A Techno-Economic-Environmental Feasibility Study of Residential Solar Photovoltaic/Biomass Power Generation for Rural Electrification: A Real Case Study

Abstract

To avert climate change, there has been a rise in the usage of green energy sources that are also beneficial to the environment. To generate sustainable energy in a financially and technically efficient manner, our research attempts to close the gaps. The potential of green sources like photovoltaic (PV) and biomass for a rural community southwest of Sohag Al Gadida City, Sohag, Egypt, is examined in this research considering its techno-economic (TE) and eco-friendly feasibility. The HOMER Pro v3.14 package is used as a scaling and optimization instrument, to calculate the price of the PV/biomass setup and the size and characteristics of its parts. This is to estimate the corresponding electrical production and reduce the total annual cost for the customer. The suggested system structure is validated through the presentation of simulation outcomes and evaluations utilizing MATLAB/SIMULINK R2022a. In addition, a TE-environmental investigation of the optimized PV/biomass structure is performed. The optimum structure is carefully chosen from the best four configurations using the demand predilection by analogy to the perfect technique based on the generation cost, operation cost, energy production, and renewable fraction. The results also indicate that using hybrid PV/biomass is an attractive choice with the initial capital cost (ICC: USD 8.144), net present cost (NPC: USD 11,026), a low cost of energy (LCOE: 0.184 USD/kWh), and the high renewable fraction (RF: 99.9%) of the system. The annual CO₂ emission performance of a PV/biomass system is much better than that of the grid alone and PV/diesel. This method might be applied in rural areas in other developing countries.

1. Introduction

Two major events prompted manufacturing countries to consider new and green energy (GE) as a complement to the expected growth in their national energy needs. These proceedings include the current global energy calamity and the growing awareness of the influence of fossil fuel (FF) emissions on the environment. To mitigate the potential harm caused by these emissions, anti-pollution regulations are currently being discussed and enacted into laws by the governments of industrialized countries [1,2,3]. One of the major energy sources that contribute to climate change is FFs. The energy information administration of the US has projected that energy consumption in global markets will increase by 57% from 447 quadrillion British Thermal Units (BTUs) in 2004 to 702 quadrillion BTUs in 2030 [4,5].

The globe’s population is growing at a fast rate, which is the primary driver of this rise. The fact that there currently exist over seven billion individuals on Earth and that number is predicted to grow by one billion every 12 years makes finding a remedy to this issue challenging [6]. There is a boost in the search and exploitation of GE sources due to the rise in global energy utilization (EU). In a century, the accessibility of fossil fuel-based energy might not be able to keep up with the constant rise in the EU [6,7]. Increasing the availability of GE forms is therefore a very attractive approach. To substitute conventional FFs, scientists and policymakers are searching for alternative GE sources like PV, wind power, biomass, and tidal power. Since GE forms are clean, kind to the environment, and help stop climate change, they are seen as one of the primary solutions to this issue. Many research projects and studies related to GE are being implemented by researchers in this field [8].

Accordingly, the most important research contributions of this paper are as follows:

• It provides a first-of-its-kind comparison of on/off-grid PV/biomass power generation to meet the electric load of residential buildings for rural electrification in Egypt.
• It conducts a feasibility study for an HGEF using HOMER Pro software and arrives at an optimal solution.
• Economic and microeconomic parameters based on the real market of Egypt are used, except for the default values in the HOMER Pro software.

2. Literature Analysis

Among the various GE projects being implemented around the planet, PVs hold great promise [9,10,11,12]. PV is a green energy technology that supports household electricity utilization. Getting energy from the sun at a price that is profitable, or even beneficial, as compared to other GE sources, is the overarching objective of photovoltaic innovation. In certain environments, PV power generation has become feasible; nevertheless, due to its widespread use in remote areas, numerous constraints must be investigated from an official, practical, and financial standpoint [13,14,15]. Compared with traditional technologies, PV technology has obvious environmental advantages in terms of energy generation. PV systems operate quietly and do not emit toxic gases or greenhouse gases (GHGs). PV power generation is an emission-free process. However, the common drawback of all solar power systems is that the production hinges on the availability of PV radiation [16,17,18]. However, the countries of the Middle East, especially Egypt, are among the countries with the highest accessibility of solar radiation throughout the year, which gives them a competitive advantage over other countries, as shown in Figure 1 [19].

