Feasibility Study of the Grid-Connected Hybrid Energy System for Supplying Electricity to Support the Health and Education Sector in the Metropolitan Area

Abstract

One of the biggest issues impeding a country’s progress is the lack of power. To overcome this issue, hybrid renewable energy systems (HRES) play an important role. Due to rising consumption and diminishing resources globally, sustainability has recently attracted more attention. Bangladesh has access to renewable energy sources, including solar, micro-hydro, biomass, wind, and others. The objective of this research is to minimize the net present cost (NPC), cost of energy (COE), and CO₂ emissions of the suggested electricity network using the Hybrid Optimization Model for Multiple Energy Resources (HOMER) Pro Software. This investigation explores the possible use of a hybridized energy system (i.e., solar, wind, and diesel) with battery storage in Bangladesh’s northern area. Utilizing HOMER Pro software, an optimal grid-connected system is chosen after evaluating the techno-economic viability of several configuration options. For the Rangpur metropolitan region, seven distinct grid-connected solutions with stationary renewable sources are simulated. The HRES is designed to meet demands for hospital, diagnostic, school, and operation theatre loads of 3250.00 kWh, 250.00 kW maximum requirement, and 570.00 kWh, 71.25 kW maximum electricity demand, respectively. Multivariate linear regression (MLR) is used to assess the suggested optimal combination in terms of system size, cost, technical performance, and environmental stability. The findings show that the metric real-time rate (annual) has emerged as the most advantageous option since economic criteria like total NPC and COE are preferred above others.

1. Introduction

Energy is now understood to be the yardstick by which economic development and the improvement of the standard of living for everyone are measured. The world’s population is expanding, technological advances are being adopted, and electricity consumption is swelling at an astounding level, resulting in a significant divergence between supply and demand for energy. Gas and biomass are two sustainable, environmentally friendly, and renewable resources that may be used to build a more energy-efficient economy. However, when employed in a standalone, flexible framework, renewable resources are subject to several limitations. In order to solve these problems, an integration of renewable systems made of solar and wind energy resources mixed with other sources is created. Between 2018 and 2050, the world’s electricity demand is expected to rise by 50% as a result of increasing industrialization and the world population [1].

In [2], researchers illustrate the arrangement of hybrid PV/DG/Battery sources with the help of HOMER Pro software in Malaysia. They investigated the simulation to find the best alignment in terms of technological and economic points of view and found that the PV/DG/Battery becomes optimal compared to others. According to [3], the authors evaluated the network of HRES as PV/DG/Battery with the grid-connected model for better technological and economical ways to obtain the optimal HRES. According to the research in [4], the authors focused on finding the best HRES model in the constraints of the NPC and COE. They found the best-optimized results for COE of 0.05744 USD/kWh and NPC of USD 180.2 M. In [5], researchers investigated optimal HRES considering COE, NPC, and the environmental pollutants with the help of HOMER Pro software and validated with the genetic algorithm (GA) for optimizations. As a result, they obtained that PV/DG/pump hydro storage (PHS) was the most economical, and the value became COE as 0.345 USD/kWh. In [6], it has been analyzed and obtained as the most economical configuration of the PV/WT/DG/LA-Battery in terms of the lowest economic values of NPC and COE. According to [7], researchers exhibit the analysis between the PV/DG and the WT/DG. They investigate the former to become optimized compared to the others in terms of reducing emissions using HOMER software. In reference [8], the authors observed the six different arrangements of the HRS and simulated them with the HOMER Pro software. The results of the simulations revealed that the PV/DG/Battery exhibited the lowest value of COE at 0.238 USD/kWh and NPC at USD 233,693. Table 1 represents the summary of the Hybrid energy system for off-grid and on-grid systems that have been published in recent years.

Even though electricity accessibility is a basic human right, numerous sections of society do not have access to electricity. Contrary to many rural communities, metropolitan parts of cities often enjoy a constant supply of energy 24/7, 365 days per year. According to a 2019 World Bank estimate, 17.47% of rural inhabitants and 10% of the global population did not have access to power [9]. In other places, the length of power outages has quadrupled, adding to the anguish of those who must endure the heat at night. In a double blow, water pumps are inoperable without power. The intensive care units (ICUs), coronary care units (CCUs), and operating rooms are now operating on generators in Bangladesh’s cities, but these services might be interrupted in the event of a protracted power loss. It will be challenging for hospitals to continue providing their medical services if there is a prolonged power outage.

Table 1. Summary of hybrid energy system using different methods.

