Essay: Optimal grid-connected PV system and benefits

1. INTRODUCTION AND BACKGROUND OF THE STUDY
Introduction
A grid-connected PV systems generates electricity from the energy that is made available by the sun, and the electricity is then converted into grid-compliant AC by an inverter. The process of PV electrical generation itself is entirely pollution-free but the manufacturing and system setup of PV arrays will impose some environmental cost. An increase in portion of renewable energy (RE) contribution in the national grid is beneficial to both economics and politics, thus reducing the nation dependency on conventional fossil fuels will lessen the fossil economic effects to the nation.
Background
Residential and commercial buildings account for about thirty-three percent of the total global carbon emissions. Considering the massive development in new construction in economies in transition, and the inefficiencies of the current building stock worldwide, if nothing is done, carbon footprint from buildings will increase significantly in the next 20 years. Therefore, if targets for the carbon emission reduction are to be met, it is obvious that decision-makers should cope with the emissions from the building sector. Reduction of carbon footprint from buildings must be a base of every national climate change strategy. It must be noted that the generation of electricity itself is the main cause of carbon emissions, unless it comes from renewable energy sources such as wind or solar power plants. (Ataei et al., 2015)
The City of Windhoek’s energy demand has grown significantly and continues to even grow at a more accelerated rate due to the large number of people that flock from the villages to the city in search for green pastures. This increases the burden on the national grid, pressuring the country to import more its electrical energy from the Southern African Power Pool (SAPP). This brings about increased energy security risks for the country, resulting from the current electrical energy generation constraints in South Africa, coupled with rising electricity prices and infrastructure constraints. Hence, the increasing price of electricity, in turn may negatively affect the local industrial, commercial and domestic consumers.
The grid-tied PV systems have become a favored method of reducing the carbon footprint and the burden on the national grid at residential and commercial buildings. Maerua Mall and the Grove Mall of Namibia are exemplary cases, which have demonstrated that the installation of large rooftop PV arrays reduces the burden on the national grid by reducing their own daytime consumption. Therefore, meeting the increasing electricity demand in a sustainable and cost-effective way and decreasing the dependency on power imports from SAPP. However, one of the key challenges towards mass adoption of this technology, which is proposed at Goreangab (Windhoek), is the high investment cost and the uncertainty about whether the systems are economically viable. The question of economic viability quickly becomes complex: The acquisition of necessary information to determine the economic feasibility is but the first challenge. With that knowledge at hand, a simulation software, HOMER, can then be applied to predictively simulate the most economically sensible grid-connected PV system. It is at this simulation attempt that this research seeks to answer the question: What is the optimal grid-connected PV system rating to be installed at Goreangab (Windhoek) to ensure maximum economic viability?
Investigating this question provides a multitude of key questions to be answered, questions such as: What are the economic and environmental benefits of installing a grid-connected PV system as compared to the current system, which is a grid-only system? What is the effect of feed-in tariffs, whereby the investors will be remunerated for feeding their surplus electricity back into the grid? What is the payback period of investing in grid-connected PV system?
Statement of the problem
1. The City of Windhoek faces a high energy demand due to influx of people into the city, causing an increased burden on the national grid.
2. Namibia faces energy challenges due to its dependence on importing the bulk of its electricity, over 50%, from the Southern African Power Pool (SAPP). This brings about increased energy security risks for the country.
3. The increasing price of energy consumed from the grid is negatively affecting the local consumers, especially those with low incomes.
4. Since the majority of the imported electricity is generated from fossil fuels. This increases the carbon footprint emission, which may cause environment problems.
Research questions
In the light of the problem statements above, the research questions are as follows:
1. What is the optimal grid-connected PV system rating to be installed at Goreangab (Windhoek) to ensure maximum economic viability?
2. What are the economic and environmental benefits of installing a grid-connected system as compared to the current system, which is a grid-only system?
3. What is the effect of feed-in tariffs, investors will be remunerated for feeding their surplus electricity back into the grid?
