1. Introduction

2. Research area and data

2. Materials and Methods

2.6. Sizing of the Solar Array and System Design at Khartoum

To prevent undersizing, divide the total average daily energy demand by the efficiency of the system components to get the daily energy requirement from the solar array:

$${\mathbf{E}}_{\mathit{a}\mathit{r}\mathit{r}\mathit{a}\mathit{y}}=\frac{\mathbf{d}\mathbf{a}\mathbf{i}\mathbf{l}\mathbf{y} \mathbf{a}\mathbf{v}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{g}\mathbf{e} \mathbf{e}\mathbf{n}\mathbf{e}\mathbf{r}\mathbf{g}\mathbf{y} \mathbf{c}\mathbf{o}\mathbf{n}\mathbf{s}\mathbf{u}\mathbf{p}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}}{\mathbf{p}\mathbf{r}\mathbf{o}\mathbf{d}\mathbf{u}\mathbf{c}\mathbf{t} \mathbf{o}\mathbf{f} \mathbf{c}\mathbf{o}\mathbf{m}\mathbf{p}\mathbf{o}\mathbf{n}\mathbf{e}\mathbf{n}\mathbf{t} \mathbf{e}\mathbf{f}\mathbf{f}\mathbf{i}\mathbf{c}\mathbf{i}\mathbf{e}\mathbf{n}\mathbf{c}\mathbf{i}\mathbf{e}\mathbf{s}}=\frac{3500}{0.9\times 0.8\times 0.8}=3.8 {\mathrm{KWh.Day}}^{-1}$$

Then the peak power is:

$${\mathbf{P}}_{\mathit{p}}=\frac{\mathbf{d}\mathbf{a}\mathbf{i}\mathbf{l}\mathbf{y} \mathbf{e}\mathbf{n}\mathbf{e}\mathbf{r}\mathbf{g}\mathbf{y} \mathbf{r}\mathbf{e}\mathbf{q}\mathbf{u}\mathbf{i}\mathbf{r}\mathbf{e}\mathbf{m}\mathbf{e}\mathbf{n}\mathbf{t}}{\mathbf{m}\mathbf{i}\mathbf{n}\mathbf{i}\mathbf{m}\mathbf{u}\mathbf{m} \mathbf{p}\mathbf{e}\mathbf{a}\mathbf{k} \mathbf{s}\mathbf{u}\mathbf{n}-\mathbf{h}\mathbf{o}\mathbf{u}\mathbf{r}\mathbf{s} \mathbf{p}\mathbf{e}\mathbf{r} \mathbf{d}\mathbf{a}\mathbf{y}}=\frac{5173}{3.84}=1508 \mathrm{W}$$

The total current needed for a DC-voltage of 24 is I = 1608/24= 67 A, according to the chosen panel KC50T [http://www.kyocerasolar.com/pdf/specsheets/KC50T.pdf]. The number of parallel panels, Np=67/7.45=40, and the number of series panels, Ns=24/24=1, are both close to 40, indicating that there are 40 total panels needed (220),also [10] Design the array and the number of modules based on their load requirements, which means module sizing is affected by the factors mentioned above. While performing the above calculation, an average efficiency of 12% was assigned to the PV modules used in the current installation.

Table 2. Details of requirements of the system.

Table 2. Details of requirements of the system.

Component	Model	W/Ah	A	V	Size	WeigtLb/kg	Warranty Year	Photo
Panels	KC50T	180W	7.45	24	65.3×32.6×1.81	43	25
Batteries	UB-8D AGM	250 Ah	~	12	20.5 x 10.5 x 10 475	167	1.0
Regulators	Xantrex C60	~	60	24	08 x 5.0 x 2.5	2.5	2.0
Inverter	Latronics LS - 3024	3000 W	~	24/220	14.6 x 15.2 x 7.0	24 kg	3.0
Wires	#02,#10 AWG	Diameter= 6.54304 mm, Area= 32.0 mm2 ,Diameter= 2.59 mm, Area= 5.27

3. Results and Discussion

3.2. Load Profile

Figure 5 shows the average monthly load values including the maximum and minimum loads for each month. The average hourly load is between 2.2W and 3.9W and the average for the year is 31.0W. The average daily loads are presented in Figure 14 below. The minimum load is never equal to zero. From the tracker and annual Schreiber driven by its own solar panels to charge the minimum base is the result of was light. The charge has a maximum of about 90W. A total of 28 Values greater than 90 W should be further investigated. The month of maximum load to be studied is February and September. Representative diurnal load profile for summer, winter and midseason represented by respectively June, January and May; this allows hourly maximum and minimum values to be noted. There is never a zero load, indicating that some lights are left on throughout the day and night. There is a different load profile to the design load of 7 hours use per day. Since the beginning of the monitoring period, the villagers requested the ability to switch the lights on and off according to their needs, as their mud and stone house are very dark inside even during the day. The maximum and minimum values indicated by the error bars show that Last profile remains relatively constant over time, with increases in Morning and afternoon and a bathroom in the middle of the day and Night.

