar angles, i.e., what azimuth and tilt angles, are required in order to maximize revenue are explored. There are expected to be increases in the total annual revenue when the modules are faced westward, and this has already been shown for other jurisdictions besides Alberta, including California [13], Ontario [14], and Austria [15]. It has also been shown that for certain local climatic conditions that a south-southwest (SSW) azimuth can actually produce more annual solar insolation as well [16], which would further increase the annual revenue. However, these researchers only performed simulations for the output of a single module and did not include many important factors that are relevant to utility-scale PV power plants, such as the effects of different module technologies, different types and sizes of inverters, and the presence of inter-row shading. This research takes into account all of the major factors and parameters important in determining the performance of a complete utility scale power plant. Previous research has proven the basic premise that optimizing revenue in an open market regime requires a departure from the traditionally optimal azimuth orientations. However, there is uncharted territory when optimizing the azimuth and tilt angles, as well as modules, inverters, and inter-row spacing with respect to the total revenue generated in a large utility-scale array with deregulated pricing. The goal of this analysis is to setup multiple versions of a PV power plant by varying one major parameter at a time. In order to cover all major parameter variations currently used in commercial PV system design, there will be approximately 40 different versions. Then, for each of these conditions, full annual performance simulations are pursued, over a wide range of azimuth and tilt angle selections. These results will then be weighted with the historical hourly prices of 9 energy, the goal being the determination of the azimuth and tilt angles for maximum revenue. Aggregation of these results will then provide us with insight into the overall annual financial gain (or loss) resulting from adjusting the azimuthal and tilt angles in order to maximize the annual revenue as opposed to the annual energy produced. It will also provide us with the actual values of the azimuthal and tilt angles which result in these maxima. 1.3 Thesis organization This thesis is organized in the following manner. In Chapter 2, the background for this work is provided. Then, in Chapter 3, the simulation technique, employed for the purposes of this analysis, is detailed. In addition, the parameters important for this purpose are introduced. In Chapter 4, the results of these simulations are analyzed and the dependence of the annual financial output on the azimuthal and tilt angles is explored. Finally, in Chapter 5, the conclusions of this analysis are presented, and some suggestions for further exploration are made. 10 Chapter 2: Background PV technologies found their genesis with development of the solar cell in 1954 by Chapin et al. [17]. This initial device, which arose from the advances in microelectronics that had occurred, was fabricated using crystalline silicon (c-Si). Since that time, there have been many advances in PV technology. These advances seem likely to continue for the foreseeable future. This chapter will first provide background information on the major components of a utility-scale PV array. It will then demonstrate how these systems have traditionally been optimized and why it is important to perform further study into their optimization due to the increased presence of deregulated energy markets. Subsequently, a literature review, on PV system optimization, with respect to tilt and azimuthal angles selection in deregulated energy markets, will be conducted. 2.1 The photovoltaic effect Each PV cell in a solar module is an electronic device which converts incoming solar radiation into electricity through the PV effect. The core of a typical cell is a diode formed by a silicon p-n junction [18]. This is created by layering n- and p- type semiconductors produced through a method called doping. Doping is when impurities are added to a semiconductor to change its electrical properties. Near the interface of the two layers, excess electrons diffuse into the p-type material from the n-type, and excess holes diffuse into the n-type material from the p-type. This creates a positively charged area on the n-type side and a negatively charged area on the p-type side, and therefore forms an electric field at the junction. If electro-magnetic radiation with photon energies in excess of the energy band gap of the semiconductor material illuminates such a cell, electrons will be ‘knocked’ loose; thereby forming electron-hole pairs. These pairs 11 will be influenced by the electric field direction and will start to flow if an external circuit is connected. The electrons eventually return to the cell after completing the circuit to recombine with the holes, thereby generating DC electricity [19]. 2.2 PV technology and innovation Commercially available PV modules are designed with a variety of different semiconductor materials and with various layer configurations based on the simple p-n junction. The technology types have been separated into the following categories by the National Renewable Energy Laboratory (NREL): Multijunction Cells, Single-Junction GaAs, Crystalline Si Cells, Thin-Film Technologies, and Emerging PV. For the purposes of this particular dissertation, only Crystalline Si Cells and Thin-Film Technologies will be discussed. These two types are further classified, as shown in Table 2-1. Table 2-1 Classification of PV Technologies Crystalline Si cells C-1 Single crystal (concentrator) C-2 Single crystal (non-concentrator) C-3 Multicrystalline C-4 Silicon heterostructures Thin-film technologies TF-1 CIGS (concentrator) TF-2 CIGS TF-3 CdTe TF-4 Amorphous Si:H (stabilized) 12 Types C-1 and TF-1 will not be discussed, since concentrator technology is not typically used for cloudy, northern climates. Type C-4 is not currently in large scale commercial production, and therefore, is not really relevant for this dissertation. For the commercial and utility scale PV arrays targeted in these simulations, types C-2, C-3, TF-2, TF-3, and TF-4 will be examined. Justification for focusing on these technology types is due to their widespread usage as shown in the annual global PV production of the top 5 technology types in Figure 2-1 [20]. Figure 2-1 PV production by technology – percentage of global annual production in 2017. Original figure created from publicly reported data [20]. The online version of this figure is in colour. Both single crystal (also known as monocrystalline) and multicrystalline modules are made by stringing multiple cells together and laminating them behind glass. Monocrystalline cells are made using wafers cut from a pure single crystal silicon ingot for the p-n junction. Multicrystalline cells are made using wafers cut from pure silicon which has been melted into bricks. CdTe, CIGS, and a-Si modules are made from depositing thin films of semiconducting material onto large area sub- or superstrates. CdTe uses Cadmium Telluride for the p-n junction, CIGS uses Copper Indium 13 Gallium Selenide, and a-Si uses amorphous silicon. Each of these technology types have their own nuances and distinctions when it comes to real-world performance, and these will be discussed further in Section 4.3. The best published PV cell efficiencies for the different types of technologies, corresponding to PV cells between 1975 and 2017, are depicted as a function of the year in Figure 2-2. Figure 2-2 Best PV cell efficiencies of different technologies recorded between 1975 and 2017. This plot is courtesy of the National Renewable Energy Laboratory, Golden, CO. The online version of this figure is in colour. 2.3 The classical PV system Figure 2-3 gives an overview of a typical grid connected utility scale solar power plant. There are additional sub-categories of solar energy systems, such as off-grid power, consumer electronics, and specialty high-tech uses. The technical details of these systems will not be discussed within the scope of this research. The general definition for a grid-connected 14 commercial or utility scale PV power plant is a peak capacity above ~100kW and a 3 phase (3P) Point of Common Connection (PCC). For these systems, the DC electricity produced by the solar module, as outlined in the previous section, is converted to AC electricity through the use of one or more inverters, thereby allowing the electricity to be transmitted and distributed to existing grid loads/customers. On either side of the inverter, there are DC and AC disconnects to allow for servicing of the equipment when necessary. After the AC disconnect, there are service panels, which allow for any local AC loads to utilize the solar power. Before connection to the grid, there are step-up transformers, which change the voltage to the appropriate value specific to the distribution system. Finally, there is the AC utility meter which can be installed either before or after the step-up transformers, depending on the particulars of the local utility. This is what measures the amount of power being produced by the PV array, and is ultimately what determines the revenue paid to the solar park by the entity purchasing the renewable energy generated. There are also additional items needed to complete a grid-connected solar park in order for it to be operational and approved by all relevant governing bodies. These include a mechanical structure to mount the modules (shown as mounting racks in Figure 2-3 [21]), fusing as per the electrical code, and the AC and DC cabling required to transfer the energy between all the separate devices listed in the previous paragraph. All of these items are necessary in order to build a PV system, besides the solar modules themselves, and are collectively referred to as the balance of system (BOS). As the price of PV modules has steadily dropped, BOS elements have started to play an important role in lowering the overall cost of solar power. Together, they constitute a larger proportional cost of the complete system than the panels themselves. This is yet another reason why it is important to optimize 15 Figure 2-3 Overview of a solar PV power plant. This figure is adapted from Lumby et al. [21]. The online version of this figure is in colour. 