1 RENEWABLE ENERGY IRE MINI PROJECTS DESIGN A
1 201200047 SEPHELANE N 30.08.2018 SE6300 INTRODUCTION TO RENEWABLE ENERGY IRE MINI PROJECTS DESIGN A HYBRID RENEWABLE ENERGY SYSTEM FOR A SMALL VILLAGE VILLAGE ASSIGNED: LINAKENG, Thaba-Tseka2 1. TITLE DESIGN OF AN OFF-GRID HYBRID SOLAR-WIND-HYDRO SYSTEM WITH A DIESEL AND BATTERY BACK-UP, FOR THE COMMUNITY OF LINAKENG, THABA-TSEKA, LESOTHO 2.
ABSTRACT Off-grid hybrid renewable energy systems have proven to be the most efficient way to supply electricity to remote areas where access to grid connection might be costly and impractical. These systems take advantage of the available renewable energy resources that are in abundance in these locations such as Solar, wind and water. These systems are also very environmentally friendly, and assist in improving the lives of the people in these isolated areas. In this paper, the community of Linakeng in Thaba-Tseka, Lesotho, with a primary domestic load of 204 kWh/ day and a peak of 83kW, will be the target for design and simulation of a hybrid wind-solar-hydro system that has a generator and battery back-up.
The system design was based on the area’s annual average solar radiation of 5.35 kWh/m2/d, wind speed averaging about 2.63 m/s annually and flow rates averaging about 707 L/s annually. Simulation and design of the system was done using HOMER and the resulting optimal systems were those that had an LCOE of 0.
243 $/kWh and 0.498 $/kWh and an NPC of $185,752 and $381,317 for the lowest and highest architectural system respectively. These systems were able to meet their load demands, with a significant and efficient use of the renewable energy systems and a low emission. Sensitivity variables were taken into consideration to meet economic and resource fluctuations and these included the primary load, the solar radiation, wind speed and Flow rate. 3. INTRODUCTION 3.
1 COUNTRY OVERVIEW Lesotho is not an exception in the global Environment concerns that have been increasing since the 1990s. The evident potential threats brought about by pollution, the greenhouse effect and others, prompt an urgent solution in the energy sector, to switch to alternative energy resources in order to reduce the excessive use of fossil fuels such as coal, natural gas and oil, which effectively reduces the rate of Global warming 1. Lesotho’s energy heavily depends on supply from the ‘Muela Hydropower station as well as imported electricity from South Africa and Mozambique. This landlocked country also relies on biomass (wood and Dung) and Petroleum as other sources of energy. Majority of the country’s population resides in rural mountainous areas (about 70% population). The country’s economic growth is viewed as steady, however, it has declined since 2011 and as a result, unemployment and poverty levels have become high. 3.
2 ENERGY CONCERNS Imported electricity constitutes about 87% of the overall cost of electricity in the country (2017-2018 LEC tariff determination). This can be a challenge, especially in a poverty ridden country due to the high cost of electricity purchase, as tariffs for this imported energy ranges from about 0.77 to 1.50 per kWh as compared to the 0.
13 per kWh cost from the ‘Muela hydropower station. Moreover, the current import of energy is still not sufficient to meet the overall load demand of the country (only about 60% of the urban region has electricity access and in the rural areas, it is only about 18%) and therefore there is an increased reliance on biomass and Petroleum. This leads to a rapid decline in biomass stock (the country loses, on average, about 0.5% of forest cover every year).3 In addition, most people live far from the National grid connection and thus, those who reside in rural mountainous areas do not have access to electricity, however, these areas have proven to possess sufficient supplies of renewable energy resources such as wind, solar and water. In order to get electricity, these areas could either extend the grid transmission lines or use stand-alone systems. The latter seems rather inconvenient from the financial point of view because the cost of extending the national grid transmission escalates with distance.
