# Introduction ealth facilities are community institutions where reliable and sustainable energy requires particular attention. Energy in health facilities is a critical enabler of access to many medical technologies, and thus to health services access. Studies conducted by World Health Organization (WHO) indicate that electricity access have a significant impact on some key health service indicators, such as: prolonging night-time service provision; attracting and retaining skilled health workers to a facility; and providing faster emergency response, including for childbirth emergencies. Without energy, many life-saving interventions cannot be undertaken (WHO, 2014).Modern energy provision is therefore a critical enabler of universal access to health care and universal health coverage. The problems that health facilities encounter in Nigeria based on power supply are found throughout much of the world. Where there is a central power system, it is unreliable. Bringing power from the central grid to rural health facilities is not economically feasible in many cases. Hybrid systems designed with Hybrid Optimization Model for Electric Renewables (HOMER) can be cost effective and robust, solving both these issues simultaneously. Many studies throughout the world have used HOMER software to investigate the optimal design of proposed hybrid energy systems (HESs). Halabi Each study proposed certain components that differed from others and the simulation was conducted for a specific area. Ani (2014) used HOMER to model energy map for off-grid health clinics in Nigeria. The author found that the most ideal solutions for the sites were hybrid systems (PV/diesel; PV/wind/diesel) with a battery backup. Malika (2016) also used HOMER to model renewable energy systems for rural health clinics in Algeria. The study focused on the optimization and the cost analysis of renewable energy hybrid systems for electricity production at rural health clinics situated in coastal, high plains and desert regions of Algeria, represented by Algiers, Ghardaia and Djanet. Regarding the cost of fuel in different regions of Algeria, the optimized renewable energy systems found for Algiers and Ghardaia are composed of PV systems, wind generators and batteries, while for Djanet it is a PV system and batteries. Ani and Emetu (2013) used the HOMER software to model robust, reliable energy systems for a rural health service facility in the southern part of Nigeria. Although HOMER is increasingly used for state-of-the-art microgrid design, these examples go back to HOMER's roots as a tool for electrification. The benefits of electrifying rural health clinics can literally mean the difference between the health clinics standards of the "dark ages" compared to that of the modern world. By having electricity, a health clinic will have prolonged opening hours. With more electricity, health clinics could access basic medical devices and appliances, such as vaccine refrigerators, as well as general equipment such as water sterilization, heating, cooling, and ventilation. An international donor agency United States Agency for International Development (USAID) (2016)has been working with a health clinic in the Kalahari Desert of Botswana to improve local health care service delivery. The health clinic is not connected to the grid and currently utilizes a diesel generator to partially meet its energy needs. The donor agency decided to explore different options for upgrading the clinic's power generation systems, by comparing costs for a variety of different energy generation systems that can meet 100% of the clinic's load using HOMER program. Evaluating the energy generation options, the resulting cost estimates show that the lowest cost system is a PV/diesel-battery hybrid system. A dieselbattery system costs 13% more than this hybrid system because the added fuel cost over the life of the system is more than the savings in initial PV investment, and a PV-battery system costs about 28% more than the leastcost design. In diesel only system, the cost of energy from a diesel system with no batteries is over twice the cost from a diesel-battery system. The calculations demonstrate that because of fuel and maintenance costs, the system with the lowest capital cost is not the system with the lowest lifetime cost of energy. World health organization (2016) conducted a simulation to compare the costs of different stand-alone power supply technologies to a hypothetical health clinic in rural Kenya, using HOMER Power System Design tool. The simulation tested and compared power supply arrays reliant upon a fuel-based generator, PV and generator combinations, and PV only, with and without battery backup; and looked at costs of the different supply options (both initial and long-term), as well as the pollution and climate emissions. The simulation further explored these supply options for two demand scenarios: one using conventional medical devices and one using more energy-efficient medical devices that reduce the clinic's overall energy demand. The simulation provides an interesting example of how optimal combinations of photovoltaic and diesel generation with appropriate energy storage can yield multiple gains: lower overall cost of energy, a shift to renewable energy, and a reliable supply for all health facility energy needs. The simulation also demonstrates how investments in more energy-efficient medical devices, can help reduce the required capital investment in energy supply for a rural health clinic. The results of the simulation demonstrate that the best combination remains energy efficiencies + more efficient supply configurations. # a) Description of HOMER Software HOMER is a computer model developed by the United States (U.S.) National Renewable Energy Laboratory (NREL) to assist in the design of power systems; evaluate technical (power system's physical behavior) and financial (power system's life-cycle cost, which is the total cost of installing and operating the system over its life span) options for on-grid and off-grid power systems, for distributed generation and standalone applications. This software application (HOMER) helps to facilitate the comparison of power generation technologies across a wide range of applications. It allows one to model the performance of a power system configuration and determine its technical feasibility and life-cycle cost; compare many different design options based on the satisfied technical constraints at the lowest life-cycle cost; and assists in understanding and quantifying the effects of uncertainty or changes in the inputs. In 1993, NREL developed the first version of HOMER for the U.S. Department of Energy (DOE) for renewable energy programs. The developed design tool (HOMER) was used in predicting the long-term performance of hybrid power systems and to understand the tradeoffs between different energy production configurations. HOMER has a user-friendly windows-based interface with a library of input data files and users can readily model new applications. HOMER simulate different system configurations, or combinations of components, and generates results that can be viewed as a list of feasible configurations sorted by net present cost. It displays simulation results in a wide variety of tables and graphs that help one compare configurations and evaluate them on their economic and technical merits (Getting Started Guide, 2005). # b) Literature Review Based on literature search, many case studies concentrate on the use of HOMER at the national, regional, and