key: cord-0757683-6i22z7gw authors: Vaziri Rad, Mohammad Amin; Panahi Vaghar, Mouzhan; Kouravand, Amir; Bellos, Evangelos; Kasaeian, Alibakhsh title: Techno-economic evaluation of stand-alone energy supply to a health clinic considering pandemic diseases (COVID-19) challenge date: 2022-06-30 journal: Sustainable Energy Technologies and Assessments DOI: 10.1016/j.seta.2021.101909 sha: 2395fd5971fd9b44fa6ade7358da0a46c8276a07 doc_id: 757683 cord_uid: 6i22z7gw The increase in the number of patients in health care centers boosts electricity consumption. Such a load jump also adversely affects the energy supply, in particular, in rural off-grid systems. To overcome the mentioned challenges, some innovative and practical approaches with available optimization tools should be employed. This study addresses the possibility of developing a challenge prediction-based method for optimizing a reliable and affordable hybrid renewable energy system (HRES) to aid with energy challenges associated with pandemic conditions. The results indicate that the PV/diesel/battery hybrid system with a maximum energy cost of $0.141/kWh and a renewable fraction of more than 50% can meet demand even during the most severe load jumps. Furthermore, the fuel constraints during pandemic years can increase the energy costs up to 2.5 times, and the required photovoltaic installation capacity by about four times. Due to the 20.1% PV output boost, Vertical-axis tracking systems are recommended in areas with limited PV installation space. It is concluded that, by considering the likely effects of pandemics, the supplied energy cost to the rural health clinic equipment and water treatment loads would be between $0.113–0.200/kWh. The COVID-19 outbreak in numerous parts of the world has impacted various aspects of human life [1] . The energy industry is one of the sectors which has been affected and experienced significant changes since late 2019. Global energy demand is expected to fall by 6% in 2020, compared to 2019. Despite declining overall energy demand, the residential and medical sectors increased their energy consumption in 2020. Thus, industrial and commercial needs have decreased, while medical and residential demands have increased [2] . So the main question is how energy systems, particularly standalone systems with limited flexibility, will cope with this demand increase. Numerous studies on domestic energy consumption during the COVID-19 period have been conducted. For instance, a study conducted in Barcelona, Spain, estimated thermal energy consumption and CO 2 emissions increased by 182% in the residential sector [3] . Another study concluded that the lockdown period resulted in a 115% increase in electricity consumption of residential buildings in India [4] . The international energy agency (IEA) [5] released the first quarter 2020 statistical data, indicating that global coal demand fell by 8% in the second quarter of 2019 compared to the first quarter, owing to significant declines in electricity demand and industrial coal demand. Additionally, global oil demand fell by 5% due to travel restrictions. Nuclear energy and natural gas demand were also in a similar state. However, the renewable energy demand increased by approximately 1.5% in the first quarter of 2020, compared to the same period in 2019. Based on the IEA reports [5] , renewable energy has been the most resilient energy source to the Covid-19 conditions. The first reason is that, renewable electricity is ineffective when the demand is reduced for the other applications of renewable energy. Secondly, they are given priority due to the low operating costs and some environmental regulations. Furthermore, because of the global clean energy roadmap, the renewable energies production capacity has increased slightly. During the pandemic, the energy industry was also affected from the supply chain point of view. Insufficient energy access can speed up the virus spread (such as ; hence the energy supply security is more critical than ever [6] . Investigations have shown that residents at the houses with energy problems have often lower levels of health and are more vulnerable to pandemic diseases. Also, the energy access is a vital issue in the proper availability of medical facilities and appropriate services quality. Hospitals and health centers consume energy for medical services and equipment, sterilization, disinfection, and other therapeutic operations [7] . Consequently, access to the most flexible energy systems during the pandemic condition has become a worldwide challenge [8] . The discussed pandemic problems and advantages of renewables for these conditions require HRES as efficient and flexible systems. During pandemics such as COVID-19, areas without access to the electricity grid require greater attention than ever. Adequate power supply to rural health clinics is a major need [9] . A hybrid energy system can be the most reliable choice for the