GE production in Egypt has made remarkable progress over the past five years; this contributes to saving fuel and reducing the import bill while continuing to reduce carbon emissions. Egypt intends to raise the share of electricity production from GE sources in the power production mix to at least 42% by 2035, compared to 20% in 2023, according to data seen by the Energy Research Unit [20]. The state launched the National Project for Egyptian Rural Development in 2019 to help the neediest rural communities to develop, eliminate poverty, and create job opportunities, in order to provide a decent and sustainable life for citizens. The development of rural areas (RAs) depends heavily on electricity, as it is the driving force for any economic growth. In RAs, most persons face issues related to frequent power outages and meager power quality because the traditional grid is far from the specific location. Hence, a stable and superior electricity feed is crucial and essential to support sustainable expansion. Hence, energy challenges in such areas can be solved by implementing a microgrid power system (MGPS) [21].

This work offers a design of an off-grid hybrid green energy farm (HGEF) to provide electric power for a residential house in an RA, as shown in Figure 2. The off-grid HGEF system is suitable for RAs and remote locations where grid connectivity is difficult to integrate or where the grid is unstable. The off-grid HGEFs can also be made completely self-reliant by incorporating more battery capacity. Planned load profiles and PV system design utilizing HOMER Pro software are included in the system, which consists of PV panels, a biomass generator, batteries, and a converter that regulates electricity flow between the AC and DC buses. An off-grid HGEF requires more initial cost when compared to a grid-connected HGEF system, but it saves energy even when sunlight is not available [22]. The main benefits of the off-grid HGEFs are as follows: it is an environmentally friendly energy source; it is a completely independent system, which does not require traditional grid supplies to function; extra energy is kept in the battery for several days; it is best suited for RAs where the grid is placed at difficult locations such as in mountains or several islands; no government permissions or approvals are required for standalone systems; no power outages take place due to traditional grid failure; the energy stowed in the battery is used in the night; and it is the best replacement for diesel generators. As a result, a thorough analysis of earlier research using the HOMER Pro program was carried out in this field to identify the shortcomings that needed to be rectified with further advancements. A flow chart illustrating the overall methodology is shown in Figure 3. To address the demand for RAs,

Table 1. Average monthly values of daily solar radiation over Sohag Al Gadida City.

Month	Clearness Index	Daily SR * [kWh/m2/Day]	Daily Temperature ** [°C]
January	0.593	3.850	12.420
February	0.657	5.020	14.090
March	0.675	6.150	18.240
April	0.670	6.940	23.680
May	0.666	7.370	28.100
June	0.720	8.130	30.390
July	0.710	7.910	31.210
August	0.709	7.500	30.810
September	0.709	6.740	28.580
October	0.685	5.520	24.640
November	0.630	4.240	18.840
December	0.584	3.570	13.980

* NB: Monthly averages for global horizontal SR over 22 years (July 1983–June 2005). ** NB: Monthly averages for air temperature over 30 years (January 1984–December 2013).

, as shown in Appendix A, highlights prior pertinent works produced in the past five years on optimal HGEF design and techno-economic-environmental analysis utilizing the HOMER Pro program [23,24].

Based on the literature survey of previously published works mentioned in Appendix A, the following can be observed:

• By reviewing most of the previously published works, the PV/biomass renewable integration system has not yet been evaluated in Egypt.
• To enhance the HGEF in Egypt, no thorough TE-environmental assessment based on weather information has been carried out.
• There is no comprehensive comparison of HGEF based on TE-environmental factors, determining whether the on-grid or off-grid mode of operation represents the most cost-effective solution.

This paper is organized as follows: Section 1 and Section 2 present an overview of the literature review and theoretical background, including research contributions. Section 3 explains the methodology applied in designing and sizing the PV/biomass system, including study site selection and load evaluation. Section 4 illustrates the TE-environmental investigation of the addressed system, a summary of all components, and a sensitivity analysis. Section 5 provides the design of the HGEF and the outcomes of simulation and modeling implementation usng SIMULINK/MATLAB software. Finally, Section 6 addresses the conclusions.

3. Off-Grid PV/Biomass System Design

This section presents the feasibility study criteria and PV/biomass system sizing.