rural communities for households scale (Olubayo et with only a few research efforts directed at healthcare facilities. Furthermore, the strategies for modelling and simulation of alternative energy systems for powering health facilities have not been comprehensively studied. Razmjoo and Davarpanah (2018) studied four different models of hybrid renewable energy systems with a combination of photovoltaic panels, wind turbine, and diesel generators for residential application in Damghan city. Simulation, optimization, and modeling procedures were done by HOMER software. The simulation results show that among three hybrid systems investigated, PVwind system has the highest value of electrical production with 18,478 kWh/yr and the PV-diesel system has the lowest value of electrical production with 9,876 kWh/yr. Moreover, from the environmental view, the PV-Diesel system is highest with2,402 kg/yr and the PVwind system has the lowest pollution rate, i.e., 0%. Shezan et al (2016) carried out a research to analyze the performance of an off-grid PV (photovoltaic)-winddiesel-battery hybrid energy system for a remote area located in the state of Selangor, Malaysia. The system was designed as well as simulated to support a small community considering an average load demand of 33 kWh/day with a peak load of 3.9 kW. The simulation and optimization of operations of the system was done by HOMER application using the real time field data of solar radiation and wind speed of that area. The simulation ensures that the system is suitably feasible with respect to net present cost (NPC) and carbon dioxide (CO2) emission reduction purpose. The result shows that NPC and CO2emission can be reduced about 29.65% and 16 tons per year respectively compared to the conventional power plants. The NPC of the optimized system has been found about USD 288,194.00 having the per unit Cost of Energy (COE) about USD 1.877/kWh. The analyzed hybrid energy system might be applicable for other region of the world where the climate conditions are similar. Similar research was conducted in Shezan and Ping (2017) which uses the HOMER to design an off-grid Hybrid PV-Wind-Biomass-Diesel Energy Systemin order to support a small community having an average load demand of 80kWh/d with a peak load of 8.1 kW. The simulation ensures that the system is economically and environmentally feasible with respect to NPC and CO2 emission limitations. The result shows that the NPC of the optimized system has been found to be about USD 160,626.00, having the per unit COE of USD 0.431/kWh. In a related research, a PV-wind-diesel system was designed by Bernal-Agustín et al. (2006) to minimize the total cost of system installation and to reduce the pollutant emissions by using HOMER software. The results demonstrated the practical utilization of the method used. Bekelea and Boneya (2011) designed a wind-PV hybrid power system for supplying electricity for a community living in Ethiopian remote area using HOMER. Moreover Kusakana and Vermaak (2013) used HOMER to investigate the possibility of using hybrid PVwind renewable systems to supply mobile telephone stations in remote areas of Congo. Li et al. (2013) presented a techno-economic feasibility study of an autonomous hybrid wind/PV/battery power system for a household in Urumqi, China using Hybrid Optimization Model for Electric Renewable (HOMER) simulation software. They recommended a hybridwind/PV/battery system with 5 kW of PV arrays (72% solar energy penetration), with a one wind turbine of 2.5 kW (28% wind energy penetration), 8 unit batteries each of6.94 kWh and 5 kW sized power converters for the household. Furthermore Ngan and Tan (2012) analyzed the implementation of hybrid photovoltaic (PV)/wind turbine/diesel system in JohorBahru, Malaysia and used "HOMER" software. Bad awe et al. (2012) integrated and optimized a hybrid wind and solar energy system to an existing diesel generator with a battery backup to supply power to telecommunication towers using HOMER software. Their results indicated that the hybrid renewable energy system is a cost effective solution. HOMER software is one of the tools being used by WHO for designing renewable energy health facilities. HOMER can evaluate a range of equipment options over varying constraints to optimize small power systems for health facilities. HOMER's flexibility makes it useful in the evaluation of design issues in the planning and early decision-making phase of health facility electrification projects. Therefore, the aim of this paper is to design energy system options that can optimize health services delivery, using HOMER software and demonstrates in detail the depth of the software application; teach health workers step by step on how to model renewable energy health facilities. The objective of this research is to ensure the uninterrupted power supply to the health facilities in the remote and decentralized areas, to ensure environmentally safe energy system, to reduceCO2 and other greenhouse gas (GHG) emissions, to reduce the COE and improving the NPC. # II. # Methods and Materials a) Modeling of Alternative Energy System Components Before going to the computer simulation, modeling of alternative energy system components is done step by step as described below. The proposed system contains wind energy sub-system, PV subsystem, diesel generators unit and battery storage subsystem. Step One: Defining the Power System This schematic (Add/Remove Equipment to Consider) shown in Figure 1 represents all of the technology options for a power system design. The power system design can be defined by clicking the Add/Remove button. For this exercise, we selected a number of different components as shown in Figure 1 by clicking the check box of each of the following: Primary Load 1 (Health Facility Load), PV, Wind Turbine 1 (BWC Excel-R), Converter, Generator 1 (diesel generator) and Battery 1 (Surrette 6CS25P). At the bottom of the window select 'do Not Model Grid', and Click OK to return to the Main window. Year 2021 Step Two: Defining the Facility Load and its Location The load inputs describe the electrical demand that the power system must be served according to a particular schedule. In this case, clinic personnel must examine their facility's specific needs and discuss them with energy design experts. The needs assessment will include an inventory of the types of equipment used in the facility, the power required to operate each device, and the average "daily load", or the amount of power required to operate equipment under normal working conditions. Therefore, in this study, the hourly load profile of each load [shown in Table A1 of the supplementary data] needs to be entered into primary load according to the setup defined in the schematic and model. To find wattage information for a given A1 of the supplementary data, which was used to define the hourly profile, and the random variability parameters was set to 0% for accurate power load measurements as indicated in Figure 2. # Strategic Thinking and Adjusting for Change Once a facility has comprehensively analyzed the energy requirements of its daily operations, it must be determined whether those demands are likely to change. One must think strategically about the possibility that energy demands may increase due to the addition of new services or extended operating hours. # b) Power Generation Options After determining the facility's typical daily energy usage as described above, it is time to evaluate the energy technologies available to electrify the