deprived areas in pandemic conditions. Also, the increased energy demands of the health care centers due to the COVID-19 pandemic must have been considered in obtaining the appropriate capacities of the system components. It should be noted that the hybrid optimization of multiple energy resources (HOMER) software includes a microgrid modeling tool that enables the developing countries to increase the reliability of their energy systems in response to the demand changes [10] . According to the Iran Electrical Industry Syndicate [11] , the Coronavirus has harmed this industry drastically. Several consequences include the damage to the electrical industry construction projects, supply chain difficulties, electrical industry import and export problems, and power outages. According to this organization's estimations, in 2021, there is a 30% probability of grid uncertainty and power outage under the impact of disruption in rural electrical infrastructure development. This problem can damage vital sectors such as hospitals and service centers. One of the primary strategies to deal with COVID-19 ′ s energy challenges is adding energy storage units for health applications. Batteries are counted as an essential part of the off-grid HRES facilities. The batteries can overcome intermittent renewable energy generation challenges and compensate for sudden load increases [12] . A fundamental question is the ability of optimized battery capacity in HRESs to respond the severe load jumps in an emergency such as the COVID-19 pandemic. In the previous studies, the use of some equipment such as bio generators [13] , diesel generators [14] , and fuel cells [15] have been effective in increasing the flexibility of hybrid systems. These methods can be helpful for the electrification of some essential applications such as rural health clinics. Several papers examined the techno-economic implications of electrifying health clinics in rural areas using HRESs. For example, Al-Karaghouli et al. [16] presented an optimal model for meeting the power demand of a health clinic in rural southern Iraq using a photovoltaic solar system. Using the HOMER software, it was determined that the electricity cost of the presented method was $0.238/kWh. Also, AL-Shammari et al. [17] proposed a cost-effective hybrid energy system with a 100 percent renewable component, to meet the power demand of a rural health clinic in southeastern Iraq, using HOMER software. The proposed system, which consisted of photovoltaic panels, wind turbines, and batteries, was found to have a cost of energy (COE) of $0.547/kWh and a net present cost (NPC) of $72878. As demonstrated by the former literature review, the possibility of severe load jump on clinics' energy demand was not considered when determining the HRES appropriate capacity. This paper investigates the increased power consumption of medical centers, as a result of COVID-19. Some studies have discussed the effect of possible pandemics on the design of health clinic energy systems. Chowdhury et al. [18] optimized a proposed standalone HRES using HOMER software. During the COVID-19 period, this hybrid system, comprised of photovoltaic/wind/battery energy sources, met the energy demands of a temporary medical center in Bangladesh. The optimal Levelized COE of HRES was $0.468/kWh. The previous studies on rural healthcare designs did not account for fuel scarcity as an energy challenge during pandemic conditions. Tsao et al. [8] identified the energy supply and demand risks, caused by COVID-19 when designing a reliable renewable-energy-based energy supply for pandemic conditions. Alsagri et al. [19] also investigated some methods for managing excess electricity, assuming the possibility of electrical load jumping for a fuel cell/photovoltaic/diesel/battery system. The generated electricity was used to power a medical center in Saudi Arabia during the COVID-19 outbreak crisis. The HOMER software demonstrated a reliable and cost-effective energy system with an energy cost between $0.105-0.120/kWh. However, the effect of fluctuating fuel prices, the availability of space for photovoltaic installation, or the impact of severe load jumps were not completely investigated. Table 1 summarizes the recent research works on rural health facility power supply. Although the mentioned studies did not consider the fuel shortages, space constraints for installing the PVs, and load shock in pandemic conditions, the current investigation attempts to address these issues. The current study examines the effect of load jump and fuel shortage as the primary energy challenges caused by COVID-19 on the technoeconomic specifications of a standalone HRES. This issue has been addressed for the first time using the HOMER software's multi-year tool and sensitivity analysis feature. Additionally, due to the increased capacity of photovoltaic installations during epidemic conditions, the effect of using various types of tracking