3.1. Solar Radiation in Egypt

The solar charts indicate that Egypt is one of the republics in the Sunbelt that relishes a high intensity of direct solar radiation (SR), as the typical SR ranges between 2000 and 3200 MWh/m²/year. Sunshine lasts from 9 to 11 h with a few overcast days throughout the year [25]. Solar energy (SE) shows high energy generation potential, reaching an economic potential of about 74,000 TWh/year [26,27,28]. Egypt’s solar map is shown in Figure 4.

3.2. Site Location

The proposed location for this case study is a rural village located southwest of Sohag Al Gadida City, Sohag, Egypt. Sohag Al Gadida City is located at 26°26′08″ N latitude and 31°40′19″ E longitude; it is also about 61 m above sea level, as shown in Figure 5. The SR weather can vary from urban to RAs [29,30]. Therefore, knowing the SR climate in the region is vital for determining the performance and sizing of systems.

3.3. SR Data for the Site

The “insolation value” refers to the quantity of usable sunshine that the panels can receive on a typical day in the least favorable month of the year. For evaluation, the most challenging month is chosen to make sure that the system will function all year long. In Sohag Al Gadida City, in December, the median daily sunlight is 6 h. An alternative way to comprehend the sunshine figure is as kWh/day of SE coming on each m² of PV panels at a latitude tilting [31]. SR data for the proposed site were obtained from the NASA Surface Meteorology and SE Database website. Table 1 shows the typical monthly values of global SR over Sohag Al Gadida City [32]. The chart shows that there are a lot of SE events at this location, particularly in the summer, when the mean daily SR in June was 8.13 kWh/m²/day [33,34]. Moreover, Figure 6 displays the monthly mean of the horizontal radiation for 22 years.

3.4. Load Data Analysis

The “bottom-up” strategy, which anticipates each daily load and adds them together to get an average daily total, is the recommended way for calculating PV system loads (Figure 7). This approach is straightforward for PV systems intended to supply basic loads like lights, TVs, satellite receivers, laptops, refrigerators, fans, and other appliances. Summer lasts for eight months, from March to October, whereas winter lasts for four months, from November to February. The suggested design process will be based on

Table 2. Daily electrical consumption of various appliances in the home.

Devices	Number of Devices	Power [W]	Daily Operating Time [h/d]	Average Daily EC [Wh]
Indoor lighting	10	12	12	1440
Outdoor lighting	5	18	6	540
Ceiling fan	4	50	10	2000
Refrigerator	1	50	24	1200
TV and sat-receiver	2	65	12	1560
Laptop	2	20	6	240
Phone chargers	3	18	1	54
Electric stove	1	500	1	500
Water heater	1	500	1	500
Washing machine	1	1000	2	2000
Other loads	-	500	1	500
Total		2733		10,534

, which lists the average daily electrical consumption (EC) of various household appliances (such as lights, televisions, laptops, refrigerators, fans, and other loads). The household’s typical daily total load demand is approximately 2733 W. Nevertheless, as Figure 7 illustrates, these loads only operate momentarily rather than continuously.

Lastly, a normal day’s estimation of each load in the house must be made and added up, as indicated in Table 2. The power usage (kWh/day) for each type of load in the house for each of the four seasons was calculated to create the daily load profiles. In comparison with the other seasons, summer has the greatest energy use (12.691 kWh/day).

3.5. PV Array Selection

PV solar panels are the only source to cover the daily energy demand of a home. So the PV module should be carefully selected according to several points. First, the PV module must have high efficiency, especially in the worst conditions. Also, solar insolation data should be collected according to the site and analyzed so that the design works effectively. Finally, a high-quality PV module must have a low degradation level to prevent energy loss during its lifetime of operation.

Table 3. Main characteristics of the selected PV module.