Hybrid Energy System	Study Area	Connectivity Type	Methods/ Software	Load Demand	Objective Function: COE(USD/kWh)	Year	Ref.
PV/WT/ Battery	Spain	Off/On-grid	HOMER	Household	0.36	2022	[10]
PV/DG/ Battery	Ecuador	Off-grid	HOMER	Community	0.421	2022	[11]
PV/DG/Wind/Battery	Addis Ababa, Ethiopia	Off-grid	HOMER	Vehicle Charging	0.196	2022	[12]
PV/DG/Wind/Battery	Island in the Philippines	Off-grid	HOMER	Community	0.167	2022	[13]
PV/DG/Wind/Battery	Bangladesh	Off-grid	HOMER	Community	0.358	2022	[14]
PV/DG/Wind/Battery	Nigeria	Off-grid	HOMER	Household	0.564	2022	[15]
PV/Biogas/ Battery	Ghana	Off-grid	HOMER	Household	0.265	2022	[16]
PV/DG/Wind/Battery	Nigeria	Off-grid	HOMER	Household	0.269	2022	[4]
PV/Wind/DG/Battery	Cameroon	Off-grid	HOMER	Household	0.331	2021	[17]
PV/Wind/DG/Battery	West Bengal, India	Off-grid	HOMER	Household	0.197	2018	[6]
PV/DG/ Battery	Nigeria	Off-grid	HOMER	Household	0.218	2019	[18]
Wind/PV	Turkey	Off-grid	HOMER	Household, School, Mosque	0.365	2022	[19]
PV/DG/ Battery	Chad	Off-grid	HOMER	Household	0.323	2022	[20]
PV/DG/ Battery	Nigeria	Off-grid	HOMER	Household	0.490	2022	[3]
PV/DG/Wind/Battery	Milwaukee, USA	Off-grid	-	Household	0.43	2021	[21]
PV/DG/ Pico-Hydel	Kerala, India	Off/On-grid	HOMER	Household	0.15	2012	[22]
PV/DG/Wind/Battery	Uttarakhand, India	On-grid	MATLAB	Household	0.25	2021	[23]
PV/DG/Wind/Battery	Nigeria	On-grid	HOMER/ MATLAB	Household	0.147	2022	[24]
PV/Wind/Bio-mass/Hydro	Cameroon	Off-grid	HOMER	Household	0.256	2018	[25]
PV/DG/ Battery	Tazouta, Morocco	Off-grid	HOMER	Household	0.356	2019	[26]
PV/Wind/ Diesel	Muqdadiyah, Iraq	Off-grid	HOMER	Household	0.321	2016	[8]

Table 1. Summary of hybrid energy system using different methods.

Hybrid Energy System	Study Area	Connectivity Type	Methods/ Software	Load Demand	Objective Function: COE(USD/kWh)	Year	Ref.
PV/WT/ Battery	Spain	Off/On-grid	HOMER	Household	0.36	2022	[10]
PV/DG/ Battery	Ecuador	Off-grid	HOMER	Community	0.421	2022	[11]
PV/DG/Wind/Battery	Addis Ababa, Ethiopia	Off-grid	HOMER	Vehicle Charging	0.196	2022	[12]
PV/DG/Wind/Battery	Island in the Philippines	Off-grid	HOMER	Community	0.167	2022	[13]
PV/DG/Wind/Battery	Bangladesh	Off-grid	HOMER	Community	0.358	2022	[14]
PV/DG/Wind/Battery	Nigeria	Off-grid	HOMER	Household	0.564	2022	[15]
PV/Biogas/ Battery	Ghana	Off-grid	HOMER	Household	0.265	2022	[16]
PV/DG/Wind/Battery	Nigeria	Off-grid	HOMER	Household	0.269	2022	[4]
PV/Wind/DG/Battery	Cameroon	Off-grid	HOMER	Household	0.331	2021	[17]
PV/Wind/DG/Battery	West Bengal, India	Off-grid	HOMER	Household	0.197	2018	[6]
PV/DG/ Battery	Nigeria	Off-grid	HOMER	Household	0.218	2019	[18]
Wind/PV	Turkey	Off-grid	HOMER	Household, School, Mosque	0.365	2022	[19]
PV/DG/ Battery	Chad	Off-grid	HOMER	Household	0.323	2022	[20]
PV/DG/ Battery	Nigeria	Off-grid	HOMER	Household	0.490	2022	[3]
PV/DG/Wind/Battery	Milwaukee, USA	Off-grid	-	Household	0.43	2021	[21]
PV/DG/ Pico-Hydel	Kerala, India	Off/On-grid	HOMER	Household	0.15	2012	[22]
PV/DG/Wind/Battery	Uttarakhand, India	On-grid	MATLAB	Household	0.25	2021	[23]
PV/DG/Wind/Battery	Nigeria	On-grid	HOMER/ MATLAB	Household	0.147	2022	[24]
PV/Wind/Bio-mass/Hydro	Cameroon	Off-grid	HOMER	Household	0.256	2018	[25]
PV/DG/ Battery	Tazouta, Morocco	Off-grid	HOMER	Household	0.356	2019	[26]
PV/Wind/ Diesel	Muqdadiyah, Iraq	Off-grid	HOMER	Household	0.321	2016	[8]

From Table 1, it has been found that optimization has been conducted in many areas and under various conditions by utilizing renewable energy as well as non-renewable energy sources. The above simulations have been carried out for household load, community load, vehicle charging load, etc. However, in this study, the hospital load along with the operation theater (OT) load have been considered. The analysis for the hybrid energy system has been done with the HOMER Pro software and simulated for grid-connected (simple rate, real-time rate, scheduled rate, and grid extension) systems. The optimized results have been compared with the multivariate linear regression (MLR) that has not been applied for assessment with HOMER Pro software. This analysis has been a great way to investigate the quick and accurate optimization of hybrid energy systems.

2. Methodology

2.1. Proposed Power System Design

A PV and battery storage system is a direct source of DC load, whereas an AC load is coupled to a diesel-powered generator. The operating room load is attached to the DC source, whereas the hospital, diagnostic, and educational loads are related to the AC source. A bidirectional converter is used to convert the DC produced by the PV array into AC for residential applications. To provide the insufficient quantity of needed energy and/or store the system’s surplus energy, a power storage technology, such as a battery, is utilized. The proposed system of the design is shown in Figure 1.

The Framework of the Hybrid Energy System

As illustrated in Figure 1, the research demonstrates the consistent architecture of optimizing methodologies for a hybrid power system to meet the load requirements of the selected on-grid area. Using HOMER involves three major stages. The structure of the system architecture (Figure 1), the required electric demand, and the available RE sources (solar radiation, wind data, and temperature data) at the selected location must all be assessed before conducting a HOMER study. The hardware components indicated in

Table 2. Hospital loads.