modes has been investigated to make the best use of an available space. The study case is a rural area in Iran, experiencing severe fresh water and energy shortages. The obtained results have been extrapolated to other parts of the world, taking into account various solar radiations. The proposed hybrid system consists of a photovoltaic array, a diesel generator, and a battery bank, which is one of the most common hybrid systems for off-grid applications. The results of this study are hoped to demonstrate the critical nature of considering epidemic conditions when designing standalone HRESs. Furthermore, the proposed method attempts to identify a costeffective solution to this challenge. The main purposes of this study are focused on the suggestions for solving energy problems in poverty-stricken areas in the face of epidemics. Such objectives are achieved based on a challenge prediction method for optimizing a hybrid renewable system. In the presented techno-economic evaluation, the effect of fuel shortage and load jump, as the potential pandemic challenges on the designing of standalone HRESs, are investigated. Furthermore, comprehensive sensitivity analyses are conducted to determine the most cost-effective and reliable method of powering off-grid medical units. It should be noted that the governments and investors can effectively address the mentioned power supply challenges by utilizing a pre-made software, as described in this study. In this section, the methodology for optimizing the hybrid energy system's techno-economic performance is descibed. As such, Sections 2.1, 2.2, and 2.3 introduce the case study, the electrical load, and the load jump assumptions, respectively. Then, in section 2.4, the energy system's component equations and economic objectives are presented. Following that, Sections 2.5 and 2.6 discuss the input data and the proposed HRES configuration, respectively. Iran has an appropriate potential for solar energy resource, based on the world's photovoltaic power potential map (Fig. 1) . Furthermore, Iran's solar radiation conditions are close to those found in most regions of West Asia, Africa, and Oceania. To generalization of results to other parts of the world, the sensitivity analyses consider all possible solar radiations in the range of 4 to 6 kWh/m 2 /day. Also, a deprived area with severe water stress has been selected to more efficiently estimate the proposed energy system's capability to supply the rural regions. The Chutani village, as a deprived area in the Sistan and Baluchestan province, near the Oman Sea, is investigated. This village has an estimated population of more than 400 people, a tropical climate, and limited access to water and electricity. It is worth noting that the lack of access to facilities leads to many health problems in such villages. To obtain water, residents need to dig pits (referred to as Houtag) to collect rainwater, which poses health risks [28] . The annual average solar radiation in the Chutani village is 5.4 kWh/m 2 /day with a clearness index of approximately 60%, indicating the area's excellent solar radiation potential ( Fig. 2 (a) ). This city's average annual temperature is around 26 • C, reaching as high as 50 • C in the summer. However, the average yearly wind speed is about 4 m/s, indicating that this region's wind resource has a limited potential ( Fig. 2 (b) ) [29] . At certain times of the year, the high temperatures reduce the output power of photovoltaic panels [30] . This issue is considered in the current optimization. The electricity supply of a standalone outpatient clinic is considered in this paper due to the critical nature of medical services in remote areas. So, a small medical clinic with the size of two combined 40-ft containers (approximately 56 m 2 in area) has been selected. The clinic's standard capacity is four patients and four employees, but can be increased to ten patients during an emergency, such as a pandemic. Table 2 estimates the energy consumption of a typical health clinic. Additionally, the power consumption profile of this load is determined using the HOMER software library and an actual outpatient clinic in a climate similar to that of the selected area (Fig. 3) . Consequently, the annual average consumed energy is 25.76 kWh/day, with a peak of approximately 2.1 kW. Moreover, due to the critical need for clean water in arid rural areas, an electrical load equal to the demand of a brackish water reverse osmosis desalination (BWRO) is considered. Taking the minimum amount of freshwater, needed for each resident, to be approximately 0.3 m 3 , the total daily freshwater requirement for 14 residents would be equal to 4.2 m 3 . The BWRO unit consumes up to 2.5 kWh/m 3 of energy [32] , which causes an increase in the total electrical demand by about 10.5 kWh/day. Also, it consumes a maximum peak of 0.8 kW for pumping fluid between a water resource, desalination device, and storage tank. This