4. What is the payback period of investing in grid-connected PV system?
Significance of the study
The findings of the study are envisaged to boost the confidence of investors about the growth of renewable energy in Namibia. The study will further assist the government in attaining its goals and targets of the national renewable energy policy.
Limitation of study
Some of the challenges that the researcher may face include: firstly, finding the data to calculate the load profile of the households in the study area, this is because the study area has a large population, hence getting a significant sample size from the population may be quite challenging.
Another challenge is the researcher’s lack of experience in using the simulation software tool. Hence the researcher might either need to broaden his knowledge on the software tool or may need get assistance from an experienced user.
Delimitation of study
The research study is only bound to the study area, which is Goreangab (Windhoek). It is also worth mentioning that the analysis of the grid-connected PV system has no physical energy storage element, but it will utilize the grid utility as the virtual energy storage where the system distributes the extra generated electricity and consumers are compensated in terms of reduced electricity charge. Lastly, the effects of electricity tariffs having the possibility of being time-dependant when determining the load profile of the study will be ignored for the sake of simplicity.
Research aims
In this research, the study will aim to determine the feasibility of the solar grid-tied system in Goreangab (Windhoek) via a simulation software known as HOMER and its financial performance will be compared with the current system, which is grid-only system, based on the cost of electricity, electricity production and consumption, and the emission of pollutant. In addition, the payback period of the grid-tied PV system is also determined.
2. LITERATURE REVIEW
Overview
The literature study presents previous work done on the subject of this research study, as well presenting research on different fields from which features will be implemented in this research proposal.
Previous works
A review of previous work in the field is presented. The focus of each related research project is discussed. The similarities and shortcomings of the research relevant to this research study are discussed. Notable points relevant to this research study are mentioned.
Grid-connected PV (GCPV) systems may be classified into two types: with battery-bank storage or without, the first having the advantage of supplying power to critical loads when the utility grid is facing outages. Both systems utilize effectively the inverters which are crucial elements that cannot be neglected. Most inverters in grid-connected applications carry a very high efficiency, in order to maintain stability with the network. The point of connection between the utility and PV-generator may have minor distortion which could lead to significant effects. These distortions could either be caused by the girds instability, or the PV output fluctuation and/or losses (Ibrahim et al., 2014). The function of the inverter is not only convert AC to DC power but also to synchronize the PV output in frequency and phase with the grid. Finally, protection is also a function that inverters are in charge of separating the PV panels from the utility grid, with the help of fuse switches on both sides, and disconnecting whenever high levels of instability occurs (Kaundinya et al., 2009). Also, in case of frequent shortages in the utility, a hybrid standalone-grid-connected system should be used. This is to ensure continuous supply to the load after the shortage occurs.
Another classification could be a utility interactive grid-connected systems (Shalwala & Bleijs, 2009). In this case, excess energy is sold to the utility, but if the load is unmet then the utility will feed in the system. The grid-connected system may be incapable to satisfy the entire load demand of the application that it is intended to serve, in that case it could be utilized to offset the electricity costs only. This could be very beneficial to reducing the costs of operating the applications (Verma et al., 2011). Having this system could also help reduce the bills by avoiding the utility charge during peak demand hours. Dual metering in such systems is important so as to track the performance of the GCPV system. GCPV system could be set in a plant setup or building integrated. The later, have the advantage of saving space and its costs not because it does not include the structure to mount the PV roof. As for plant mode, more costs could be required from the investors. Figure 1 and 2 show simple block diagrams of GCPV systems with and without battery storage, respectively.
Figure 1 GCPV system with battery storage
Figure 2 GCPV system without battery storage
Eltawil & Zhao (2010), conducted a research to establish higher performance ratio for GCPV systems along with factors such as overall efficiency, life cycle costs, cost of energy, energy payback period etc. Investigations were performed on the potential problems in high penetration levels and islanding prevention methods of grid-connected PV systems. The idea is to provide critical review to illustrate the importance of such systems. More focus given to the conversion efficiency and total harmonic distortion. The review highlighted the conversion efficiency which exceeds 90% and maintain a total harmonics distortion (THD) of harmonics less than 5% over the literature. The authors recommended the use of inverters at unity power factor, then concluded with presenting issues to be addressed such as excessive over-sizing of PV generator to inverters. Authors claim that islanding does not form a technical barrier to large-scale applications in PV systems.
