Optimal design, prefeasibility techno-economic and sensitivity analysis of off-grid hybrid renewable energy system

ABSTRACT

This work aims to find an optimal hybrid renewable system design using solar, wind energy, battery storage, thermal loads, thermal load controller (TLC), boiler, and a diesel generator (DG) for the considered site. The prefeasibility techno-economic analysis has been carried out using HOMER software to meet the load demand requirement of the village. For identifying winning system architecture, minimum net present cost (NPC), lowest cost of energy (COE), and the highest renewable fraction (RF) are used as the criteria. The obtained results show that the least cost-optimal hybrid system consists of 614kW-PV,850kW-WT,800kW-DG, 1212 no. of batteries and 591 kW converter along with TLC-2000kW, having minimum NPC: $5.48M, least COE: $0.272/kWh and highest RF:91.6% can provide 92% reliable power supply to onsite load demand of 4502.95kWh/day with 70% renewable sources in the considered site. This paper also presents a sensitivity analysis of the hybrid system with variations in load demand, diesel fuel price, project lifetime, and interest rate.

KEYWORDS:

• Net present cost
• techno-economic potential
• cost of energy
• hybrid renewable energy system
• optimization
• renewable energy

1. Introduction

The fossil fuels used primarily for power generation are coal, diesel, petroleum, and natural gas. However, due to a sudden increase in power demand in developing countries, exhaustibility, and price changes, fossil fuel depletes quickly and cannot meet the required needs. So, it has become critical to conserve fossil fuels and consider using renewable energy resources (RERs) as an alternative. Nowadays, it is challenging to supply power for increasing energy demand rapidly using only conventional energy sources. As a result, there is an urgent need to consider energy sources that are both efficient and clean. Solar, wind, tidal, geothermal, and biomass are important renewable energy sources to replace fossil fuel power generation (Halabi and Mekhilef 2018). In (Dawoud, Lin, and Okba 2018), authors have presented various optimisation techniques and principles for the physical modelling of renewable energy resources. However, in practice, power resulting from solar photovoltaic (PV) and wind turbine (WT) generators are dependent on climate variations. Furthermore, both solar and wind systems are unreliable if storage batteries or backup distribution generators (DGs) are insufficient.

In (Al Busaidi et al. 2016), the authors have reviewed the various hybrid renewable energy systems (HRES) for electrical power supply usage. As a result, several methods were implemented for sizing the various components of the system during design. Moreover, in (Rad et al. 2020), the authors found a cost-effective method of delivering energy and water for residential and health care containers in Iran. Additionally, they have estimated the influence of various parameters changes (project lifetime, dispatch strategies control, and salvage cost) on finding the winning system configuration and analyzing how system economic cost varies.

Due to rapidly rising oil and coal prices, transmission line expansion costs, and power demand usage have led to the design of an HRES that combines two or more RERs, including battery storage and DGs for continuous power generation. As a result, this HRES can minimise COE and carbon emissions and deliver a reliable power supply to end-users compared to conventional energy sources. As a result, various optimisation approaches such as HOMER software and metaheuristics algorithms have been implemented by researchers in the past for designing and finding an optimal HRES architecture based on available renewable potentials such as solar irradiation, wind speed, and temperature at considered sites (Senthil Kumar et al. 2018). In (Farahi and Fazelpour 2019), authors have been implemented the feasibility of providing electricity from an HRES comprising PV/WT/DG/Battery configuration to meet the load demand in Iran.

In (Ashrafi Goudarzi et al. 2019), the authors have been designed an optimal HRES that can meet the energy demand of a 5-story residential building in Tehran. In the first stage, the author had calculated the energy consumption of the building using Design Builder software. Then, HOMER software is applied to perform techno-economic analysis and obtain an optimal feasible HRES that can meet the energy demand of buildings located in the considered site. Furthermore, in (Tezer, Yaman, and Yaman 2017), the authors have been investigated various optimisation techniques from the past to the present in order to solve problems related to sizing the system in such a way minimising energy costs, system management to stabilise the uncertainty of energy produced by renewable sources, and finally reducing greenhouse gas emissions from HRES. Finally, in (Ammari et al. 2021), the authors have focused on four critical categories of HRES, which mainly focus on sizing, optimisation, control, and energy management. Therefore, It is observed that viable software is characterised by flexibility in simulation, doesn’t need much time, and is easy to use, but a simple optimisation equation limits this method.

1.1. Related work

The hybrid systems can be categorised as stand-alone and grid-connected. The most reliable technology for rural areas to provide continuous power supply at lower COE is the stand-alone HRES (Muh and Tabet 2019). In (Iqbal et al. 2018), the authors have used Homer Pro to build HRES and perform sensitivity analysis to guarantee that the designed system was robust and cost-effective. The results show that system cost is 180.2 M USD for a peak load of 73.6 MW and LCOE: 0.05744 $/kWh. Table 1 summarises the related works published on modelling the HRES using HOMER Pro software for obtaining the optimal system to the considered site based on available renewable energy sources.

Table 1. Related recent research published on designing the HRES design based on available renewable energy sources.

Authors/year	Winning system architecture	Approach	Site selected	Potentials available and type of load	Conclusion
Baseer, Alqahtani, and Rehman (2019)	PV/WT/DG/battery.	HOMER	Jubail Industrial City, Saudi Arabia To design an optimum HRES	Annual Solar radiation:5.72 kWh/m2./day Average annual wind speed:5.68 m/s. Loads: Domestic: 11,160 kWh/day, Community:4865 kWh/day, and Commercial: 3288 kWh/day.	Optimal system: PV/WT/DG/battery. RF: 100%, lowest COE: $0.25, NPC: $555,492 Saves 2800 tons carbon emissions/year
Rad et al. (2020)	PV/WT/biogas DG/fuel cell.	HOMER	Iran Design an optimal HRES based on economic and environmental points.	Annual Solar radiation: 3.74 kWh/m^2/day Average annual wind speed:4.5 m/s. Domestic load:361 kWh/day	COE: 0.164 - 0.233 $/kWh. Release less carbon emission
Jeyasudha et al. (2020)	PV/bio-gasifier/Battery/Converter.	HOMER	Korkadu village, Puducherry, India. To design an optimum design.	Annual solar radiation: 6.53 kWh/m^2. Average annual wind speed: 4 m/s. Loads(kWh/day): Domestic:176, agriculture: 4.5 and commercial: 12.	Optimal HRES: 50kW-PV/10kW-biogasifier/50kW-converter/300 no. of batteries each 16kWh. System releases low carbon emissions and has high RF: 87.0%. LCOE is Rs 7.886/kWh NPC is Rs 5,926,132.
Jahangir, Shahsavari, and Vaziri Rad (2020)	PV/WT/Battery/converter	HOMER	Gulf of Oman: city of jask, Persian Gulf: city of genaveh, Caspian Sea: the city of Anzali. To design an optimal HRES, including Wave Energy Converter/PV/WT/Battery and perform a prefeasibility techno-economic analysis system.	Annual solar radiation: Jask: 6.18 kWh/m^2 Genaveh:5.45 kWh/m^2 Anzali: 3.85 kWh/m^2 Average annual wind speed: Jask: 6.58 m/s. Genaveh:5.32 m/s. Anzali: 5.5 m/s. Electrical load: 37500.00 kWh/day.	Case1: City of jask, Optimal system (PV-13,032 kW/3 WT of 660 kW /411 batteries 167Ah/6886 kW converter system). Least COE: 0.219/kWh, initial cost: $33.7 and NPC: $44.1. Case2: City of Genaveh, Optimal system (PV-14,387 kW/3 WT of 660 kW /428 number of batteries 167Ah/6862 kW converter system). Least COE: 0.233/kWh, initial cost: $35.9, and NPC: $46.9. Case3: City of Anzali, Optimal system (PV-7221 kW/14 WT of 660 kW /454 number of batteries 167Ah/5878 kW converter system). Least COE: 0.242/kWh, initial cost: $39.6 and NPC: $48.8.
Salameh et al. (2020)	PV/DG /Battery /converter.	HOMER	City of khorfakkan, Sharjah To design a stand-alone solar PV system with single and dual-axis solar trackers.	Annual Solar radiation: 8.30 kWh/m^2 /year. Annual average load demand 37.75 MWh	COE:0.25$/kWh. The results show that HRES with dual-axis solar PV provides a low electricity cost of 250 $/MWh and an emission decrease of 69.6%.
Konchou et al. (2021)	PV/DG/Battery/converter	HOMER	MAKENENE, Cameroon Design an optimal HRES based on available renewable sources.	Annual Solar radiation: 800 W/m^2. Average annual wind speed:3.5 m/s. Domestic loads: 50.22 kWh/day.	More economical system. COE:0.132$, NPC: 38,817$. Emission 2 kg/year
Sawle et al. (2021)	PV/WT/Micro Hydro turbine/ Biogas Gen./Battery storage/DG/converter	HOMER	Ukai, Gujarat, India. Design an optimum design based on available renewable energy sources and techno-economic analysis is performed	Annual Solar radiation: 5.30 kWh/m^2. Average annual wind speed:5.63 m/s. Domestic loads: 898 kWh/day.	More economical system. COE:0.196$, NPC: 831,217$. operating cost:36,184$. RF:81.2%.
Pujari and Rudramoorthy (2021)	PV/Battery storage/DG/Converter	HOMER	Kanadripalle village, Andhra Pradesh India. Design an optimum design based on available renewable energy sources and techno-economic analysis is performed	Annual Solar radiation: 5.18 kWh/m^2. Average annual wind speed:3.76 m/s. Total loads: 332.97 kWh/day.	More economical system. COE:0.217$, NPC: $3,41,280. operating cost:8,144$/year. RF:96.6%.
Aziz et al. (2019)	PV/Battery storage/DG/Converter	HOMER	Diyala, Muqdadiyah district, Iraq. Design an optimal HRES based on available RERs and techno-economic analysis is performed under LF, CC strategy, and combined.	Annual Solar radiation: 5.02 kWh/m^2/day. Ambient temperature: 36.15°C. Total loads: 145.00 kWh/day.	More economical system. COE:0.21$/kWh, NPC: $110,191. Carbon emission: 27,678 kg/year. RF:35.6%.