load is considered as a deferrable load which must be provided with its required daily capacity and does not require a specific time of day to be supplied. As a result, this demand can be met during the off-peak hours or with excess electricity generated by the HRES. Theoretically, the maximum amount of ability to increase the nominal capacity of patients' admission in rural clinics would be equal to the maximum increase in the energy consumption of the clinics. In Iran, the small rural health clinics, that normally serve up to 5 patients, can accommodate even up to 15 patients (based on their available space) in a pandemic situation. Observing the energy consumption bills in some grid-connected rural clinics has also shown an increase of about 40 to 135 percent in the months of the epidemic, compared to the normal months. Therefore, in this study, three worst-case scenarios, which include the increase of 50%, 100%, and 150% in energy consumption, are considered to perform the simulation. By default, the HOMER software considers the worst-case scenario for the power peak to provide the highest power supply reliability. Thus, it can be assumed that these numbers indicate both the average increase in the load profile consumption and the greatest shocks to the largest power consumption peaks in pandemic conditions. In the HOMER software, the maximum stochastic behaviors for a load profile compared to the forecasted demand profile are limited by two main parameters. The first one is the "day to day variability", which shows the largest possible percentage of the stochastic behaviors of the load profile in two consecutive days of a month. The second one is the "time step variability", which shows the largest possible percentage of the stochastic behaviors of the load profile in two consecutive hours of a day. Based on the NREL's (US National Renewable Energy Laboratory) database, the HOMER software suggested 18% and 21% for these two variables, respectively. It should be noted that these two parameters also affect the maximum possible value for the stochastic behavior of power peaks. As an example of a daily load profile, Fig. 4 shows the average energy consumption in July, a month in which a high energy consumption occurs due to the high ambient temperature. As it can be realized, a 100% load jump can significantly increase the peak of power consumption. For the higher reliability of optimization, this random behavior is considered for all months of the year as well as all the studied scenarios. The following section details the primary formulas, used by HOMER, to calculate the output power-related parameters. • PV module The software determines the photovoltaic's output power, efficiency, and cell temperature using equations (1) to (3) [33] . (1) where f PV denotes the module's derating factor (%),Y PV denotes the module's rated capacity (kW), and G Total , and G T.STC are the incident solar radiations on the photovoltaic array at the real and standard test conditions (kW/m 2 ), respectively. Additionally,μ is the temperature coefficient of the power (%/ • C), and T c and T c.STC are the cell temperatures under the real and standard test conditions ( • C) [34] . where η PV,STC is the PV's efficiency obtained under the standard test conditions (%),η PV is the real efficiency (%), and T STC and T amb are the test condition and ambient temperatures ( • C), respectively. Also, V NOCT is the wind speed under the NOCT conditions (m/s), V is the real wind speed (m/s), áá is the cooling effect correction factor, and a and b are the PV constants [34] . where T c,NOCT and T a,NOCT represent the cell temperature and ambient temperature at the nominal operating cell temperature (20 • C), respectively. In addition, η mp,STC denotes the PV's efficiency at its maximum power point (%), α is the module's solar absorptance (%), and τ is the solar transmittance of any cover over the PV array (%) [34] . The following equation calculates the global incident radiation on the surface of the photovoltaic module on an hourly basis: where β is the surface slope ( • ),G b is the beam radiation (kW/m 2 ), G d is the diffuse radiation (kW/m 2 ), ρ g is the ground reflectance (%),A i is a measure of the atmospheric transmittance of beam radiation (anisotropy index), and R b is the ratio of beam radiation on the tilted surface to beam radiation on the horizontal surface. These values are highly dependent on the PV system's tracking mode. The inverter's output power is calculated using Eq. (5), where P inv.in is the inverter's input power (kW) and η inv is the inverter's efficiency (%) [34] . • Diesel generator Diesel generators (DGs) are added to hybrid systems to increase the power supply process's reliability, cost-effectiveness, and flexibility. Equations (6) and (7) calculate the generator's hourly output power and duty factor, respectively: where F DG (t) is the hourly fuel consumption (L/h), P R is the generator's