2.4 PV energy market The typical method of monetizing an installed solar array is to be paid for the kWh of energy it produces. This can be done via a Power Purchase Agreement (PPA), a Feed-in-Tariff (FIT), or by getting paid market value for the energy. PPAs are typically signed between large utilities and solar farm operators. These agreements pay the operators very low rates, which are calculated to make a minimum amount of profit to satisfy any investors over the lifetime of the system. This can be accomplished due to the economies of scale of these systems, which are typically in the 10 to 1,000 MW range. An example would be Solar Star in California, the largest PV power plant in the United States, and under a PPA to Southern California Edison [22]. FITs 16 are typical for areas where the local governing body has agreed to pay a certain rate to any approved solar installation, done in order to stimulate the solar economy and increase the percentage of renewable energy in that area. The value of these is typically above the going energy rate and is usually for residential to mid-sized commercial systems. An example would be the province of Ontario from 2009 – 2016 [23]. Due to solar power achieving grid-parity in many regions recently, these types of agreements are diminishing. The increasingly more common way of receiving remuneration for the energy being generated, is to get paid at whatever the going rate is per kWh in the local jurisdiction. This is known as net-metering for smaller installations, with an example for this type of system being British Columbia [24]. In an increasing number of jurisdictions worldwide, such as Alberta, this rate per kWh fluctuates hourly, which is where this research becomes relevant. There are additional financial vehicles such as rebates, tax credits, etc. which add to the overall economic viability of the PV system, however they are beyond the scope of this thesis. In 2001, the province of Alberta implemented a fully deregulated electricity market, finishing a process started in 1996 [6]. This system allows for the buying and selling of electricity on the open market, known as the electricity pool, and permits the connection of multiple electricity generators, known as Independent Power Producers (IPPs), to the electricity grid in order to facilitate this. The AESO describes this system as follows: “The pool price is the dollar cost of a megawatt hour of electricity at the end of a given hour that is paid to electricity generators for supplying electricity by retailers (such as your local service provider). Typically, retailers purchase this electricity to supply residential and business customers, as well as large industrial customers.” [25] 17 This allows for IPPs, such as commercial and utility-scale PV arrays, to sell the energy they generate at the current market value. It therefore follows that it is important determine when the market price of electricity is valued at its highest, and produce the most energy at these times to maximize overall revenue. The 10 year annual average of the normalized hourly cost of electricity can be calculated from the historical AESO electricity price data [26], and is shown in Figure 2-4. It can be seen that there is a noticeable peak in the early evening hours attributed to home appliances such as air conditioning and public utilities such as street lighting [25]. The same figure also shows the path of the Sun (in terms of height and azimuth) on several key days throughout the year in Edmonton, AB. PV modules produce the most power when they are more directly oriented to the sun, as will be further discussed in Section 3.1.5. Therefore, in order to maximize annual revenue for this type of energy market, it would make sense to orient the PV modules more directly towards the location of the Sun in the western sky during the times of peak pool pricing. 18 Figure 2-4 Solar paths and 10 year annual average of normalized hourly cost of electricity in Edmonton (lat. 53.5 °N, long. 114.1 °W, alt. 739 m). The online version of this figure is in colour. 2.5 Current literature on PV system orientation optimization Since the introduction of PV in the late 1950’s, and its subsequent possibility as a large scale grid connected energy source, it has been of utmost importance for engineers to increase the overall value offered by PV to achieve this goal. This has been addressed by increasing the quantum efficiency of solar technology in laboratory conditions, lowering installed costs via decreased production costs per Watt-peak (Wp) of PV module capacity and BOS components, and maximizing the economic value through the ideal implementation of PV in real-world applications. The first target has been constantly strived for in laboratories worldwide, as discussed in Section 2.2. The second target has been realized through a consistent drop in cost of all physical components of a complete PV system, and while it has been argued that PV modules are not yet 19 commodified [27], it is only a matter of time until all the necessary electrical hardware components go this direction. The third target is where this research aims to contribute to the overall academic knowledgebase of PV, as it postulates that optimizing the orientation of an array for a particular set of local conditions can potentially increase the annual financial gain of a PV system. For arrays on flat wide-open land, which is typical for utility scale systems, this orientation optimization is realized without incrementally increasing its cost, and therefore the overall value of the system is increased. There have been many different methods proposed for determining the optimum tilt angle to maximize annual production [28]. These maximize the objective function of the annual incoming radiation on a tilted plane using the path of the sun through a clear sky, and provide empirical equations used to predict the ideal tilt angle for any latitude. All of these methods assume a due south orientation for the azimuth, which is the general rule-of-thumb for PV system design in the northern hemisphere [29]. Some researchers have concluded that varying the tilt angle at mid-latitudes is generally irrelevant, producing on average less than 6% difference in annual energy output [30]. However, with utility-scale solar, a 6% difference is very important with respect to overall system value. Take for instance the ~750 MWp Solar Star project previously mentioned. Having an annual output hovering around ~1,700,000,000 kWh, and an estimated PPA of ~0.05 $/kWh, an average of 6% in increased production could be the ~$5M yearly revenue difference between a ‘go’ or ‘no-go’ when determining the financial viability of such a solar array. As such, any system design modification, resulting in an incremental increase in the projected annual revenue of a PV project without increasing the overall installation cost, can be considered an avenue worth pursuing. 