In (Mandal, Das, and Hoque 2018), authors have been studied the potential application of an HRES (PV/Wind/Diesel) with battery storage in Bangladesh's northern region. According to the obtained results, the optimised HRES consists of 73 kW PV arrays, a 57 kW DG, a 387-kWh battery bank, and 28 kW inverters with a minimum COE of $0.37$/kWh and NPC of $357,284. This system also reduces carbon emissions by almost 62 percent compared to the current kerosene used. In (Haratian et al. 2018), the authors have designed an off-grid HRES and performed the techno-economic analysis to determine a cost-effective system in the KhshU site. The simulation results reveal that PV/WT/battery system has a minimum NPC:8173$ and the least COE:0.546 $/kWh. In addition, they found that wind speeds ranging from 3 to 6 m/s had no significant impact on the NPC & COE of the system. Using HOMER, a stand-alone HRES was designed in (Ariyo et al. 2018) for the Senate Building, University of Ilorin, Nigeria. The results show that the PV/DG/battery system yields a minimum LCOE of 0.283 $/kWh. It was also discovered that this site has a low average wind speed of 2.22 m/s, making it unsuitable for power generation using a wind energy system.

Based on studies discussed on stand-alone (off-grid) in Table 1, it could be concluded that HRESs design based on the available potential in the considered region provides an optimal economic solution for the government and investors (Vishnupriyan and Manoharan 2018). Additionally, the primary optimisation objective to evaluate HRES is the life-cycle cost for installation and operating the system over its lifetime. As a result, stand-alone system scenarios were considered in this study to demonstrate the effectiveness of system performances and economic variations if system parameters are changed during the design process.

The primary purpose of this HRES design is to fully utilise RERs to supply the power continuously with a high-reliability level for the considered site. For the first time, in this work, a prefeasibility techno-economic analysis and optimal design of different HRES incorporated along with sensitivity parameters (such as diesel fuel price, load demand, project lifetime, interest rate, PV derating factor, wind turbine hub height/lifetime, and grid power price) variations is investigated for stand-alone mode. The simulated systems are filtered and ranked for the best wining system configuration based on economic parameters (NPC, COE), REF, carbon emission, and unmet load percentage.

2. Methodology and materials

To obtain an optimal HRES design, a basic and integrated framework should be developed based on the potential available (solar irradiation, wind speed, and temperature) at the considered area, financial risk, environmental policies, and available RE resources. The key inputs of HRES design and planning need to determine daily load demand profile considering seasons variations, meteorological data in site, the specific area where PV/WT should install, and present power generation capacity. The winning system architecture among the various configurations can be accomplished from simulation, optimisation, and sensitivity analysis performed by HOMER (Sen and Subhes 2014). NPC, COE, O&M, and operating costs were obtained from the simulation process. After designing the optimal hybrid system and performing techno-economic analysis by HOMER. It is also necessary to perform sensitivity analysis for the designed system to measure the financial sensitivity in contrast to the uncertainty of system parameters. This analysis provides future investment ideas and information about system planning expansion and policy decisions. The main objectives of this research work are outlined as follows:

• Optimal hybrid system design, prefeasibility techno-economic and sensitivity analysis of various HRES configuration by considering renewable energy sources, seasonal load demand inputs, differences in diesel prices, the lifetime of the project change, interest rate, PV derating factor variation, and uncertainty in system parameters.

• To obtain an optimal stand-alone HRES for the considered site based on the economic (minimum NPC and low LCOE) and technical performances (renewable fraction and percentage of unmet loads).

2.1. Problem definition

The prefeasibility techno-economic analysis of stand-alone renewable energy systems (HGRES) is performed through the cost minimisation function as. $Costminimizingfunction=min{\sum }^{H}RESelementscost(t)$The total cost of the HRES includes ‘total cost of solar PV arrays, total Wind turbine cost, Diesel generator cost, number of batteries cost, converter cost, and thermal load controller cost.’ The TNPC and LCOE mainly depend on the total annualised cost of the system. The proposed optimisation process performed in this work mainly depends on ‘the lowest NPC and LCOE, total capital cost, initial cost, O&M cost, replacement cost and salvage cost of different components, energy sources over the project lifetime.’ Ranking of different HRES configurations obtained from the optimisation process is done depending on TNPC. The TNPC was calculated by estimating the system's total annualised cost and LCOE. To calculate TNPC, Equation (1) is used (Mudgal, Reddy, and Mallick 2019; Chen, He, and Li 2017; Peter Lilienthal 2016): (1) $D_{NPC}={\displaystyle \frac{D_{ann,total}}{CRF(j,L_{proj})}}$(1) where $D_{ann,total}$: total annualised cost,$j$: annual real interest rate (discount rate),$L_{proj}$: project lifetime, and $CRF(j,N)$: capital recovery factor with $j$ in % of interest rate.

The capital recovery factor ($CRF$) were calculated by Equation (2): (2) $CRF(j,N)=\left[{\displaystyle \frac{j{(1+j)}^N}{{(1+j)}^N-1}}\right]$(2) where $N$: number of years, and $j$: the annual real interest rate. The drop-in interest rate may cause a decrease in $CRF$ and this, in turn, leads to an increase in NPC.

The LCOE is defined as ‘the average cost per kWh of useful electrical energy produced by the system’ and calculated by Equation (3): (3) $LCOE=\left[{\displaystyle \frac{D_{ann,total}}{E_{ls}+E_{grid}}}\right]$(3) where$E_{ls}$: electrical energy served by microgrid system,$E_{grid}$: the amount of electricity sold to the grid by the microgrid.

The renewable fraction is defined as ‘the fraction of the energy delivered to the load that originated from renewable power sources’ and calculated by Equation (4): (4) $F_{rene}=\left[1-\left({\displaystyle \frac{E_{nonrene}+H_{nonrene}}{E_{served}+H_{served}}}\right)\right]$(4) Where $E_{nonrene}$: ‘non-renewable electrical production (kWh/yr),$H_{nonrene}$: non-renewable thermal production(kWh/yr),$E_{served}$: Total electrical load served(kWh/yr),$H_{served}$: Total thermal load served(kWh/yr)’.

3. Assumptions and model inputs

3.1. Study area

The site chosen for this work is situated in chintalaya palle village, Andhra Pradesh, India. These are located at a latitude and longitude of 15°1.3'N, 78°6.4'E, with an altitude of 243 m above sea level, as depicted in Figure 1. As per census 2011 info, nearly 4094 people lived in 986 houses at the considered site. The primary occupation of people living was agriculture farming, digging granite stones in mines, and working as labourers in cement factories. Also, this site is surrounded by stone mining factories and rice mills. Therefore, integration of RERs is an optimal way to build stand-alone HRES to meet the increasing demand of residential, community, commercial, and agriculture and deliver a continuous power supply under emergency conditions.

Figure 1. Geographic map of the study area.

3.2. Load estimation

An onsite field survey accomplishes the load data collected at the considered site for 24 h. This information was fed into HOMER, and its scaled annual average load demand (kWh/day) is considered into two different load variables (such as:100%, 125%) compared to the baseline case. HOMER will estimate given load data into 8760 hourly load values for the entire year using hourly load profile and incorporating random variability factors (i.e. ‘day-to-day (10%) and time-step-to-time step variability (60 min)’). The estimated load data consumption for domestic, community, commercial loads for 24 h were tabulated in Table 2(a) as shown below. This table provides the daily load demand of different electrical components for each hour (CFL bulbs, tube-lights, LED bulbs, TV, ceiling fans, exhaust fans, radio, refrigerators, air coolers, electric iron box, motor pumps, and mix grinders).

• The energy demand in kWh is calculated using equation

  Table 2. (a) Load data of Domestic, Community, commercial and agricultural loads.

  Load categories	Loads (Appliances)	Power consumption kWh/day
  Household Loads	CFL bulbs	30.1
  	Tubelight (Fluorescent)	55.8
  	LED Bulbs	36.06
  	Ceiling fans	438
  	Exhaust fans	40.5
  	TV	228
  	Radio	16.2
  	Refrigerator	372
  	Air cooler	121.2
  	Electric Iron box	117
  	Mixer Grinder	156
  	Motor pumps	375
  	Phone charger	34.05
  	Computer Desktop	55.2
  	Electric rice cooker	100.8
  	Air conditioner	700
  	Water heater	310
  	Total (kWh/day)	3185.91
  Community Loads	Schools	100.2
  	Health Clinic centre	129.13
  	Worship Places	52.12
  	Street Lights	65
  	Total (kWh/day)	346.45
  Commercial loads	Small business centres	126.2
  	Computer & printer service	59.08
  	Wielding shop & Rice mills	352.18
  	Total (kWh/day)	537.46
  Daily load (kWh/day)		4069.82

Energy Demand (kWh) = [Load demand (kW) * Duration (h)*quantity of device ON]

➢	Power consumption by tube light is equal to 40 watts.
➢	The duration of tube light usage is 1 h.
➢	Number tube light ON for 1 h  = 150 units.

Solution:

Then energy demand (kWh) = 

[power consumed by tube light (kW)*duration (h)*quantity of tube lights ON]

 =  [(0.04 kW) *(1 h) *(125 tube lights)]

 = 5 kWh.

This is how we have estimated the load demand of the domestic, community, and commercial loads for 24 h. In addition, tables A1, A2, A3, and A4 in the appendix provide information about estimating domestic, community, and commercial load consumption each hour based on electrical devices for a considered site approximately.

The total electricity used in the village for residential purposes (such as lighting, cooking, phone charging, and so on) is known as the domestic load. It is observed that the peak & daily load demand of these loads obtained from HOMER were 630.56 and 3185.9 kWh/d, respectively. Similarly, the community and commercial peak and daily load demand were 167.26 and 883.91 kWh/day. Water pumping motors used for irrigation purposes come under agriculture loads (deferable load). It is noticed that peak demand is 13.05 kW and daily demand is 23.56 kWh/d, respectively, for the considered site.

Figure 2a, b, and c were attained by entering estimated load demand data into the HOMER. The figures illustrate daily, seasonal, and yearly domestic, community, and commercial load profiles monthly attained by random variability factors. Additionally, Figure 3. depicts the information in the appendix about the estimated daily load profile at the chosen site.

Figure 2. (a) The info of domestic loads daily, seasonal, and yearly profile. (b) The info of community and commercial loads daily, seasonal, and yearly profile. (c) The yearly profile of agriculture loads.

Figure 3. Daily load profile.

Thermal loads:

Thermal load is the demand for heat energy and later utilised for room space heating, industrial process, and hot water heating. This load can be served by the boiler and a generator from which waste heat can be recovered or by surplus electricity. These loads are assumed to be 5% of the total electrical loads, and information about these load profiles obtained from HOMER for 24 h is depicted in Figure 4. It can be noticed that the scaled annual average of this load is 409.59 kWh/d, with a scaled peak load of 61.78 kW having a load factor of 0.28. Thus, the main aim of including this load type in the HRES design is to examine the significance of surplus energy feeding the thermal load connected to the system.