Components	Quantity	Power (W)	Total Power (W)	Operating Hours per Day	Energy Used (kWh/d)
Led Lights	700	10	7000	10	70
Fan	500	80	40,000	10	400
Exam Light	12	20	240	2	0.48
Microscope	9	30	270	2	0.54
Rotator	8	60	480	1	0.48
AC Refrigerator	20	500	10,000	8	80
Centrifuge	10	600	6000	1	6
Spectrophotometer	10	63	630	1	0.63
Autoclave	6	630	3780	1	3.78
Dental Chair	9	710	6390	0.5	3.20
Compressor	12	370	4440	2	8.88
Jet Sonic Cleaner	5	45	225	2	0.45
Amalgam Filling Machine	4	80	320	1	0.32
X-Ray Machine	4	200	800	1	0.80
Cd4 Machine	3	200	600	4	2.4
Hematology Analyzer	4	230	920	4	3.68
Blood Chemical Analyzer	4	45	180	6	1.08
Air-Conditioning Unit	20	1500	30,000	8	240
Computer	20	120	2400	4	9.6
Radio	6	30	180	8	1.44
Water Pump	6	3730	22,380	5	111.9
Cell Phone Charger	300	15	4500	4	18
Miscellaneous					6.34
Total					920

(Section 2.3) have technical and budgetary details available in the HOMER application. The above-mentioned statistics are also analyzed by HOMER software, which requires a list of system structures with the lowest cost of energy (COE, USD/kWh), and net present cost (NPC, USD). To meet the user’s specified load needs and other technical restrictions like fuel cost and extra energy, detailed modeling of the technical and economic facts of each configuration is required. The optimal arrangement is selected when the results of many systems are evaluated in the post-HOMER study, taking technical and environmental factors into account. The major accomplishments from the perspectives of life cycle emission (LCE), recovery factor (RF), and society are examined. This study examined existing situations and investigated based on information about costs and the environment that had been improved, despite HOMER’s absence of social issue information. The HRES of the proposed system with the framework for the electrification of the metropolitan area is presented in Figure 2.

2.2. Demographic Information and Weather Information

This effort took into account the metropolitan area of Rangpur city in Bangladesh (25°44.6′ N, 89°16.5′ E), which is considered to be a grid-connected area. The clearness index for the chosen location is 0.65, and the annual mean sun irradiation is 4.57 kWh/square meter per day. The specified area’s monthly sun radiation is shown in Figure 3. The NASA-applied climatology is used to choose the monthly average wind resource data. The average yearly wind speed is 2.85 m/s. As indicated in Figure 4, which illustrates the monthly median wind speed of the chosen location, March provides the greatest wind velocity of 3.28 m/s, and October stands for the minimum wind velocity of 2.42 m/s. The uppermost value of the atmospheric temperature is provided in June. The average monthly ambient temperature of the specified area is shown in Figure 5. As a consequence of insufficient wind speed, it is not possible to generate electricity from wind turbines in the chosen location.

2.3. Load Assumption

This research took into account 25 schools, 15 diagnostic centers, a medical college hospital with 500 beds, a general hospital with 200 beds, and more. The combined daily electrical load demand for the hospital, diagnostic center, and school is 3250 kWh, with a peak demand of 240 kWh. It is decided by the daily and hourly data measurements. Table 2,

Table 3. Loads in ICU.

ICU Loads	Quantity	Operating Hours per Day (h/d)	Rating (W)	Wh/d
Main Monitor	1	24	200	4.8
Bed Monitor	10	24	60	14.40
Ventilator	10	24	52	12.48
Infusion Pump	10	24	52	12.48
Syringe Pump	10	24	52	12.48
Lighting Units	20	24	72	34.56
DC Shock	2	4	50	0.40
Suction	4	4	60	0.96
Electrocardiograph	2	4	60	0.48
Portable X-ray	1	0.6	3800	2.28
Blood Analyzer	1	6	2500	15
ECO Doubler	1	4	50	0.20
Portable Light	5	6	250	7.50
Total				118

,

Table 4. Diagnostic center loads.

Components	Rating (W)	Quantity	Operating Hours per Day	Total Demand (kWh/d)
Air-Conditioning Unit	1000	5	8	40
AC Refrigerator	500	1	24	12
X-Ray Machine	200	1	1	0.2
Centrifuge	600	1	1	0.6
Autoclave	630	1	1	0.63
Analyzer	275	2	4	2.2
Light	10	30	10	3
Fan	80	12	10	9.6
Water Pump	1500	1	3	4.5
Miscellaneous				5
Total				77.93

and

Table 5. School loads.

Components	Rating (W)	Quantity	Operating Hours per Day (h/d)	Total Demand (kWh/d)
Light	10	50	6	3
Fan	80	80	4	25.6
PC	125	1	6	0.75
Water Pump	1492	1	2	2.98
Total				32.33

show the workloads at hospitals, ICUs, diagnostic centers, and schools. The DC source of the system is used to calculate the electric load demand for the operating theater, which is 570 kWh/d (peaking at 71.25 kWh/d). Operation theater burden is shown in

Table 6. Operation theatre load calculation.

Components	Rating (W)	Operating Hours (h/d)	Total Demand
Batteries of the Operation Table	800	12	9.6
PC	175	24	4.2
Anesthesia Table and Monitor	276	8	2.21
Monitor (wall)	202	8	1.62
Monitor (arms) (x3)	84	8	0.67
Small Monitor (arm)	30	8	0.24
Mattress Heater	1035	8	8.28
Blood Heater	1150	8	9.2
Electrosurgical Unit	1150	8	9.2
Insufflator (Laparoscopy Tower)	220	8	1.76
Video (Laparoscopy Tower)	150	8	1.2
Xenon Light	130	8	1.04
Electrosurgical Unit	1150	2	2.3
Laser	2800	0.3	0.84
Ultrasound Machine	580	8	4.64
Total			57

. Figure 6 and Figure 7 show the daily load profile for the suggested design and operation theater load, respectively. The OT load data has been obtained from the survey and found active from 10:00 A.M. to 1:00 P.M. and 9:00 P.M. to 12:00 A.M. However, the OT was found to be off the rest of the time during the day, and for these reasons, there are gaps in Figure 7.