Parameters	Values
Max. rate power	250.29 W
Voltage at MPP (Vmpp)	30.9 V
Current at MPP (Impp)	8.1 A
Open circuit voltage (Voc)	36.6 V
Short circuit current (Isc)	8.75 A
Total energy of the array (Et)	12.691 kWh/day
Peak power of the array (Pp)	2.7 kW
Total number (TN) of modules (Nm)	20
TN of cells in series (Ns)	2
TN of cells in parallel (Np)	10

shows the main characteristics of the selected PV module. PV power output is dependent on solar irradiation (I_t) and the PV cell surface temperature (T_C) as follows [35]:

$$P_{PV(t)}=P_{PV(STC)}^{*}\times f_{PV}\times \frac{I_{(t)}}{I_{(STC)}^{*}}\times \left[1+{\alpha }_{PTC}(T_{C(t)}-T_{C(STC)}^{*})\right]$$

(1)

The symbols P*_PV, f_PV, I_(t), I*_(STC), α_PTC, and T*_C(STC) are the PV array rated capacity (W), derating factor (%), SR incident on the surface (W/m²), incident SR, power temperature coefficient (%/°C), and surface temperature, respectively.

$$T_{C(t)}=T_{a(t)}\times I_{(t)}\times \frac{T_{C(NOCT)}-20}{0.8}\times \left[1-\frac{{\eta }_{MPP}}{0.9}\right]$$

(2)

The symbols T_a(t), T_C(NOCT), and ${\eta }_{MPP}$ are the ambient temperature (°C), normal operating cell temperature (°C), and the efficiency at the MPP (%), respectively.

$${\eta }_{MPP}={\eta }_{MPP(STC)}\times \left[1+{\alpha }_{PTC}(T_{C(t)}-T_{C(STC)})\right]$$

(3)

3.6. Biomass Generator (BG) Selection

A 5-kW biomass generator, coupled with PV arrays, was chosen to satisfy the energy need (EN) of the residential home, due to the PV modules’ ability to fulfill EN over the daytime and produce no power at nighttime. In this instance, the EN is met by the biomass generator. The chosen BG can produce 2719 kWh of electricity annually at a constant generation cost of 0.633 USD/kWh, with a capacity factor of 3.1% and a load ratio of at least 25% [36].

Table 4. BG input variables for biogas.

Parameters	Value
Fuel	Biogas
Available biomass	0.1 Tonnes/day
Average price	0.001 USD/Tonne
Carbon content	55%
Density of biogas	1.2 kg/m3
LHV of biogas	5.50 MJ/kg
Gasification ratio	0.70 kg/kg
Fuel curve (FC) intercept	0.480 kg/h
FC slope	0.297 kg/h/kW

shows the main characteristics of the selected BG.

3.7. Battery Bank (BB) Selection

The BB is one of the most vital components necessary to store electrical energy in off-grid PV systems. It is used later in periods of reduced or no seclusion (at night). Furthermore, the BB must be adequately sized as it is the only source that is cast to feed a given load. If the BB is not sized correctly, loads will not be supplied with reliable power and will experience significant outages during the life of the system.

Table 5. Main specifications of the selected BB.

Parameters	Value
BB type	Lithium-ion
Nominal BB capacity	100 Ah
Nominal BB voltage (Vb)	12 V
Days of autonomy (DOA)	24 h
Charging/discharging cycles	3000
Depth of discharge (DOD)	80%
Round-trip efficiency of batteries (RTE)	85%
TN of BBs in parallel (Nbp)	6
TN of BBs in series (Nbs)	4
TN of BBs (Nb)	24

shows the main conditions of the selected BB.

3.8. Solar Inverter (SI) Selection

The SI needs to be able to grip the maximal electrical power that all electrical loads can draw when operating all at once. The total maximum power required = 5000 W, as shown in Figure 7. However, it is almost impossible to run all loads at the same time. Thus, the required SI size will be selected as shown in

Table 6. Main specifications of the carefully chosen SI.

Parameters	Value
Max. PV output power	5 kW
Max. output current protection	20 A
BB voltage	48 V
Max. charge current	75 A
Input AC voltage range	100–230 VAC
AC output voltage	100/110/220/230 VAC
SI efficiency	98%

.

3.9. Charge Controller (CC) Selection

For this system, the MPPT charge controller will be selected based on several criteria. Firstly, the charge controllers must adjust the voltage and current approaching from the PV panels that must be pumped into the BB to avert charging the BB too much and spread the operational life of the BB. Secondly, the CCs must be able to handle the maximum current that comes from the PV array. Finally, the rated power of the CC must be adequately greater than the maximum conceivable power produced by the PV array.

Table 7. Main specifications of the carefully chosen CC.