Figure 4. The info profile of thermal loads.

3.4. Resources assessment

In this work, the available renewable potentials were extracted from National Renewable Energy Laboratory & NASA using HOMER Pro software. According to obtained data in the summer season, solar radiation is feasibly obtainable for 10–12 h at the chosen site. Similarly, wind speed averages around 6 m/s, and temperature increases gradually up to 48 °C in the summer season.

3.4.1. Solar radiation data

The clearness index (CI) measures atmospheric clearness, ranging between 0 and 1. The CI differs between 0.646 in Feb and reduced to 0.409 in July due to the rainy season, and finally, the annual average value is 0.539 for the selected site. As a result, for the last 22 years, the annual average monthly solar radiation data has been 5.18 kWh/m^2/day. Figure 5 depicts the CI and average monthly solar radiation data collected from NASA using HOMER for the considered site.

Figure 5. CI values and annual average solar radiation data for the selected site.

3.4.2. Wind speed data

The wind speed at the considered site available is abundant due to the hilly area and varies from 4.45–8.98 m/s at a 50 m height. Therefore, wind potential is sufficient for installing WT to produce power to meet the load demands at the particular site. The average monthly wind speed data for the considered site extracted from NASA is depicted in Figure 6. At this location, the surface roughness is set to 0.01. According to the NASA database, 30-year average monthly wind speed data is recorded as 5.75 m/s.

Figure 6. Annual average wind speed data for the chosen site.

Temperature:

According to NASA forecast of the worldwide energy resource database for 30-year the average monthly air temperature is recorded as 27.43⁰C at the considered location. Figure 7 depicts the average monthly air temperature extracted from NASA for the site chosen to study.

Figure 7. Annual average air temperature for considered site.

3.5. Mathematical modelling, specifications, and cost details of hybrid renewable energy system components

In this research study, for supplying stable and reliable power to loads in considered site, several components such as DG, PV modules, WT, Batteries, and system converter are utilised, and detailed specifications, including the cost of each, are presented in Tables 4–6.

3.5.1. Pv array

The output power produced by the solar PV module mainly depends on factors such as ‘solar irradiance, characteristics of the PV cells (conversion efficiency, installation of PV panels and derating factor) and cells temperature.’ Eq.5 is used to calculate the PV array power (Peter Lilienthal 2016). (5) $P_{PVoutput}=Y_{PVrated}\ast d{f}_{PV}\ast \left({\displaystyle \frac{{\overline{G}}_T}{{\overline{G}}_{Tref}}}\right)[1+{\alpha }_P(T_C-T_{Cref})]$(5) where: ‘$Y_{PVrated}$: Rated PV array power at standard test conditions (kW); $d{f}_{PV}$: derating factor (%); ${\overline{G}}_T$: solar radiation (kW/m^2); ${\overline{G}}_{Tref}$: Solar radiation at standard test conditions (1000 W/m^2); ${\alpha }_P$: Temperature coefficient (% / °C); $T_{Cref}$: Cell temperature at reference conditions (25 °C);$T_C$: Cell temperature (°C).’

In this study, tata power solar system 300TP monocrystalline PERC cells were used to design HRES. The technical specifications of this PV module are represented in Table 3. In this work, to improve solar system efficiency from energy losses due to wiring connection and increased temperature, the derating factor value is chosen as 88% and ground reflectance as 25% (Kabalci 2013).

Table 3. Tata power solar system 300TP solar PV module technical specification details (solar panel details: https://kenbrooksolar.com/system/hybrid-solar-system).

Parameters	Value	Parameters	Value
Nominal Maximum Power (Pmax)	300 W	Lifetime	25 years
Optimum Operating Voltage (Vmp)	35.5 Volts	Capital cost	755.48 $/kW
Optimum Operating Current (Imp)	8.22 Amps	Replacement cost	453.28 $/kW
Open Circuit Voltage (Voc)	44.5 Volts	O&M cost	37.76 $/kW/year
Short Circuit Current (Isc)	8.65 Amps	Maximum System Voltage	1000 V (IEC) or 1000 V (UL)
Operating Temperature	−40 °C to +85 °C	Max Series Fuse Rating	15 Amps
Efficiency	15.44%	Classification	Class A
Derating factor	88%	Power Tolerance	0 / +5 W
Temperature coefficient	−0.390	Technology	Monocrystalline (Mono)
Ground reflection	25%	Dimensions	1968 × 987 × 40 mm
Frame material	Anodized Aluminum Alloy (Black)

3.5.2. Wind turbine

The power generated by a wind turbine (WT) system is calculated at each time step. The turbine's wind speed is calculated using Equation (6). (6) ${\mathit{U}}_{\mathit{h}ub}={\mathit{U}}_{\mathit{a}nem}\cdot {\displaystyle \frac{\mathit{l}n({\mathit{Z}}_{\mathit{h}ub}/{\mathit{Z}}_0)}{\mathit{l}n({\mathit{Z}}_{\mathit{a}nem}/{\mathit{Z}}_0)}}$(6) ‘Where: $U_{hub}$: Speed of wind at the Hub height (HH) (m/s); $U_{anem}$: Speed of wind at anemometer height (m/s); $Z_{hub}$: HH of the turbine (m); $Z_{anem}$: Anemometer height (m); $Z_0$: Surface roughness length (m); $ln$ (..): Natural logarithm.’

The output WT power attained at normal conditions is calculated using Equation (7) (Peter Lilienthal 2016). (7) ${\mathit{P}}_{\mathit{W}TG}=\left({\displaystyle \frac{\mathit{\rho }}{{\mathit{\rho }}_0}}\right)\cdot {\mathit{P}}_{\mathit{W}TG,\mathit{S}TP}$(7) ‘Where ${\mathit{P}}_{\mathit{W}TG}$: WT power output (kW); ${\mathit{P}}_{\mathit{W}TG,\mathit{S}TP}$: WT output at standard temperature and pressure (kW); $\mathit{\rho }$: Actual air density [kg/m^3]; ${\mathit{\rho }}_0$: Air density at standard temperature and pressure (1.225 kg/m^3).’

The power generated by WT was calculated using Equation (8). (8) $P_{wt}=\left[V^3\left({\displaystyle \frac{P_r}{V_r^3-V_{cutin}^3}}\right)-\left({\displaystyle \frac{V_{cutin}^3}{V_r^3-V_{cutin}^3}}\right)\ast P_r\right]\to ifV>V_{cutin}orV<V_r$(8) Where $P_r:$ Rated power of windmill,$V$: Wind speed at the considered site,$V_{cutin}$ and $V_{cutout}$: Cut-in and cutout of windspeed of a windmill,$V_r$: Rated windspeed of a windmill.

Gamesa G52-850 kW WT is selected in this study, manufactured by Gamesa corporation limited. This type of WT is mainly chosen for sites having low/medium wind speed and higher turbulence intensity. The WTs used in this research have a maximum power efficiency of 3.5–4 m/s and a cut-in speed of 3.5–4 m/s. The detailed technical and cost of Gamesa G52-850 kW model WT considered for this work are shown in Table 4.

Table 4. Economic and technical details of wind turbine (Gamesa G52-850 kW) (Gamesa G-52 wind turbine specifications: https://www.windpowerengineering.com/turbine/gamesa-g52-850kW).

Parameters	Value	Parameters	Value
Manufacturer	Gamesa G52	Tower height (m)	50
Rated capacity(kW)	850	Swept area(m^2)	2124
Cut-in-speed (m/s)	4	Capital cost	$822,492.00
Cut-out-speed (m/s)	25	Replacement cost	$657,993.00
Rated wind speed(m/s)	13	O &M cost/year	$61,851.40
Number of blades	3	Lifetime (years)	25
Tip speed(m/s)	84	Materials	Fiberglas/Epoxy
Hub height (m)	40/55/65	Power density(W/m^2)	400.2
Rotor diameter (m)	52

3.5.3. Diesel generator

The lifetime of diesel generators (DG) is estimated using Equation (9). (9) $R_{gene}=\left({\displaystyle \frac{R_{gene,h}}{N_{gene}}}\right)$(9) ‘Where: $R_{gene,h}$: generator lifetime (hour); $N_{gene}$: No. of hours DG operates in one year (hour/year).’

The average electrical efficiency is calculated using Equation (10) (Peter Lilienthal 2016). (10) ${\eta }_{gene}=\left({\displaystyle \frac{3.6\cdot E_{gene}}{M_{fuel}\cdot LHV{F}_{fuel}}}\right)$(10) Where: ‘$E_{gene}$: Total annual electrical production (kWh/year); $M_{fuel}$: Total annual fuel consumption (kg/yr); $LHV{F}_{fuel}$: The lower heating value of the fuel (MJ/kg);.’

The fuel price of diesel in the Andhra Pradesh market rate in march 2021 is Rs 83.95 per litre ($1.15/l). The CAT-1000 kVA-50Hz-CP diesel generator is taken for this analysis to meet the load under any conditions. The caterpillar company has designed this generator. The cost details for one unit generator available from web source are taken as initial capital cost: $82,249.20, replacement cost: $57,574.44, operation & maintenance cost per hour: $126.00, and fuel price per litre: $1.15 (https://www.abhishekengg.com/caterpillar-make-400-to-5000-kva-lt-dg-set.html#1000-kva-caterpillar-diesel-generator). The properties of fuel used in this type of generator have values such as lower heating 43.4 MJ/kg, carbon content 85%, density 800 kg/m^3, and sulfur content 0.45%. This generator lifetime in hours is chosen as 90,000.00 and at a minimum load ratio of 25%.

3.5.4. Battery

During emergencies conditions battery can provide the necessary backup power. Based on energy generation and load consumption, the no. of batteries integrated into the system and the Battery's state may be determined. The lifespan of a battery is determined by characteristics such as lifetime throughput and battery float life. The battery storage bank lifetime is estimated using Equation (11) (Lilienthal 2016). (11) $R_{batte}=\{\begin{array}{c}{\displaystyle \frac{N_{batte}\cdot Q_{lifetime}}{Q_{thrpt}}iflimitedbythroughput}\\ R_{batte,f}iflimitedbytime\\ MIN\left({\displaystyle \frac{N_{batte}\cdot Q_{lifetime}}{Q_{thrpt}}\cdot R_{batte,f}}\right)iflimitedbythroughputandtime\end{array}$(11) ‘Where,$R_{batte,f}$: Storage float life (year); $R_{batte}$: storage bank life (year); $N_{batte}$: No. of batteries in the storage bank; $Q_{lifetime}$: Lifetime storage throughput (kWh); $Q_{thrpt}$: Annual storage throughput (kWh/yr).’