20 It has been demonstrated that local meteorological conditions, such as morning cloud cover, and environmental conditions, such as smog, have an impact on PV system performance irrespective of the price paid per kWh [31]. Using only the National Solar Radiation Database (NSRDB) as an input, the azimuthal angle which results in the maximum annual energy production output can vary between ±15 º from due south based on local conditions throughout the US. Such data shows that more research is needed to determine the optimum orientation to maximize the overall value for complete systems at specific locations. Further details on this relationship is explored by predictions made in California for optimal tilt and azimuth based on local Time of Use (TOU) energy pricing, which found that the azimuth can be up to 10 degrees west for the best revenue-based results [13]. Similar methods were used to determine optimal orientation angles at slightly higher latitudes in Ontario using both fixed pricing and TOU energy pricing. These resulted in angles between 1-6 degrees east and west of due south and slightly less than latitude. [14] There have also been studies performed in Germany and Austria that measured the optimum angle and orientation to optimize the total cost of electricity from an electrical system’s perspective, and not just for the individual solar farm owner [15]. These simulations generally show that for increased solar penetration of the grid the azimuthal angles increasingly deviate from due south and the tilt angles become steeper. This research presented in this thesis differs from most other similar solar placement work such as [32] in that the production of AC electricity (after module, inverter, and other electrical losses) is considered when calculating the overall system value, and not just the DC power from the panel. There has been work done using a complete PV system model which covers multiple locations in the US and takes into account TOU electricity data [33], however they only model a single type of module, inverter, and system design, whereas this research aims to see if different 21 types of inputs and technologies, as would be chosen during the process of a utility scale PV system design, effect the results. Most importantly they do not incorporate inter-row shading, and subsequent parameters such as the Ground Coverage Ratio (GCR), which is further described in Section 3.1.3, and is a significant part of large system design in northern latitudes. While there is research exploring the specific influence of these large system parameters on energy generation [29], there is none which takes into account tilt and azimuthal orientation, inter-row shading and tilt trade-off, technology selection, full electrical system design, and TOU electricity rates for any specific location. This thesis intends to explore simulations which take all of these factors into account and cycle through a range of tilt and azimuthal angles in order to maximize overall system value, by maximizing the annual revenue generated by the system. Before discussing the simulations performed, it is instructive to note that for the purposes of this thesis all systems were assumed to have static angles with respect to azimuth and tilt positioning. Dynamic angular positioning has been pursued and documented by a number of researchers since the inception of PV technology. It has been shown that there are consistent performance improvements [32], but these are rarely worth the additional costs incurred based on the current economies of overall system hardware. Therefore, given the state and projected future of the PV value proposition, a fixed angular orientation results in the maximum value for such projects. Accordingly, static angular positioning will be focused on for the remainder of this thesis. 22 Chapter 3: Simulating the PV System The prospect of harvesting energy from the Sun through the use of PV technology has captured the human imagination since its genesis in the 1950’s. Initial applications focused on extra-terrestrial satellite systems, where cost is no object. The oil shortages that occurred in the 1970’s prompted the expansion of PV into the realm of consumer electronic products, such as calculators and watches. More recently, PV has drawn the attention of those who wish to use it for utility-scale energy generation. As shown in Figure 3-1 [34], global solar PV production has increased exponentially in recent years, and moved beyond the small-scale production model of its early days, and now large-scale solar farms are being considered. Indeed, the research taking place in the major PV research centres of the US and the EU have focused on such large-scale PV energy generation deployment, and today utility-scale PV systems are viewed as being a quiet, reliable, and cost effective alternative to fossil fuel energy generation. 23 Figure 3-1 Global solar PV cell manufacturing: annual production, manufacturing shares, and major manufacturing countries. Figure from © Binz, C., Tang, T., & Huenteler, J. (2017). Spatial lifecycles of cleantech industries – The global development history of solar photovoltaics. Energy Policy, Vol. 101(Feb 2017). 386-402. Page 390. Adapted with permission from publisher [34]. Workers in the PV field have found it instructive to employ simulations of the performance of prospective PV systems prior to their actual fabrication. Such simulations have provided guidance on the ideal choice of components, architecture, and electrical and mechanical setup in 24 order to maximize the desired metric for different local conditions. The results presented in this thesis are built upon such simulations. In this chapter, the elements of the simulations performed are presented. These simulations are primarily aimed at determining the economic benefit accrued from varying the tilt and azimuthal angles of a range of different PV configurations. Data, drawn from the governing equations, which describes the output energy as a function of time, and real-world historical energy price data, are the primary data streams employed for the purposes of this analysis. This chapter is organized in the following manner. In Section 3.1, the process of PV system optimization is featured. Then, in Section 3.2, the simulation workflow is presented. Finally, in Section 3.3, the simulation parameters are detailed. 3.1 PV system optimization In order to maximize the overall effectiveness of an installed PV array, it is important for engineers to carefully optimize every parameter when designing the system. Typically, systems are optimized in order to maximize one of several different parameters: the specific yield (kWh/kWp), the levelized cost of electricity (LCOE), or the return on investment (ROI). The specific yield is the ratio of annual output energy to the total installed DC capacity. A description of how the DC capacity of a module is measured in kWp is given in Section 3.1.1. The annual output energy of the system is typically maximized in order to maximize the specific yield, since the DC capacity for any given system is fixed. For systems which are paid a fixed price per kWh generated, the revenue is maximized when the annual output energy is maximized. This is not the case in Alberta, where the price of electricity fluctuates throughout the day, and therefore, the maximum revenue is not necessarily achieved when the annual output energy is 25 maximized. Since the objective is to optimize the overall value of the PV system, it can be seen that the specific yield is not the correct metric to maximize for this particular situation. The LCOE is the cost per kWh of electricity generated that takes into account a combination of capital costs, operation and maintenance (O&M), performance, and fuel costs [21]. A simple formula for this is as follows: LCOE = Sum of costs over lifetime ÷ Electrical energy produced over lifetime (3.1) It is often taken as a proxy for the average electricity price the system must receive in a market in order to break even over its lifetime. Therefore, for jurisdictions where the price of electricity fluctuates hourly, the LCOE is not the ideal metric to optimize to maximize the overall value of the system. The ROI for a PV array is typically expressed as the payback time required in order to recoup the initial investment in the system [35]. A simple formula for this is as follows: Payback time = Cost of system ÷ Annual PV revenue (3.2) Typically, the annual revenue is calculated using the total energy output multiplied by the rate of electricity. However, when both the energy and the price of energy fluctuate, it must be done by multiplying both values together over the time intervals over which they fluctuate and then summing the values. For purposes of this dissertation, the cost of the system is considered fixed. Therefore, we can see that in order to lower the payback time, thereby maximizing the value of the system, we must aim to maximize the annual revenue. No matter which objective function is chosen, there are several major component selections and design parameters of a PV system which influence its performance: modules, inverters, electrical setup, civil layout, and orientation. 26 3.1.1 Modules As was discussed in Section 2.2, there are many types of different PV module technologies which can be chosen. Due to the continuously dropping cost of polysilicon, the majority of modules currently being used for PV installations are either mono- or multicrystalline silicon. However, there are many other options, each having its own advantages and disadvantages. The important items to note here are that thin film modules are generally less efficient, and therefore, will have a larger overall footprint. Also, thin film a-Si modules typically have better low light and hot weather performance [36], and CIGS modules are more highly dependent on orientation parameters and environmental conditions [37]. The overall power of modules is measured in Wp (Watt-peak) and at Standard Test Conditions (STC), which is sometimes interchanged with the term Standard Reporting Conditions (SRC). This is the metric used when modules are purchased, and how overall system size is measured and reported [38]. The parameters for these conditions are at 25°C, a wind speed of 1 m/s and AM 1.5. In actuality, the prevailing real-world conditions rarely match STC, and therefore, it is important to simulate how the system responds to these real world conditions in order to determine the overall effectiveness of the design choices. For purposes of this research, we will be varying the different types of modules and including all major technology types relevant to large grid-connected systems, as outlined in Section 2.2. 