Parameters	Value
CC manufacturer	Sunny island
CC type	MPPT
Nominal voltage	180–230 V
Max. continuous power	2500 W
Input voltage range	110–230 V
BB capacity	100 Ah
Max. BB charging current	75 A
BB voltage range	36–60 V
CC efficiency	95%

lists the specifications associated with the CC.

4. Techno-Economic (TE)-Environmental Analysis (EA)

In this part, the TE-EA of a 5 kW off-grid PV/BG as implemented in the HOMER Pro simulator is described. To estimate the performance of on/off-grid PV/BG, programs like HOMER, RET Screen, PV system, etc., are used [37,38]. HOMER Pro software provides a way to assist in the design of the most cost-effective power system built on the proportions of each component in the system and the power source data. HOMER also allows for the comparison of a wide range of design possibilities chosen for their scientific and financial viability. The suggested system as it appears in the HOMER Pro simulator is depicted in Figure 8. An isolated PV system is made up of PV panels, a BG, a SI, CCs, and BB. This configuration delivers electrical energy to the load at the lowest NPC [31].

Table 8. The optimization results of HOMER.

Architecture				Cost				System		Biomass Generator
PV (kW)	BG (kW)	Battery (Number)	Converter (kW)	NPC (USD)	COE (USD)	OC (USD/Year)	ICC (USD)	RF (%)	TF (L/Year)	Hours	Production (kWh)	Fuel (L)
5.75	……	15	1.66	USD 10.332	USD 0.173	USD 203.88	USD 7.696	100	0	……	……	…….
5.94	1.5	15	1.83	USD 11.026	USD 0.184	USD 225.26	USD 8.114	99.9	2.40	2	3	2.40
……	1.5	5	1.5	USD 486.637	USD 8.13	USD 37.493	USD 1.950	0	3.995	3.329	4.994	3.995
39	1.5	……	1.87	USD 751.342	USD 12.55	USD 56.247	USD 24.213	0	5.974	4.978	7.467	5.974
……	1.5	……	……	USD 1.27 M	USD 21.22	USD 98.283	USD 250	0	10.512	8.760	13.140	10.512

Biomass generator: BG; Net present cost: NPC; Cost of energy: COE; Operating cost: OC; Initial capital cost: ICC; Renewable fraction: RF.

also shows the optimization results against its life cycle cost.

4.1. Technical Feasibility Assessment

Solar panels are highly susceptible to shadowing. Then, in PV array (PVA) installation, it is crucial to avoid being in the shadows, according to a technical study. A solar module is made up of many PV cells that are linked in series with metallic objects to produce a voltage that can be used. The other cells will have to lower their power to match the PV cell’s output if it loses power as a result of shade. Stated differently, there is no distinction between the cell and PV module halves. The PVA needs to be oriented correctly, that is, south at an angle equal to the latitude of the area, to receive the maximum amount of solar energy possible over the year. Generally speaking, PV panels should be horizontally inclined with a slope that is 15° higher than the longitude to work better during the winter. Conversely, the inclination angle of the PV panels should be 15° less than the longitude if the PV system is to be utilized in the summer, which is the best strategy for enhancing the summer efficiency of the PV panels [39,40]. The amount of SR that reaches the solar PVA in a day at the ideal angle can be used to calculate the sunlight period of the solar PVA intended for a residential setting:

$$t=\frac{\theta \times E_s}{1000\times \raisebox{1ex}{$W$}\!\left/ \!\raisebox{-1ex}{$m^2$}\right.}$$

(4)

The symbols t, ϴ, and E_S are the sunlight period of the panel (h), optimal angle (°), and SR energy (W/m²), respectively.

Based on the greatest current and BB voltage that the PVA generates at 25 °C, the greatest amount of power that the array can produce may be calculated. The following is one way to put this:

$$P_{PV}=I_{PV(max)}\times V_B$$

(5)

where P_PV, I_PV(max), and V_B are the maximal power (W), maximum current (A), and battery voltage (V), respectively.

The length of the PVA’s lighting and the highest electrical output accessible determine how much energy the array can produce each day:

$$E_{PV}=P_{PV}\times t$$

(6)

where E_PV is the amount of electricity (Wh).