In this study, the luminous 200Ah,12 V, ILTT25060 model Lead Acid battery of 200 Ah maximum capacity is used during the design of HRES. The technical specifications of the Battery are shown in Table 5.

Table 5. Technical details of battery (luminous 200Ah,12 V, ILTT25060 model) (https://kenbrooksolar.com/price-list/luminous-solar-panels-inverters-batteries-price#Luminous_Solar_Battery_Price).

Parameters	Value	Parameters	Value
Manufacturer	luminous	Maximum Operating Temperature	−35°C / −31°F | 50°C / 122°F
Nominal capacity(kWh)	3.12	Height	412mm
Nominal voltage (V)	12	weight	62.5 kgs
Maximum capacity (Ah)	200	Capital cost ($)	342
Efficiency (%)	80	Replacement cost ($)	34.20
Maximum charge current(A)	80	O &M cost/year ($)	68.41
Self Discharge	5% per month	Lifetime throughput (kWh)	1704.90

3.5.5. Converter

In this research, a generic large free converter is selected, and default cost details as recorded in HOMER pro software are provided in Table 6 (Peter Lilienthal 2016).

Table 6. Technical details of Generic large free converter (Lilienthal 2016).

Parameters	Value
Rated power (kW)	1
Capital cost ($)	200
Replacement cost ($)	175
O &M cost ($)	5/year
Efficiency	95%
Lifetime, years	15

3.5.6. Boiler

The boiler used in this hybrid design serves as a backup source of heat that can serve the required amount of thermal load whenever needed. HOMER needs to add a boiler whenever a thermal load is coupled to the system. The thermal load controller (TLC) permits surplus electrical production to serve loads connected to the thermal bus. In this work, a generic TLC of 1 kW is chosen from HOMER, and default cost details as recorded in software are initial capital cost $200 per 1 kW, replacement cost $200, and operation and maintenance cost per year $20.00/year for each converter a lifetime of 20 years (Peter Lilienthal 2016).

3.5.7. System economics

The nominal discount rate is considered 8% and 10%, and the inflation rate is 2% expected. The annual capacity shortage is assumed to vary from 0% to 10% in this designed hybrid system. The project lifetime of HRES design in this work is taken as 15 and 25 years. The operating reserve is considered 80% of solar and 70% of wind power output, 10% of the hourly load, and the minimum renewable fraction as 40%.

3.6. Other constraints

In this work, the constraints assumed while performing analysis are taken as follows: Maximum annual capacity storage is 10%, load in current time steps as 10% along with annual peak load as 5%, percentage solar power output as 80%, minimum renewable fraction as 40%, and wind power output as 75%.

3.6.1. Sensitivity variables

Sensitivity analysis allows monitoring the impact of certain input variables on system design and the system's economy. To assess their influence on the optimised HRES configuration, various input values and related economic parameters are changed within a given range. This work's sensitivity analysis aims to know the optimal value of a particular variable used in designing the best HRES configuration. Furthermore, this analysis will provide clear information during the optimisation process on how the obtained financial and technical results vary depending on different input assumption values and help design a perfect HRES system for the considered site.

Therefore, different project lifetimes were considered during this study's design and simulation process, such as 15 and 25 years. This variation in project lifetime will provide assistance to identify if the optimised HRES configuration is more cost-effective and feasible than the grid extension or not. In addition to project lifetime, for the first time, the sensitivity analysis is performed for different input variables variation in this research such as annual average load demand, diesel price, % PV derating factor, WT hub height/lifetime, annual interest rate, and a lifetime of the Battery. Table 7 provides changed input values during sensitivity analysis, HRES design, and techno-economic analysis.

Table 7. Sensitivity variables.

Sensitivity variables	Change in values to perform sensitivity analysis
	Case 1(a)	Case 1(b)
Load demand (kWh/day)	4097.36 (100%)	5110.82 (125%)
Diesel fuel price ($/L)	1.15	1.37
Project lifetime (year)	25	15
Interest rate (%)	8	10
PV derating factor (%)	88	75
Lifetime of Battery (years)	12	15
Wind turbine: hub height(metres)/lifetime(years)	40/25	50/15

4. Results and discussions

In the current study, simulation, optimisation, and sensitivity analysis are the three principal tasks performed using HOMER Pro software to obtain various HRES configurations (Lambert, Gilman, and Lilienthal 2006). The obtained economic parameter results in the optimisation process, such as NPC, LCOE, operating costs, and renewable energy fractions (REF), were used to rank and find out a winning HRES architecture. Furthermore, in this research work, prefeasibility techno-economic analysis of stand-alone HRES is performed, including the sensitivity parameters in order to obtain an optimal hybrid system for the considered site.

In case-1, the diesel generator and renewable energy source-based stand-alone system model with thermal load controller and boiler(off-grid) are designed as shown in Figure 8. The simulation was carried out using HOMER, based on components input specifications, cost, available potentials, load demand, and other constraints. It creates various HRES designs that are suitable and reliable for meeting the load demand at the chosen location.

Figure 8. The schematic diagram of the stand-alone microgrid model.

4.1. Analysis of stand-alone HRES model performance

Figure 8 depicts the designed off-grid connected hybrid model. The summary of obtained results for two subcases, taking into account base and sensitivity analysis is shown in Table 8 below.

Table 8. Summary of techno-economic and sensitivity analysis of standalone HRES.

Total average load demand(kWh/day)				Project lifetime (years)	Interest rate (%)	Diesel price($/L)	PV derating factor (%)	Wind turbine		lifetime of battery (years)	NPC ($)	COE ($)	REN. Frac. (%)	CO2 (Kg/yr)	Total fuel (L/yr)
Domestic	Community	Agriculture	Thermal					hub height (m)	lifetime (years)
3185.9	883.9	23.56	409.59	25	8	1.15	88	40	25	12	5.48M	0.272	91.6	48,072	26,681
3982.38	1104.88	23.56	511.98	15	10	1.37	75	50	15	15	5.01M	0.299	89.3	87,690	44,484

Case 1(a): stand-alone HRES model base-case analysis

For this case study, the lifetime of the system is considered 25 years, with an annual real interest rate of 8%, diesel price as 1.16$/L, PV derating factor as 88%, the lifetime of a battery as 12 years, and wind turbine hub height and lifetime as 40 m,25 years. Therefore, the obtained optimal system for this case consists of 614 kW of PV arrays, one unit of 850 kW wind turbine, one unit of 800 kW of diesel generators, 1212 number of 12 V batteries, 591 kW of power converters, and 2000kW of TLC having the lowest NPC and COE value as $5.48M and $0.272 respectively. Table 9 is obtained by filtering the overall simulation results, and the HRES comparison is performed based on NPC, COE, renewable fraction, CO2 emission, and total fuel cost. The total NPC of a system is ‘the present value of all the costs acquired over its lifetime, minus the current value of all the revenue it earns. Costs include capital costs, replacement costs with the waste disposal, operation with maintenance costs, and fuel costs’.

Table 9. Overall optimisation results of case 1(a) different HRES models ordered by NPC, COE.

Scenario	TATA-PV (kW)	WT G52 (850 kW)	CAT-1000 kVA-CP (800 kW)	Lumi-ILTT 12 V Battery	Generic Converter (kW)	Thermal load controller(kW)	Boiler fuel consumption(L)	Cost/NPC ($)	Cost/COE ($/kWh)	Operating cost ($/yr)	Initial capital ($)	System/Ren Frac (%)	System/Total Fuel (L/yr)	Carbon dioxide emission (kg/yr)	Excess electrical (%)	Unmet load (%)	Dispatch
PV/WT/DG/Batt /TLC/BO/CONV	614	1 U	1 U	1212	591	2000	8,487	5.48M	0.272	246,247	2.30M	91.6	26,681	48,072	53.9	0	LF
PV/Batt/TLC /BO/CONV	1705	–	–	2222	585	4000	13,646	6.27M	0.329	255,622	2.97M	93.0	13,646	−16.9	36.4	4.98	LF
WT/DG/Batt/ TLC/BO/CONV	–	1 U	1U	1191	813	2000	10,145	6.81M	0.340	381,585	1.87M	68.8	109,787	263,327	42.0	0	CC
PV/DG/Batt/ TLC/BO/CONV	1478	–	1U	2316	646	1000	12,784	6.98M	0.349	360,572	2.32M	84.9	50,511	99,690	27.2	0	LF
WT/Batt/TLC /BO/CONV	–	3U	–	874	644	4000	6802	7.19M	0.383	270,415	3.70M	96.5	6,802	−8.44	79.9	5.96	LF
PV/WT/Batt/ TLC/BO/CONV	1365	1U	–	2185	644	4000	7666	7.26M	0.364	288,733	3.53M	96.3	7,666	−9.52	64.9	0.021	LF
PV/WT/DG/ TLC/BO/CONV	1365	3U	1U	—	322	4000	814	17.3M	0.882	991,964	4.45M	43.0	242,203	640,107	85.4	0	LF

NOTE- 1 U: 1 unit of an 850-kW wind turbine and 1 unit of an 800-kW diesel generator.

The NPC of optimal system obtained in case 1(a) is $5.48M, which is the least expensive NPC in comparison with other system configurations such as: PV/Batt/TLC/BO/CONV: $6.27M, WT/DG/Batt/TLC/BO/CONV: $6.81M, PV/DG/Batt/TLC/BO/CONV: $6.98M, WT/Batt/TLC/BO/CONV: $7.19M, PV/WT/Batt/TLC/BO/CONV: $7.26M and PV/WT/DG/Batt/TLC/BO/CONV: $17.3M. The Levelized COE of this optimal HRES resulted from Table 9 is $0.272/kWh; it is the least expensive COE in comparison with other system configurations such as PV/Batt/TLC/BO/CONV: $0.329/kWh, WT/DG/Batt/TLC/BO/CONV: $0.340/kWh, PV/DG/Batt/TLC/BO/CONV: $0.349/kWh, WT/Batt/TLC/BO/CONV: $0.383/kWh, PV/WT/Batt/TLC/BO/CONV: $0.364/kWh and PV/WT/DG/Batt/TLC/BO/CONV: $0.882/kWh. Figure 9 illustrates obtained the optimal stand-alone HRES result for case 1(a) with the least expensive NPC, COE, and REF for considered sites using HOMER.

Figure 9. The Homer optimisation results of case 1(a) hybrid stand-alone microgrid model PV/wind/diesel/battery system/thermal load controller/boiler.