3.1.2 Inverters There are 3 major types of inverter topologies used for PV installations: microinverters, string inverters, and central inverters. Each has their own advantages and disadvantages and are 27 also typically suited for different scales of installations. Irrespective of the topology, every grid-connected commercially available inverter has a feature called the Maximum Power Point Tracking (MPPT). Since PV modules have a nonlinear I-V curve with respect to irradiance and temperature, it is important to make sure that they are producing the maximum amount of power for the given conditions [39]. This is achieved through an MPPT system, which adjusts the operating voltage (or current) to be as close as possible to where the module is producing the maximum amount of power (PMPP), as shown in Figure 3-2. This is achieved through a number of different methods, but is beyond the scope of this research. However, it is important to note that there is a relatively new methodology being implemented using string optimizers in place of MPPT systems, and this type of design will be tested in one of the simulations. Figure 3-2 Characteristic PV module power curve [39]. Microinverters typically range in size from 100 to 600 W of output AC power, and are connected to a single PV module. (Some manufacturers connect up to 2-3 modules to a single 28 microinverter.) For utility scale PV systems, it is rare for micro-inverters to be implemented due to cost concerns. String inverters typically range in size from 2 to 60 kW of output AC power, and are connected to the PV modules through several parallel “strings” of multiple modules in series. Each string typically ranges in size from about 7-14 modules in series, and then between 1-6 of these strings are connected in parallel to each inverter. Further details of the DC electrical architecture are discussed in the following section (3.1.4). This string-based wiring allows for the maximum system voltage to be realized while still being within the regulatory limits set by the electrical code of the majority of jurisdictions worldwide. The value which typically determines the maximum number of modules per string is the combination of temperature and insolation which produces the highest open-circuit voltage (VOC) for each module. Semiconductors, and therefore p-n junctions which exhibit the PV effect, as described in Section 2.1, operate more efficiently at lower temperatures. Therefore, it follows that PV modules will produce a higher voltage in environmental conditions which are cold and bright, such as a brisk and clear morning in late spring in Edmonton. This results in smaller string sizing for these locations, which usually results in a slight decrease in efficiency. This is because the resistive losses are proportional to the square of the current flowing through the circuit. Therefore, if an additional module can be placed in the circuit, the current (and losses) will essentially be the same, and there will be one more module’s worth of power produced in that string, and the overall efficiency will be increased. These factors all need to be taken into account when choosing the appropriate inverter setup for the system. Central inverters typically range in size from 500kW to 2MW of output AC power, and are the most of efficient of the 3 types in ideal operating conditions. However, their real world operating efficiency is typically much more sensitive to perturbations like shading, such as that 29 which occurs when a cloud slowly passes by, thereby shading only a portion of the large amount of connected modules at a time. The maximum power point gets averaged out for all the modules powering that inverter, and they hold a much larger number of strings at PMPP. Therefore, if one module in the series is shaded, the entire branch is held at that lower voltage, resulting in a lower power output. For the purposes of this analysis, we will consider all three topologies to ensure that all possible options are explored. Since the system size chosen for the majority of the simulations is in the ~190kW range, string inverters will be chosen for the majority of the simulations, as this is the typical choice for this size. It also lends itself well to effectively varying other desired parameters, such as different MPPT configurations for the inverters, as some inverters have multiple MPPT inputs. 3.1.3 Civil Layout The major civil layout parameter for a typical PV array is the inter-row spacing, which is also represented as the Ground Coverage Ratio (GCR). There can also be landscape design concerns, such as the geometry, slope, and soil conditions of the land being used. However, for purposes of this research, we will be assuming a flat and easy to work with landscape, and have designed every simulation to have as close as possible to a perfect square layout, as shown in Figure 3-3. 30 Figure 3-3 Typical layout of the physical position of the modules for the systems being simulated. The online version of this figure is in colour. Since the output of the array is directly affected by any shading of the modules, and inter-row shading is directly affected by the GCR, we will be varying this parameter for the simulation. The GCR is calculated by taking the array length and dividing by the row pitch, as shown in Figure 3-4 [40]. This is the ideal parameter to focus on when determining the civil layout for purposes of these simulations, as it does not depend on the array tilt which will be continuously varied. 31 Figure 3-4 Calculation of the array GCR. This figure is adapted from Deline et al. [40] . The online version of this figure is in colour. 3.1.4 Electrical Design The electrical setup of a PV system typically involves designing 5 subsections: the DC topography up to the AC/AC inverter, the PV-specific subsection of the AC topography up to the general AC utility meter, a portion of the grid topography to the Point of Common Connection (PCC) with the utility grid, the grounding and fault protection requirements, and any instrumentation. A layout of these different sections has already been shown in Figure 2-3. For these simulations, we will only be considering the first two sections of the design, as their parameters are the ones which primarily influence the performance criteria. The overall performance is highly dependent on the inverter and module choice, however, the electrical design can also have a marginal effect on overall system performance. This margin becomes very 32 important when maximizing the system impact and financial performance, as this area can typically realize the highest electrical performance gains for the least amount of incremental cost increase. The main variable parameters for these topographies are: • System maximum DC voltage: This depends on the jurisdiction and can be either 600 or 1000 VDC. For this research, 600 VDC was used in order to cover the widest set of potential applications. • The size and circuit diagram of the module strings: These decisions are influenced directly by the physical location parameters of the modules, the type of modules, the type of inverters, and the local climate conditions. For the purposes of this research the module strings were sized to the standard maximum length to comply with the Canadian Electrical Code (CEC), which is based on the chosen modules, inverters, and typical Edmonton temperatures conditions taken from the Canadian Weather Energy and Engineering Dataset (CWEEDS) [41]. The physical layout of the strings is important to optimize during the system design, as when a single module is shaded in the string it limits the current of the whole string to the level of the shaded module. An example of two identical systems with a different physical string layout is shown in Figure 3-5. Each colour represents a block of modules connected in series, and it can be seen that any shading would affect the two designs differently. In order to determine any possible influence of the string layout on the results, several of the most common types of string layouts and physical module interconnections were simulated. However, to ensure that for each simulation only one design parameter was changed each time, care was taken with each iteration that the string length and circuit diagrams were kept identical. 33 Figure 3-5 Vertical string layout (a) vs. horizontal string layout (b). Each colour is a different string of 14 modules connected in series. The online version of this figure is in colour. • The ratio of maximum DC module output power vs. maximum AC inverter rated output power: This value is dependent solely on the number and size of modules as measured at STC, and the rated power of the inverters used. Inverters have typical efficiency curves which peak lower than their peak power ratings, as shown in Figure 3-6 [42]. Also, since the PV modules are only occasionally producing at their peak power due to varying irradiation conditions throughout each day, it follows that it makes sense to design a system with a higher inverter rated output power than system module power. Therefore, this parameter is varied through a range of values for a portion of the simulations to determine its impact on the overall system output. (a) (b) 34 Figure 3-6 Archetypal basic inverter efficiency curve [42]. 