2.4. Hardware Components for Modeling

The hardware components for the modeling are presented in

Table 7. Data about the technical and financial aspects of the embedded systems.

Components	Description	Capital Cost (USD/kW)	Replacement Cost (USD/kW)	Operation and Maintenance	Lifetime
Solar Cell	Name: Generic Flat Plate PV Panel Type: Flat Plate Rated Capacity (kW): 1 Manufacturer: Generic	1000	USD 0.00	10 USD/y	25 y
Non-renewable Energy Sources (DG)	Name: Auto-size Genset Capacity: 340 kW Fuel: Diesel Fuel Curve Intercept: 7.23 L/h Emissions: CO (g/L Fuel): 16.5	370	290 USD/kW	0.050 USD/h	15,000 h
Lead Acid Battery	Nominal Voltage (V): 12 Nominal Capacity (kWh): 1 Nominal Capacity (Ah): 83.4 Roundtrip Efficiency (%): 80 Maximum Charge Current (A):16.7 Maximum Discharge Current (A): 24	300	400 USD/kW	10 USD/y	10 y
Converter	Name: System Converter Capacity (kW): 1 Conversion Efficiency: 95%	300	300 USD/kW	USD 0.00	15 y
Discount Rate (%)	7.09
Fuel Cost (USD/L)	1.14

. In this case, solar cells (PV), non-renewable energy sources, diesel generators (DG), and lead acid batteries as storage and converter have been taken into account for the hybrid energy system [5,27,28].

2.4.1. Modeling of Solar PV

A solar PV module’s performance varies depending on the operational and environmental circumstances. A generic 1 kW flat plate PV module is utilized in this hybrid system. Solar radiation strikes it, and other eco-friendly issues affect how much power the PV system produces. Formula to determine the PV element’s outcome [29]:

$$P_{pv}=Y_{PV}{f}_{PV}\left(\frac{{\stackrel{-}{G}}_T}{{\stackrel{-}{G}}_{T,STC}}\right)\left[1+{\alpha }_p\left(T_c-T_{c,STC}\right)\right]$$

(1)

where Y_PV is the PV element’s authorized capability, or its wattage under typical test circumstances [kW], f_PV is the strength reduction factor for PV [%], ${\stackrel{-}{G}}_T$ is the specific time step in the process, solar radiation strikes the PV array [kW/m2], ${\stackrel{-}{G}}_{T,STC}$ is absorbed radiation under typical test circumstances [1 kW/m²], α_P is the thermal energy coefficient [°C], T_c is the specified time step to take Photovoltaic temperature [°C], T_c,STC is the Photovoltaic module temperature during routine testing [25 °C]. The formula may be used to get the cell temperature Tc [°C] from the solar module’s equilibrium [30]:

$$\tau \alpha G_T={\eta }_c{G}_T+U_L(T_c-T_a)$$

(2)

where τ is any cover’s ability to block sunlight from reaching the Solar panel [%], α is the photovoltaic element’s sunlight absorptance [%], G_T is the PV module being hit by sun energy [kW/m²], η_c is the PV module’s conversion efficiency [%], U_L is the rate of heat transmission to the environment [kW/m² °C], T_c the heat of PV cells [°C], T_a is the surrounding warmth [°C]. According to the aforementioned formulae, the renewable radiation received by the photovoltaic system is in equilibrium, and the electrical output plus the heat transfer to the surroundings. Solving this expression for module temperature will result in [30]:

$$T_c=T_a+G_T(\frac{\tau \alpha }{U_L})(1-\frac{{\eta }_c}{\tau \alpha })$$

(3)

In the expression of the previous section, HOMER estimates that photovoltaic conversion efficiency has 0.9. This presumption somehow does not produce a sizable inaccuracy, since the term “ηc/τα” is minor in comparison to unity. As it does under the direction of a maximum power point tracker (MPPT), HOMER expects that the photovoltaic panel constantly works at its MPPT. In other words, HOMER posits the constant equality of the power density and the MPPT efficiency [30]:

$${\eta }_c={\eta }_{mp}$$

(4)

where η_mp is the Photovoltaic element’s effectiveness at its MPP [%].

Therefore, researchers may substitute MPP for ηc with $N_{mp}$ in the formula for the cellular temperature to produce [30]:

$$T_c=T_a+(T_{C,NOCT}-T_{a,NOCT})(\frac{G_T}{G_{T,NOCT}})(1-\frac{N_{mp}}{\tau \alpha })$$

(5)

2.4.2. Modeling of DG

When renewable energy systems (RESs), such as solar panels and battery banks, are incapable of fulfilling the load demand, the function of a DG is to provide the necessary power. As soon as the auto-size DG is chosen, HOMER determines the DG’s capacity using the peak electric demand (+10%). The hybrid system used in this case has a lifespan of 15,000 h and 340 kW DG. The DG is predicted to be 38% efficient. The generator’s minimum load ratio is 10%. The anticipated amount of fuel used to produce power is as follows [31]:

$$F=F_0{Y}_{gen}+F_1{P}_{gen}$$

(6)

where F is the amount of fuel usage [L/h], F₀ is the access factor for the generator fuel curve [L/h-kW_rated], F₁ is the inclination of the generator fuel graph [L/h-kW_output], Y_gen is the generator’s maximum power output [kW], P_gen is the generator’s yield for this time interval [kW]. With perhaps a concentration of 820 kg/m³, diesel fuel has a significant impact on the price of 43.2 MJ/kg. The duty factor (DF) (kWh/start-stop/y) in the research that follows is the proportion of power production by additional main forces to the overall beginning, and it may be computed utilizing the formula [31]:

$$DN=\frac{Pgen}{Ns/s}$$

(7)

where Ns/s denotes the number of starts and stops [32].