3.3 WHY RENEWABLE ENERGY? Renewable energy sources use environmentally friendly technologies to produce energy, they are inexhaustible at all, and are non-polluting 2. The application of renewable energies in off-grid systems, however, might prove to be challenging because of their dependence on the nature of the source as well as the geographical and weather conditions of an area. In spite of this, the electrification of remote villages would be a major step in the improvement of lives of the people in the community as well as in improving the community’s economy. Access to electricity would be beneficial for improving sectors of agriculture, health, food preservation as well as creating job opportunities for the community.
3.4 PURPOSE OF THE STUDY The main objective of this mini-project is to design an off-grid hybrid renewable energy system that can generate and provide cost-effective electricity to rural areas, specifically and as assigned, to the Linakeng community in the Thaba-tseka district, Lesotho. This is to be accomplished by first studying raw measured data for wind, solar and river flow rates (of Linakeng River) of the village and estimating the load demand for the area required for the domestic use of the electricity. This raw data will assist in simulating a hybrid renewable energy system that consists of Hydropower, Wind power and solar power that also incorporates a diesel generator used as a back-up, Batteries for storage and a converter. Simulation and Optimization of this system will be done using Hybrid Optimization Model for Electric Renewables (HOMER). 4.
LITERATURE REVIEW There is a substantial amount of research that has already been done for off-grid hybrid renewable systems, all in which simulation and optimization of these systems was done using HOMER. For example, for this thesis work, Bahta 2 Simulated and optimized a wind turbine-photovoltaic-diesel generator-battery bank-converter for the rural community of Haressaw among the sub-districts of Atsbi district in the regional state of Tigray, Ethiopia. During optimization of this power system, a Primary load demand of 1505kWh/day, a peak load of 284kW, a deferrable energy of about 17kWh/day and a deferrable peak load of 3.6kWh was involved.
HOMER modeling tool was used to design the off-grid system with Wind and Solar energy being the primary sources supply electricity directly to the load and to charge the battery bank when excess generation is happening as well as engaging the use of the generator during peak demand times. The study suggested that the community’s energy requirements was for lighting, water pumping, school and health clinic equipment load, television, radio, flour milling machines and local food (enjera) baking. During power system set-up, simulation and optimization of the study was done based on the electricity load, climatic data sources, economics of the power components and other parameters in which the NPC has to be minimized to select an economic feasible power system as well as taking into account capacity shortage, renewable fraction, excess electricity, COE, diesel fuel consumption to check the technical capability so as to select a system that is sound in techno-economic aspects.4 Research for rural dwellers in Nigeria was also conducted by Chiemezuo et al 3.
The Unwana community in Ebonyi state was selected for the study. The area was found to have an estimated 100 households, 8 shops, a community hall, one school, a community health center, a community water project, street lights and miscellaneous load. With this, the average daily demand for the area was estimated to be 240.
1 kWh/day with a peak demand of 41.6 kW. HOMER pro software was used to design, simulate and optimize the best hybrid system to satisfy the demand. This study found that the system with the lowest Net present cost (NPC) was that which consisted of one 20kW biogas generator, one 10kW biogas generator, 4.6kW PV panels, 31 strings of 12V, 1kWh lead acid battery and 8.
65kW converter. It was also observed that 6.3% of the total power is derived from solar and the remaining 93.7% from biomass. A case study was conducted on an island residence by Oulis et al 4 and a hybrid AC/DC micro-grid was designed using HOMER pro.
The design was done using Photovoltaic arrays, a diesel generator, AC load and battery energy storage for provision of energy even during long periods of low sunshine. HOMER pro tool was used to identify a design that was the most cost-effective. A sensitivity analysis was also carried out to evaluate the system’s sturdiness against Fluctuations in fuel costs and PV generation.
Bashiru Olalekan carried out an optimization analysis of a stand-alone hybrid system of the Senate Building at the University of Ilorin, Nigeria 5. The energy demand for the area was averaged daily at 1520kWh during a typical dry season, 712.5kWh during a rainy season and 212.8kWh during the weekdays. The aim of the study was to meet the energy demand of the area at the lowest possible cost of energy and using HOMER, it was observed that a PV-diesel-Battery system gives the most ideal results for the case study. Sensitivity analysis was also carried out to determine to determine the condition under it would be most feasible to include wind power in the system design, technically and financially.