The REF of the optimal HRES obtained for this case is 91.6%, which is the fraction of energy delivered to the loads (domestic load (kWh/d): 3185.9, Community load (kWh/d): 883.9, agriculture load(kWh/d):23.56 and thermal load (kWh/d):409.59) that originated from renewable sources. The REF of the other systems configurations resulted is as follow: PV/Batt/TLC/BO/CONV:93.0%, WT/DG/Batt/TLC/BO/CONV: 68.8%, PV/DG/Batt/TLC/BO/CONV: 84.9%, WT/Batt/TLC/BO/CONV: 96.5%, PV/WT/Batt/TLC/BO/CONV: 96.3% and PV/WT/DG/Batt/TLC/BO/CONV: 43.0%. Therefore, results conclude that the renewable potential available at the site is abundant and fully utilised for power generation.

From the simulation results obtained, the total amount of fuel consumed by the PV/WT/DG/Batt/TLC/BO/CONV system is 26,681 L/year, which is the average amount of fuel consumed by the generator per kWh of electricity generated for one year. It is concluded that the total amount of fuel consumed by this optimal system is the minimum amount in comparison with other HRES configurations such as PV/Batt/TLC/BO/CONV: 13,646 L/year, WT/DG/Batt/TLC/BO/CONV: 109,787 L/year, PV/DG/Batt/TLC/BO/CONV: 50,511 L/year, WT/Batt/TLC/BO/CONV:6,802 L/year, PV/WT/Batt/TLC/BO/CONV: 7,666 L/year and PV/WT/DG/Batt/TLC/BO/CONV: 242,203 L/year. Therefore, if fuel consumption by the system is less, the cost of the system and carbon emission release will reduce.

Case 1(b): stand-alone HRES model with sensitivity analysis applied to design

For this case, the project lifetime is considered 15 years, the annual real interest rate as 10%, diesel price as 1.37 $/L, PV derating factor as 75%, the lifetime of a battery as 15 years, and wind turbine hub height and lifetime as 50 m,15 years. Therefore, the obtained optimal system for this case consists of 753 kW of PV arrays, one unit of 850 kW wind turbine, one unit of 800 kW diesel generators, 1396 number of 12 V batteries, 725 kW of power converters, and 2000kW of TLC along with boiler fuel consumption as 11,298L has lowest NPC and COE value as $5.01M and $0.299 respectively.

The NPC of optimal HRES obtained from simulation results in this case 1(b) is PV/WT/DG/Batt/TLC/BO/CONV: $5.01M, which is the least expensive NPC in comparison with other HRES configurations such as: WT/DG/Batt/TLC/BO/CONV: $6.18M, PV/Batt/TLC/BO/CONV: $6.23M, PV/DG/Batt/TLC/BO/CONV: $6.93M, PV/WT/Batt/TLC/BO/CONV: $7.09M, WT/DG/TLC/BO: $13.1M, PV/WT/DG/Batt/TLC/BO/CONV: $6.47M. The Levelized COE of HRES in this case obtained from the HOMER simulation result listed in Table 10 is $0.299/kWh; it is indicated that this is the least expensive COE in comparison with other system configurations such as WT/DG/Batt/TLC/BO/CONV: $0.371/kWh, PV/Batt/TLC/BO/CONV: $0.394/kWh, PV/DG/Batt/TLC/BO/CONV: $0.418/kWh, PV/WT/Batt/TLC/BO/CONV: $0.428/kWh, WT/DG/TLC/BO: $0.800/kWh and PV/WT/DG/Batt/TLC/BO/CONV: $0.389/kWh. Figure 10 illustrates the various optimal stand-alone HRES from HOMER for case 1(b).

Figure 10. The Homer optimisation results of the case 1(b) hybrid stand-alone microgrid model PV/wind/diesel/battery system/thermal load controller/boiler with sensitivity variable.

Table 10. Overall optimisation results of case 1(b) different system models ordered by NPC, COE.

Scenario	TATA-PV (kW)	WT G52 (850 kW)	CAT-1000 kVA-CP (800 kW)	Lumi-ILTT 12 V Battery	Generic Converter (kW)	Thermal load controller(kW)	Boiler fuel consumption(L)	Cost/NPC ($)	Cost/COE ($/kWh)	Operating cost ($/yr)	Initial capital ($)	System/Ren Frac (%)	System/Total Fuel (L/yr)	Carbon dioxide emission (kg/yr)	Excess electrical (%)	Unmet load (%)	Dispatch
PV/WT/DG/Batt /TLC/BO/CONV	753	1 U	1U	1396	725	2000	11,298	5.01M	0.299	291,352	2.50M	89.3	44,484	87,690	46.1	0	LF
WT/DG//Batt/ TLC/BO/CONV	–	2U	1U	1637	970	2000	9,537	6.18M	0.371	381,605	2.88M	87.0	58,100	128,334	62.5	0	LF
PV/Batt/ TLC/BO/CONV	2457	–	–	2742	717	4000	16,944	6.23M	0.394	288,586	3.74M	93.1	16,944	−21.0	37.5	4.9	LF
PV/DG/Batt/ TLC/BO/CONV	1811	–	1U	2907	811	1000	16,206	6.93M	0.418	477,452	2.81M	82.0	77,366	161,616	18.3	0	LF
PV/WT/Batt/ TLC/BO/CONV	2343	1U	–	2592	651	4000	9,855	7.09M	0.428	310,406	4.41M	96.2	9,855	−12.2	65.1	0.02	LF
WT/DG/ TLC/BO	—	3U	1U	–	–	4000	23.2	13.1M	0.800	1.13M	3.35M	41.7	306,082	808,871	78.7	0	LF
PV/WT/DG/Batt TLC/BO/CONV	852	2U	1U	1637	1002	4000	8,173	6.47M	0.389	293,605	3.93M	92.8	27,123	50,073	69.1	0	CC

NOTE- 1 U: 1 unit of an 850-kW wind turbine and 1 unit of an 800-kW diesel generator.

The REF of the optimum system in this case obtained is 89.3%, which is the fraction of energy delivered to the loads (domestic load (kWh/d): 3982.38, Community load (kWh/d): 1104.88, agriculture load(kWh/d):23.56 and thermal load (kWh/d):511.98) that originated from renewable sources. The REF obtained for other systems configurations from HOMER is as follow: PV/WT/DG/Batt/TLC/BO/CONV:89.3%, WT/DG/Batt/TLC/BO/CONV: 87.0%, PV/Batt/TLC/BO/CONV: 93.1%, PV/DG/Batt/TLC/BO/CONV: 82.0%, PV/WT/Batt/TLC/BO/CONV: 96.2%, WT/DG/Batt/TLC/BO: 41.7% and PV/WT/DG/Batt/TLC/BO/CONV: 92.8%. Therefore, the obtained optimal system in this case is fully utilising RERs available at considered site for meeting load demand.

In this case, the total amount of fuel consumed by the optimal PV/WT/DG/Batt/TLC/BO/CONV system obtained is 44,484 L/year, which is ‘the average amount of fuel consumed by the generator per kWh of electricity generates for one year.’ Therefore, it is concluded that the total amount of fuel consumed by this optimal system is the minimum amount in comparison with other HRES configurations such as WT/DG/Batt/TLC/BO/CONV: 58,100 L/year, PV/Batt/TLC/BO/CONV: 16,944 L/year, PV/DG/Batt/TLC/BO/CONV:9,855 L/year, WT/DG/Batt/TLC/BO: 306,082 L/year and PV/WT/DG/Batt/TLC/BO/CONV: 27,123 L/year. Therefore, fuel consumption reduction will reduce system costs and carbon emissions into the environment.

4.3. Summary of component cost

Table 11 provides obtained NPC summary of various components in terms of USD used in optimal design of HRES. (Such as TATA power solar system 300 TP, WT Gamesa-G52(850 kW), CAT-1000kVA-50Hz-PP diesel generator, Lumi-ILTT 25060-12 V Battery, Generic large free converters, and generic thermal load controller). From Table 11, it is noticed that NPC of optimal system obtained from simulation results for case 1(a) of TATA power solar system-300 TP, Gamesa-G52(850 kW) WT, CAT-1000kVA-50Hz-PP diesel generator, Lumi-ILTT 25060-12 V Battery, Generic large free converters, generic boiler and generic thermal load controller were $763,379.58, $1,622,077.00, $796,087.50, $1,550,007.43, $191,986.38, $105,332.25 and $455,655.59. From this table, it can also be concluded that during the system's total lifetime, the Battery must be changed with a replacement cost of $73,505.17.

Table 11. The total NPC and annualised costs of each component used in the optimal stand-alone HRES for Case 1(a) are summarised as.

Total NPC (case 1(a))
Name	Capital	Replacement	O & M	Fuel	Salvage	Total
Battery -inverlast ILTT 25060	$414,504	$73,505.17	$1,071,858.15	$0.00	$9,859.89	$1,550,007.43
CAT-1000 Kva-50Hz-PP	$82,249.20	$0.00	$456,082.78	$270,475.19	$12,719.67	$796,087.50
Gamesa G52-850kW	$822,492	$0.00	$799,585.00	$0.00	$0.00	$1,622,077.00
Generic boiler	$0.00	$0.00	$0.00	$105,332.25	$0.00	$105,332.25
Generic large free converter	$118,177.72	$43,872.25	$38,193.61	$0.00	$8,257.20	$191,986.38
Tata power solar systems 300TP	$463,740.21	$0.00	$299,639.37	$0.00	$0.00	$763,379.58
Thermal load controller	$400,000.00	$127,522.94	$0.00	$0.00	$71,867.36	$455,655.59
System	$2,301,163.13	$244,900.36	$2,665,358.91	$375,807.44	$102,704.11	$5,484,525.72
Annualised costs (case 1(a))
Battery -inverlast ILTT 25060	$32,063.70	$5,685.95	$82,912.92	$0.00	$762.71	$119,899.86
CAT-1000 Kva-50Hz-PP	$6,362.34	$0.00	$35,280.00	$20,922.44	$983.92	$61,580.85
Gamesa G52-850kW	$63,623.36	$0.00	$61,851.40	$0.00	$0.00	$125,474.76
Generic boiler	$0.00	$0.00	$0.00	$8,147.91	$0.00	$8,147.91
Generic large free converter	$9,141.56	$3,393.71	$2,954.44	$0.00	$638.73	$14,850.99
Tata power solar systems 300TP	$35,872.34	$0.00	$23,178.42	$0.00	$0.00	$59,050.75
Thermal load controller	$30,941.75	$9,864.46	$0.00	$0.00	$5,559.25	$35,246.95
System	$178,005.04	$18,944.12	$206,177.18	$29,070.35	$7,944.61	$424,252.08

Figure 11(a) and (b) show the NPC of each component used in the best off-grid connected HRES design for the site under consideration. It can be noticed that annualised costs obtained for components TATA power solar system 300 TP, WT Gamesa-G52(850 kW), CAT-1000kVA-50Hz-PP diesel generator, Lumi-ILTT 25060-12 V Battery, Generic large free converters, generic boiler, and generic thermal load controller of this optimal system were $59,050.75, $125,474.76, $61,580.85, $119,899.86, $14,850.99, $8,147.91, and $35,246.95 respectively. The total annual cost of this optimum HRES is estimated at $424,252.08.