2.4.3. Modeling of Battery

The summation of the accessible and constrained power determines the overall quantities of electricity held in the storage system during any specific moment [33], therefore,

$$Q=Q_1+Q_2$$

(8)

where $Q_2$ is the constrained power and $Q_1$ is the power that is currently accessible. The following formula, which employs mathematics, provides the extreme energy that the store potentially receives during a particular period of time [34].

$$P_{batt,dmax,kbm}=\frac{-kc{Q}_{max}+k{Q}_1{e}^{-k∆t}+Qkc(1-e^{-k∆t})}{1-e^{-k∆t}+c(k∆t-1+e^{-k∆t})}$$

(9)

Furthermore, one may demonstrate that the following formula gives the highest amount of energy the storage component can disperse over a certain duration of time (δt) [34].

$$P_{batt,cmax,kbm}=\frac{k{Q}_1{e}^{-k∆t}+Qkc(1-e^{-k∆t})}{1-e^{-k∆t}+c(k∆t-1+e^{-k∆t})}$$

(10)

The permitted limit for the energy into or out of the storing deposit in almost any single phase is provided by the two equations that came before it. Required charge capacity is subject to two extra restrictions imposed by HOMER. Recognition of segment is necessary for figuring out the maximum voltage and current levels for further details. The aforementioned formulas are used by HOMER to quantify the number of accessible and constrained electricity at the conclusion of the sampling interval after determining the optimum charge or discharge electricity [35].

$$Q_{1,end}=Q_1{e}^{-k∆t}+\frac{(Qkc-P)(1-e^{-k∆t})}{k}+\frac{Pc(k∆t-1+e^{-k∆t})}{k}$$

(11)

$$Q_{2,end}=Q_2{e}^{-k∆t}+Q\left(1-c\right)\left(1-c\right)\left(1-e^{-k∆t}\right)+\frac{P(1-c)(k∆t-1+e^{-k∆t})}{k}$$

(12)

In Equations (11) and (12), Q₁ is the electricity accessible [kWh] at the start of the time interval, Q₂ is the initial constrained electricity [kWh] for the time interval, Q_1,end, is the electricity obtainable [kWh] at the conclusion of the time phase, Q_2,end is the confined electricity [kWh] at the period step’s conclusion, P is power entering (positively) or exiting (negatively) from the reserve in kW, Δt is the period stage’s duration [h].

2.4.4. Modeling of Inverter

Each network with components, including both AC and DC, needs a conversion. The inverter’s primary function is to convert DC current from the DC bus into AC for AC loads. The formula to calculate terminal voltage electricity is as follows [33]:

$$P_{in}=\frac{P_{out}}{P_{inv}}$$

(13)

where $P_{in}$ is the entrance of Dc electricity to the converter in kW, $P_{out}$ is the AC output power [kW], $P_{inv}$ is the inverter efficiency (95%).

2.5. Net Present Cost (NPC)

The present value of all the expenses connected with establishing and maintaining the element throughout the course of the development is subtracted from the present value of all the returns it generates throughout that same period to get the constituent’s NPC (also known as life-cycle cost). The NPC is found utilizing the following formulas and is used to order the outcomes of the HOMER analysis [36,37]:

$$C_{NPC}=\frac{C_A}{CRF(i,N)}$$

(14)

$$CRF\left(i,N\right)=\frac{i{(1+i)}^N}{{(1+i)}^N-1}$$

(15)

$$i=\frac{\acute{i}-f}{1+f}$$

(16)

where $C_A$ is total annualized cost (USD/y), CRF is capital recovery factor, i is annual real interest rate [%], $\acute{i}$ the nominal interest rate [%], F is annual inflation rate [%], N is the project lifetime (in the year). In this study, a discount rate of 7.09% and an annual inflation rate of 7.48% are considered.

2.5.1. Cost of Energy (COE)

HOMER defines the COE as the platform’s median price for every kWh of usable generated electricity [38].

$$COE=\frac{C_{ann,tot}-C_{boiler}{H}_{served}}{E_{served}}$$

(17)

where $C_{ann,tot}$ is the overall structure cost on a yearly basis [USD/y], $C_{boiler}$ is the marginal cost of a boiler [USD/kWh], $H_{served}$ is the served thermal load overall [kWh/y], $E_{served}$ is the served electricity consumption overall [kWh/y].

2.5.2. Life Cycle Emission (LCE)

The quantity of corresponding ${\mathrm{C}\mathrm{O}}_2$ releases from the electricity required to create, transmit, and recycle the infrastructure was calculated in this study using LCE [33]. To determine LCE, the following Equation (18) has been used:

$$LCE=\sum _{i=1}^{x}BiEl$$

(18)

where El (kWh) provides information regarding the amount of electricity generated and preserved in each piece of equipment or item, while Bi (kg ${\mathrm{C}\mathrm{O}}_2$-eq/kWh) shows the system’s comparable ${\mathrm{C}\mathrm{O}}_2$ emissions during a lifetime, the number x represents the number of components utilized to define the structure.