The results obtained in the study showed an energy system that would cost 0.283kWh. This cost, compared with 0.087kWh currently charged by electric facilities in Nigeria, would not compete. 5.
METHODOLOGY 5.1 RENEWABLE ENERGY RESOURCE DATA To begin, the Load demand of the community was determined by estimating the use of electricity for an individual household through assuming the type and number of appliances and electrical equipment a family might typically possess. This was done using Excel through a matrix system were the load profile has 2 Matrices, the 1st Matrix (nA x 1, where nA is the number of appliances) represents Appliance power and the 2nd (24 x nA) is the time / extent of use for the appliance. This was then used to predict the electricity Load demand for the community of about 55 households (as determined by census, 2006). Table 1: Appliance Power Matrix for the 55 households of the Linakeng Community Appliance Type Number Power Rating Total Power Energy saver lights 188 11 2068 Refrigerators 30 100 3000 iron/electric jugs 35 1000 35000 DVD/Decoder 15 10 150 Amplifier 0 600 05 Radio/Speakers 12 100 1200 TV 10 60 600 Computer 0 100 0 stove plates 16 1000 16000 Table 2: Load Profile 5.2 RENEWABLE ENERGY RESOURCE DATA To design this system, research on the suitable hybrid system that would be applicable to the Linakeng community was conducted using Global Horizontal Irradiance (GHI) data and wind speed data obtained for the European Union Meteorological data site by entering the coordinates of the area under study. The Flow rate of the Linakeng River was estimated using annual Precipitation data for the Thaba-Tseka district.
Table 3: Hydro, Solar and Wind data Month Hydro (L/s) Solar (kWh/m2/d) Wind (m/s) January 1,410.3 7.1 2.1 February 1,200.1 6.8 2.
2 March 1,040.4 5.2 1.9 Hour Load (kW) Hour Load (kW) 0000 – 0100 1.11 1200 – 1300 12.56 0100 – 0200 1.
11 1300 – 1400 12.56 0200 – 0300 1.11 1400 – 1500 12.
56 0300 – 0400 1.11 1500 – 1600 4.56 0400 – 0500 13.
90 1600 – 1700 3.06 0500 – 0600 42.91 1700 – 1800 21.13 0600 – 0700 18.40 1800 – 1900 20.92 0700 – 0800 4.
02 1900 – 2000 4.92 0800 – 0900 4.56 2000 – 2100 4.92 0900 – 1000 4.56 2100 – 2200 4.92 1000 – 1100 4.
56 2200 – 2300 1.11 1100 – 1200 4.56 2300 – 0000 1.116 April 590.
3 4.1 2.6 May 230.7 4.2 2.
1 June 150.6 3.2 2.6 July 80.6 4.0 2.0 August 300.
6 4.2 2.5 September 310.2 6.2 4.
1 October 830.2 6.3 2.9 November 1,140.
0 6.7 3.1 December 1,230.8 6.3 3.5 5.
3 RENEWABLE ENERGY EQUIPMENT COST The cost of wind, solar and Hydro equipment was researched and for each price, a margin of Fluctuation in cost was applied where relevant. Some of the cost estimations were done using ren21 (2018). Table 4: Component Costs Diesel Converter Battery PV Hydro Wind Size (kW) 1.00 1.00 1 1.00 25 1.
00 Capital ($) 110 100 400 600 13300 800 Replacement ($) 110 100 400 300 13000 600 O;M ($/yr or $/hr) 0.05 10 40 60 600 80 Lifetime (yrs) – 15 10 25 25 15 Head or Hub (m) – – – – 18 60 Fuel price ($/L) – – – – – – 5.4 DESIGN AND SIMULATION Hybrid Optimization Model for Energy Renewable (HOMER) software tool was then used to design and simulate the off-grid hydro-wind-solar system with generator and battery back-up. 6. RESULTS After adding all the components required, the resulting schematic hybrid is shown in Fig 1.7 Figure 1: Schematic of the hybrid wind-Solar-Hydro Plant Fig 2 shows the optimization results for the simulation, arranged from lowest to highest according to the Cost of Electricity (COE) Figure 2: Optimization Result 7. DISCUSSION AND ANALYSIS 7.