3.1.5 Orientation & Solar geometry The angle of incidence of solar irradiation that a module experiences changes as a function of the time of day, the day of the year, and the latitude of the locality. Figure 2-4 shows the angular paths of the Sun in the sky on different days of the year for Edmonton, AB. Since there is generally a strong decrease in performance at high angles of incidence [37], it is in the designer’s best interest to optimize the orientation of the modules to most effectively satisfy the objective function of the system. Typically, this is the annual energy production, however in this case we would like to maximize annual system revenue with respect to varying local hourly energy prices. Figure 1-5 shows how orientation parameters for an array are typically defined, with the tilt/slope angle being β, and the calculated azimuthal angle being γ. The optimal β is typically calculated using a system of equations which include the direct, diffuse, and albedo components of the solar irradiation equations for various sky conditions [43], with the result usually being several degrees below the latitude of the location. Typically, γ is set to 0°, or due south in the northern hemisphere, in order to maximize the amount of direct exposure to the Sun during its daily path across the sky. 35 For situations other than maximizing the annual energy production there is no clearly defined method available to estimate the ideal angles. Therefore, for purposes of this research, both the tilt and azimuth will be varied for each simulation across a wide enough range to find the maxima of both the annual energy and revenue. 3.2 Simulation workflow The components of the simulated system parameters can be categorized into the following three main sections: technology, design, and location. Figure 3-7 provides a visual representation, using standard symbols of flowchart diagrams, to show how portions of each of these major pieces were put together to provide the resulting data. The parameters were varied one at a time for each major batch simulation, and the variables were changed in small increments through a large range until the desired maxima were found. The figure highlights the path taken by the variables through the simulation process in red. The process of simulating the electrical production of the PV system was performed by commercially available software, which is further discussed in Section 3.3.1. This value is defined as the annual energy generated, or EG. The portion of calculating the value of energy produced by the PV system was performed in Microsoft Excel with the data from the simulations, and by using Equation 3.3. 𝑅𝑆 = 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 = ∑ (∆𝑡 ∗ 𝑃𝑜𝑤𝑒𝑟𝑖 ∗ 𝑃𝑟𝑖𝑐𝑒𝑖)8760𝑖=1 (3.3) The system Revenue, or RS, is the value desired, and it is to be compared between different simulations on an annualized basis. The Δt is the time step, which in this case is 1 hour, and corresponds to the smallest available time step in the raw data for both energy prices and meteorological data input into the PV system simulations. This is also why i varies from 1 to 8,760, as that corresponds to the number of total hours in a typical year. The Power is the 36 instantaneous power produced consistently over the given time period by the system as modeled by the simulation, and has units of W. The Price is the value paid for a generator’s grid-connected energy by the Alberta Power Pool (APP), and has units of $/Wh. Figure 3-7 Diagram showing the process taken to simulate the annual energy and revenue of multiple array orientations for each different type of system design. The online version of this figure is in colour. 37 3.3 Simulation parameters This discussion on the simulation parameters is divided into four components; software specifics, meteorological data, specifics to the Alberta electricity market, and detailed variable selections. These sections are presented sequentially. 3.3.1 Software There are many different type of software tools presently available to simulate and model PV systems. After a review of the current commercial and research-based offerings it was decided to go with PVSyst. The decision was based on several years of industrial experience simulating systems in real-world applications. It is a tool which offers a wide range of parameter inputs, the ability to do batch simulations with a single type of variable input, and proven algorithms for the different portions of the system needing to be mechanically and electrically modeled [44]. It also includes very comprehensive calculations on every important loss a PV system experiences. An example of the loss diagram used in Simulation 1 is shown in Figure 3-8. It is the de-facto leader when it comes to model accuracy as evidenced by multiple personal experiences, where lenders would only provide financial backing if they were satisfied with the modeled results performed specifically by a PVSyst simulation for the particular solar asset in question. 38 Figure 3-8 Typical loss diagram for an example system taken from PVSyst simulation software. 3.3.2 Meteorological data There are several major components of meteorological data for a specific location which are needed by simulation software in order to effectively model the electrical output of a PV module. These values are the global direct radiation, the global diffuse radiation, and the temperature [43]. These values are shown for the city of Edmonton, AB over varying timescales in Figure 3-9, with these particular values taken from a Canada Environment Weather station in nearby Stony Plain, AB [45]. The values for the three major types of data needed are not always available for the timescales required for simulations, and sometimes daily or monthly data needs to be converted into hourly data. A full discussion on how this is accomplished is beyond the scope of this research, however, there is scientific software which can perform these estimations, and for this application a particular package called Meteonorm was used. For the purposes of this research there were three different meteorological datasets used all with data points measured in close proximity to Edmonton. These are all based on historical weather station data from Environment Canada. 39 Figure 3-9 Historical meteorological data for Edmonton: a) Monthly total radiation, b) Daily global radiation, c) Monthly temperature, d) Daily maximum and minumum temperatures The online version of this figure is in colour. 3.3.3 Specifics to the Alberta electricity market Figure 1-4 shows the average pricing of the APP. These values were tabulated by taking the past ten years of hourly data, which are listed in $/MWh and directly from the AESO website [26]. The minimum system size to be paid the marginal price of Alberta’s wholesale electricity market is 150 kW [46]. For this reason, the average size for the complete system models needed to be larger than this. After some experimenting, 189 kW was chosen due to it being a size which lended itself well to multiple technology types having the same size, resulting in a close to square footprint, and also was a reasonably sized minimum building block to be used for simulating larger multi-MW sized systems. 3.3.4 Detailed variable selections In Table 3-1, a detailed list of all the parameters and variables for each of the 44 simulations performed for the purposes of this thesis is presented. For each simulation, one parameter was changed, which is indicated by the cell being highlighted. For Simulation 1 and Simulation 11 the 40 simulation number is highlighted to show that for these are the base cases for the two major types of architectures being simulated: a single row of modules, and a full multi-row system. This will be further discussed in Chapter 4. For some simulations, several variables were changed to quantities which had been used in previous simulations (Simulation 25 and Simulation 35). This was to see if stacking up several changed variables would have a non-linear outcome on the results. This proved not to be the case. It is also worth noting that more single crystal and multicrystalline modules were simulated due to their much greater market share. 41 Table 3-1 Parameters and variables for each simulation. The online version of this table is in colour. 