Celik (2006), performed a techno-economic assessment of grid-connected PV system in Turkey. The system is considered interactive with the grid and with battery storage. A presentation of weather data analysis was provided and different configurations of this system were considered, i.e. battery removed from the system. The author used two different sizing approaches. First one, was sized so that energy production and energy demand were equal annually. While the second assumed the equality to occur in the worst month of the year. The study concluded with cost of electricity appearing to be higher per kWh for the GCPV system by 3–4 times more than utility electricity. The author attributes such results to high taxes and governmental subsidies on electricity. Sidrach & López (1998), performed an evaluation of GCPV systems in Spain for a 2 kWp installed system. The average daily supply to the grid was found to be 7.4 kWh, while a 4.1–8% of monthly average value of system efficiency was achieved. The system uses a single-phase inverter for grid-connection which serves the purpose of tracking the maximum power point of PV. The study concludes that inverter efficiency and threshold are very important in such system. More waste of energy occurs due to exceeding that threshold where sometimes an entire day could be wasted.
Al-Sabounchi et al. (2013), designed and performed performance evaluation of a GCPV system in UAE under hot weather condition for a period of one year. Evaluations extends to power production, energy, conversion efficiency and effect of ambient temperature. Dust deposition caused a significant decrease in the performance, which is a major concern for PV implementation in desert zones. The authors highlight the importance of dealing with the impact of dust on the PV panels, which causes the highest reduction to be reached in July, along with effect of high temperature and humidity, which reached around 27%. Tsang & Chan (2013), proposed a novel design of a control scheme of a three-level GC inverter with a maximum power point tracking (MPPT) function which uses four power switches and a simple integral controller for overall system, while for the inverter it uses current and voltage loop controllers. The study develops this design, then test its performance with a PV simulator to validate its effectiveness. However, it did not implement it in an installed GCPV system or mention the efficiency.
Sulaiman et al. (2012), presented a novel sizing methodology for GCPV system with different types of PV and inverters to be considered. The design was carried through EP program and aimed to either optimize the technical or economic aspects. The use of an interactive sizing algorithm ISA presented an issue which was time. Therefore, authors developed EPSA to solve that problem. Two cases were investigated with one being made to maximize yield factor YF for technical improvements, and the other to maximize Net Present Value (NPV) for economic improvement.
Beser (2010), presented a single-phase multilevel inverter for GCPV system. The system was experimentally tested for validation and it showed a low THD on output voltage and current of load. While Kim et al. (2009), provided an evaluation of GCPV system types, operator state and PV performance for two systems which are installed in Daegu Metropolitan City. Many factors were taken into consideration such as ambient temperature, plane horizontal irradiance and tilt angle. The study performs power efficiency calculation and compares the two systems in terms of output on monthly basis. Annual power generation efficiency was around 10.8% for each system. 80% of the total costs came from the PV and inverter.
Mondol et al. (2007), performed simulation to predict GCPV system performance using TRNSYS software then conducted experimental testing to validate and/or compare the results. The study aims to explore the simulation accuracy with real data. The simulation results were found to be more valid in summer than it was in winter. To ensure accuracy of simulation, the authors developed a global diffuse correlation module that is location-specific. Al-Hasan et al. (2004), performed evaluation of a GCPV system in Kuwait and optimized the electrical load pattern. The authors accounted for weather data and load analysis. From the study, the GCPV systems caused reduction in electricity peak demand load.