facility. Rural health clinics have a number of options available to supply reliable electricity. The best option for a given application depends on energy technology drivers such as the capital cost, operating cost, reliability, and durability. Capital Cost is the initial cost to purchase and install the equipment. Power equipment -including generators (PV panels, wind turbine, and diesel), inverters, charge controllers, and batteries -can vary greatly in cost and quality. In many cases, higher-quality models will cost more, but can have a greater return on investment in terms of greater reliability of power and longer system lifetimes. Costs also vary considerably based on the local market. Operating Cost includes the cost of fuel (where applicable), operations and maintenance, and parts purchased for repairs. Operating costs will vary more than capital costs, due to differences in: ? Fuel prices over time and from location-to-location. ? Use patterns -systems will experience more or less stress in a given day, based on the number of hours they operate, the amount of power they provide, and the type of equipment drawing loads (e.g., high intensity equipment such as sterilizer oven (laboratory autoclave), as opposed to low-intensity equipment such as lighting); and ? Environmental conditions. Reliability is expressed as a fraction of time the equipment is available to provide power. Generators need to be taken off-line for service periodically; wind and solar power systems require optimal weather conditions to operate at maximum efficiency. Systems can generally achieve greater reliability by adding backup components; hybrid systems (include photovoltaic panels and/or a wind turbine, batteries, and a generator) which has the ability for one system to support the other provide greater flexibility, although this generally increases cost and complexity. Durability is the typical system lifetime, expressed either in years or in hours of run-time (for engine generators). # Facility Location -The Case Study Sets of Locations in Nigeria The locations of the hypothetical health facility are chosen to reflect the various geographical and climatic conditions in Nigeria. Nigeria is divided into three main climatic regions: the equatorial climatic region where the global solar radiation ranges from 4.1 to 4.9 kWh/m 2 /day, the tropical climatic region where the global solar energy is around 5kWh/m 2 /day, and finally the arid climatic region where the global solar radiation is higher than 5kWh/m 2 /day, while the wind is characterized by a moderate speed (2.4 to 5.4m/s) as can be seen in (Ani, 2014). These locations for the hypothetical health facility are: Nembe (Bayelsa State) in the equatorial climatic region, Abaji (Abuja, FCT) in the tropical climatic region, and Guzamala (Borno State) in the arid climatic region. Step Three: Renewable Energy (Wind and Solar) Resources The availability of renewable energy system (RES) at a location differs considerably from location to location. This is a vital aspect in the development of the power system. The performances of solar and wind energy components are influenced by the geographical location and climatic conditions. As RES (solar and wind) are naturally available and intermittent, they are the best option to be combined into a hybridized diesel system. These resources (solar and wind) depend on different factors such as the amount of solar energy available is dependent on climate and latitude (your specific location on the Earth's surface), the wind resource is influenced by atmospheric circulation patterns and geographic aspects; in turn influences when and how much power can be generated. The specific geographical locations (latitude and longitude) of the health facility based on solar and wind resources are discussed below. # c) Wind Resources Wind resources can be determined by using the National Aeronautics and Space Administration (NASA) Surface Meteorology and Solar Energy database. Therefore, to determine the wind resources of a location, log into the NASA Meteorology website (http://eosweb.larc.nasa.gov/) and enter the coordinates and select the annual wind speed average, which (the annual wind speed average) is a good indicator of the suitability of the installation of a wind turbine in any given location. Generally, values above 7m/s with few months below 5m/s are considered adequate for satisfactory results (Kassam, 2011). Once the data is received, click on the wind resource icon in HOMER and click on the "enter monthly averages" and enter the monthly wind data, then fill the Altitude and Anemometer Height with the appropriate information. For this exercise, a hypothetical site coordinates of 4° 17' N latitude and 6° 25' E longitude for Nembe (Bayelsa State), of 9° 00' N latitude and 7° 00' E longitude for Abaji (Abuja, FCT), and latitude 11° 05' N, longitude 13° 00' E for Guzamala (Borno State), were used as indicated in Figure 3. # d) Solar Resource Solar resources can be obtained directly via internet by HOMER from the NASA Surface Meteorology and Solar Energy database by entering the GPS coordinates. NASA provides extensive information on solar resources by month for any location in the world (http://eosweb.larc.nasa.gov/sse/). Using the coordinates from the wind resources above, the annual solar radiation of these areas are4.12kWh/m²/d for Nembe (Bayelsa State), 5.45 kWh/m²/d for Abaji (Abuja, FCT), and 5.90 kWh/m²/d for Guzamala (Borno State) which can be seen from Figure 4. Kassam (2011) advised that the average radiation should have a constant trend and that the annual radiation should be above 5kWh/m 2 /d in order to have a reliable source of power coming from the photovoltaic panels. Step Four: Diesel Price Diesel price has a significant impact on the running costs of a system equipped with a diesel generator (Kassam, 2011).The diesel price can be added by clicking the diesel icon in HOMER, then click on the "Diesel Inputs" and enter the diesel price.In the exercise,1$US/L for Nembe, 1.5$US/L for Abaji and 1.8$US/L for Guzamala, were considered as reference price for the diesel as shown in Figure 5. Step Five: Economics The economic factors of the project shown in Figure 6 can all be defined by clicking on the Economics icon in HOMER .In this exercise, a real annual interest rate of 6% was assumed. The real interest rate is equal to the nominal interest rate minus the inflation rate. The appropriate value for this variable depends on current macroeconomic conditions, the financial strength of the implementing entity, and concessional financing or other policy incentives. Also, the project lifetime was set to 20 years -inline with the expected life span of the renewable equipment (wind turbine, photovoltaic system). Diesel generators and batteries have usually a much lower lifetime, which largely depends on the Step Six: Equipment Diesel Generator The Generator in the power system can be defined by considering the Size, Cost, Replacement Cost, Operating Lifetime, and Operations & Maintenance (O & M) expenditure of the generator. In generator-only systems, the generator must be sized to handle the peak expected load, but the system frequently runs at lower loads at reduced efficiency. Therefore, in such situation, a battery bank can be added to a generator-based system to reduce run time and save fuel costs. The addition of batteries to a diesel system are often a good investment in terms of fuel savings. In this exercise, by clicking on the Generator icon, a2.5kVA diesel generator has been considered. The