42 (Continued…) 43 44 (Continued…) 45 Chapter 4: Results & Discussion Solar energy offers an energy source of virtually unlimited and untapped potential. PV technology operates quietly and efficiently, providing a renewable energy source to all who subscribe. It has been recognized as one of the most promising energy sources for the future, and developments in PV technology will undoubtedly dominate technological innovation for many years to come. In this chapter, the simulation results are presented. For the purposes of this analysis, these simulations correspond to a sampling over the technological selections and design approaches in play in the field today. A variety of operating conditions are also considered. The goal is to identify opportunities for the optimization of PV system performance. The robustness of the results to changing the input data, i.e., the meteorological and electricity price data, is also examined. The conclusions of this analysis are presented in the subsequent chapter. This chapter is organized in the following manner. In Section 4.1, the general approach taken to interpret the results is featured. Then, in Section 4.2, a discussion of the statistical variance is detailed. Finally, in Section 4.3, the individual results are presented, which is followed by the aggregate results in Section 4.4. 4.1 General approach The goal of this research is to provide some baseline results for deciding whether or not to adjust the installed orientation of a PV system in order to maximize the financial yield of such a system, as opposed to maximizing the energy yield. We do this by providing a sequence of baseline results in a jurisdiction where the price of energy varies with market conditions. For our particular case, data from Edmonton, AB is selected. This analysis is performed across a wide range of different system configurations, commonly used in real-world designs, with each 46 subsequent simulation changing one parameter at a time. In addition, the robustness of the results to financial and meteorological data variations is also examined, to further understand if there are any significant changes in the overall results due to the vagaries of the variables involved. Each of the configurations are slightly different in terms of components and design, and they did not all have the exact same overall installed DC module wattage, known as nameplate capacity. It was not possible to make each simulation setup to have the same nameplate capacity due to the inherent nature of PV system design. For example, when changing from c-Si to CdTe modules, the VOC will be different, resulting in a different number of modules per string, and this typically results in a slightly different nameplate capacity. All of the simulated systems which were directly compared were very close in size, however it would not be meaningful to compare the exact energy output for systems with different nameplate capacities. Therefore, the specific yield was calculated for each simulation, both for the energy produced and for the revenue generated. This is a common practice in industry used to compare systems and is achieved by dividing the energy and the revenue values by the nameplate capacity in order to have a figure in terms of kWh/kWp, i.e., a system with an annual energy yield of 5,000 kWh and a size of 4 kWp would have a specific energy yield of 1,250 kWh/kWp. This value is then used to be able to accurately compare results across systems with slightly different sizes. Details can be seen in the numerical results, which are tabulated in Error! Reference source not found.. For purposes of this research, the results needed to be further normalized, as each different type of system configuration/design will produce different values for the specific energy yield, due to some technologies and designs being more effective for the specific meteorological conditions. We needed to evaluate and display the increase in specific annual yield each system would experience irrespective of its comparative performance with other systems, and therefore, the 47 absolute values in the graphs were normalized so that the maxima were at unity. Absolute results are also depicted. 4.2 Statistical Variance For the purpose of this analysis, there are several variables which will impact the variance of the results. For the calculation of the annual generation, the variance will be dominated by the meteorological year-to-year variability. Additional uncertainties include PV modules and parameters, long-term degradation, and soiling. These deviations represent random variability of the uncertainty from year-to-year, not the absolute uncertainty, and therefore will not be included. The meteorological data variability is dependent on the input data. If data for a specific year is used, then there is no variability since it represents the exact conditions for that specific year. If the data is an average over several years then it assumed to follow a Gaussian (or "normal") distribution. In this study, there were 3 different sets of meteorological conditions used. Each of these sets are representative of data over several years. Unfortunately, the variability of the years these datasets were taken from is not available. This is common for such datasets, but to compensate as such, it has been shown that the variability is dependent on the annual irradiance [47]. However, such an analysis is beyond the scope of this research, therefore, the variance for this term is assumed to be zero. 4.3 Individual Results We start the individual analysis with the consideration of the conditions in Simulation 1, which are outlined in Table 4-1. 48 Table 4-1 Conditions for Simulation 1 Simulation # 1 size (nominal) 14 kWp module type / brand polycrystalline silicon / generic module size 250 Wp # of panels 56 inverter type / brand string / generic inverter size 12 kW # of inverters 1 # of strings 4 panels per string 14 overbuild 1.17 row spacing & civil setup single row / no shading ground coverage ratio N/A string setup N/A additional differences none AESO electricity price data 10 year hourly average simulated azimuth range 0° – 39° simulated tilt range 35° – 54° For this initial simulation, it was important to set a baseline, from which one can then change parameters in the subsequent simulations and look for any differences in the results. Therefore, the most generic and typical design parameters were used for this baseline case. This includes the module size and type, the inverter size and type, and the string sizing and resulting overbuild. It was chosen to only use a single inverter as to make this system the smallest possible unit of a larger system. It was also chosen to orient the modules in a single-row with no inter-row shading, as would typically be experienced in a real-world commercial installation. This is also to set a baseline and see if once the system grows in size, and includes multiple rows, if there will be any changes in the results. This single-row setup means that GCR and string setup are also not applicable. This unshaded, single-row setup is used on the first ten simulations. The electricity 49 price data chosen as a baseline is the hourly rate averaged over 10 years from the AESO online database. With the system designed, and all the parameters input into the modelling software, the simulation is then run for a range of tilt and azimuth values, which are incremented by 1° for each computation. In order to lower computational time, a range of values were chosen in which the two maxima were expected to land. This is very important, as these simulations can take quite some time. This is especially true once the software is required to calculate the inter-row shading effects of the larger arrays in Simulations 11 through 44, as well as additional design features included in some configurations, such as string voltage optimizers. The time on a single CPU for each simulation ranged from 45 minutes to 8.5 hours. For this initial simulation, a tilt range of 35-54 degrees and azimuth range of 0-39 degrees were chosen. The numerical results are shown in Table 4-2, and provide the tilt and azimuth configurations, where both the annual energy and annual revenue are maximized. The maximum energy is achieved at a configuration which is expected based on common design principles for the northern hemisphere, namely facing the modules due south and tilting at latitude. The values of 3° and 51° are very close to the 0° of due South and Edmonton’s latitude of 53°. The maximum revenue is achieved at values of 15° and 46°. It can be seen that there is roughly a 1% gain when optimizing orientation for annual revenue. This revenue improvement may seem insignificant, however, since it is realized with an effective zero increase in installation cost, it becomes a very attractive and important gain to the overall value of the PV system. The azimuth being 15° West of due South was expected, based on the initial hypothesis that facing the modules in a more westerly orientation would capture additional sunlight in the evening hours. This is when electricity typically has a higher value, as shown by looking at Figure 1-4 and seeing that the 50 highest prices paid for electricity are generally in the afternoons of the summer months, which can be attributed to the air conditioning load during this time. The ideal tilt being about 5° lower than normal was not initially predicted, however, it can be explained through the fact that this tilt angle provides more direct incident solar radiation during the early afternoon hours in the summer when energy prices are high. Table 4-2 Tabular results for Simulation 1 Base Case - Maximum Energy Tilt for Maximum Energy 51° Azimuth for Maximum Energy 3° Annual Revenue $1,815 Annual Energy 22,840 kWh Optimized Case - Maximum Revenue Tilt for Maximum Revenue 47° Azimuth for Maximum Revenue 15° Annual Revenue $1,835 Annual Energy 22,560 kWh Comparison Base Case Specific Energy Yield 1631 kWh/kWp Optimized Case Specific Energy Yield 1611 kWh/kWp Percentage Change in Specific Energy Yield -1.241% Base Case Specific Revenue Yield 129.6 $/kWp Optimized Case Specific Revenue Yield 131.0714286 Percentage Change in Specific Revenue Yield 1.102% The results for Simulation 1 are further analyzed in Figure 4-1, which presents the sensitivity of both maxima, via contour lines for the annual revenue and a colour map for the annual energy. An important feature to notice on the graph is that there is not a significantly large change in the maximum energy or the maximum revenue with changing tilt and azimuth. This is shown by both the contour lines and colour fields having a very small scale as they drop off from the maxima. 