rated power (kW), also C a and C b are the generator manufacturer's constant parameters range from 0.085 to 0.250 [35] . Additionally, DF is the duty factor, which is the ratio of the supplementary initial shakers' produced power (kWh) to the total annual start/stops of generator (N SS ). • Battery bank Energy storage units (battery banks) are added to hybrid energy systems to improve the reliability of the power supply process. They store excess electricity and release it when there is insufficient power to meet the demand [36] . The battery state of charge represents the energy, stored in the bank, and varies according to the charge or discharge state, as defined by Eqs. 8 and 9 [37] . where SOC(t) denotes the unit state of charge at time t, P bat denotes the charge quantity of the storage unit (kWh), and (t − 1) represents a previous time step. Furthermore, η Cbat and η Dbat are the charge and discharge efficiencies (%),and σ is the self-discharge rate of the battery. Moreover,D Load (t) indicates the demand at each time step, and N represents the number of units. • Economic objectives The NPC and COE are the primary economic objective functions in current optimization. The NPC is the project's total cost over its lifetime, as calculated by Eq. (10) [38] . The term C ann denotes the annualized total cost ($/year) [39] , and CRF (i,n) demonstrates the capital recovery factor, calculated by Eq. (11) [40] . Also, i is the real interest rate, which is calculated based on Eq. (12) by f (annual inflation rate (%)) and i • (nominal interest rate (%)), and n is the lifetime of the project (year). The COE is a critical factor in determining the cost-effectiveness of a hybrid system, which is the average cost ($) of useful electricity per kWh produced, which can be calculated using the following equation [25] : where C ann.t is the annualized total cost (including the componentrelated expenses such as capital, replacement, and maintenance ($)), and E served is the annual served energy (kWh). Table 3 illustrates each component selection and economic input data, based on the local market situation. The assumed inflation and nominal discount rates are 15% and 18%, respectively. Due to the region's low fuel prices, the diesel fuel price is estimated to be $0.2/L. Furthermore, the project's lifetime is assumed to be equal to the solar panel's (20-year) lifetime, to avoid salvage value harming the accuracy of calculations. It is worth noting that the allowable annual loss of power supply is considered to be zero due to the selected load's essential operation. This section introduces the HOMER's overall optimization process. The importance of considering the effect of pandemics on an outpatient health clinic's energy consumption is then discussed. Overall optimization process The optimization process is depicted in Fig. 5 . The first step specifies the HRES's initial parameters, including the electrical load, ambient temperature, solar radiation, computational constraints, and economic and technical factors. Then, in the second step, all possible system's configurations with varying energy unit sizes are selected. Various states would occur at each time step depending on the input parameters and the component's chosen sizes. If the photovoltaic system generates more energy than the remaining demand, the excess energy is sent to the energy storage device. The battery bank's state of charge (SOC) is checked if the PV's output power is less than the electrical load. If the battery's SOC is sufficient, the storage device will discharge to meet the load; if the SOC is insufficient, the fuel generator will operate to meet the remaining demand. After repeating the optimization process for 8760 h per year and the entire duration of the project, the optimization objective (energy cost) for each simulated scenario will be calculated. As a result, the optimal scenarios are classified according to the hybrid systems' lowest energy cost. • Pandemic assumption Pandemics such as COVID-19 have historically resulted in a severe increase in the energy demand of healthcare facilities. This increase in load results from the increased number of patients and the use of medical equipment. However, the grid-connected clinics are much less likely to face this issue. However, the sudden increase in the demand is a significant issue for standalone clinics, particularly in rural areas. Thus, HOMER software is used to estimate the effect of a 50% load jump (low shock), a 100% load jump (medium shock), and a 150% load jump (severe shock) on the techno-economic optimization of a standalone rural outpatient medical clinic. The current approach provides the energy required for the clinic equipment and water treatment. It should be noted that the main optimization objective of the current study is the cost of energy, which is a major challenge in rural and remote areas, especially for the developing and underdeveloped countries. Obviously, designing