Ramli et al. (2014), investigated the optimal PV, inverter and PV/inverter sizes for a grid-connected PV system in Makkah, Saudi Arabia by using HOMER as a software tool. Net present cost, renewable electricity fraction, excess electricity, and carbon emissions were the major key performance parameters that have been considered in determining the optimal system configuration. The optimum PV array and inverter sizes for a GCPV system have been obtained with the unmet load and excess electricity percentages as the main constraints. An optimum system configuration, with unmet load and excess electricity of 0% for serving electricity in Makkah city with a peak load of 2200 MW, is obtained for 2200 MW PV size and 2200 MW inverter size, i.e. for the ratio of R=1.00. However, the inverter size can be lower than 2200 MW if the NPC is the main factor to be taken into account when selecting the system for implementation.
Kazem et al. (2017), performed a design and techno-economic evaluation of a Grid-connected PV system in Adam city, Oman with a size of 1 MW. The numerical simulation was made using MATLAB developed code. The optimum array size was found to be 250 W p with around 4000 modules to satisfy the 1 MW. While, the optimum inverter size is about 800 kW. The system had a payback period of 10 years and a CoE of 0.2258 USD/kW h. The system was feasible and showed great promise for the city of Adam.
Authors Location Year PV size (kW) Battery storage (with/without) Inverter efficiency (%) CoE (USD/kWh)
Sidrach & López Spain 1998 2.000 Without
Al-Ismaily & Probert Oman 1998 1.200-3.000 Without 0.210
Lazard Atlanta 2000 1.000 Without 0.412
2001 0.412
2002 0.373
Al-Hasan et al. Kuwait 2004 1000-6000 Without –
Celik Turkey 2006 2.000 With 95-98 0.440
3.034 With 0.500
2.000 Without 0.710
3.034 Without 0.950
Mondol et al. Northern Ireland 2007 13.000 Without 80-90 –
Kim et al. Korea 2009 2.000 With 92 0.824
2.000 With 91 0.531
Beser et al. Turkey 2010 1.000 Without 97-99 –
Al-Badi et al. Oman 2011 5.000 Without 0.210
Bouzguenda Oman 2012 1.000 With 0.304
Al-Badi et al. Oman 2012 43.000 With 0.327
Sulaiman et al. Malaysia 2012 5.040 Without –
1.936 Without –
Tsang & Chan China 2013 – Without –
Al-Sabounchi et al. UAE 2013 36.000 Without –
Ramli et al. Saudi Aribia 2014 2200,000 Without 90 –
Kazem et al. Oman 2017 1000 Without 95 0.2258
Table 1 Summary of literature review: Previous works
From the table above the following critical analysis can be made:
1. The use of numerical simulation to optimize GCPV systems is very important to improve efficiency and reduce costs.
2. Using real time data of the ambient temperature, plane horizontal irradiance and tilt angle gives a more accurate simulation of the overall system.
3. Inverters carry significant effect on the overall system performance and efficiency.
4. Ensuring a better life cycle cost and cost of energy will encourage investment in solar energy.
5. Inverter efficiency is in the range of 90–95%, which is relatively high.
6. The cost of energy (CoE) is in the range of 0.210–0.950 USD/kWh.
Grid connections and tariffs
This section provides research on how residences connect to and interface with the grid. New developments and the introduction of Advanced Metering Infrastructure (AMI), that make more complex tariff structures possible, are researched. The feed-in tariff structure is explored to understand how electricity is exported back to the grid.
Tariffs
Initial exploratory research regarding electricity consumption tariffs date back half a century (Houthakker, 1951). A review of tariff structures done in 2002, Borenstein et al., (2002), considers the implementation of dynamic pricing, as opposed to more traditional methods such as flat tariffs. Borenstein et al.’s report discussed the tariff structure at the hand of the economics of electricity:
“Depending on one’s view, either the most natural or the most extreme approach to price-responsive demand is real-time pricing of electricity…”
The reason for this contrast is discussed here. The grid requires the capacity to handle the highest demand for electricity. Larger grid capacity implies higher capital cost required to construct this capacity. The capital costs of the electric utility need to be paid by the electricity profits; therefore peak demand causes higher electricity costs. This makes real-time pricing ideal, which implements increased tariffs for energy consumption during peak time periods. However, electricity demand changes almost continuously, implying that consumers could possibly have extreme difficulty managing electricity expenses. This necessitates the need for developing suitable tariff structures such as flat tariffs and TOU tariffs.