minimum allowable load on the generator has been set as 30% of its rated capacity and has 20,000 operating hours life time. The default efficiency curve present in HOMER was used, as often vendors do not provide comprehensive details to design a specific efficiency curve. Generators typically have low capital cost compared to other alternatives, but higher operating costs due to the need for fuel(USAID, 2016). Initial capital cost will vary with the size and type of generator, while the Operating costs will vary depending on the level of usage. Generators have several maintenance requirements; and maintenance checks will vary depending on the design of the engine and its duty cycle (whether it is a primary energy source or a back-up/emergency unit). Engine oil and the oil filter should be changed after approximately 1,000 hours of operation. Meanwhile, it is often difficult to determine the generator hourly maintenance cost (the expense and transport associated with acquiring diesel) due to many health facilities have contract with companies or individuals to supply diesel to the generator at the facility and also manage the generator's O&M services. In the exercise, an hourly O&M cost of 0.1 US$ were assumed as indicated in Figure 7. ----------------------------------(1) For better performance and higher efficiency the diesel generator will always operate between 80 and 100% of their kW rating, where DEG ? is the diesel generator efficiency. # f) Converter Converter in the power system represents both an inverter (for the conversion of generated direct lobal Journal of Researches in Engineering ( ) Volume Xx XI Is sue III Version I J Year 2021 current (DC) power into required alternating current (AC) power; both for energy flowing directly to the load from the PV and for energy transiting through the battery), and a rectifier (for the conversion of generated AC power in order to charge the battery).It is a device that can convert electrical power from ac to dc in a process called rectification and from dc toac in a process called inversion (Shezan et al., 2016). When defining the converter, the key parameters to consider are the energy conversion efficiencies, Size, Cost, and O&M Cost as shown in Figure 8. # Figure 8: HOMER input for converter g) Mathematical Model of Converter In the proposed scheme converter contains both rectifier and inverter. PV, wind energy generator and battery sub-systems are connected with DC bus while diesel generating unit sub-system is connected with AC bus. The electric loads connected in this scheme are DC loads (Ani, 2015). The rectifier is used to transform the surplus AC power from the diesel electric generator to DC power of constant voltage. The diesel electric generator will be powering the load and at the same time charging the battery. The rectifier model is given below(Ani, 2015): ------------------------(2) ( ) ( ) -----------------------------------( ------------------------( ( ) ( ) REC IN REC OUT REC t E t E ? × = ? ? -t E t E AC SUR IN REC ? ? = - # h) Storage System -Battery Battery is a device that stores energy and makes it available in an electrical form. Batteries are not a power technology, but a means of storing the power produced by other systems, such as photovoltaic and/or wind systems, hybrid systems. The battery stores the generated electricity during the availability of the renewable energy sources and the stored energy supplied to the consumer whenever required (Partha and Nitai, 2020). For some renewable energy clinics, batteries can be used to provide backup power during surges and outages. However, if renewable energy is absent for a lengthy period of time, some other system (generator) would be useful to recharge the batteries. Batteries' lifetimes are partly dependent on the cycling (charging and discharging) they experience. HOMER provides a library of several predefined batteries, and users can add to the library if necessary. When choosing from the library, the key parameters to consider or include are the Nominal Capacity, Voltage, Round Trip Efficiency, Minimum State of Charge, Capacity Curve and Lifetime Curve (User Manual, 2016). By clicking on the battery icon (selected or created) in HOMER we are able to define the battery bank used in the power system by entering the cost of the battery, the lobal Journal of Researches in Engineering ( ) Volume Xx XI Is sue III Version I J Year 2021 number of batteries per string and the number of strings of batteries. Therefore, in this exercise, a single string of Surrette 6CS25P battery, 6V/1,156Ah sealed lead-acid batteries were considered as indicated in Figure 9. # Figure 9: HOMER input for storage battery i) Mathematical Model of Storage Battery HOMER uses the Kinetic Battery Model (Manwell and McGowan, 1993) to determine the amount of energy that can be absorbed by or withdrawn from the battery bank each time step. HOMER determines the total amount of energy stored in the battery at any time as the sum of the available (energy that is readily available for conversion to DC electricity) and bound energy (energy that is chemically bound and therefore not immediately available for withdrawal) by the following equation(HOMER help, 2015;User Manual, 2016): -------------------------------------------------(5), where: 2 1 Q Q Q + = -- 1 Q is the energy in kWh that is readily available for conversion to DC electricity. # 2 Q is the energy in kWh that is chemically bound and therefore not immediately available for withdrawal. Each hour of the simulation, the maximum amount of power that the battery bank can withdraw (or absorb) is being calculated using maximum discharge (or charge) power. The maximum discharge (or charge) power varies from hour to hour according to its state of charge and its recent charge and discharge history, as determined by the kinetic battery model. Using the kinetic battery model, the maximum amount of power that the battery can discharge over a specific length of time t ? is given by the following equation(User Manual, 2016): ------------------------------------------( ------------(7) ( ) ( ) t k t k t k t k # j) Mathematical Model of Charge Controller To prevent overcharging of a battery, a charge controller is used to sense when the batteries are fully charged and to stop or decrease the amount of energy flowing from the energy source to the batteries. The --------------------------( --------------( is the amount of surplus energy from DC sources, kWh (Ani, 2015). ( ) ( ) CC IN CC OUT CC t E t E ? × = ? ? --8) ( ) ( ) ( ) t E t E t E DC SUR OUT REC IN CC ? ? ? + = -- # k) Photovoltaic (PV) System Photovoltaic (PV) Systems generate electricity from sunlight collected by solar panels. Solar panels are available in different shape and size. For instance, the size of the PV system depends on the power requirement of the health facility and its location. PV systems are highly modular, so the system can be customized to cover power demand of the health facility and add units if the power demand increases. PV systems typically have higher capital costs, but lower operating costs when compared to other energy generation options. Solar panels has the lowest O&M cost due to it does not require extensive maintenance; only to remove dust from the panels twice a year (Kassam, 2011). For the facility under analysis we assumed for the photovoltaic panels a derating factor of 90% and expected lifetime of twenty years as indicated in Figure 10. The cost of PV panels was estimated as US$ 0.600/Wp based on prices cited by Nigerian suppliers (Solar Power Systems Components, 2015). This was adjusted upward to US$ 2/Wp (US$ 2,000 per kWp) to account for other support components that are required, also known as balance of system (BOS) parts, such as cables, charge controller with Maximum Power Point Tracker, lightening protection, as well as delivery/labour and installation costs. -----------------------------------------------(10), where: ave H is the monthly average radiation on the horizontal surface of the earth (kWh/m 2 /day) ave o H , is the extraterrestrial horizontal radiation, meaning the radiation on a horizontal surface at the top of the earth's atmosphere (kWh/m 2 /day) Mathematically, the output of the PV array are calculated by HOMER using the following equation(Help Manual, 2015): ( ) [ ] STC c c P STC T T PV PV PV T T G G f Y P , , 1 ? + ? ? ? ? ? ? ? ? = ? ------ (11), where # Wind Turbine Wind Turbine converts the wind energy to the mechanical torque, which rotates the shaft of an electrical generator to generate electrical energy (Hur, 2018;Zammit et al., 2017).The wind turbine in the power system generation can be defined by considering the wind turbine power curve, cut-in speed, and cut-out speed of the wind turbine. HOMER provides a library of several wind turbines already defined to choose from (select), but the wind turbine of ones interest (not found in the library)can be defined (create) based on the manufacturer specifications. By clicking on the wind turbine icon (selected or created) in HOMER, we are able to define the wind turbine used in the power system by entering the quantity (number of wind turbine), Capital Cost, Replacement Cost, Operations & Maintenance (O&M) expenditure, Operating Lifetime, and Hub height of the wind turbine. Full details of the chosen wind turbine can be seen by clicking the 'Details' button. For the health facility in this exercise, a BWC Excel-R 7.5kW wind turbine (mounted on a standalone monopole) was considered as shown in Figure 11using the default model present in HOMER library. Due to the moving parts, maintenance for wind turbines is somewhat higher than for PV systems. Wind speed increases with height above ground. This is because; the ground-level obstacles such as vegetation, buildings, and topographic features tend to slow the wind near the surface. Since the effect of these obstacles decreases with height above ground, wind speeds tend to increase with height above ground. This variation of wind speed with height is called wind shear. HOMER uses wind shear to calculate the wind speed at the hub height of the wind turbine. Wind energy engineers typically models wind shear using the logarithmic profile mathematical model. The logarithmic profile (or log law) assumes that the wind speed is proportional to the logarithm of the height above ground. The following equation gives the ratio of the wind speed at hub height to the wind speed at anemometer height(HOMER help, 2015): -------------------------------(12), where: A2 of the supplementary data. ( ) ( ) ( ) ( ) o anem o hub anem hub z z z z z v z v / ln / ln = -- Step Seven: Calculate Results HOMER model can be used to compare costs for a variety of different energy generation systems that can meet 100% of this clinic's load. By clicking the Calculate button, the program runs the simulation of different permutations of all possible configurations of system types based on the inputs provided. After the design system had completely simulated, the best possible system configurations will be determined under optimization process. HOMER sorts the feasible cases in order of increasing net present (or lifecycle) cost. This cost is the present value of the initial, component replacement, operation, maintenance, and fuel costs(User Manual, 2016). HOMER lists the optimal system configuration, defined as the one with the least net present cost, for each system type. In this investigation, and by Ani (2014)an attempt was made to determine for each region and regarding the selected components, which of the mix renewable energy (PV/wind/battery) or hybrid system (PV/wind/diesel/battery; PV/diesel/battery; wind/diesel/ battery) is the optimal power system. A 60% renewable fraction was used as criteria for the energy solution. The components needed to satisfy the health facility centre's annual load of 7,082kWh are shown in Figure 12. The equation for estimating the level of optimization of any energy solution being considered for a health clinic and location is derived as below: # c) The Annualized Cost of a Component The annualized cost of a component includes annualized capital cost, annualized replacement cost, annual O&M cost, emissions cost and annual fuel cost (generator). Operation cost is calculated hourly on daily basis (Ani, 2015): d) Annualized Capital Cost The annualized capital cost of a system component is equal to the total initial capital cost multiply by the capital recovery factor. Annualized capital cost is calculated using (Ani, 2015): ------------------------- The annualized replacement cost of a system component is the annualized value of all the replacement costs occurring throughout the lifetime of the project, minus the salvage value at the end of the project lifetime. Annualized replacement cost is calculated using (Ani, 2015): --------------------------------------( 14) rep f , a factor arising because the component lifetime can be different from the project lifetime, is given by (Ani, 2015): ------------------------------------------( 15) rep R , the replacement cost duration, is given by (Ani, 2015): ( ) proj cap acap R i CRF C C , ? = --( ) ( ) proj comp rep rep arep R i SFF S R i SFF f C C , , ? ? ? ? = --( ) ( ) ? ? ? ? ? = > = 0 , 0 0 , , , rep rep rep proj rep R R R i CRF R i CRF f --? ? ? ? ? ? ? ? ? = comp proj comp rep R R INT R R -----------------------------(16), # ( ) SFF , the sinking fund factor is a ratio used to calculate the future value of a series of equal annual cash flows, is given by(User Manual, 2016): -------------------------------( 17) The salvage value of the component at the end of the project lifetime is proportional to its remaining life. ( ) ( ) 1 1 , ? + = N i i N i SFF -- Therefore the salvage value S is given by(User Manual, 2016): ------------------------------------------(18), where rem R , the remaining life of the component at the end of the project lifetime is given by(User Manual, 2016): comp rem rep R R C S ? = -- ( ) -------------------------( The operating cost is the annualized value of all cost and revenues other than initial capital costs and is calculated using (Ani, 2015): ------------------------------- Annualized cost of a component is calculated using (Ani, 2015): -----------------------------------------------------( 22) Annualized total cost of a component is calculated using (Ani, 2015): ----------------------------------------- rep proj comp rem R R R R ? ? = --( ) [ ] ? ? = = ? ? ? ? ? ? =emissions aop arep acap ann C C C C C + + + = --( ) ? = + + + = c N c emissions c aop c arep c acap c tot ann C C C C C 1 , , , , , ), The mathematical model derived estimates the life-cycle cost of the system, which is the total cost of installing and operating the system over its lifetime. The output when run with HOMER software/tool will give us the optimal configuration of the energy system that takes into account technical and economic performance of supply options (rated power characteristics for solar Photovoltaic (PV), power curve characteristics for wind turbine (WT), fuel consumption characteristics for diesel generators (DG) and minimum and maximum state of charge (SOC) of a battery bank), the 20-year life cycle cost (LCC) of equipment, locally available energy resources (hourly solar insolation data (W/m 2 ), hourly wind speed (m/s), as well as cost of fossil fuels), environmental costs, and system reliability. IV. # Simulation Simulation process determines how a particular system configuration, a combination of system components of specific sizes, and an operating strategy that defines how those components work together, would behave in a given setting over a long period of time. HOMER simulates the operation of a system by making energy balance calculations for each of the 8,760 hours in a year(User Manual, 2016). For each hour, HOMER compares the electric demand of the health facility in the hour to the energy that the system (energy option) can supply in that hour, and calculates the flows of energy to and from each component of the system. After running the simulations, HOMER sorts the feasible cases in order of increasing NPC (or lifecycle cost). This cost is the present value of the initial, component replacement, operation, maintenance, and fuel costs. HOMER lists the optimal system configuration, defined as the one with the least net present cost, for each system type. It is possible to display the overall (entire) list of configurations or to show the categorized lists (the best solutions from an economic perspective) per system design. Overall list of configuration is the simulation results, while the categorized lists is the optimization results.In this exercise, the optimization result panels in Figure 13 show the categorized list displayed four By clicking on each of the displayed solution we can access a comprehensive set of data providing high level of detail on each system component such as economical information essential to run a thorough business case. HOMER's main financial output is the total NPC and COE of the examined system(s) configurations (Farid et al., 2017). The Cost summary and Cash Flow details represent a practical starting point for developing a customized business case, including financial indicators such as ROI (Return on Investment), payback period and NPV (Net Present Value), in comparison to the diesel generator only base case that will enable decision makers within health organizations to make accurate investment decisions. In addition it is possible to display many other relevant data concerning renewable (PV and wind turbine) equipment details (i.e. electrical production, hours of operation, etc.), diesel generator (i.e. diesel generator hours of operation, fuel consumption), batteries performances (battery state of charge histogram, etc.), and emissions, etc. These data were organized and presented in the supplementary data. V. # Results and Discussion Results in the supplementary data (Tables A3 -A14) show that the study of electrification options for the hypothetical health facility at various geographical locations (Nembe (Bayelsa State) in the south, Abaji (Abuja, FCT) in the centre, and Guzamala (Borno State) in the north) in Nigeria illustrate that the percentage of energy generated by both the solar and the wind renewable energy components of each of the hybrid system types tends to vary with the locations of the health facility; and that both the lifetime costof different energy system and the environmental impact of the hybrid energy system types studied vary significantly with the locations of the health facilities due to availability of the renewable energy resources and climatic conditions. Therefore in setting up power system for off-grid rural health facility in Nigeria, the following options were to be chosen based on different regions as this depends on climatic conditions and available renewable energy resources. ? Equatorial Region has two options (PV/diesel, and PV/wind/diesel) ? Tropical Region has also two options (PV/diesel, and PV/wind/diesel) ? Arid Region has four options (PV/diesel, PV/wind, PV/wind/diesel, and wind/diesel) VI. # Conclusion This paper is a resource for health professionals seeking to electrify health facilities that currently lack power. A case study of a hypothetical off-grid health facility at various geographical locations in Nigeria was used to illustrate the stepwise approach to electrification of health facilities and demonstrate the utility of a modeling tool to assist in the critical task of system design. Information was provided to help the health professionals weigh the pros and cons of various energy systems with a focus on appropriate solutions and special considerations for off-grid rural health clinics. When considering the lifetime cost of different system designs, a modeling program HOMER is a valuable tool. HOMER simplifies the task of determining the most suitable combination of renewable source to supply a given load, and is, therefore, a useful tool in systems load sizing. The product could be used during the process of design, energy analysis and simulation of electrical power process in hybrid and stand-alone systems for energy supply of daily needs and technological processes in health clinics. ![et al. (2017), Ansong et al. (2017), Sigarchian et al. (2015), Rezzouk and Mellit (2015), and Madziga et al. (2018) used HOMER to analyze the performance of suggested HESs to find the optimal configurations.](image-2.png "") 1![Figure 1: Add/Remove window of different components device, check the user's manual or it may be written on the back of the device. A Health facility load profile depends on multiple parameters including lighting for child delivery and emergency night-time care, refrigeration for blood and vaccines, sterilization facilities, electricity for simple medical devices, etc., and for this reason, it is important to outline an accurate power profile in order to dimension correctly the renewable components for the facility. The adoption of energy efficiency can reduce energy consumption and increase renewable energy penetration (Babatunde et al., 2019; Olubayo et al., 2019). It can also reduce cost of energy and emissions. The simulation concerns a standard health facility described by (Ani and Emetu, 2013). Obviously, many rural health clinics in Nigeria have the same electrical load data with that of Ani and Emetu (2013) which can be used in the establishment of a general case study for the electrical load data; therefore it was used by Ani (2014)to study three hypothetical off-grid remote health clinics at various geographical locations in Nigeria; which will be use here as an example to the study. The electrical load (Power supply requirements) data for a Health facility is shown in TableA1of the supplementary data, which was used to define the hourly profile, and the random variability parameters was set to 0% for accurate power load measurements as indicated in Figure2.](image-3.png "lobalFigure 1 :") 2![Figure 2: HOMER output for the Primary load](image-4.png "Figure 2 :") 3![Figure 3: HOMER output graphic for wind speed profile in (a) Nembe, (b) Abaji, and (c) Guzamala](image-5.png "Figure 3 :") 4![Figure 4: HOMER output graphic for Solar (clearness index and daily radiation) profile in (a) Nembe, (b) Abaji, and (c) Guzamala](image-6.png "aFigure 4 :") 5![Figure 5: HOMER input for diesel price profile in (a) Nembe, (b) Abaji, and (c) Guzamala](image-7.png "Figure 5 :") ![Journal of Researches in Engineering ( ) Volume Xx XI Is sue III Version I J Year 2021 conditions where the equipment operates and the maintenance performed (Kassam, 