There is a traditional notion that lights protect villagers from evil spirits. This may be at least partially responsible for this phenomenon. Due to the fact that nearly fifty percent of the residences in the villages keep their WLEDs on all night, the load profile provides strong evidence that this may be taking place. Because not all families are devoutly religious followers of Hinduism, there is a difference in the degree to which they are terrified of malevolent spirits. [12]. This may explain why around half of families leave their lights on overnight. There is a correlation between the average load and the seasons, with a significant increase in the average daily load during the winter months despite the fact that the actual light consumption pattern varied throughout the day and not all lights were on simultaneously. Consequently, the average daily load has stayed constant, varying between 450Wh and 722Wh, with a monthly average of 370Wh.

3.2.1. Array voltage sizing

Figure 6 shows the result of sizing the voltage of the whole array, which has 10 polystirine modules. This was done by simulating the fixed input data of the pv modules, which are a fundamental part of the array. The homer was used to simulate the selected data of the module, the inverter, and the rest of the system. As you can see above, the system was designed based on the load requirements of the house in the city of Alazhari. The system and the battery were then simulated using homer. Because of this simulation, the design for the battery was found.

3.2.2. Probable Monthly Averages For The Battery Bank Voltage.

As can be seen from Figure 7. The BB voltage is expected to remain fairly constant throughout the year. The BB voltage annual average is expected to be 25.85V.

As can be seen from Figure 7 January is a winter month, thus the battery voltage is typically fully charged. There are no extended times when the BB’s charge level is low. On purpose, the battery voltage drops to 25.2 V on the 9th and 30th of the month. Battery function is generally good, with flight movements BB voltage of 26.2V and no extended intervals of low state of charge. The expected homer value of 25.9V, which can be seen in Table12 in 5.1, is extremely close to the annual average BB voltage. that happened The fact that the batteries are frequently full and must rely on the CC to lower the power output of the PV array shows that the PV system is too large for the current load requirements. According to[13]), the PV system has been purposefully expanded so that it can supply all of the daily lighting needs in 4-5 years. Energy demand has risen by an anticipated 5–10% p.a. as the village population has grown (now at a rate of 2% p.a.). The simulation results contain a wealth of important information and quantify system losses at every level, making it possible to pinpoint system design flaws. By contrasting the performances offered under realistic circumstances over the course of an entire year, this should lead to a thorough comparison between various technical solutions that might be used. Inverter and grid injection losses have been put in place. When the transformer is exterior, they could include wire losses. Exhaustive loss diagram Figure 4.22, which quantifies all loss effects, provides a thorough understanding of the design quality of the PV system. Losses on each subsystem can be aggregated or enlarged with specific contributions, and the overall loss rate was 78.6%. As shown in Figure 4.23, the yearly PV system energy output ranges from 0.5 to 2.17 kWh/kWp, with an average annual performance ratio of 78.6% (1.3 kWh/kWp). Figure 4.22’s performance ratio illustrates how well-built a PV system is, and a value of 78.6% is high-quality. According to Goetzberger & Hoffmann (2005), the typical value of the performance ratio is between 60 and 80%. This demonstrates that due to variables like conduction losses, contact losses, the module and inverter efficiency factor, component faults, etc., around 21.3% of the solar energy that was present during the studied period was not transformed into useful energy.

3.2.3. Expected PV Output

The annual average production of the 300Wp PV array is anticipated to be 343,3 kWh. This equals 940.5Wh/day, or 343.3/365 days. As expected, the PV output does not coincide with the global sun radiation on the tracking axis. This could imply that the batteries are frequently full, reducing the PV. Figure 8. depicts the average daily production (Wh) of the 300Wp PV array for each month, as well as the monthly worldwide sun radiation values on the POA. Figure 6-70 makes this outcome crystal obvious.

Figure 8. Normalized and Loss Factor.

Figure 8. Normalized and Loss Factor.

Figure 9. System Output Energy.

Figure 9. System Output Energy.

3.2.3. Size Inverter

As a result of using Homer, it was discovered that as energy increased in KW/H, the array power increased systematically as shown in Figure 9 . However, as energy increased to 2.5 KW/H with 0.025 KW, the distribution of output power decreased to 2.00 KW/H with 0.27 KW, and finally to 0.04 KW/H with 0.04 KW. When the energy increased more than the inverter’s design anticipated, the result was consistent with the inverter’s ability to limit the array’s power at 0.030. [23] as seen in the figure below

Figure 9. inverter output distribution.