Figure 11. (a). Components total NPC cost of obtained optimal HRES for case 1(a). (b) Components annualised NPC cost of optimal HRES for case 1(a).

Case 1(b): Table 12 provides the obtained NPC results summary of various components used to design optimal stand-alone HRES for sensitivity cases. From Table 12, it can be noticed that NPC obtained for case 1(b) of TATA power solar system-300 TP, Gamesa-G52(850 kW) WT, CAT-1000kVA-50Hz-PP diesel generator, Lumi-ILTT 25060-12 V Battery, Generic large free converters, generic boiler, and generic thermal load controller were $770,208.47, $1,272,216.90, $984,035.63, $1,349,329.33, $176,338.59, $93,735.36, and $367,780.98.00 respectively. It has been determined that the Battery will need to be replaced at the cost of $51,666.36 during the project's lifetime.

Table 12. Total NPC and annualised costs obtained for each component of optimal stand-alone HRES for case 1(b) (sensitivity variables were included in this case).

Total NPC (case 1(b))
Name	Capital	Replacement	O & M	Fuel	Salvage	Total
Battery -inverlast ILTT 25060	$477,432.0	$51,666.36	$825,321.29	$0.00	$5,090.32	$1,349,329.33
CAT-1000 Kva-50Hz-PP	$82,249.20	$0.00	$525,939.41	$392,903.67	$17,056.65	$984,035.63
Gamesa G52-850kW	$822,492.0	$0.00	$534,524.45	$0.00	$84,799.55	$1,272,216.90
Generic boiler	$0.00	$0.00	$0.00	$93,735.36	$0.00	$93,735.36
Generic large free converter	$145,009.10	$0.00	$31,329.49	$0.00	$0.00	$176,338.59
Tata power solar systems 300TP	$568,579.27	$0.00	$245,594.18	$0.00	$43,964.98	$770,208.47
Thermal load controller	$400,000.00	$0.00	$0.00	$0.00	$32,219.02	$367,780.98
System	$2,495,761.57	$51,666.36	$2,162,708.83	$486,639.03	$183,130.53	$5,013,645.26
Annualised costs (case 1(b))
Battery -inverlast ILTT 25060	$55,245.06	$5,978.47	$95,500.36	$0.00	$589.02	$156,134.87
CAT-1000 Kva-50Hz-PP	$9,517.30	$0.00	$60,858.00	$45,464.04	$1,973.68	$113,865.66
Gamesa G52-850kW	$95,172.97	$0.00	$61,851.40	$0.00	$9,812.41	$147,211.97
Generic boiler	$0.00	$0.00	$0.00	$10,846.40	$0.00	$10,846.40
Generic large free converter	$16,779.43	$0.00	$3,625.23	$0.00	$0.00	$20,404.66
Tata power solar systems 300TP	$65,791.98	$0.00	$28,418.43	$0.00	$5,087.32	$89,123.09
Thermal load controller	$46,285.18	$0.00	$0.00	$0.00	$3,728.16	$42,557.02
System	$288,791.93	$5,978.47	$250,253.41	$56,310.44	$21,190.57	$580,143.67

Figure 12 depicts a results summary of the NPC of each component when a sensitivity case is considered. From this table, it can be noticed that annualised costs of TATA power solar system 300 TP, WT Gamesa-G52(850 kW), CAT-1000kVA-50Hz-PP diesel generator, Lumi-ILTT 25060-12 V Battery, Generic large free converters, generic boiler, and generic thermal load controller of this optimal system were $89,123.09, $147,211.97, $113,865.66, $156,134.87, $20,404.66, $10,846.40, and $42,557.02 respectively. The annual cost of this optimal HRES is estimated as $580,143.67.

Figure 12. Components total NPC cost of obtained optimal HRES for case 1(b).

4.5. Electric summary of optimal hRES

Table 13 reveals obtained simulation results of optimal HRES generation, consumption, excess and unmet loads. From this Table 13, it can be noticed that the total annual generation for case 1(a) stand-alone HRES is 3,467,437.00 kWh per year, power consumed by the loads is 1,494,072.00 kWh per year, and excess generated power from the system is 1,867,732.00 kWh/year. Similarly, for case 1(b), the power generation obtained is 3,712,924.00 kWh per year, power consumed by the loads is 1,865,438.00 kWh per year, and the systems have an excess power generated of 1,712,368.00 kWh/year. The monthly generation of different energy sources used in this optimal stand-alone HRES is depicted in Figure 13.

Figure 13. Monthly average electricity generation from various sources of optimal stand-alone HRES.

Table 13. Obtained electric summary of optimal HRES configuration

Excess and Unmet loads
	Case 1(a)	Case 1(b)
Quantity (kWh/yr)	Value
Excess Electricity	1,867,732.00 (53.9%)	1,712,368.00 (46.1%)
Unmet Electric Loads	0	0
Capacity Shortage	0	0
Production Summary
Component	Production (kWh/yr) & Percent
TATA power solar system-300TP	939,376.00 (27.1%)	1,016,244.00 (27.4%)
CAT-1000kVA-50Hz-PP	58,056.00 (1.67%)	107,789.00 (2.90%)
Gamesa G52-850kW	2,470,006.00 (71.2%)	2,588,891.00 (69.7%)
Total	3,467,437.00	3,712,924.00
Consumption Summary
Component	Consumption (kWh/yr) & Percent
AC Primary Load	1,485,484.00 (99.4%)	1,856,850.00 (99.5%)
DC Primary Load	0	0
Deferrable Load	8,588.00 (0.575%)	8,588 (0.460%)
Total	1,494,072.00	1,865,438.00
Renewable fraction	91.6%	89.3%

It is noticed that for case 1(a), power generated by PV modules (Tata power solar system 300TP) is 939,376 kWh per year (27.1%), diesel generator (CAT-1000kVA-50hz-PP) is 58,056.00 kWh/year (1.67%). More amount of power is generated by a wind turbine (Gamesa-G52-850kW) is 2,470,006.00 kWh/year (71.2%). Similarly, for case 1(b) is power generated by PV modules (Tata power solar system 300TP) is 1,016,244 kWh per year (27.4%), diesel generator (CAT-1000kVA-50hz-PP) is 107,789.00 kWh/year (2.90%) and more amount of power is generated by wind turbine (Gamesa-G52-850kW) is 2,588,891.00 kWh/year (69.7%).

4.6. Greenhouse gases emissions

Tables 14 provide the greenhouse gases emitted values for this optimal HRES, such as ‘carbon dioxide, carbon monoxide, unburned hydrocarbons, particulate matter, sulfur dioxide, and nitrogen oxides.’ According to this table, the PV/WT/DG/Batt/TLC/BO/CONV configuration designed was emitting CO₂ into the atmosphere at a rate of 48,072 kg/yr, which is less than the WT/DG/Batt/TLC/BO/CONV system, which emits 263,327 kg/yr.

Tables 14. The info of greenhouse gases emissions of optimal HRES.

Pollutant	Quantity		Unit
	Case 1(a)	Case 1(b)
CO2	48,072	87690	kg/yr
Carbon Monoxide	39.3	68.3	kg/yr
Unburned Hydrocarbons	1.26	2.22	kg/yr
Particulate Matter	2.69	4.66	kg/yr
Sulfur Dioxide	119	218	kg/yr
Nitrogen Oxides	399	682	kg/yr

Similarly, for case 1(b), PV/WT/DG/Batt/TLC/BO/CONV configuration designed is emitting CO₂ contains 87,690 kg/yr into the atmosphere, which is less quantity when compared to WT/DG/TLC/BO system release 808,871 kg/yr.

4.7. Compare economics

The TNPC is the most important economic outcome of HOMER software simulation findings (https://www.abhishekengg.com/caterpillar-make-400-to-5000-kva-lt-dg-set.html#1000-kva-caterpillar-diesel-generator). The economic comparison results for the base case and the current HRES designed in this study are summarised in Table 15.

Table 15. Summary of Economics results for the base case and current HRES system.

	Case 1(a)		Case 1(b)
Terms	Base Case	Current System (Lowest cost system)	Base Case	Current System (Lowest cost system)
NPC	$6.81M	$5.48M	$6.93M	$5.01M
LCOE (per kWh)	$0.340	$0.272	$0.418	$0.299
CO2 Emitted (kg/yr)	263,327	48,072	161,616	87,690
Fuel Consumption (L/yr)	109,787	26,681	77,366	44,484
Renewable fraction (%)	68.8	91.6	82.0	89.3

4.7.1. Optimal off-grid HRES obtained from homer to the considered site

The obtained optimal stand-alone HRES for case 1(a) comprises 614 kW of PV arrays, 850 kW of WT (1 unit), 800 kW of DG (1 unit), 1212 no. of 12 V batteries, 591 kW of power converters, and 2000kW of TLC is having minimum NPC and COE value of $5.48M and $0.272 respectively. For economic comparison of case1(a), the base-case system chosen in this study is the WT/DG/Batt/TLC-BO/CONV system (rated as:850 kW/800 kW/1191 number/2000kW/813 kW). The NPC and COE of the base system configuration are $6.81M and $0.340/kWh, respectively. The difference in investment costs between the base case and designed optimal PV/WT/DG/batt/TLC-BO/CONV configuration can roughly recover in three years. And this system has a high rate of return: 31.0%, interest rate: 28%, and a simple payback of 3.2 years. As a result, it is concluded that the fuel consumption and carbon emissions of this HRES (PV/WT/DG/batt/TLC-BO/CONV) configuration are lower than those of the base system.