The policy decisions regarding tariff structures are in some companies addressed through demand-side management (DSM). Demand side management is a type of management that utilities apply to affect load usage patterns to meet objectives such as lowering peak or shifting peaks (Strbac, 2008). DSM addresses grid constraints through various methods, of which one is implementing the correct tariff structure. These objectives of DSM policy implementation are demonstrated in Figure 3. DSM does have the potential to produce side-effects and should be applied correctly.
Figure 3 DSM load manipulation objectives
The previous discussion highlighted some of the challenges and reasons for different tariff structures. A list is now given of collected methods, supplemented by Reiss & White (2005) and Borenstein (2008):
Flat tariffs
Throughout the day, a singular tariff is charged for the electricity
Increasing block pricing
This tariff structure is characterised by the various tiers at which per-unit electricity charge is increased as the consumer passes the specified usage tier.
Time-of-use tariffs
Electricity is priced at various costs, depending on the time of day. The electricity cost is usually highest at time when the electricity consumption peaks, while during off-peak (usually night) an off-peak tariff is present.
Critical peak pricing
For a specified duration of time when the electricity peak is highest, tariffs are hiked to discourage consumers from consuming electricity.
Real-time pricing
Electricity tariffs may be set for a specific hour, half-hour, or moment of the day. Pricing is usually based on the demand for electricity. The tariff is set an agreed-upon time before the tariff is implemented.
The problem of distributing electricity across several income groups lead to apparent problems. To solve the socio-economic problem, block tariffs have been implemented, charging higher tariffs for electricity consumed beyond specified tiers (Herriges & King, 1994).
Feed-in tariffs
Feed-in tariffs (FiT) allow electric utilities to remunerate clients for feeding electricity back into the grid. Countries that show quick adoption of PV has shown to most likely have a FIT policy implemented (Klein et al., 2007). These policies need to be managed closely to ensure sustainable implementation: As part of the implementation, FIT needs to constantly be adjusted downwards to compensate for cheaper PV systems (Prest, 2012).
The metering infrastructure on the grid connection should be able to support the feed-in of electricity. One implementation of net metering is where instantaneous power from the grid is measured and the electricity meter starts rolling back if more electricity is generated than used locally. A second implementation of net metering is where energy feed-in and consumption is measured over periods of time, e.g. over a half-hour or hour period. Policies may, in some cases, only remunerate customers as long as more energy is consumed from the grid than is fed into the grid. Opposed to this policy, in an attempt to drive market adoption of renewable technologies, a policy could be implemented where utilities pay a flat rate for electricity fed into the grid, providing stability for customers that purchase a PV system. Policy design for this mechanism faces issues such as whether energy can accumulate into a state of “credit”, and how long the credit is valid for (Eid et al., 2014). Countries with established FiT schemes have imposed further regulations to create sustainable and fair tariffs for FiT – holding the grid clients accountable for additional fees that contribute to network maintenance cost, etc (Picciariello et al., 2015).
Simulation Software Tool
Various software packages have been developed to calculate the performance for PV systems. Calculating the PV system performance from measured solar data requires refined models, which has been the sole focus of research projects. The software packages in Table 2 each has their own methodology and require specific inputs to calculate the PV system performance.
Software Cost Origin Input Output
PVWatts
Free Developed at NREL Not possible to specify own data. Accepts closest TMY2 weather data file in database.
Monthly or Hourly PV system output.
PVsyst Paid Commercial Product Meteorological data. Monthly yield and detailed system information such as losses.
SAM
Free Developed at NREL A variety of weather data files, including TMY2 and TMY3. Also possible to create and input own TMY3 data. Monthly or hourly yield, system performance, and economic results
e.g. payback times.
SunSim Program not available to the author, software packaged used for University of Cape Town research study
SolarGIS Paid Commercial product offering online access to tools for designing PV systems Meteorological and Solar data provided at a price. PV energy calculator and PV performance assessment tool.