The PVA’s energy-gathering capacity declines in proportion to the PV technique’s technology efficacy:

$${\eta }_e={\eta }_i\times {\eta }_{ccd}\times {\eta }_b\times {\eta }_{ca}$$

(7)

where ${\eta }_e$, ${\eta }_i$, ${\eta }_{ccd}$, ${\eta }_b$, and ${\eta }_{ca}$ are the efficiency of the equipment, inverter, CCdevice, BB, and the cable.

The temperature outside has an impact on the PV cell’s efficacy. Values acquired in a laboratory setting at a fixed temperature of 25 °C are used to calculate catalog data for PVA efficacy. The nominal operating cell temperature (NOOCT) of the PV cell is the temperature at which it operates in laboratory trials. A temperature difference of 20 °C between the PVA and the surrounding air is sufficient. The PVA’s functioning temperature can be determined using Equation (8) in a variety of ambient temperature conditions.

$$T_p=T_a+(T_{NOCT}+20)$$

(8)

where T_P is the PV panel temperature (°C), and T_a is the ambient temperature (°C).

The following formulas illustrate how a temperature differential can affect the voltage and current values that a PVA can produce:

$$V_{ocv2}=V_{ocv}-\Delta T\times 0.0842$$

(9)

$$I_{scc2}=I_{scc}-\Delta T\times 0.0086$$

(10)

where V_ocv is the open-circuit voltage (V) and I_scc is the short-circuit current (A).

The efficacy of the PVA is calculated using the following equation:

$${\eta }_{PVarray}=\frac{V_{ocv2}\times I_{scc2}}{V_{ocv}\times I_{scc}}$$

(11)

Despite being positioned at the ideal angle, the PVA receives changing angles of sunlight throughout the day. Consequently, when the angle deviates by 15° from the optimal angle, the PV system’s efficacy drops by 5%. The following formula establishes the PV system’s total efficacy:

$${\eta }_{te}={\eta }_{ea}\times {\eta }_p\times {\eta }_e$$

(12)

where ${\eta }_{te}$ and ${\eta }_{ea}$ are the total efficiency of the PV system and the efficiency of the angle inclination.

When calculating the total number of PV modules needed, the highest possible output needs to be considered. The following formula can be used to calculate the necessary quantity of solar PV modules to satisfy the energy requirements of such a system:

$$n_{PV}=\frac{E_r}{E_{PV}\times {\eta }_{te}}$$

(13)

where $n_{pv}$ is the number of PV modules, and E_r is the amount of energy required (Wh).

4.2. Economic Feasibility Assessment

The following equations are utilized in the model for evaluating a system’s net present cost (NPC), cost of energy (COE), and operational cost (OC) for financial evaluation. NPC is calculated as the total lifespan current expenditures of the system less the actual value of the earnings generated by the system [41]. As seen in Figure 9, current costs consist of capital costs, operation and maintenance (O and M) costs, and replacement costs (RC). As a result, the suggested technique only generates salvage profit for the duration of the gadget’s life. The total annual cost (TAC), which can be found using the following calculation, must be ascertained before the price of COE is known.

$$TAC=CR{F}_{(i,n)}\ast NPC$$

(14)

where CRF attitudes for capital recovery factor are determined by Equation (15).

$$CR{F}_{(i,n)}=\frac{i\times {\left(1+i\right)}^n}{{\left(1+i\right)}^{n-1}}$$

(15)

where $i$ is the annual real discount rate, as calculated in Equation (16), and $n$ is the year’s number.

$$i=\frac{i^{\prime }-f}{1+f}$$

(16)

where $i$ is the token discount rate, $i^{\prime }$ is the rate at which we can pirate cash, and $f$ is the price rise rate. Afterwards, using TAC and energy aided (E_served), the COE per kWh formed by the system is calculated using Equation (17).

$$COE=\left(\frac{(\raisebox{1ex}{$TAC$}\!\left/ \!\raisebox{-1ex}{$yr$}\right.)}{E_{served}(\frac{kWh}{yr})}\right)$$

(17)

In addition to COE and NPC, one other crucial energy system statistic is OC, which is the yearly total of all expenses and income, excluding annual capital costs, which are calculated by multiplying ICC by the capital recovery factor (CRF). For each component, NPC calculations are carried out using additional equations in HOMER Pro in accordance with Equations (3) to (5); it is advised to consult the HOMER Pro instruction manual [42]. Because BGs require more fuel than other HRES components, their running costs may be greater. At 250 USD/kW, batteries are the most expensive to replace. Based on Figure 9 and

Table 9. TE-environmental parameters.