1 AVERAGE kWh/d, AVERAGE kW AND LOAD FACTOR Table 5 shows values for the average kWh per day, the average kW, the Peak and the Load Factor. The average kWh/ per day shows that the daily load consumption for the Village is estimated at around 204 kWh/day. The 8.
50kW average is the estimated consumption per household. The peak, indicates the amount of consumption when most people are using electricity, which was found to be 82.7kW. The load factor of 0.
10 indicates a 10% efficiency on the system. This is not ideal as it indicates that use of electricity is inefficient, relative to what it could be if the peak demand was in control. An increased load factor is more ideal because it will decrease the average unit cost of the kWh and thereby increasing savings 6 Table 5: Baseline and Scaled amounts for average kWh/d, average kW and load factor Baseline Scaled Average (kWh/d) 204 204 Average (kW) 8.51 8.50 Peak (kW) 82.8 82.7 Load factor 0.
103 0.1038 7.2 DAILY PROFILE, SEASONAL PROFILE AND PLOTS OF LOAD PROFILE (HOURLY, MONTHLY) The daily profile shows a typical load consumption in one day for this village. In figure 3, the hours between 2200-2400hrs and 0000-0400hrs show a very low consumption of energy and this might be due to the fact that at those hours, most people are asleep. The peak hour was found to be between 0500-0600 as indicated in the figure and this might be an indicator that this is the hour that most people wake up to perhaps prepare for school or go to work. Figure 3: Daily Profile. A seasonal profile for the scaled data monthly averages in Figure 3 below, show the average value (in kW) for load demand for every month of the year.
These do not indicate significant change in energy consumption between seasons, however, this might be subject to change, as people become more accustomed to the use of electricity, because, a change in energy usage is expected as lifestyle changes7. Figure 4: Scaled data Monthly averages 7.3 DENSITY MAP, PROBABILITY DENSITY FUNCTION (PDF), CUMULATIVE DISTRIBUTION FUNCTION (CDF) AND DURATION CURVE (DC) Figure 5 shows the Density map. This shows the load density at every hour of the day and according to this figure, the load density is mostly kept at minimum values between 0kW and 27kW throughout the days of a year. Figure 6 is the Probability density Function and it shows the probability of having a particular load demand at any given time.
The graph indicates that the highest probability of about 25% is on a load demand between 0kW and 2.5kW. This again, is proof of inefficiency of the system. Figure 7 shows a cumulative distribution function, which accounts for the area under the PDF graph. This graph shows that the maximum load that is able to be reached by the system is just above 80 kW. A duration curve, as shown in Figure 9,9 shows how the load will vary with time.
It defines the energy/power requirement of a load in terms of maximum demand, total energy requirement and the distribution of energy demand 8. In the figure, the load demand of the community looks to decrease with time. Figure 5: density Map Figure 6: Probability density distribution (PDF) Figure 7: Cumulative distribution Function10 Figure 8: Duration curve 7.
4 RENEWABLE ENERGY RESOURCES 7.4.1 SOLAR RESOURCE A relationship between the amount of sunshine and its effect on Solar radiation data has long been established 9.
Specific to the area under study, an average annual global horizontal radiation of 5.35kWh/m2/d is proven to be a sufficient resource to supply solar power. Also, generally, Lesotho has an abundant solar energy resource, with more than 300 days of sunshine. Sunshine durations range from 10.2 to 13.8 hours per day for both highlands and lowlands and this, further reassures a consistent supply of solar energy10. During the months of April to August, the global horizontal radiation falls below average as seen in figure 9.