HOMER
Pro Free Originally developed at the NREL, and enhanced and distributed by HOMER Energy Meteorological data, different options for PV systems, cost of electricity, load profile, etc. Can optimise PV plant size, provide economic results.
RETScreen Free Natural Canada
Resources Has a database with weather station data, and can accept satellite data from the NASA meteorology data set. Cost and financial analysis, risk analysis, etc.
Table 2. Software applications to estimate the performance of PV systems
Each software package provides different results and statistical values. In this study, the software package which is of interest is HOMER Pro.
HOMER is primarily an optimisation software package which simulates different RES system configurations and scales them on the basis of net present cost (NPC), which is the total cost of installing and operating the system over its lifetime. Depending on the input data and constrains imposed by the user, HOMER firstly assesses the technical feasibility of the RES system (i.e. whether the system can adequately serve the electrical and thermal loads and any other constraints imposed by the user), and then estimates the system’s NPC. Besides the electric load to attend, the user has to specify the “search space”, i.e., the sizes and/or quantities of the different components of the RES system (wind generators (WT), photovoltaic array (PV), batteries, inverters, electrolyser, and generator) that will be used to calculate the optimal system design. It also performs sensitivity analysis to evaluate the impact of a change in one or more of the input parameters. (Amaral, 2011).
For this research, HOMER was chosen because it can optimise the grid-connected PV system and it also provides economic results, which is the main objective of this research study.
Financial indicators
The costs and benefits of a solar PV system throughout its lifetime is analyzed and assessed by HOMER using the following financial indicators (Kassim et al., 2015):
Net Present Value
Net Present Value (NPV) represents the life cycle cost of the system. The calculation assesses all costs occurring within the project lifetime, including initial set-up costs, component replacements within the project lifetime, maintenance and fuel. Future cash flows are discounted to the present. HOMER calculated NPV according to the following Equation 1:
(1)
where, TAC is the total annualized cost ($) (the sum of the annual costs of each system component).
The Capital Recovery Factor (CRF) is calculated as:
(2)
where, N is the number of years and “i” is the annual real interest rate (%).
Cost of Energy
Cost of Energy (CoE) is the cost of generating electricity. The Life Cycle Cost (LCC), unit cost and payback period criteria are used and the LCC is obtained using the formula below:
(3)
The capital cost (Ccapital) of a project includes the initial Ccapital for the equipment, system design and installation. The Ccapital is usually considered as a single payment made on the first year of the project. The maintenance cost (CO&M) is the sum of all O&M costs incurred yearly. Examples for O&M costs include operator salary, inspections, insurance and property tax. The replacement cost (Creplacement) is the sum of equipment replacement costs incurred over the lifetime of the system. A good example of a Creplacement expense is the battery, which requires replacement once or twice during the entire lifetime of the system.
These costs normally occur at specific predicted years and the entire cost is often covered by the predicted yearly expenses. Finally, the salvage value (Csalvage) of a system refers to its net worth in the final year of the life-cycle period. Assigning a Csalvage of 20% of the original cost for mechanical equipment that can be moved is a common practice. The Csalvage can be modified depending on other factors, such as obsolescence and equipment condition. After calculating the LCC, the unit CoE produced by the system can be calculated as follows:
(4)
The material cost of solar panels and inverters is not the only expense encountered during the construction of solar power plants. Costs involved in construction, such as electrical tools (step-up transformers, high-tension switchgears, or cables), land acquisition and management costs must be considered.
Simple Payback Period
Simple Payback Period (SPP) represents the number of years required for the cash flow to equal the total investment. The basic assumption of the SPP method is that the more quickly the cost of an investment can be recovered, the more desirable the investment is. The equation for SP is given below:
(5)
However, this indicator ignores the time value of money and it also ignores all cashflows that may accrue to the project after the payback period. As a result, it is used as a measure of risk assessment.
3. METHODOLOGY
This section discusses the research methodology and design adapted in this research. It describes the various techniques and strategies that will be utilised for gathering the necessary data. It centres on explaining how the primary and secondary data are to be collected.