Figure 9. inverter output distribution.

The simulation for the module in which we utilized the average annual energy output results in a maximum average power generation of 25 kw/m2. The average minimum power generated, which was measured in May, June, July, and January, was 20 kw/m2. It was reported in February. This was the outcome of a real experiment conducted by the researcher, and the outcome of a simulation of the overall system energy production as electrical power was 7600 kWh/year. [21].

3.2.4. Average daily PV array energy output values for each month.

The average amount of power that a 300Wp PV array makes in a year is 1260Wh/day. PV array power follows the same seasonal pattern as GCS global solar radiation, with the most power coming from PV arrays in April, May, October, and November, which are in the middle of the season. General input menu of the homer software for generating weather data based on the location of Khartoum. The average values from the last 10 years will be used to figure out the hourly values. The high horizon and inclination will also be taken into account.As details in Figure 10 and Figure 11 Figure 12.

Figure 10. daily input /output diagram.

Figure 10. daily input /output diagram.

Figure 11. system output power distribution.

Figure 11. system output power distribution.

Figure 12. Daily system output.

Figure 12. Daily system output.

Figure 13. Array power distribution.

Figure 13. Array power distribution.

This similar with [21] and that power output is not used up to the full extent of what it could do. Because the batteries are full, a lot of power is being wasted. This is normal for the first few years of the GCS PV system, since it was made to meet the full energy needs of the local community after 5 years of growth in population and demand. Figure 13. The PV power measured is more than what the PVSyst5.0 simulation predicted. This difference could be because the charge controller doesn’t always work the way it should.

3.2.5. Simulation of the PV System

This design used the computer program simulators PVsys, the design includes 23 separates’ module in a single house. PVsyst is a PC software package for the study, sizing, simulation and data analysis of complete PV systems. It is a tool that allows to analyse accurately different configurations and to evaluate its results in order to identify the best technical and economical performances of different technological options for any specific part. The simulation results include a great number of significant data, and quantify the losses at every level of the system, allowing identifying the system design weaknesses. This should lead to a deep comparison between several possible technological solutions, by comparing the available performances in realistic conditions over a whole year. Losses between inverters and grid injection have been implemented. These may be either wiring losses when the transformer is external. Detailed loss diagram Figure 14 gives a deep sight of the quality of the PV system design, by quantifying all loss effects. Losses on each subsystem may be either grouped or expanded with detailed contributions it was 78.6%. The range of annual PV system energy output is between 0.5 - 2.17 kWh/kWp and average annual performance ratio of the PV system is 78.6 % (1.3 kWh/kWp) as shown in Figure 4.23. The performance ratio in Figure 14 and 15 shows the quality of a PV system and the value of 78.6% is indicative of good quality. Usually the value of performance ratio ranges from 60-80% similar to Goetzberger & Hoffmann (2005) detailed. This shows that about 21.3% of solar energy falling in the analysed period is not converted into usable energy due to factors such as losses in conduction, contact losses, the module and inverter efficiency factor, defects in components, etc.

Figure 14. Normalized and Loss Factor.

Figure 14. Normalized and Loss Factor.

Figure 15. System Output Energy.

Figure 15. System Output Energy.

3.2.6. Simulate the economic value

The most energy-efficient system for Khartoum has been optimized using HOMER while taking various load and wind-and-PV combination factors into account. The proposed scheme is depicted in the HOMER simulation tool in Figure 1. The National Renewable Energy Laboratory (NREL), USA’s HOMER software was used to identify the most energy-efficient renewable-based hybrid system solutions for Khartoum. HOMER is a micro-power optimization model. It analyses acceptable technology solutions based on cost and resource availability and includes a number of energy component models. [24].

HOMER Depending on the energy resources available in particular places, the Plan offered optimal layouts of suitable power plants. [26]. Analysis has been done for a single home user as well as combinations of 10, and 50 home users to find the most economically and technically viable options. HOMER makes a model of each system configuration by simulating its operation hour by hour over the course of a year. It is worked out how much renewable power is available and compared to how much electricity is needed. [26].

3.3. Renewable Resources

Due to the lack of hourly data, monthly averaged global radiation data from HOMER has been used. This data introduces a clearness index based on the latitude information of the chosen site, and the values of this index are displayed in Figure 15. Using the Graham algorithm, HOMER generates the synthesized 8760 hourly values for a year. [24] Consequently, a data sequence with realistic day-to-day and hour-to-hour fluctuation and autocorrelation is produced. In addition to height = 30 m, elevation = 3 m ASL, and surface roughness = 0.01 m, measured data from (SEI) were utilized to calculate the monthly mean wind speed (1999-2007).[27]. These monthly average data were generated by HOMER depending on additional parameters, such as Weibull factor "k" = 1.8 and autocorrelation factor, as depicted in Figure 4. [28] Certain systematic outcomes are dependent on the place at which the analysis is conducted. Combining renewable energy generating with conventional wind and PV power generation will make renewable energy sources’ electricity more reliable and economical. [28].