Similarly, for case 1(b), when sensitivity variables (change in input variables) are considered, the obtained system comprises 753 kW of PV arrays, 850 kW of WT (1 unit), 800 kW of DG (1 unit), 1396 no. of 12 V batteries, 725 kW of power converters and 2000kW of TLC is having lowest NPC and COE value as $5.01M and $0.299 respectively. For economic comparison of case1(b), the base-case system taken in this study is the PV/DG/Batt/TLC-BO/CONV system (rated as 1811kW/800 kW/2907 number/2000kW/811 kW). The NPC and COE of the base system configuration are $6.93M and $0.418/kWh. The difference in investment costs between the base case and designed optimal PV/WT/DG/batt/TLC-BO/CONV configuration can nearly recover in three years. And this system has a high rate of return: 42.0%, simple payback of 2.9 years, and rate of interest: 35%. It is noticed that carbon emissions and fuel consumption of this HRES (PV/WT/DG/batt/TLC-BO/CONV) are lower than those of the base system.

4.8. Comparison of proposed research work model to other models that have been implemented in literature:

The HOMER software tool has been used commonly to execute several stand-alone HRES configuration prefeasibility techno-economic analyses for rural electrification in India. Table 16 shows the prefeasibility techno-economic analysis of the existing and proposed model. According to simulation results, the PV/WT/DG/Battery/Converter/TLC/boiler design is the best off-grid HRES in case 1(a), with the lowest COE, highest RF, lowest unmet load percentage, and lowest carbon emission. In addition, many HRES models have been developed and integrated with various power sources based on potential availability (such as ‘solar irradiation, wind speed, temperature’) in the selected area before being deployed to meet load demands.

Table 16. Comparison of existing and proposed models in terms of available potentials

Parameters	Existing model		Proposed model
Optimal system	PV/DG/ WT/Battery /Converter (Das et al. 2017)	PV/DG/ WT/Battery /Converter (Pradhan, Mohanty, and Kar 2017)	PV/WT/DG/Battery/TLC/Boiler/Converter system (case 1(a))
Solar irradiation (kWh/m^2/day)	4.56	4.84	5.35
Clearness index	0.51	0.522	0.555
Wind speed (m/s)	2.47	3.93	5.75
COE($/kWh)	0.280	0.201	0.272
NPC ($)	612,280	237,064	5.48M
Renewable fraction (%)	60	43.8	91.6
CO2 Emitted (kg/yr)	34,234	76,011	48,072

Table 16 shows that the proposed off-grid HRES in case 1(a) is optimal based on the available potential at the considered location. Compared to other works performed in (Das et al. 2017; Pradhan, Mohanty, and Kar 2017), the proposed HRES model is the best feasible configuration for the chosen site, with the lowest COE: 0.272$/kWh and the highest RF: 91.6. As a result, these models were optimally feasible for the available potentials of 5.35 (kWh/m^2/day) solar irradiation and 5.75 m/s wind speed. Therefore, the government can suggest the proposed hybrid configurations for rural electrification in order to provide a reliable power supply at a low energy cost at the considered site.

5. Conclusions

Maximising the technological, economic, and environmental benefits of hybrid renewable energy systems has become a major concern for designers. In this paper, for the first time, the hybridisation of different energy sources such as WT/PV/Battery/TLC/Boiler/converter system to supply power for household load demands in chintalaya palle village, Andhra Pradesh, India, was studied from technical, economic, and environmental perspectives for various RE-based stand-alone scenarios. HOMER software is used for microgrid modelling due to its public availability and ease of implementation. The study compares various configurations to determine the winning architecture hybrid system solution with the lowest COE, lowest NPC, and highest renewable fraction. To determine the best configuration scheme for stand-alone hybrid systems, each optimal configuration's technical and economic advantages are assessed. A sensitivity analysis was performed to outline the impact of design parameter uncertainty on HRES design and cost.

The following are the main conclusions of this work:

1. The obtained results show that least cost-optimal stand-alone HRES consists of 614kW-PV,850kW-wind turbine,800kW-Diesel generator,1212 number of batteries and 591 kW converter system along with thermal load controller-2000kW rating having minimum NPC: $ 5.48M, least COE: $ 0.272/kWh and highest RF: 91.6% can provide 92% reliable power supply to onsite load demand of 4502.95kWh/day with 70% renewable sources in the considered site.

2. The stand-alone models with moderate battery size tend to be more efficient and cost-effective than a system without batteries.

3. Meanwhile, integrating RERs has yielded various benefits, such as providing hundred percent required power to fed load demand with excess energy of 53.9%, reducing the release of the harmful GHG emissions into the environment, and also minimising fuel consumption by nearly 15% when compared to base case system for the stand-alone scenario.

4. Therefore, RE-based hybrid systems in the considered site can provide at least 85% of the onsite electricity for load demand, with 80–92% of renewable energy fraction. In addition, the designed optimal hybrid systems are more environmentally friendly as it emits less carbon emission for the considered site.

Acknowledgment

The authors are extremely grateful to the chancellor, vice-chancellor, Vellore institute of technology, Vellore, for providing the VIT seed grant and the excellent infrastructure facilities and encouragement that have made this research work possible.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Appendix

Appendix 1

Table A1: Load data for period of 24 hours kWh/day

Time	Power consumptions kWh/day
	Domestic	Community	Commercial	Total
	L1	L2	L3	L=L1+L2+L3
01:00	37.725	6.12	0.64	44.485
02:00	49.725	6.12	0.64	56.485
03:00	49.725	6.12	0.64	56.485
04:00	47.35	6.12	0.64	54.11
05:00	33.85	6.66	0.64	41.15
06:00	301.9	10.11	0.64	312.65
07:00	116.7	6.13	2.04	124.87
08:00	112.65	14.43	16.96	144.04
09:00	88.95	18.26	58.42	165.63
10:00	83.52	28.09	62.46	174.07
11:00	69.585	31.52	63.26	164.365
12:00	127.545	29.2	63.06	219.805
13:00	270.26	12.83	13.12	296.21
14:00	237.16	15.02	13.12	265.3
15:00	64.5	26.5	59.62	150.62
16:00	87.3	30.02	61.9	179.22
17:00	39.06	21.39	63.06	123.51
18:00	135.585	19.43	27.04	182.055
19:00	353.88	13.55	13.92	381.35
20:00	226.395	11.85	10.8	249.045
21:00	306.795	7.52	2.84	317.155
22:00	244.5	6.62	0.72	251.84
23:00	51.525	6.42	0.64	58.585
00:00	49.725	6.42	0.64	56.785

Table A2: Load demand of Domestic loads for 24 hours

Appliances & Power(W)	CFL bulbs			Tubelight (Fluorescent)	LED bulbs			Ceiling Fan	Exhaust Fan	TV	Radio	Refrigerator	Air cooler	Electric Iron Box	Mixer Grinder	Motor pump	Phone charging	Computer desktop	Electric rice cooker	Air conditioner	Water heater	Total kWh/day
	10	20	30	40	5	15	25	100	100	200	40	200	200	600	200	500	5	200	800	3500	1000
Quantity	120	105	75	225	210	120	75	360	75	195	90	126	66	105	162	165	480	45	75	100	200
Usage(hr/day)	12	12	12	12	12	12	12	14	5	10	5	16	10	2	6	5	17	7	2	4	2
Time
01:00	0.9	0	0	0	0.825	0	0	36	0	0	0	0	0	0	0	0	0	0	0	0	0	37.725
02:00	0.9	0	0	0	0.825	0	0	36	0	0	0	0	12	0	0	0	0	0	0	0	0	49.725
03:00	0.9	0	0	0	0.825	0	0	36	0	0	0	0	12	0	0	0	0	0	0	0	0	49.725
04:00	0.4	0	0	0	0.825	0	1.125	33	0	0	0	0	12	0	0	0	0	0	0	0	0	47.35
05:00	0.4	0	0	0	0.825	0	1.125	30	0	0	0	0	0	0	0	0	1.5	0	0	0	0	33.85
06:00	0	0	0	2.4	0	0	1.5	24	0	0	0	0	0	0	0	82.5	1.5	0	0	0	190	301.9
07:00	0	0	0.45	3.6	0	0	1.5	0	0	0	3	24	0	0	0	82.5	1.65	0	0	0	0	116.7
08:00	0	0	0.9	5.4	0	0	0.75	0	9	12	3.6	25.2	0	54	0	0	1.8	0	0	0	0	112.65
09:00	0	0.6	1.35	6	0	0	0	0	0	18	3	25.2	0	0	33	0	1.8	0	0	0	0	88.95
10:00	0	0.72	0	0	0	0.9	0	0	0	24	0	25.2	0	0	24	0	2.1	6.6	0	0	0	83.52
11:00	0	0.96	0	0	0	1.125	0	0	9	24	0	25.2	0	0	0	0	2.1	7.2	0	0	0	69.585
12:00	0	0.6	0	0	0	0.945	0	0	9	18	0	24	10.8	0	0	0	2.4	9	52.8	0	0	127.545
13:00	0	0.6	0.9	0	0	1.26	0	0	0	0	0	22.8	10.8	0	24	67.5	2.4	0	0	140	0	270.26
14:00	0	0.6	0	1.8	0	0.66	0	21	0	0	0	22.8	13.2	0	0	0	2.1	0	0	175	0	237.16
15:00	0	0	0.9	3.6	0	0	1.2	24	0	0	0	21	12	0	0	0	1.8	0	0	0	0	64.5
16:00	0	0.6	0.9	4.8	0	0	1.2	24	0	12	0	21	0	0	21	0	1.8	0	0	0	0	87.3
17:00	0	0.96	0.9	4.8	0	0.9	1.2	0	0	0	0	21	0	0	0	0	2.1	7.2	0	0	0	39.06
18:00	0.3	0.96	1.35	4.8	0.45	1.125	1.2	0	0	18	3.6	25.2	0	0	0	67.5	2.1	9	0	0	0	135.585
19:00	0.3	1.08	2.25	6.6	0.45	1.35	1.5	0	6.75	36	0	25.2	0	63	30	0	2.4	9	48	0	120	353.88
20:00	0.3	1.2	1.62	7.2	0.45	1.575	1.5	33	6.75	36	3	25.2	0	0	24	75	2.4	7.2	0	0	0	226.395
21:00	0.3	1.2	0.9	4.8	0.45	1.395	1.65	33	0	30	0	21	0	0	0	0	2.1	0	0	210	0	306.795
22:00	0.2	0	0.9	0	0.45	0	0.75	36	0	0	0	18	13.2	0	0	0	0	0	0	175	0	244.5
23:00	0.9	0	0	0	0.825	0	0.6	36	0	0	0	0	13.2	0	0	0	0	0	0	0	0	51.525
00:00	0.9	0	0	0	0.825	0	0	36	0	0	0	0	12	0	0	0	0	0	0	0	0	49.725
Total load (kwh/day)	6.7	10.08	13.32	55.8	8.025	11.235	16.8	438	40.5	228	16.2	372	121.2	117	156	375	34.05	55.2	100.8	700	310	3185.91
	30.1				36.06