2011).](image-8.png "lobal") 6![Figure 6: HOMER input for economics](image-9.png "Figure 6 :") 7![Figure 7: HOMER input for fuel generator e) Mathematical Model of Diesel Generator Hourly energy generated by diesel generator ( ) DEG E](image-10.png "Figure 7 :") ![length of the time step [h]. P is the power [kW] into (positive) or out of (negative) the battery bank using kinetic battery model. max Q is the total capacity in kWh of the battery bank. Q is the total amount of energy in kWh in the battery at the beginning of the time step k is the rate constant [h -1 ] which is a measure of how quickly the battery can convert bound energy to available energy or vice-versa. c is the battery capacity ratio [unitless].Likewise, the maximum amount of power that the battery can absorb over a specific length of time t ? is given by the following equation(User Manual, 2016):](image-11.png "") ![Journal of Researches in Engineering ( ) Volume Xx XI Is sue III Version I J Year 2021 model of the charge controller is presented below (User Manual, 2016):](image-12.png "lobal") 10![Figure 10: HOMER input for solar photovoltaic l) Mathematical Model of the Monthly Average Clearness Index andSolar Photovoltaic Generator HOMER uses the extraterrestrial horizontal radiation ( ave o H , ) to calculate either the monthly average clearness index ( T K ) or the monthly average global solar radiation ( ave H ). For instance, when you enter](image-13.png "Figure 10 :") ![ave o H , to find T K . If you enter T K , HOMER multiplies it by](image-14.png "") ![, to find ave H (User Manual, 2016). The monthly average clearness index can be calculated using the equation (HOMER help, 2015; User Manual,](image-15.png "H") ![is the rated capacity of the PV array, meaning its power output under standard test conditions [kW] PV f is the PV derating factor [%] T G is the solar radiation incident on the PV array in the current time step [kW/m 2 ] STC T G , is the incident radiation at standard test conditions [1 kW/m 2 ] P ? is the temperature coefficient of power [%/°C] c T is the PV cell temperature in the current time step [°C] STC c T , is the PV cell temperature under standard test conditions [25 °C] III.](image-16.png "PVY") 11![Figure 11: HOMER input for wind turbine](image-17.png "Figure 11 :") ![hub z = the hub height of the wind turbine [m] anem z = the anemometer height [m] o z = the surface roughness length [m] at the hub height of the wind turbine [m/s] at anemometer height [m/s] ( ) ... ln = the natural logarithm The surface roughness length is a parameter that characterizes the roughness of the surrounding terrain. The representative surface roughness lengths taken from Manwell, McGowan, and Rogers (2002) are shown in Table](image-18.png "") 12![Figure 12: The network architecture (HOMER simulator) for the hybrid energy system of the health facility centre b) Mathematical Cost Model (Economic & Environmental Costs) of Energy SystemsThe equation for estimating the level of optimization of any energy solution being considered for a health clinic and location is derived as below:](image-19.png "Figure 12 :") Nagger et al., 2018; Bahramara et al., 2016; Adetunji etal., 2018; Al-Hamdani et al., 2016; Hassan et al., 2016;Rajbongshi et al., 2017; Ronad and Jangamshetti, 2015;Jamalaiah et al., 2017; Rohit and Subhes, 2014;Deshmukha and Singha, 2019; Al Garni et al., 2018;Zahboune et al., 2016; Mohammed et al., 2013;Shahinzadeh et al., 2015a; Shahinzadeh et al., 2015b) A2lobal Journal of Researches in Engineering ( ) Volume Xx XI Is sue III Version I J3 4 5 6 7 8 9 10 11 12 13 141 1 1 1 4 2 1 1 1 1 2 1 Very smooth, ice or mud 575 28 15 10 10 Sterilizer Oven (Laboratory Centrifuge Hematology Mixer Microscope Security light Lighting Autoclave) 1,564 Incubator 400 Water Bath 1,000 Communication via VHF Radio Stand-by 2 Transmitting 30 Desktop Computer 200 Printer 65 Terrain Description Calm open sea Blown sea Snow surface Lawn grass Rough pasture Fallow field300 575 28 15 40 20 1,564 400 1,000 2 30 400 65 0.00001 m z 0 0.0002 m 0.0005 m 0.003 m 0.008 m 0.010 m 0.03 m5 2 2 5 12 7 1 24 1 24 4 5 3(10.00hr -15.00hr) (12.00hr -14.00hr) (10.00hr -12.00hr) (09.00hr -14.00hr) (18.00hr -6.00hr) (09.00hr -16.00hr) (12.00hr -13.00hr) (0.00hr -23.00hr) (14.00hr -15.00hr) (0.00hr -23.00hr) (09.00hr -13.00hr) (09.00hr -14.00hr) (09.00hr -10.00hr; 13.00 -15.00hr)J ( ) Volume Xx XI Is sue III Version I lobal Journal of Researches in EngineeringCrops0.05 mFew trees0.10 mMany trees, few buildings0.25 mForest and woodlands0.5 mSuburbs1.5 mCity center, tall buildings3.0 m © 2021 Global Journals ## Acknowledgements I would like to express my deep appreciation and gratitude to Dr. Carlos Dora for inviting me to the World Health Organization (WHO) expert consultation on energy access in health care facilities Geneva, Switzerland -24-26 March 2015. I would also like to thank Elaine FLETCHER for all her technical support of me attending the WHO consultation, which provided me with ideal opportunities to present my work and meet experts in the field and the chance to acquaint myself with new research dimensions from around the world. ## Supplementary Data Electrical Load Data * Cost benefit analysis of self-optimized hybrid solar-wind-hydroelectrical energy supply as compared to HOMER optimization AAcakpovi EBHagan MichaelM B doi: 10. 5120 International Journal of Computer Applications 114 18 2015. 20081-2133 * Developing a Microgrid for Tafelkop using HOMER KEAdetunji OAAkinlabi MK ICABCD) IEEE 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems 2018. 2018 * Optimal design and analysis of grid-connected photovoltaic under different tracking systems using HOMER AlGarni HZAwasthi ARamli M Energy Convers. Manag 155 2018. 2018 * Optimal sizing of photovoltaic systems using HOMER for Baghdad AHAl-Hamdani AFDawood KNAbdullah SMMousaui International Journal of Computation and Applied Sciences 1 2 2016. 2016 * Energy Optimization At GSM Base Station Sites Located In Rural Areas.Doctoral Dissertation VincentAni Anayochukwu 2015 UNN Nsukka University of Nigeria * Energy Optimization Map for Off-Grid Health Clinics in Year 2021 VincentAni Anayochukwu 2014 * Nigeria. International Journal of Renewable Energy Research (IJRER) 4 1 * Simulation and Optimization of Photovoltaic/Diesel Hybrid Power Generation System for Health Service Facilities in Rural VincentAni &Anayochukwu AliceEmetu Nnene 10.7770/ejee-V1N1-art521 Environments Electronic Journal of Energy and Environment (EJEE) 1 1 2013 * Techno-economic analysis of a hybrid system to power a minein an off-grid area in Ghana MAnsong LDMensah MAdaramola Sustain. Energy Technol. Assess 23 2017. 2017 * Decarbonisation of Electricity Generation: Efforts and Challenges OBabatunde JMunda YHamam Carbon Footprints Berlin/Heidelberg, Germany Springer 2019 * Optimization and a comparison between renewable and non-renewable energy systems for a telecommunication site MEBadawe TIqbal GMann 25th IEEE Canadian conference on electrical and computer engineering (CCECE) Montreal, Canada IEEE 2012. April 29 -May 2, 2012 * Optimal planning of hybrid renewable energy systems using HOMER: A review SBahramara MPMoghaddam MRHaghifam Renew. Sustain. Energy Rev 62 2016. 