This section further describes the design and methodology to be used for this study. It explains the research philosophy and design survey question, and the reason for using quantitative research approach. The section describes the research population and sample size and research instruments.
Research design
In order to achieve the objective of determining the feasibility of the solar grid-tied system in Goreangab (Windhoek), quantitative research design has to be applied to determine the load profile of field of study.
To obtain the average load and energy consumption data for Goreangab residential area, it is required that load forecasting should be carried out for the households in the study area. The load forecasting is necessary for obtaining the information regarding the load demand and the total population under consideration. In this study, load forecasting will be carried out in a form of a survey which will be conducted through interviews and questionnaires.
Through the process of load assessment, the average load demand and average power consumption are determined using the data obtained during the load forecasting process. This is necessary for accurate power system sizing and modelling to ensure a reliable grid-connected PV system. Therefore a load assessment will be carried out to determine the load profile of the study area.
Population
Population is defined as a group that is subjected to a research interest. Sometimes it becomes impractical to study an entire population when the size of population is large. As a result, the researcher can determine the average of group to consider for the study and then the researcher may make general findings based on a sample.
Goreangab (Windhoek) was chosen as the field of study. The residential area has a total of about 3000 households.
Sample
A sample is a subset of a population selected for the study where questionnaires and interviews will be administered. A sample is chosen carefully in consideration that such sample will represent the population under study. Convenience sampling is known to be most attractive type of probability sampling, whereby each member in a population is chosen by chance and each member has an equal chance of being selected.
The researcher wishes to take a sample of 50 households to establish the load profile of the study area. The convenience sampling technique will be used to select the respondent of the study.
Research instruments
Researcher may employ various instruments and techniques within the quantitative approach of data collection. In this study, the following research instruments are to be employed: questionnaires, interviews and the HOMER software tool.
4. PROCEDURE
This section describes the manner in which research instruments will be administered to collect data.
Questionnaires
The questionnaires consists of various questions designed to gather data for the purpose of analysis on the research topic. These questionnaires are then distributed to the respondents for completion. In this study, the questionnaires will consist of both close-ended and open-ended questions.
Interviews
The researcher will conduct structured interviews for the purpose of verification of the questions that will be set in the questionnaires. The interviews will allow immediate feedback and furthermore, the interviews will provide rich data to the researcher.
HOMER software tool
Another instrument that will be used in this research, is the Hybrid Optimization Model for Electric Renewables (HOMER) software tool, developed by the National Renewable Energy Laboratory (NREL). As observed from the literature review, the HOMER simulation software performs the analysis of the grid-connected PV systems by simulating the system operation and cost evaluation for the project’s lifetime. This simulation requires data on initial capital, O&M, as well as replacement costs.
Figure 4 shows the procedure HOMER uses for simulation of data collection for a grid-connected PV system.
Figure 4 HOMER simulation design flow chart
HOMER inputs:
To use HOMER, the researcher is required to provide the model with inputs, which describe technology options, component costs, and resource availability. HOMER uses these inputs to simulate different system configurations, or combinations of components, and generates results that the researcher can view as a list of feasible configurations sorted by net present cost. (HOMER Energy, 2016)
Load profile
The daily load profile will be obtained from the estimated hour of use for the electrical appliances. The electricity demand in the study area may vary depending on various factors. The reasons for the variations in the electricity consumption in may be due to the human behaviour, climatic changes etc.
Solar resource data
In HOMER, the solar resource input can be represented by either the solar radiation data or the clearness index. To model a system containing a PV array, the HOMER user must provide solar resource data for the location of interest. Solar resource data indicate the amount of global solar radiation that strikes Earth’s surface in a typical year. There are two ways to create solar baseline data: users can directly use HOMER to synthesize data from NASA surface meteorology and solar energy database, or users can import hourly radiation data from a file.
Project Constraints
The constraints menu in HOMER allows a modification to the system constraints which are conditions the systems must satisfy. HOMER discards systems that do not satisfy the specified constraints, so they do not appear in the optimization results or sensitivity results.