Table A3: Community load data

Load category	Schools (3)											Health clinic centre										Workship Places(temple,church,mosque)							Street lights	Total kWh/day
Appliances & Power(W)	Tubelight (Fluorescent)	LED bulbs	Ceiling Fan	Computer	Printer & scanner	MIC set	TV	water pump	Refrigerator	LCD projector	Cooler	Tubelight (Fluorescent)	LED bulbs	Ceiling Fan	Refrigerator for vaccines	Electric heater	Micro Scope	Body scanning device	Computer & printer	Air Conditioner	Water pump	Tubelight (Fluorescent)	LED bulbs	MIC SET	TV	Water pump	Ceiling Fan	High intensity lamps	LED bulbs
	40	20	100	200	340	150	200	500	200	220	200	40	20	100	200	1000	90	1500	300	2000	500	40	10	150	200	500	100	200	20
Quantity	36	70	36	12	6	3	6	3	3	3	3	10	16	10	4	4	4	4	5	2	2	15	45	3	3	3	20	6	250
Usuage(hr/day)	8	16	11	8	6	2	5	2	10	6	6	14	18	14	24	13	10	5	11	9	2	16	24	10	4	2	15	4	13
Time
01:00	0	0.3	0	0	0	0	0	0	0	0	0	0	0.32	0	0.2	0	0	0	0	0	0	0	0.3	0	0	0	0	0	5	6.12
02:00	0	0.3	0	0	0	0	0	0	0	0	0	0	0.32	0	0.2	0	0	0	0	0	0	0	0.3	0	0	0	0	0	5	6.12
03:00	0	0.3	0	0	0	0	0	0	0	0	0	0	0.32	0	0.2	0	0	0	0	0	0	0	0.3	0	0	0	0	0	5	6.12
04:00	0	0.3	0	0	0	0	0	0	0	0	0	0	0.32	0	0.2	0	0	0	0	0	0	0	0.3	0	0	0	0	0	5	6.12
05:00	0	0.3	0	0	0	0	0	0	0	0	0	0	0.32	0	0.2	0	0	0	0	0	0	0.24	0.6	0	0	0	0	0	5	6.66
06:00	0	0.6	0	0	0	0	0	0	0	0	0	0	0.32	0	0.2	0	0	0	0	0	0	0.24	0.6	0.45	0	1.5	1.2	0	5	10.11
07:00	0	0.6	0	0	0	0	0	0	0	0	0	0.4	0.32	1	0.2	1	0	0	0	0	0	0.36	0.6	0.45	0	0	1.2	0	0	6.13
08:00	0.8	0	2	0	0	0	0	1.5	0.6	0	0	0.4	0.32	1	0.4	2	0	0	0.9	0	1	0.36	0.6	0.45	0.6	0	1.5	0	0	14.43
09:00	0.6	0	3.6	1.8	0	0.45	0	0	0.6	0	0	0.4	0.32	1	0.6	2	0.36	0	0.9	2	0	0.48	0.6	0.45	0.6	0	1.5	0	0	18.26
10:00	0.6	0	3.6	1.8	2.04	0	0	0	0.6	0.66	0.6	0.4	0	1	0.8	3	0.36	3	1.5	4	0	0.48	0.6	0.45	0.6	0	2	0	0	28.09
11:00	0.6	0	3.6	2.4	2.04	0	1.2	0	0.6	0.66	0.6	0.4	0	1	0.8	4	0.36	4.5	1.5	4	0	0.36	0.3	0	0.6	0	2	0	0	31.52
12:00	0	0.3	3.6	2.4	2.04	0	1.2	0	0.6	0.66	0.6	0.4	0	1	0.8	4	0.36	6	1.2	2	0	0.24	0.3	0	0	0	1.5	0	0	29.2
13:00	0	0.3	3.6	0	0	0	0	0	0.6	0	0	0.4	0	1	0.8	2	0.09	0	0.9	2	0	0.24	0.3	0	0	0	0.6	0	0	12.83
14:00	0	0.3	3.6	1.8	0	0	0	0	0.6	0	0	0.4	0	1	0.8	2	0.18	0	0.9	2	0	0.24	0.6	0	0	0	0.6	0	0	15.02
15:00	0	0.3	3.6	1.8	2.04	0	1.2	0	0.6	0.66	0.6	0.4	0	1	0.8	3	0.36	3	1.2	4	0.5	0.24	0.6	0	0	0	0.6	0	0	26.5
16:00	0.6	0	3.6	2.4	2.04	0.45	1.2	0	0.6	0.66	0.6	0.4	0.32	1	0.8	4	0.36	3	1.5	4	0	0.24	0.6	0.45	0.6	0	0.6	0	0	30.02
17:00	0.8	0	2	2.4	2.04	0	1.2	0	0.6	0.66	0.6	0.4	0.32	1	0.6	2	0.36	0	1.2	2	0	0.36	0.6	0.45	0.6	0	1.2	0	0	21.39
18:00	0.8	0	2	0	0	0	0	1.5	0	0	0	0.4	0.32	1	0.4	1	0.18	0	0.9	0	0	0.48	0.8	0.45	0.6	1.5	1.5	0.6	5	19.43
19:00	0.6	0	0	0	0	0	0	0	0	0	0	0.4	0.32	1	0.2	1	0	0	0	0	0	0.48	0.8	0.45	0.6	0	1.5	1.2	5	13.55
20:00	0	0.8	0	0	0	0	0	0	0	0	0	0.4	0.32	1	0.2	0	0	0	0	0	0	0.48	0.8	0.45	0	0	1.2	1.2	5	11.85
21:00	0	0.8	0	0	0	0	0	0	0	0	0	0	0.32	0	0.2	0	0	0	0	0	0	0	0.6	0	0	0	0	0.6	5	7.52
22:00	0	0.8	0	0	0	0	0	0	0	0	0	0	0.32	0	0.2	0	0	0	0	0	0	0	0.3	0	0	0	0	0	5	6.62
23:00	0	0.6	0	0	0	0	0	0	0	0	0	0	0.32	0	0.2	0	0	0	0	0	0	0	0.3	0	0	0	0	0	5	6.42
00:00	0	0.6	0	0	0	0	0	0	0	0	0	0	0.32	0	0.2	0	0	0	0	0	0	0	0.3	0	0	0	0	0	5	6.42
Total load (kwh/day)	5.4	7.5	34.8	16.8	12.24	0.9	6	3	6	3.96	3.6	5.6	5.76	14	10.2	31	2.97	19.5	12.6	26	1.5	5.52	12	4.5	4.8	3	18.7	3.6	65	346.45

Table A4: Commercial load data

Load category	Small business centres (20 shops)					Computer and printer services (4)					Rice mills and Wielding shop (2)						Total kWh/day
Appliances & Power(W)	Tubelight (Fluorescent)	LED bulbs	Ceiling Fan	Water pump	Refrigerator	Tubelight (Fluorescent)	LED bulbs	Ceiling Fan	Computer, printer & scanner	Water pump	Tubelight (Fluorescent)	LED bulbs	Water pump	Ceiling Fan	Rice griding motors	Wielding Motors
	40	20	100	500	200	40	20	100	540	500	40	20	500	100	7500	4500
Quantity	60	65	60	10	10	12	16	16	6	2	20	20	2	10	5	2
Usuage(hr/day)	8	24	13	1	14	13	12	13	13	1	8	12	1	10	7	8
Time
01:00	0	0.4	0	0	0	0	0.16	0	0	0	0	0.08	0	0	0	0	0.64
02:00	0	0.4	0	0	0	0	0.16	0	0	0	0	0.08	0	0	0	0	0.64
03:00	0	0.4	0	0	0	0	0.16	0	0	0	0	0.08	0	0	0	0	0.64
04:00	0	0.4	0	0	0	0	0.16	0	0	0	0	0.08	0	0	0	0	0.64
05:00	0	0.4	0	0	0	0	0.16	0	0	0	0	0.08	0	0	0	0	0.64
06:00	0	0.4	0	0	0	0	0.16	0	0	0	0	0.08	0	0	0	0	0.64
07:00	0	0.8	0	0	0	0	0.16	0	0	0	0	0.08	1	0	0	0	2.04
08:00	1.2	0.8	4	5	1.6	0.16	0.16	0.8	2.16	1	0	0.08	0	0	0	0	16.96
09:00	1.6	0.8	4	0	1.6	0.32	0	0.8	2.16	0	0.64	0	0	0	37.5	9	58.42
10:00	1.6	0.4	5	0	2	0.48	0	1.6	3.24	0	0.64	0	0	1	37.5	9	62.46
11:00	1.4	0.4	6	0	2	0.48	0	1.6	3.24	0	0.64	0	0	1	37.5	9	63.26
12:00	1.2	0.6	6	0	2	0.48	0	1.6	3.24	0	0.64	0	0	0.8	37.5	9	63.06
13:00	1.2	0.4	5	0	2	0.32	0	0.8	2.16	0	0.64	0	0	0.6	0	0	13.12
14:00	1.2	0.4	5	0	2	0.32	0	0.8	2.16	0	0.64	0	0	0.6	0	0	13.12
15:00	1.2	0.4	5	0	2	0.32	0	0.8	2.16	0	0.64	0	0	0.6	37.5	9	59.62
16:00	1.2	0.4	5	0	2	0.32	0.24	1.6	3.24	0	0.8	0	0	0.6	37.5	9	61.9
17:00	1.6	0.6	5	0	2	0.48	0.24	1.6	3.24	0	0.8	0.4	0	0.6	37.5	9	63.06
18:00	1.6	1	6	0	2	0.48	0.32	1.6	3.24	0	0.8	0.4	0	0.6	0	9	27.04
19:00	1.2	1.2	5	0	1.6	0.32	0.32	0.8	2.16	0	0.32	0.4	0	0.6	0	0	13.92
20:00	1.2	1.2	3	0	1.6	0.32	0.32	0.8	2.16	0	0	0.2	0	0	0	0	10.8
21:00	0	0.8	0	0	1.6	0	0.24	0	0	0	0	0.2	0	0	0	0	2.84
22:00	0	0.4	0	0	0	0	0.24	0	0	0	0	0.08	0	0	0	0	0.72
23:00	0	0.4	0	0	0	0	0.16	0	0	0	0	0.08	0	0	0	0	0.64
00:00	0	0.4	0	0	0	0	0.16	0	0	0	0	0.08	0	0	0	0	0.64
Total load (kwh/day)	17.4	13.8	64	5	26	4.8	3.52	15.2	34.56	1	7.2	2.48	1	7	262.5	72	537.46