2.6. MLR Comprehensive in Machine Learning

2.6.1. Regression

Two theories are approached using regression [39]. First, regression assessments are frequently utilized for forecasting and prediction, where machine learning is heavily used. Second, in some circumstances, regression analysis can be utilized to identify the causal links between the factors that are distinct and reliant. It is crucial to note that regressions by themselves just reveal relationships of a defined database containing several components and an outcome variable.

2.6.2. Multivariate Linear Regression (MLR)

A statistical method called MLR uses several explanatory variables to predict the outcome of an answer variable. Modeling the linear relationship between the to-be-analyzed independent variables (x) and dependent variables (y) is the goal of (MLR) [40]. The MLR fundamental model is: y = β₀ + β_1X1 + ⋯ + β_mx_m + ε. The equation for calculating the regression coefficient matrix is [41]:

$$\widehat{\beta }={\left(X^{T}X\right)}^{-1}{X}^{T}y$$

(19)

$$\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e},\beta =\left[\begin{array}{c}{\beta }_0\\ \begin{array}{c}{\beta }_1\\ ⋮\end{array}\\ {\beta }_m\end{array}\right],X=\left[\begin{array}{cc}\begin{array}{c}1\\ 1\end{array}& \begin{array}{c}{x}_{11}\\ x_{21}\end{array}\\ ⋮& ⋮\\ 1& x_{n1}\end{array}\begin{array}{cc}\begin{array}{c}{x}_{12}\\ x_{22}\end{array}& \begin{array}{c}\dots \\ \dots \end{array}\\ ⋮& ⋮\\ x_{n2}& \cdots \end{array}\begin{array}{c}{x}_{1m}\\ \begin{array}{c}{x}_{2m}\\ ⋮\end{array}\\ {\beta }_{nm}\end{array}\right],Y=\left[\begin{array}{c}{Y}_1\\ \begin{array}{c}{Y}_2\\ ⋮\end{array}\\ Y_n\end{array}\right]$$

The MLR optimization technique’s operational process is depicted in Figure 8. In general, MLR can be broken down into the following processes [42]:

It is simple to graphically describe any results and important factors with heatmaps. While there are various methods to portray data, heatmaps provide a high-dimensional view of the data points and the relationships between them. Specific variables can be used in the rows, columns, and diagonal of data analysis heatmaps. This heatmap was produced in this study by putting several simulation data types into MLR (machine learning). It demonstrates how our input and output parameters are related to one another (COE, NPC, emission, production, renewable fraction, etc.). The connections between the parameters are displayed in Figure 9.

3. Results

With an inventory of system configurations and their capabilities organized by lowest COE and NPC, HOMER calculates the value and simultaneously determines the viability of a hybrid system (HS) over the course of a year. HOMER Pro software has seven options available for considering the grid-connected hybrid system. They are simple rate (on metering, off metering), real-time rate (monthly and annual), scheduled rate (monthly and annual), and grid extension. Seven alternative scenarios are assessed among several configured energy systems to investigate the structure that is both technologically and cost-effectively optimal. The COE, NPC, renewable dissemination, additional power, LCE, operational emissions, duty factor, and surplus energy are the primary considerations for the proposed HES. Even though HOMER modeled several parts of the power scheme, it only shows the possibilities for feasible hybrid approaches. To determine the best system configuration, various circumstances are discussed and contrasted in the subsequent subsections based on expenditures, ecological releases, and, consequently, performance.

3.1. Case-I: Simple Rate (Net Metering Off)

In this study, the hybrid system design based on a PV/Battery/DG was taken into consideration. As illustrated in Figure 10 and Figure 11, the COE of the system is 0.0291 USD/kWh, and its NPC is USD 2,301,523. It is made up of a 1775 kW PV module, two batteries, a 340 kW DG, and a 1078 kW inverter (

Table 8. Summary of simulation methods.

	Simple Rate (Net Metering Off)	Simple Rate (Net Metering On)	Real-Time Rate (Monthly)	Real-Time Rate (Annual	Scheduled Rate (Monthly)	Scheduled Rate (Annual)	Grid Extension
PV (kW)	1775	1723	1837	1947	1896	1896	1282
DG (kW)	340	340	340	340	340	340	340
Storage (kWh)	2	4	180	224	178	178	1277
Converter (kW)	1078	1080	1053	1066	1073	1073	248
PV Production kWh/y	2,605,033	2,528,449	2,464,878	2,857,550	2,782,585	2,782,585	1,881,802
DG Production kWh/y	-	-	63,831	55,364	59,483	59,483	572,181
Renewable Fraction (%)	78.5	78.1	75.2	78.1	77.7	77.7	59.03

). Figure 12 shows that among the seven situations, the lowest ${\mathrm{C}\mathrm{O}}_2$ emissions (349,525 kg/y) exist. A total of 2,605,033 kWh of power was produced overall in this hypothetical situation. Furthermore, 80.1% of the total electricity is supported by the PV, while the continuing 19.9% is obtained via grid purchases. It is also clear from the results that this approach is employed to completely fulfill the overload need, with the extra energy output amounting to 3.56 percent of the overall energy produced. DGs are not used to generate power.

3.2. Case-II: Simple Rate (Net Metering On)

The hybrid system configuration based on a PV/Battery/DG was taken into consideration. It is evident from Figure 11 that this arrangement has a COE of 0.0198 USD/kWh and an NPC of USD 1,539,620 (Figure 10). It consists of a 1080 kW inverter, four batteries, a 1723 kW PV module, and a 340 kW DG (Table 8). Figure 12’s findings show that of the seven scenarios, the one with the second lowest ${\mathrm{C}\mathrm{O}}_2$ emissions (351,020 kg/y) is the metering on. In total, 2,528,449 kWh of power was produced overall in this hypothetical situation. In addition, 79.5% of the total power is supported by the PV, and the residual 20.5% is obtained from grid purchases. Additionally, it is evident from the results that this technique is employed to generate 2.85% of the total amount of energy produced in excess. DGs are not used to generate electricity.