This may be due to the fact that days are shorter in winter than they are in summer because of the trajectory of the earth (it is tilted at an angle). This, however can be compensated for by the other renewable energy resources, as this is a hybrid system. Figure 9: Monthly average radiation 7.
4.2 WIND RESOURCE The wind resource was found to average around 2.63 m/s annually. This is very inefficient as the minimum speed to start a wind turbine is 2m/s and thus the wind speed would only be used to start a wind turbine and not to generate any power. To commence generation of power, the speed of wind was found to have to11 range around 3.5m/s and the optimal generation of power was found to be at a speed of around 10-15 m/s11. Figure 10: wind resource 7.
4.3 HYDRO RESOURCE Annual flow rates for the generation of hydro power have proven to be the most reliable renewable energy resource among the hybrid. The average flow rate was found to be 707 L/s and this is very satisfactory, as the minimum annual average flow rate required for a given head to generate a maximum power output of 25 kW is 150 L/s 12, which is considered the smallest economically viable hydropower system13. Smaller amounts of flow rate are experienced during the months between May and September. This might be due low rainfalls during winter seasons.
The flow rate data was however, determined using precipitation rates and this might be an inaccurate measure of flow rate, and thus, not a true reflection of the actual flow rate of Linakeng River. Figure 11: Hydro resource 7.5 SYSTEM ARCHITECTURE WITH THE LOWEST LCOE The architectural system with the lowest LCOE was found to be one with 0.243 $/kWh as the cost.
The net present cost for this system is $185,752. It estimates the present value of all the costs of installing and operating the Component over the project lifetime, minus the present value of all the revenues that it earns over the project lifetime14 and is calculated, as per table 6. This system took into consideration all hybrid components at the lowest overall cost. It is an established fact that a system with the lowest NPC yield higher profit and thus the system is most ideal 5. This system, however, is more capital intensive than the system architecture that has the highest LCOE cost due to investment in the renewable energy equipment cost, civil work costs and other factors that contribute to setting a system in place. The production of electricity of the system is summarized in figure 13.
The PV array was found to contribute to 3% of the electricity generated, wind turbine generate 28% of the electricity, the hydro turbine accounts12 for 69% and the generator only constitutes 1%. Excess electricity produced accounted for 91.8% of the electricity (see Figure 12). This might prove to be very inefficient, however, this excess might be used to accommodate a growth in population, or be used to feed to other neighboring communities. The renewable energy fraction was found to be 0.987 and this is very satisfactory, because it means that the system is majorly dependent on the renewable resources and thus reducing the use of Diesel and only using it as a back-up.
This also implies a significant reduction in CO2 and other emissions, as seen in table 7. In conclusion, the wind turbine and PV array contributed low energy to the proposed hybrid system when compared to the hydro system output energy. Table 6: Component costs for system with the lowest LCOE Component Capital ($) Replacement ($) O ; M ($) Fuel ($) Salvage ($) Total ($) PV 6,000 0 7,670 0 0 13,670 PGE 20/25 5,600 2,337 4,747 0 -435 11,976 Hydro 13,300 0 7,670 0 0 20,970 Generator 5,500 0 15,020 66,543 -278 55,440 Trojan L16P 16,000 13,923 20,453 0 -1,864 86,696 Converter 1,500 626 1,918 0 -116 3,927 System 47,900 16,886 57,206 66,453 -2,693 185,752 Figure 12: Production, Consumption and Quantity of electricity for system with Lowest LCOE Table 7: Emissions Pollutant Emissions (kg/yr) Carbon dioxide 11,408 Carbon monoxide 28.2 Unburned hydrocarbons 3.12 Particulate matter 2.
12 Sulfur dioxide 22.9 Nitrogen oxides 25113 Figure 13: Monthly average Electric production 7.6 SYSTEM ARCHITECTURE WITH THE HIGHEST LCOE The system with the highest LCOE was found to be one with 0.498 $/kWh as the cost. The net present cost for this system is $381,317 as seen in table 8 .This system does not take into consideration all hybrid components and instead, it only simulates hydro and diesel resources.