an energy system without considering the possibility of epidemics can have two main consequences for a standalone health clinic. Firstly, the power outages' possibility, due to the demand increase, leads to high social costs by endangering the lives of patients. Secondly, the use of additional power generation equipment with non-optimal capacity, which leads to a significant increase in the energy costs. Therefore, the present study aims to introduce the optimal hybrid system, taking into account the mentioned challenges. Some researchers have compared the HOMER software's technoeconomic output with various optimization algorithms. The HOMER algorithm has demonstrated higher optimization speed and reliability than other well-known optimization algorithms such as GA and PSO [44] . Furthermore, Sinha and Chandel [45] concluded in a review of software tools for HRES optimization that HOMER software is highly accurate at determining the techno-economic characteristics of an energy system. An HRES consisting of photovoltaic PV/DG/battery, optimized with the particle swarm optimization (PSO) and an adaptive version of the marine predators' algorithm (AMPA), is selected to verify the accuracy of the current study's calculations. From Table 4 , it is concluded that the outputs of these two optimization algorithms differ by less than 5%, using the HOMER algorithm. Additionally, the outputs of the HOMER algorithm in the current study model differed slightly from those in the study of Yu et al. [46] . The main reason for this slight mismatch would be the difference between the numbers of iterations (focus factor) for the simulation process. It should be noted that, in the current study, the focus factor of HOMER software is set at 5% (high). These values demonstrate the simulation model's accuracy. In section 4.1, optimization is performed without considering the effect of a pandemic, and the use of various tracking modes in conjunction with the PV module is analyzed. Then, in section 4.2, the impact of load jump and fuel limitation on the techno-economic characteristics of the HRES is examined as two possible effects of pandemic diseases. The technical performance of the proposed optimum scenario is discussed in section 4.3, and the results are generalized to other possible conditions using the sensitivity analysis in section 4.4. The optimal scenarios for supplying the health clinic's load, using the HOMER optimization algorithm, are summarized in Table 5 , without considering the effect of a pandemic. Thus, by combining a 5.2 kW PV, a 5-kWh battery bank, and a 2 kW DG, the hybrid energy system can generate electricity at the cost of approximately $0.133/kWh and a 46.2% RF. In the second case, removing the battery bank reduces the flexibility of the power supply. The limitations of using solar panels, during sunny hours, and the lack of storage devices result in an Table 3 Technical and economic characteristics of the hybrid system component. approximately 6% increase in the COE and an 18% decrease in the RF. The pure DG scenario has a 27% higher COE and over 4480L annual fuel consumption, posing a challenge to the regions with limited access to fuel resources. By comparison, the optimal scenario consumes 50.5% less fuel than the pure DG scenario. Due to the critical nature of a continuous and reliable electricity supply for the medical clinic, the simulation was done without considering any annual capacity shortage. Accordingly, the high capacity of PV and battery devices is needed for the PV/battery scenario, causing the COE and NPC to be unaffordable. Due to the limited roof space, available on the residential containers, installing PV panels presents a challenge for the hybrid system. Thus, due to the importance of making the best use of an available space, Table 6 illustrates the HRES's techno-economic characteristics when different PV tracking modes are applied in the optimal scenario (installation of a 5.2-kW PV). The results indicate that the vertical tracking system (VTS) with a hybrid COE of $140/kWh and a component COE of $0.074/kWh performs the best economic performance among the continuous adjustment tracking modes. The dual-axis tracking system (DTS), which improved the PV output power by 29.8%, is the bestperforming mode from technical point of view. However, due to the lower installation cost, the fixed tilt structure unit was more costeffective than the tracking-base unit. The installed module's capacity and the price of diesel fuel are two factors that affect the economic efficiency of various tracking modes in the HRES, as directly and indirectly. Fig. 6 (a) illustrates the effect of the installed PV's capacity on the COE and RF of a hybrid system with a roof-top space availability of 6 to 60 m 2 . As a result, the optimal capacity for PV, VTS, and DTS would approximately be 5 kW, 4 kW, and 3 kW, respectively. Due to the higher cost of tracking systems than the fixed