3.3. Case-III: Real-Time Rate (Monthly)

The PV/Battery/DG constructed amalgam structure configuration was considered. It is evident from Figure 10 and Figure 11 that this arrangement has an NPC of USD 3,717,828 and a COE of 0.0512 USD/kWh. It is made up of a 340 kW DG, 180 batteries, an 1837 kW PV module, and a 1053 kW inverter (Table 8). Based on the results in Figure 12, the scenario with CO₂ emissions (387,111 kg/y), a total of 3,151,033 kWh was produced. The impact from the PV is 78.2% of the aggregate energy, 2.03% from DG, and the rest of 19.7% arrives from the grid purchase. It is also demonstrated by the findings that this framework is employed to satisfy consumers’ load needs and that excessive energy output accounts for 8.66% of the overall energy produced.

3.4. Case-IV: Real-Time Rate (Annual)

The hybrid system arrangement based on a PV/Battery/DG was considered. This structure has an NPC of USD 3,464,268 and a COE of 0.0445 USD/kWh, as shown in Figure 10 and Figure 11. It consists of a 1066 kW inverter, 224 batteries, a 1947 kW PV component, and a 340 kW DG (Table 8). The scenario with ${\mathrm{C}\mathrm{O}}_2$ emissions (366,026 kg/y) is shown in Figure 12. In this hypothetical situation, 3,151,033 kWh were generated altogether. A total of 78.2% of the power is contributed by solar panels, 2.03% by DGs, and the remaining 19.7% is obtained from grid purchases. It is also clear from the results that this arrangement is employed to completely meet the load need and that excess energy production accounts for 8.66% of the overall energy produced.

3.5. Case-V: Scheduled Rate (Monthly)

In this study, the configuration of the PV/Battery/DG hybrid approach is considered. From Figure 11, this structure has a COE of 0.0449 USD/kWh and an NPC of USD 3,463,741 (Figure 10), which comprises 340 kW DG, 1896 kW PV segment, 178 batteries, and 1073 kW inverters (Table 8). Results from Figure 12 indicate that the emission of ${\mathrm{C}\mathrm{O}}_2$ is 370,677 kg/y, and 3,493,847 kWh of electricity was produced overall in this hypothetical situation. The involvement from the PV is 80.9% of the overall energy and 1.73% from DG, and the rest of 17.4% comes from the grid purchase. It also becomes clear from the results that this approach is employed to gratify overall capacity needs and that the extra energy yield accounts for 11% of the overall electricity production.

3.6. Case-VI: Scheduled Rate (Annual)

It is similar to Scheduled Rate (Monthly). In this study, we considered the configuration PV/Battery/DG-based hybrid system. From Figure 11, this approach has a COE of 0.0449 USD/kWh and an NPC of USD 3,463,741, which consists of a 340 kW DG, 1896 kW PV module, 178 batteries, and 1073 kW inverters (Table 8). Figure 12 indicates that the emissions of ${\mathrm{C}\mathrm{O}}_2$ 370,677 kg/y and 3,493,847 kWh of power are produced overall in this hypothetical situation. The influence from the PV is 80.9% of the total power and 1.73% from DG, and the rest of 17.4% comes from the grid purchase. However, it is obvious from the findings that this approach is employed to fully satisfy the demand need and that the additional power production accounts for 11% of the overall electricity production.

3.7. Case-VII: Grid Extension

The PV/Battery/DG-based HS arrangement has been taken into consideration. According to Figure 11, this network has the greatest COE of the seven scenarios at USD 0.283/kWh. The NPC of USD 10,354,990 is also the highest (Figure 10). The system consists of a 340 kW DG, 1282 kW PV, 1277 batteries, and 248 kW inverters (Table 8). Figure 12 indicates that the emissions of ${\mathrm{C}\mathrm{O}}_2$ (429,928 kg/y) are the highest. This circumstance generates 2,453,982 kWh of total electricity. The influence from the PV is 76.7% of the overall power and 23.3% from DG. However, it is apparent from the study that this framework is employed to completely satisfy the power need, with the excess power output accounting for 40.2% of the overall energy produced. The simulation results are shown in Table 8. The demand loads for the system have been fulfilled by the installation of PV, DG, storage, and converter. To meet the 1775 kW PV loads, 444 solar panels (4 kW each) are required to install. Similarly, the quantity of DG, storage, and converter has also been presented in Table 8.

3.8. Optimal Method: Real-Time Rate (Annual)

The reliability, environmental emissions, and economic parameters were used in this study to choose the best possible solution. From the summary table, the case simple rate (both net metering on and off) has the lowest COE, NPC, and emission. However, it has been found that there is no electricity production from DG in those two cases. Therefore, cost and emission are lower than in the other cases. The user might specify a sell-back price and a stable power pricing in the simple rates feature. As the price is not constant for 25 years project lifetime and there are also some limitations using simple rate mode, therefore, simple rate mode is not used as an optimal process. Among the other cases, the configuration Case-IV: real-time rate (annual) is considered a cost-effective hybrid system because of its lowest COE of 0.0445 USD/kWh and NPC of USD 3,464,268. ${\mathrm{C}\mathrm{O}}_2$ emission (366,026 kg/y) is also the lowest among the cases. Grid power pricing that might vary from one time step to the next is modeled by real-time rates. Numerous pricing and limitations may be regulated throughout this option using various parameters. Underneath the specifications toolbar for real-time rate (annual), the advanced grid dynamic includes a number of options, including selling ability, acquisition capabilities, interconnected transmission cost, backup power cost, highest economic grid purchases, and a number of sophisticated control factors that can be adjusted depending on whether the correspondence determines to buy or sell electricity or charging-discharging batteries relying on the power system percentage. For most situations, the 7% return on investment (ROI) is considered an outstanding return rate, and 5% is generally seen as a respectable return rate. This ROI information is shown in Figure 13. The payback period is the amount of time required to recoup an investment or the amount of time required for an investor to break even. Longer payback periods are unfavorable, whereas shorter payback periods make investments more appealing, as shown in Figure 14. The maximum power (in kW) that may be supplied to the grid is known as the sale capacity. The greatest power that may be purchased from the grid is known as the Purchase Capacity. The maximum purchases and sales have been found for simple rate. Due to the limitations of considering all the parameters in simple rate, the second-best real-time rate has been taken as the most significant for meeting the load demand shown in Figure 15.