This system is very impractical, despite the fact that it is less capital intensive than the system with the lowest LCOE. The high NPC in this system is incurred through the high cost of fuel that will be used to run the generator (see table 8). In this system, production of electricity of the system is summarized in figure 15. Hydro turbines account for 93% of the electricity and the generator accounts for only 7%. The PV array and the wind turbines were excluded in this simulation (Figure 14). An Excess of about 89.
1% is produced and the renewable energy fraction was found to be 0.934 and this is still very satisfactory, even though the renewable energy fraction of the lowest LCOE system is slightly more ideal. Emissions in this system are slightly higher than those in the lowest LCOE architecture as seen in table 9, however, they are still of an acceptable margin. It would be ideal for a system to have a lower diesel fuel consumption because, low consumption of diesel and a higher energy generation from renewable sources is considered a good choice, because burning of diesel oil is the major source of environmental pollution and the emission of polluting elements. Again, the availability of diesel might prove to be a challenge, especially in rural areas due to reasons that vary from road access to the areas and high cost of transport to these remote areas 2 In conclusion, the cost of LCOE of this system is high due to installed PV and wind systems that are not in use so perhaps it would be a more viable solution if PV, wind and converter systems are excluded in simulation because as seen in both architectural systems, most energy is obtained from the hydro output.
Table 8: Component costs for system with the highest LCOE Component Capital ($) Replacement ($) O ; M ($) Fuel ($) Salvage ($) Total ($) Hydro 13,300 0 7,670 0 0 20,970 Generator 1 5,500 7,957 71,683 275,543 -335 360,347 System 18,800 7,957 79,353 275,543 -335 381,31714 Figure 14: Production, Consumption and Quantity of electricity for system with highest LCOE Figure 15: Monthly average electric production 7.7 SENSITIVITY ANALYSIS Sensitivity variables assist in exploring the effects of the changes in the resources and the economy. These variables show a range for which it would make sense to include renewable energy in the designing of the system15. In this hybrid, the sensitivity parameters that were taken into consideration include; solar radiation, Diesel price, Flow rate and the Primary load. The figure below (fig. 16) shows a surface plot graph showing Diesel price ($/L) variation with flow rate (L/s) while keeping the primary load and the Global solar radiation fixed.
This plot is superimposed with the levelized cost of electricity (LCOE). This plot shows how the LCOE changes as diesel price and Flow rate is varied and as seen in the figure, a change in diesel price from 0.8 to 1.2 $/L leads to an increase in LCOE from 0.186 to 0.214 $/kWh.
When flow rate is varied from 650 to 750 L/s, the LCOE reduces from 0.186 to 0.185 $/kWh. Increasing both parameters would effectively lead to an increased LCOE of 0.
213 $/kWh (from a cost of 0.0186 $/kWh), which means that the diesel price carries more weight in terms of increasing LCOE than Flow rate does in decreasing the cost of LCOE.15 Figure 16: Optimal system type 8. CONCLUSION The Purpose of this mini- project was to design an off-grid hybrid system for the community of Linakeng, Thaba- tseka, a 55 household community. Renewable energy resource data for the community was obtained using the EU meteorological data service for wind and solar resources and precipitation data for the district was used to estimate the flow rate of the Linakeng River.
A load profile was established and with all this data, a simulation was made using HOMER to give out a simulation that had a primary load of 204 kWh/day with a peak of 83 kW. Using the resources, optimal system types of 0.243 $/kWh and 0.
498 $/kWh as the lowest and highest system architectural LCOE respectively, were obtained. Both systems provided enough electricity to meet the load demand, with a very significant excess that could be used to accommodate a population increase. Emissions in these systems were also reduced and this is a good thing, as it would lead to a cleaner environment. The optimal system had sensitivity variables to accommodate economic changes and resource fluctuations and in this optimal system, diesel price and flow rate were varied while keeping the primary load and Global solar radiation fixed.
This plot was superimposed with the LCOE and it was observed that a change in diesel price from 0.8 to 1.2 $/L lead to an increase in LCOE from 0.
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