structures, installing additional PV capacity increases the COE significantly. The PVs' main function is to provide power during the day. Consequently, increasing the installed capacity gradually increases the RF, as the additional generated power is considered as a surplus electricity when the batteries are fully charged. As illustrated in Fig. 6 (b) , rising the diesel fuel prices from subsidized to the international levels result in a reduction in a fuel consumption in the hybrid system. As a result of the increased installation capacity of PV modules, the COE would increase. At a $0.1/L diesel fuel price, the pure DG scenario with a COE of $0.130/kWh would be optimal. On the other hand, the annual fuel consumption is significantly reduced at a $0.6/L diesel fuel price; which means that, in the higher fuel prices, the higher PV capacities could be optimal. Pandemic disease challenge Fig. 7 depicts the annual power generation profile of the optimized configuration without considering the pandemic impacts. This figure illustrates that DG is used less during the day due to solar panel generation. However, the generator supplies most of the load during the night. This performance demonstrates the superior capability of the PV and DG hybridization, which has been proposed to many off-grid health centers to date. However, rising the fuel consumption, rising the fuel prices, and increasing the electrical demand all pose risks to the reliability and costeffectiveness of an optimized energy hybrid system. Today, challenges imposed by a pandemic (such as COVID-19) must be anticipated, particularly for critical applications. Fig. 8 (a) and Fig. 8 (b) illustrate the effect of fuel constraints on the optimal energy cost and battery/PV capacity, respectively. As a result, the low fuel availability results in an 18% to 40% increase in the COE for the small loads. This fuel restriction requires an additional 6.1 kW to 13 kW of PV capacity and approximately 20 kWh to 30 kWh of battery capacity. On the other hand, the low fuel availability results in a 95-135% increase in the COE for large loads, an increase of about 22-31 kW additional PV installation capacity and 95-114 kWh additional battery installation capacity. These findings indicate that the fuel availability is more critical for the larger loads (large clinics), which can significantly impact the cost-effectiveness of a hybrid energy system. Moreover, due to the low local cost of diesel, a lower COE can be achieved by increasing the fuel consumption ratio, to generate a useful energy. However, ignoring the possibility of an increased fuel consumption (higher fuel availability) in an optimized hybrid configuration could result in a significant increase in the system's power shortage or cost. The HOMER multi-year analyzer is used to analyze the sudden increase in the annual energy consumption throughout the project's lifetime and determine the techno-economic impact of a 50%, 100%, and 150% load jump in electrical demand. Two primary difficulties that a standalone HRES faces with pandemic conditions are the load jumping and lack of available fuel. Accordingly, Table 7 summarizes the hybrid unit's techno-economic outputs by considering the possibility of diesel fuel shortage and the load jump. According to Table 7 , assuming no load jump, the hybrid unit consumed about 2300 L/year of diesel fuel. This unit has a 5.2 kW PV capacity, an RF of 46.2%, and a COE of $0.133/kWh. While adding a load jump of 50%, 100%, or 150% increased the installed PV's capacity Table 4 The validation of the model with the other optimization algorithms. On the other hand, if the fuel restriction is applied to any load jump from low to high, the installed PV capacity increases by approximately 75%, 136%, and 311%, respectively. Likewise, the COE increases to 0.150, 0.230, and $0.325/kWh in these cases subsequently. Without considering fuel constraints, the results demonstrate that the proposed system can withstand the largest annual increase in the electricity demand, by predicting a sudden increase in the electrical load. As a result of the optimization process, 2.4 kW additional PV panels were installed, which caused only a $0.08/ kWh increase in the energy costs. While, under the fuel constraints, more costs are required to pay for the extreme rise in the loads. Consequently, ensuring adequate fuel supply to rural areas is critical for the governments to provide necessary stand-alone applications such as health clinics. This section compares the HRES's performance, with and without load jump. Fig. 9 (a) depicts the primary demand and the demand that will be impacted by the pandemic (100% load jump). In each case, the performance of cost-effective HRES would vary. According to Fig. 9 (b), when there is no fuel restriction or load jump, the generator acts as a peak-shaving component. The DG operates during times of peak power consumption or low solar radiation. A DG will