Table 9. Summarization of real-time rate (annual).

PV (kW)	1947
DG (kW)	340
Storage (kWh)	224
Converter (kW)	1066
Grid Purchase (kWh/y)	595,829
Grid Sales (kWh/y)	1,577,275
PV Production (kWh/y)	2,857,550
DG Production (kWh/y)	55,364
Renewable Fraction (%)	78.1
COE (USD/kWh)	0.0445
NPC (USD)	3,464,268
CO2 Emission (kg/y)	366,026

shows the summary of the real-time rate (annual).

3.9. Sensitivity Analysis

A sensitivity study of the consequences of numerous issues on the overall expense and feasibility of an HS based on RE is highly recommended. The daily load requirement, wind speed, solar energy, diesel price, a number of cost inputs (capital, operational, replacement, and operation and maintenance), cost multiplier, grid expansion cost, yearly interest rate, and maximum capacity shortfall are the primary factors that are looked at. Diesel fuel costs were our primary consideration while examining the correlation between NPC and COE.

The Consequences of Higher Fuel Prices (Diesel)

The cost of diesel varies throughout the year. It is dependent on the global market. The amount of the project is influenced by changes in diesel prices. Figure 16 and Figure 17 depict how the price of diesel has an impact. Figure 16 shows that the NPC is USD 3,464,268 when the diesel price is 1.14 USD/L. When diesel costs drop to 0.90 USD/L, the NPC falls to USD 3,346,293. Similarly, when the diesel cost rises to 1.25 USD/L, the NPC increases to USD 3,507,356. Figure 17 shows that the COE is USD 0.0445 when the diesel price is 1.14 USD/L. When diesel costs drop to 0.90 USD/L, the COE decreases to USD 0.0433. Similarly, when the diesel cost rises to 1.25 USD/L, the COE increases to USD 0.0449.

3.10. Comparison between HOMER (Optimal Method) and MLR

The suggested PV/DG/Battery under the real-time (annual) method optimized by HOMER Pro software are contrasted with those optimized by the MLR technique in

Table 10. Comparison between HOMER Pro and MLR for the configuration of PV/Battery/DG.

Parameter	HOMER	MLR
COE (USD/kWh)	0.0445	0.0446
PV (kW)	1947	1975
DG (kW)	340	315
Battery (kW)	224	233
Converter (kW)	1066	1090
Renewable fraction	78.1	78.3
Emission (kg/y)	366,026	368,019
DG Generation (kWh/y)	55,364	57,371
PV Production (kWh/y)	2,857,550	2,897,153

. The comparison of COE, renewable fraction, emission, DG production, PV production, battery, and converter are shown in Table 10. It has been found that the COE in the HOMER Pro simulation is 0.0445 USD/kWh, and that of MLR is 0.0446 USD/kWh. The primary factor behind this discrepancy is that HOMER Pro employs a 340 kW DG, whereas the MLR alternative only uses a 315 kW unit. HOMER Pro and MLR employ 1947 kW and 1975 kW PV modules, respectively. HOMER Pro employs a 224 kWh battery, whereas MLR uses 233 kWh. HOMER suggested a 1066 kW converter, whereas MLR suggested a 1090 kW.

4. Conclusions

To examine the possible use of the hybridized structure for the provision of energy to the health and education sectors in urban areas, the existing research simulates a successful HES with ideal cost assessment and harmful item emission control. This analysis investigates seven different hybrid system scenarios, and an optimum setup is chosen based on lower energy prices, fewer pollutant emissions, and comparisons of the proportional benefits and negatives. This research also looks at the importance of alternative configurations in terms of LCE, renewable fraction, and other commercial and ecological parameters. A hybrid PV/Battery/DG system with a capacity of 1947 kW PV modules, a 340 kW DG set, and a 224 kWh battery bank is an extremely efficient design for meeting the 3750 kWh daily load demand. The loads are estimated from a comprehensive survey of some health and education sectors of a selected area in Bangladesh. The components of the hybrid energy system are designed using HOMER Pro software, taking into account the estimated loads and validated by MLR algorithm. Therefore, it is expected that the hybrid system can be designed with the dimensions specified in Table 8 and that the systems can be realized in practice. The following summarizes the main findings of the research:

• The optimized hybrid structure design real-time rate (annual) in this sizing optimization has a COE of 0.0445 USD/kWh and an NPC of USD 3,464,268. However, the COE and the NPC are comparatively more than the simple rate method, which is less advantageous than the real-time rate (annual) due to higher capital and replacement expenses as well as emissions of environmental pollutants. However, the proposed system is less expensive than a diesel-based energy system.
• The analysis also shows that, compared to kerosene-based lighting and grid-connected power, the improved system has reduced operational CO₂ emissions (366,026 kg/y).