HGEF Components	Parameters	Value	Unit
PV	Lifetime	25	y
	Hours of operating	4366	h/y
	Initial cost	600	USD/kW
	Replacement cost	0	USD/kW
	O and M cost	0.01	USD/kW/y
	CO2 emission	0.0225	kg/kWh
	Operation temperature	45	°C
	Efficiency	17.3	%
BG	Lifetime	216,000	h
	Hours of operating	603	h/y
	Initial cost	250	USD/kW
	Replacement cost	200	USD/kW
	O and M cost	0.59	USD/kW/y
	Fixed generation cost	0.633	USD/h
	CO2 emission	0.88	kg/kWh
BB	Lifetime	10	y
	Expected life	150,000	kWh
	Initial cost	250	USD/kW
	Replacement cost	250	USD/kW
	O and M cost	0.01	USD/kW/y
	CO2 emission	0.028	kg/kWh
	Efficiency	85	%
Converter	Lifetime	15	Y
	Hours of operating	8157	h/y
	Initial cost	300	USD/kW
	Replacement cost	200	USD/kW
	Efficiency	98	%

, which illustrate the same overall life period for the project and PV, a substitute cost of PV is USD 225.26.

4.3. Environmental Feasibility Assessment

As illustrated in Figure 10, the GHG pollution of the HGEF is assessed using both on- and off-grid methodologies. To accomplish this, the total yearly GHG pollution is calculated using the formula below [40]:

$$GHG={EM}_j\ast {\displaystyle \sum _{t=1}^{8760}{P}_j}(t)$$

(18)

The symbols j, EM_j, and P_j are the resource ranking, total CO₂ emissions (in kg/kWh), and energy produced per resource, respectively.

Table 10. The most important environmental GHG emission factors.

GHG Emission	Formula	PV/BG	PV/Diesel	Grid Only
Factors (kg/Year)
Particulate matter	PM2.5	0.00183	0.261	0.44
Carbon monoxide	CO	0.0301	4.3	4.85
Nitrogen oxides	NOX	0.0342	4.88	4.89
Sulfur dioxide	SO2	0	1.39	10
Carbon dioxide	CO2	0.9305	568	2307

compares the usual arrangement of grid units alone and PV/diesel units with on/off-grid GHG pollution.

4.4. Sensitivity Analysis (SA)

SA provides assistance in developing the proposed optimum HGEF system for the site in question. This allows observing the effects of some input variables included in the technical and economic analysis design. To investigate their impact on the ideal system, this is accomplished by assigning these variables several values within a specific range. The project term (20 and 25 years), average yearly load need (100%, 150%, and 175%), SR global horizontal, discount and inflation rates (ranging from 0 to 8), and departure factors (0.85 and 0.95) are the key factors examined. NPC and COE are thus impacted by the evaluation of many project and resource parameters. Table 9 provides a full description of the TE-environmental factors needed for HGEF modeling and simulation.

5. Simulation Results

The performance of the suggested system is investigated and tested using MATLAB/SIMULINK software, as shown in Figure 11. Table 9 lists the configuration parameters for each component used in the suggested HGEF to achieve the load needs.

The overall performance of a PV/BG is affected by the intensity of SR received by the PV modules and the ambient temperatures. Therefore, the input signals are converted to variable temperatures and different radiation levels to simulate system performance under more realistic environmental conditions, as shown in Figure 12 and Figure 13. Figure 12a shows the effect of different SR levels on the V_oc and I_sc of the module under a constant temperature of 25 °C. When SR levels gradually decrease from 1000 W/m² to 200 W/m², as shown in Figure 12a, a decrease in I_sc occurs from (52.5 A at 1 kW/m²) to (10.51 A at 0.2 kW/m²). Moreover, the module output power is also affected, as shown in Figure 12b: the DC output power decreases by 81% from 3003 W to 569.6 W.

Similarly, Figure 13a shows the impact of temperature increase on both the V_oc and I_sc of the PV module under a constant illumination of 1000 W/m². When the temperature gradually rises from 25 °C to 55 °C, Isc hardly increases by 0.81 A, as shown in Figure 13a, while V_oc decreases from 87 V to 79.84 V. Moreover, the PV module output power is also affected, as shown in Figure 13b: the DC output power decreases by 9.6% from 2998 W to 2711 W.