3.3.1. Photovoltaic Module

The annual average production of the 300Wp PV array is anticipated to be 343,3 kWh. This equals 940.5Wh/day, or 343.3/365 days. As expected, the PV output does not coincide with the global sun radiation on the tracking axis. This could imply that the batteries are frequently full, reducing the PV. Figure 6-71 depicts the average daily production (Wh) of the 300Wp PV array for each month, as well as the monthly worldwide sun radiation values on the POA. Figure 6-70 makes this outcome crystal obvious. [29]. This system provide more efficiency, planning flexibility, and environmental benefits than nonrenewable energy sources. [28] observed that PV As the price of everyday supplies rises, calculations of the electric behavior of sizable, uniformly lit-up PV arrays obtained at the scale of photovoltaic modules should become more beautiful. Cost of useable energy, which is comparable to a solar home system, ranges from 24 to 39 cents per kWh due to capacity constraints and a small amount of excess electricity. [30]that mean the scale of solar system the one of the main factor of design the hybrid system.

3.3.2. Battery with Control

Battery and controller were also formed as a significant part of the performance evaluation of a 10 kW PV power system because the system only took into account the DC load. [27]. At a cost of 10,000.00 SP per battery with charge controller, a Trojan Company isolated battery (Model: Trojan T- 105, Nominal V: 6V, Nominal Capacity: 225 Ah) was used. Optimized Homer for Algiers is made from of solar panels, wind turbines, and batteries.[31]. Local inspections and analysis using the HOMER model led to the conclusion that the following design requirements are ideal for a solar penetration of roughly 25%: Maldives PV system with 12 kWp power, 108 kWh battery capacity (50 batteries, 360 Ah, 6 V), and 12 kWp converter [27].

3.3.2. Constraints and Economics

The projected project’s duration is 25 years, with an annual real interest rate of 4%. (IEA, 2002). Due to the system’s ability to support a single user as well as a large number of users (10 to 50), but due to the user’s low load, the annual cost of operation and maintenance has been set at 500 SP. The system has adequate capacity, and its operating reserve is equivalent to 10% of its hourly load. The system, which consists of 50 households, has a low cost per kilowatt-hour, according to an analysis. [32] Table 3 displays the load demand for each group of residences, as well as the system architecture and a cost breakdown. Modeling and optimization results for the design of a hybrid system utilizing Homer software. Modelling demonstrated that, at 2004 prices, the NPC of the grid/RES hybrid arrangement is comparable to that of the grid-only supply, resulting in an RF of 73%, a payback period of 14 years, and a decrease of 65% in greenhouse gas emissions. A configuration of three Vest WECS (1.8 MW), an 800 kW converter, and three thousand five hundred batteries is anticipated to have the lowest NPC after twenty years and cost $19.1 million. This is despite the fact that a RES-only configuration has the potential to meet 100 percent of the power demand. [27] As a result, it was determined that the benefit-cost ratio and payback period were roughly 16%, 8 years, and 2 respectively. When considering energy use, environmental consequences, and remote accessibility, the results in Table 15 show that the majority of coastal regions are suitable for residential systems.[27]

It was found that, with a daily demand of 207 kWh, the combination of a 12 Kwh PV system with a battery backup capacity of 108 kWh would be ideal, provided the most suited approach for the employment of the two different sized diesel generators now available. Simply changing the diesel generator operation plan a little bit can allow for increased demand in the future. The topologies of this system demand higher initial resources but have lower overall net current costs due to the better operating efficiency of the PV generators. By reducing solar radiation, the PV system shows that system emissions are likewise reduced. The system incentive also considers providing adequate electricity for domestic usage as necessary for residents of this particular isolated area as authorized in [29].

Table 4. HOMER details results.

Table 4. HOMER details results.

Home	Load	PV module (KW)	Battery (quantity)	Initial Cost SP	Total NPC	COE (SP/ Wh)
Single	338 Wh/day 115KW Peak	0.15	2	71,890	111,470	59.5
25	6.8KWh /day 2.3KW peak	3.0	40	980,000	156,480	35.8
35	10.1KWh /day 3.5 KW	4.5	60	1178,890	2,463,300	43.8
45	13.5KWh /day 4.6 KW	6.0	80	2,188,330	2,999,590	54.7
55	16.9KWh /day 5.8 KW	7.5	100	3,567,220	2,940,985	60.1

4. Conclusion

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