51 Figure 4-1 Sensitivity of normalized annual revenue (contour lines) and normalized annual energy output (colour map) with respect to tilt and azimuth for Simulation 1. The online version of this figure is in colour. Simulations 1 through 10 were performed on single rows of modules without any shading. This was to establish a baseline and to see how RS and EG vary with the presence of inter-row shading in Simulations 11 through 44. The results of Simulations 1 through 10 are displayed in Figure 4-2. These figures display RS with respect to array tilt (β) and surface azimuth (γ) using a coloured heat map, and on the same axis they also display EG with respect to β and γ using a contour map. This allows one to see where the two maxima lie in the range of β and γ values. It can be seen for certain configurations, that before multiplying by the hourly price of energy, the optimally calculated azimuth is slightly eastward. This is likely due to there being less morning cloud cover than evening cloud cover on average, which is purely a function of the specifics of the local meteorological conditions. 52 The range of tilt for ideal orientation is tighter than the range of azimuth. This is likely due to the fact that the tilt plays a role during the part of the insolation period which has higher intensity, such as during the middle of the day. The azimuth plays more of a role during the time periods of lower insolation intensity. In the evening, there is more atmosphere to go through, which lowers the overall intensity of the solar insolation incident on the solar module, so therefore the azimuth is not as sensitive. In Figure 4-2, it can be seen that Simulations 2-10 produce orientation results very similar in response to varying input parameters, which each coincide with different PV system engineering design choices. The parameters that are varied in these simulations include whether mono-crystalline, poly-crystalline, or thin-film technology is used, as well as the number of modules per relative inverter string. Irrespective of these parameters a single, unshaded string of modules is used for this portion of the analysis. From these results, we can infer that for a primarily unshaded system, like the type typically found on a residential home, the optimum orientation for maximizing annual revenue does not depend heavily on the system design parameters. This is demonstrated by varying the primary parameters available to system design engineers: such as module technology, inverter type, physical layout, and electrical design topologies, including string layout, MPPT options, and internal module wiring. It should be noted that the single-row unshaded parameters in Simulations 1 through 10 are simulated to register a baseline, which then can be compared with the multi-row array results in Simulations 11 through 44. This provides an additional metric to interpret any cause/effect relationships. 53 Figure 4-2 Sensitivity of normalized annual revenue (contour lines) and normalized annual energy generation (colour map) with respect to tilt and azimuth for Simulations 1 – 10. One major input parameter is changed for each successive simulation to determine sensitivity of the results to system design. Details of individual simulation input parameters outlined in Table 3-1. 54 The results corresponding to Simulation 11, the baseline multi-row simulation, are presented graphically in Figure 4-3. In this case, and for all subsequent simulations, the graphic results are depicted as a 3d surface graph of the revenue, overlayed with a colour map of the energy. This method was used in order to depict the additional ‘roughness’ of the results when inter-row shading is included in the parameters. This is likely due to the effects of the shading of a single panel in a string lowering the overall current of the entire string for certain angles. This effect is can be mitigated through power conditioning methods such as string optimizers. This explains why the surface map of Simulation 26, which is simulated using such technology, is generally smoother than the others. The rest of the simulations performed for the purposes of this analysis are presented in Figures 4-4 to 4-36. The parameters that are varied in these simulations include the number of panels considered, and whether mono-crystalline silicon, poly-crystalline silicon, micro-crystalline/amorphous silicon, cadmium telluride, or copper indium gallium selenide technology is used. A variety of string designs and common electrical layouts, both physical and hardware-related, are also used for the purposes of this analysis. It is noted that for all cases the optimized revenue is only marginally better than that derived purely from sub-optimal tilt and azimuth angle selection. The details, corresponding to each simulation, are described in the corresponding figure captions. 55 Figure 4-3 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 11. Parameters: 250W Monocrystalline modules, 12kW string inverters, 189kWp DC, 1.13 DC/AC ratio, 10 year average hourly energy prices, 2x28 rows, square layout, strings wired to horizontally adjacent modules, front row is a single row with modules on bottom of rack, 1 MPPT per inverter, submodules in length, 50% GCR. The online version of this figure is in colour. 56 Figure 4-4 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 12. Parameters: Same as Simulation 11, but with 40% GCR. The online version of this figure is in colour. 57 Figure 4-5 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 13. Parameters: Same as Simulation 11, but with 30% GCR. The online version of this figure is in colour. 58 Figure 4-6 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 14. Parameters: Same as Simulation 11, but with 20% GCR. The online version of this figure is in colour. 59 Figure 4-7 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 15. Parameters: Same as Simulation 11, but with 60% GCR. The online version of this figure is in colour. 60 Figure 4-8 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 16. Parameters: Same as Simulation 11, but with 70% GCR. The online version of this figure is in colour. 61 Figure 4-9 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 17. Parameters: Same as Simulation 11, but with 80% GCR. The online version of this figure is in colour. 62 Figure 4-10 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 18. Parameters: Same as Simulation 11, but with energy prices taken as a 3 year average between 2014-2016. The online version of this figure is in colour. 63 Figure 4-11 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 19. Parameters: Same as Simulation 11, but with energy prices taken from 2017. The online version of this figure is in colour. 64 Figure 4-12 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 20. Parameters: Same as Simulation 11, but with submodules wired in width instead of length. The online version of this figure is in colour. 65 Figure 4-13 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 21. Parameters: Same as Simulation 11, but with strings aligned to vertically adjacent modules, and with the front single row being mounted at the top of the rack. The online version of this figure is in colour. 66 Figure 4-14 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 22. Parameters: Same as Simulation 21, but with submodules wired in width instead of length. The online version of this figure is in colour. 67 Figure 4-15 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 23. Parameters: Same as Simulation 22, but with 2 MPPTs per inverter. The online version of this figure is in colour. 68 Figure 4-16 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 24. Parameters: Same as Simulation 11, but with 250W Polycrystalline modules, and with the front single row being mounted at the top of the rack. The online version of this figure is in colour. 69 Figure 4-17 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 25. Parameters: Same as Simulation 22, but with 250W Polycrystalline modules, 40% GCR, and with energy prices taken as a 3 year average between 2014-2016. The online version of this figure is in colour. 