Figure 14 shows the simulation results for a period of (24 h × 60 m × 60 s), i.e., throughout a full day’s load and under variable temperatures and different radiation levels, which represent realistic environmental conditions. The power flow between the PVA, BG, BB, and load is shown in Figure 14. At the time point of 6.72 × 10⁴ s, the load power demand increases to 3886 W, as shown in Figure 14c, which exceeds the power generated by the PVA of 1873 W at the same moment, as seen in Figure 14a. Thus, the lack of power 2013 W from the BG and BB is compensated, as shown in Figure 14b,d, which means that the power is in discharge mode, as shown in Figure 14e, in which the power produced by the PVA and BG and the power injected into the BB complement each other. At the time point of 3.96 × 10⁴ s, the power demand decreases to 2279 W, as shown in Figure 14c. Energy is continuously injected into the BB because the power provided by the PVA and BG is 4534 W, as shown in Figure 13a,b, which exceeds the required load power. Thus, it is seen that the power of the BB is negative with a value of 2155 W, as shown in Figure 14d, which means that the power is being supplied (charging mode), as shown in Figure 14e.

• The proposed HGEF model attained from the HOMER program was optimally feasible and had ideal attributes for an NPC of USD 11,026, an energy generation cost of 0.346 USD/kWh, an RF of 99.9%, and a CO₂ emission of 0.9305 kg/year. The HGEF economic study revealed a payback period of 20 years and an annual real interest rate of 6% with an LCOE of 0.184 USD/kWh and an O and M cost of 50 USD/year.
• According to the previously described results, the HGEF structure involves 20 PVAs with a total DC output power of 5 kW, and a 1 kW BG with a 2 kW solar inverter to meet all electrical loads. A storage system (lithium-ion batteries) consisting of 23 BBs with a total capacity of 612.5 Ah and a total energy of 29.4 kWh at 48 V. Depending on the required electrical loads and the amount of energy generated by the proposed HGEF, the BB is charged and discharged accordingly with a storage depletion of 0.201 kWh/year for 15 years.
• Simulation results are provided to confirm the suggested HGEF configuration using HOMER Pro and MATLAB/SIMULINK software. The simulation results show that both on-grid and off-grid HRES are economically feasible and more reliable and sustainable than using grid-based electricity or PV/DG alone.
• The grid-connected HGEF provides a more reliable, unchanging, and low-priced power supply with an energy cost of 0.18–0.28 USD/kWh. However, it depends on the location of the network infrastructure and resource capabilities and accessibility.
• In contrast, the proposed isolated HGEF offers very low annual CO₂ emissions and a more independent energy supply. Nevertheless, the price of energy production is higher (COE: 0.184 USD/kWh) due to the capacity and cost of BG and BB.

6. Conclusions

This research aims to analyze the TE-EA of an off-grid PV/BG to feed the electrical load of a house in a rural village, in Sohag Al Gadida City, Egypt. The results concluded with the following:

• According to this analysis, the PV/BG hybrid configuration is the most efficient layout out of all options to satisfy the local power need at a minimal energy price. The results also indicate that using hybrid PV/biomass is an attractive choice with the initial capital cost (ICC: USD 8.144), net present cost (NPC: USD 11.026), a low cost of energy (LCOE: 0.184 USD/kWh), and the high renewable fraction (RF: 99.9%) of the system.
• The TE-EA of various off-grid HGEF strategies relying on available local resources was studied. Furthermore, a sensitivity analysis was performed for various structures to verify the effectiveness of the optimized system even under other design constraints, such as changes in project lifetime and PV array reduction factor at different % loads.
• Consequently, the decision between the two systems ought to be based on the particular requirements and constraints of the application and its place. It is crucial to remember that combining the two systems might give Egypt access to more reliable and adaptable energy sources. Therefore, the anticipated green power production system may support both the environmental and economic well-being of the RA.

It is recommended that future research examine the viability and potential of such HGEFs in various scenarios including low-cost, large-scale storage systems, such as seasonal hydrogen storage and thermal energy storage, together with fuel cells.