Economic data
The most important economic factors for HOMER are the real interest rate and the project lifetime. A real interest rate (discount rate) has to be identified for Namibia. The normal project lifetime for any PV system is between 20-30 years.
Photovoltaic data
The relevant values are needed to populate the model for the PV array: size [kW], output current (AC or DC), operational lifetime (year), derating factor [%], slope [degree], azimuth (degree W or S), ground reflectance [%], and type of tracking system used.
The PV derating factor is a scaling factor that HOMER applies to the PV array power output to account for reduced output in real-world operating conditions compared to the conditions under which the PV panel was rated. HOMER also uses a derating factor to reduce the actual output of the solar array relative to its rated capacity. The lifetime of PV modules depends on the solar cell technology used as well. For monocrystalline and polycrystalline silicon solar cells most manufacturers give a warranty of 10/90 and 25/80 which means: a 10-year warranty that the module will operate at above 90% of nominal power and up to 25 years above 80%. The ground reflectance is the fraction of solar radiation incident on the ground that is reflected. (Pavlovic et al., 2013)
Converter (Inverter) data
Inverter is a device that converts electric power from DC to AC in a process called inversion, and/or from AC to DC in a process called rectification. The inverter size, which is a decision variable, refers to the inverter capacity, meaning the maximum amount of AC power that the device can produce by inverting DC power.
The user specifies the rectifier capacity, which is the maximum amount of DC power that the device can produce by rectifying AC power, as a percentage of the inverter capacity. The rectifier capacity is therefore not a separate decision variable. The relevant values needed for this simulation are: size (kW), lifetime (year), inverter efficiency [%], and rectifier efficiency [%]. (Pavlovic et al., 2013)
5. DATA ANALYSIS
Questionnaires and interviews
To analyse the data collected from respondents, the researcher will make use of coding, transcription and thematic analysis. Face to face interviews will be tape recorded and then transcribed. The data will then be analysed by using charts to establish the load profile of the study area.
Economic analysis
The costs and benefits of the proposed grid-connected PV system are to be compared to that of the grid-only system throughout their lifetimes. Analysis and assessments is done by the HOMER software using the following financial indicators as discussed in the literature review.
Net Present Value
Net Present Value (NPV) represents the life cycle cost of the system. A high NPV value indicates that the project is able to payback the capital and interest, and able to generate wealth at the specified rate. Hence, the system with a higher NPV is more profitable.
Cost of Energy
Cost of Energy (CoE) is the cost of generating electricity. A low CoE value indicates that the project is cost-effective i.e. the system is more economical.
Simple Payback Period
Simple Payback Period (SPP) represents the number of years required for the cash flow to equal the total investment. The basic assumption of the SPP method is that the more quickly the cost of an investment can be recovered, the more desirable the investment is i.e. the system with a lesser SPP is more attractive.
GHG reduction Analysis
HOMER can simulate greenhouse gas (GHG) reductions of the energy systems. A system with a high GHG reduction is more environmental friendly.
Sensitivity Analysis
HOMER simulates each system configuration over the range of values. The researcher can use the results of a sensitivity analysis to identify the factors that have the greatest impact on the design and operation of a power system. The researcher can also use HOMER sensitivity analysis results to answer general questions about technology options to inform planning and policy decisions. (HOMER Energy, 2016)
To explore possible discrepancies in the results caused by key parameter variations, sensitivity analysis will be performed for important parameters, such as FiT degression, initial investment cost, project lifetime and electricity export cost. The FiT degression rate implies that investor profits will eventually decline.
6. RESEARCH ETHICS
The researcher guarantees confidentiality and honesty during the collection of data as proposed in this paper. The author also guarantees originality of this paper.
7. BUDGET
The availability of financial resources play a vital role in the completion of any research project, therefore it is of great importance that the researcher estimate the cost implications associated with the conduction of this research.
The researcher estimated a total amount of N$ 2 000.00 to be spent during the course of this research study.