70 Figure 4-18 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 26. Parameters: Same as Simulation 11, but with each string having a 260W AMPT string optimizer, and with the front single row being mounted at the top of the rack. The online version of this figure is in colour. 71 Figure 4-19 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 27. Parameters: Same as Simulation 11, but with 110W CdTe First Solar modules, and with the front single row being mounted at the top of the rack. The online version of this figure is in colour. 72 Figure 4-20 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 28. Parameters: Same as Simulation 11, but with meteorological data being changed to Environment Canada EPW data for Stony Plain, AB, and with the front single row being mounted at the top of the rack. The online version of this figure is in colour. 73 Figure 4-21 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 29. Parameters: Same as Simulation 27, but with 165W CIS Solar Frontier modules. The online version of this figure is in colour. 74 Figure 4-22 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 30. Parameters: Same as Simulation 11, but with 0.21kW enPhase microinverters, and with the front single row being mounted at the top of the rack. The online version of this figure is in colour. 75 Figure 4-23 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 31. Parameters: Same as Simulation 11, but with meteorological data changed to Meteonorm synthetic hourly data. The online version of this figure is in colour. 76 Figure 4-24 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 32. Parameters: Same as Simulation 29, but with meteorological data changed to Meteonorm synthetic hourly data. The online version of this figure is in colour. 77 Figure 4-25 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 33. Parameters: Same as Simulation 12, but with energy prices taken from 2017. The online version of this figure is in colour. 78 Figure 4-26 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 34. Parameters: Same as Simulation 12, but with energy prices taken from 2016. The online version of this figure is in colour. 79 Figure 4-27 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 35. Parameters: Same as Simulation 12, but with energy prices taken from 2015. The online version of this figure is in colour. 80 Figure 4-28 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 36. Parameters: Same as Simulation 12, but with energy prices taken from 2014. The online version of this figure is in colour. 81 Figure 4-29 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 37. Parameters: Same as Simulation 12, but with energy prices taken from 2013. The online version of this figure is in colour. 82 Figure 4-30 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 38. Parameters: Same as Simulation 12, but with energy prices taken from 2012. The online version of this figure is in colour. 83 Figure 4-31 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 39. Parameters: Same as Simulation 12, but with energy prices taken from 2011. The online version of this figure is in colour. 84 Figure 4-32 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 40. Parameters: Same as Simulation 12, but with energy prices taken from 2010. The online version of this figure is in colour. 85 Figure 4-33 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 41. Parameters: Same as Simulation 12, but with energy prices taken from 2009. The online version of this figure is in colour. 86 Figure 4-34 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 41. Parameters: Same as Simulation 12, but with energy prices taken from 2008. The online version of this figure is in colour. 87 Figure 4-35 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 44. Parameters: Same as Simulation 12, but with energy prices taken from 2007. The online version of this figure is in colour. 88 Figure 4-36 Sensitivity of normalized annual revenue (z-values) and normalized annual energy generation (colourmap) with respect to array tilt (β) and surface azimuth (γ) for Simulation 44. Parameters: Same as Simulation 12, but with a 174kW Solargate central inverter, which also results in a slightly different overbuild of 1.09. The online version of this figure is in colour. 89 4.4 Aggregate Results The raw data of all the simulation results is presented in Table 4-3. The simulation numbers in the table can be cross-referenced with the parameters listed in Table 3-1. The table is split into two sections, showing both the values optimized for energy output, and those optimized for revenue generated. It also calculates the percentage increase, or decrease, in both energy and revenue for each simulation. 90 Table 4-3 Tabular results of all simulations. The online version of this table is in colour. 91 (Continued…) 92 It is instructive to examine a plot of the optimum azimuth and tilt angles for both maximum energy and maximum revenue for all the simulations performed. In Figure 4-37 we see that the optimal tilt for the system does not significantly change for the two maxima. However, it can be seen, that for the maximum revenue, the azimuthal angle is generally between 14°-18° westward of due south. This corresponds well with the hypothesis that the generally higher price of electricity in the evenings can be harnessed to an overall financial advantage by facing the modules westward. Figure 4-37 Aggregated optimum orientation results of all simulations performed. Points are placed at orientations where maximum annual revenue is generated and orientations where maximum annual energy is generated. To further show that the hypothesis presented in this study is valid, we can view the frequency distribution and relevant statistical analysis values of the annual revenue gains realized across all the simulations performed, which is presented in Figure 4-38. 0102030405060-5 0 5 10 15 20 25Tilt (°)Azimuth (°)Maximum Energy Maximimum Revenue93 Count Mean Median Standard Deviation 44 0.94% 1.09% 0.47% Figure 4-38 Histogram, mean, median, and standard deviation of the revenue gains realized for all simulations performed. 0123456780 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2FrequencyAnnual Revenue Gain (%)94 Chapter 5: Conclusions & Recommendations for Further Study Solar energy provides opportunities to supply virtually unlimited amounts of power without any significant environmental consequences that typically accompany fossil fuel based power sources. At present, the potential of solar energy has been recognized and many new solar opportunities are currently being developed. It seems likely that this trend will continue many years in the future. In this thesis, we have examined whether or not an economic advantage can be accrued through the placement of solar modules. In particular, we examined how the revenues derived from a solar operation are sensitive to the azimuthal and tilt angles. These results are presented to provide best practice guidelines in order to influence the design of subsequent PV systems with similar input parameters. That being said, they should be interpreted with appropriate engineering judgement for each individual situation. 5.1 Advantages and Limitations The work presented has the advantage of being for a wide spectrum of different PV system designs and technologies. When designing a system to take advantage of the additional annual revenue available through turning modules westward, a real-world designer would have confidence that it this advantage that will manifest itself irrespective of the specific design or technology used. It is found that the best economic advantage for the Alberta energy jurisdiction is obtained using an azimuthal angle of roughly 15° West of due South, and a tilt angle that depends on the particular situation involved. This on average provides the operator with roughly a 1% economic advantage with essentially zero increase in cost. For large scale solar farm operations, this advantage could yield substantial economic benefit. 95 A limiting factor that should be noted is that the data presented is based on historical electricity price values. There is no explicit guarantee that these values will follow similar price patterns in future years owing to changing economic realities. 5.2 Possible Future Work There are several avenues which could be pursued with respect to performing additional research on this topic. One of the most obvious tasks would be to validate the findings by performing a field study. It would be much too tedious to take measurements for each azimuth and tilt angle, however, two identical systems could be constructed with angles set to the positions which achieve the maximum revenue and maximum energy and then the results obtained could be compared to the simulation results. These could then be repeated for differently configured systems. To further refine and take advantage of the results, it would make sense to develop and algorithm, or software add-on, which would streamline the simulation process outlined in this thesis. 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Thesis/Dissertation
eng
Electrical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Optimization of utility scale photovoltaic orientation parameters for deregulated energy markets in northern latitudes
Text