2016 * Design of a photovoltaic-wind hybrid power generation system for Ethiopian remote area GBekelea GBoneya Energy Procedia 6 2011 * Design of isolated hybrid systems minimizing costs and pollutant emissions JLBernal-Agustín RDufo-López DMRivas-Ascaso Renewable Energy 31 14 2006 * Arango-Manrique Adriana & Núñez ÁlvarezJosé Ricardo (2020) Energy, Economic, and Environmental Evaluation of a Proposed Solar-Wind Power On-grid System Using HOMER Pro®: A Case Study in Colombia AntonioBudes Farid Barrozo GuillermoOchoa ObregonLuisValencia Guillermo 10.3390/en13071662 2020 1662 * Design of hybrid powers ystems using HOMER simulator for different renewable energy sources HCristian NBizon BAlexandru Electronics, Computers and Artificial Intelligence(ECAI), 2017 9th International Conference on 2017. 2017 * Optimization of PV/Wind/Micro-Hydro/Diesel Hybrid Power System in HOMER for the Study Area LalDeepak DashKumar Bibhuti Bhusan AK&akella International Journal on Electrical Engineering and Informatics 3 2011. 2011 * Modeling of Energy Performance of Stand-Alone SPV System Using HOMER Pro MKDeshmukha Singha Athokpam Bharatbushan 5th International Conference on Power and Energy Systems Engineering Nagoya, Japan 2019. 2018. 2018, 19-21 September 2018. 2019 156 * An Economic Evaluation of Renewable and Conventional Electricity Generation Systems in a GuillermoFarid Barrozo Budes Valencia Ochoa& Yulineth Cárdenas Escorcia 10.12988/ces.2017.71014919 Shopping Center Using HOMER Pro® Contemporary Engineering Sciences 10 26 2017. 2005. April 12, 2020 National Renewable -National Renewable Energy Laboratory Getting started guide for Homer version 2 * Performance analysis of hybrid PV/diesel/battery system using HOMER: A case study Sabah LMHalabi SMekhilef LOlatomiwa JHazelton Malaysia. Energy Convers. Manag 144 2017. 2017 * Optimization of PV/wind/diesel hybrid power system in homer for rural electrification QHassan MJaszczur JAbdulateef Journal of Physics: Conference Series 2016. 2016 745 32006 * ManualHomer Help 2015. April 06, 2020 * User Manual 2016. July 15, 2020 HOMER * Modelling and control of a wind turbine and farm SHur Energy 156 2018 * Hybrid Optimization Model for Electric Renewables -HOMER help 2015. April 12, 2020 * Optimization and operation of a renewable energy based pv-fc-micro grid using homer AJamalaiah CPRaju RSrinivasarao 2017International Conference on Inventive Communication and Computational Technologies (ICICCT 2017. 2017 * Software Training Guide for Renewable Energy Base Station Design KassamAreef 2011. January 01, 2020. June-2011 Retrieved * Hybrid renewable power systems for mobile telephony base stations in developing countries KKusakana HJVermaak Renewable Energy 51 2013 * Techno economic feasibility study of autonomous hybrid wind/PV/battery power Year 2021 CLi XFGe YZheng CXu YRen CGSong CXYang 2013 * China. Energy 55 * Comparison between three off-grid hybrid systems (solar photovoltaic, diesel generator and battery storage system) for electrification for Gwakwani village MMadziga ARahil RMansoor South Africa. Environments 5 57 2018. 2018 * Renewable Energy Systems for Rural Health Clinics in Algeria: Homer Application Retrieved MalikaAmini January 01, 2020 * Wind Energy Explained JFManwell JGMcgowan AL 2002 Wiley New York, NY * Lead acid battery storage model for hybrid energy systems JFManwell Solar Energy 50 1993 * Optimal sizing of photovoltaic systems using HOMER for Sohar, Oman MMohammed AAziz AHAlwaeli HAKazem International Journal of Renewable Energy Research(IJRER) 3 2013. 2013 * Economically Optimal Design of Hybrid Renewable Energy Systems Using HOMER and ACO Journal of Al Azhar University Engineering Sector Nagger Hassan, Khaled Abd El_Atey & Amr Hosny Mohammed El_Enany 13 48 2018. July 2018 * Assessment of economic viability for PV/wind/diesel hybrid energy system in southern Peninsular Malaysia MSNgan CWTan Renewable and Sustainable Energy Reviews 16 2012 * Selection of a Hybrid Renewable Energy Systems for a Low-Income Household MOlubayo JosiahLBabatunde Munda & Yskandar Hamam 10.3390/su11164282 2019. 2019 11 4282 * Modeling of an intelligent battery controller for standalone solarwindhybrid distributed generation system ParthaSarothi Sikder Journal of King Saud University -Engineering Sciences 32 2020 * Optimization of PV-biomass-diesel and grid base hybrid energy systems for rural electrification by using HOMER RRajbongshi DBorgohain SMahapatra Energy 126 2017 * Developing various hybrid energy systems for residential application as an appropriate and reliable way to achieve Energy sustain ability, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 10.1080/15567036.2018.1544996 Razmjoo Ali & Davarpanah Afshin 2018 * Feasibility study and sensitivity analysis of a stand-alone photovoltaicdiesel-battery hybrid energy system in the north of HRezzouk Mellit 2015 * Algeria. Renew. Sustain. Energy. Rev 43 * Offgrid electricity generation with renewable energy technologies in India: AN application of HOMER RohitSen &Subhes CBhattacharyya 10.1016/j.renene.2013.07.028 Renewable Energy 62 2014. 2014 * Optimalcost analysis of wind-solar hybrid system powered AC and DC irrigation pumps using HOMER BFRonad SHJangamshetti 2015International Conference on Renewable Energy Research and Applications (ICRERA) 2015. 2015 * Optimal Sizing and Energy Management of a Grid-Connected Microgrid using HOMER Software ShahinzadehHossein MoazzamiMajid FathiHamid SGharehpetian Gevork B 2016. 2016 * Smart Grids Conference (SGC) Kerman, Iran Dec. 2016 Graduate University of Advanced Technology * Optimal Planning of an Off-grid Electricity Generation with Renewable Energy Resources using the HOMER Software HosseinShahinzadeh GharehpetianGevork BFathi SHamid Nasr-Azadani Sayed Mohsen International Journal of Power Electronics and Drive Systems 6 1 137 2015a. 2015 * Optimal Sizing of PV/Wind Hybrid Systems to Energy Supply of Street Lighting by using HOMER Software (A Case Study inYazd) HosseinShahinzadeh SFathi Hamid & Nasr-Azadani Sayed Mohsen 2015 The 3rd National Conference on Climate, Building and Energy Efficiency 2015b. May 2015 * Techno-Economic and Feasibility Analysis of a Hybrid PV-Wind-Biomass-Diesel Energy System for Sustainable Development at Offshore Areas in Bangladesh SK AShezan HWPing Current Alternative Energy 1 2017 * Performance analysis of an off-grid wind-PV (photovoltaic)-diesel battery hybrid energy system feasible for remote areas SK AShezan SJulai MAKibria KRUllah RSaidur WTChong RKAkikur Journal of Cleaner Production 125 2016 * Feasibility study of using a biogas engine as backup in a decentralized hybrid (PV/wind/battery) power generation system-Case study Kenya SGSigarchian RPaleta AMalmquist Pina Energy 90 2015 * Solar Power Systems Components -Solar Panels Prices in Nigeria Retrieved July 15, 2020 * United States Agency for International Development (USAID),Powering Health: Electrification Options for Rural Health Centers Retrieved January 01, 2020 * Access to Modern Energy Services for Health Facilities in Resource-Constrained Settings: A Review of Status, Significance, Challenges and Measurement www World Health Organization 2016 * Optimal hybrid renewableenergy design in autonomous system using Modified Electric System Cascade Analysis and Homer software HZahboune SZouggar GKrajacic PSVarbanov MElhafyani EZiani World Health Organization 126 2014. 2014. 2016. 2016 Energy Convers. Manag * Incremental current based MPPT for a PMSG micro wind turbine in a grid-connected DCmicrogrid DZammit CSStaines AMicallef AMaurice JLieari Energy Procedia 142 2017 * Operating Cost ($/yr) 7,103 6 302 780 * Total NPC $124,652 $142,469 $148 124 887 * Table A7: Simulation results of Economic cost for Tropical region (Abaji) * Operating Cost ($/yr) 6,597 6 342 830 * Total NPC $118,847 $142,925 $165 917 397 * Table A8: Simulation results of Economic cost for Arid region (Guzamala)