consume about 1976 L of fuel per year to perform this task. Also, during the day, the generator is rarely used (Fig. 10 (a) ). Under specific conditions, such as when faced with a pandemic, this hybrid energy configuration behaves differently ( Fig. 9 (c) ). In this regard, the DG is constantly in operation to ensure that the clinic load is reliably supplied. The generator can be switched off only when the solar radiation is high and the batteries are fully charged. It should be noted that the generator's ability to operate with different capacities (Fig. 10 (b) ) reduces the need for batteries, even during the peak times. This performance leads to a 140% increase in the annual fuel consumption. The proposed approach eliminates the need for high PV/battery installation capacity, and eventually decreases the COE. On the other hand, by assuming a fuel restriction in the years preceding a pandemic, the technical performance of the hybrid energy Techno-economic optimization of the hybrid energy system based on the fuel restriction and the load's jump in pandemic situations. system is altered even in non-pandemic (normal) years. As illustrated in Fig. 9 (d), a higher PV/battery combination capacity is required, due to the limited fuel supply. As a result of this approach, the DG plays only a backup role in the energy system, during normal years. The DG operates when solar radiation is extremely low or the battery bank is completely discharged (Fig. 10 (c) ). This performance decreases the HRES's reliance on the battery bank. The excess electricity is increased due to the increased solar capacity, particularly in normal years when the demand is not increased significantly. However, by increasing the capacity of the batteries and incorporating a deferrable load, a significant portion of this surplus electricity is consumed. Under the pandemic disease conditions, this hybrid energy configuration operates based on the DG and battery's simultaneous usage ( Fig. 9 (e) ). When there is a fuel shortage, the battery's role becomes significantly more important. Due to the increased number of battery replacements, this issue would increase the system costs. The DG uses the maximum amount of the available annual fuel, to supply the peaks (Fig. 10 (d) ) with the assistance of batteries. Due to the fuel restriction, the system requires about twice the PV capacity and about nine fold battery capacity. This issue consequences in an increase of up to 68% in the COE. This power supply management process is accomplished by predicting the possibility of pandemic diseases. Otherwise, the system faces a very high cost, due to the addition of high-capacity photovoltaic and battery systems in the fuel scarcity times. This section contains some heat maps illustrating the effect of the significant factors on the NPC and COE of the optimal HRES. Fig. 11 (a) shows the sensitivity of COE based on the PV's capital cost and the annual average solar radiation variations. Accordingly, assuming a 30% increase in the PV's capital cost and lowest solar radiation of 4 kWh/m 2 / day, as a worst-case in western Asia, the maximum COE reaches $0.149/ kWh. While the COE improves to $0.117/kWh in the best-case scenario, assuming a 30% reduction in the PV's capital cost and maximum solar radiation of 6 kWh/m 2 /day. This range of energy costs demonstrates the proposed hybrid unit's affordability in the face of possible changes. The effect of the variation of battery capital cost and diesel fuel price on the COE is depicted in Fig. 11 (b) . As can be seen from the variation in battery capital costs and the variation in diesel fuel prices from $0.1/L (subsidized rate) to $1/L (international rate), the COE varies between 0.113 and $0.192/kWh. The COE variation is less than $0.200/kWh for all possible price changes, demonstrating the economic viability of the presented hybrid system. The NPC's dependence on the ratio of the annual fuel availability to the annual average electrical demand is depicted in Fig. 11 (c) . As a result, for any load and fuel availability greater than 3800L/year, the NPC varies between $20,000 and $36,000. At the same time, NPC can be increased to $60,000 in the large loads, by increasing the fuel restriction up to 2300L/year. These changes will be more severe in cases where fuel availability is less than 2300L/year, and for the loads greater than 50 kWh/day which lead to more than $110,000 NPC. Finally, Fig. 11 (d) illustrates the NPC's variation based on the nominal discount rate and expected inflation rate. At any specified discount rate, the NPC grows in lockstep with the rate of inflation. Lower inflation rates can make the system more affordable; for example, in the countries with an inflation rate of less than 8%, the NPC can be reduced to about 30%. On the other hand, when the discount rate is low and the inflation rate is high, the NPC could be increased to more than 45%. The findings demonstrate that the proposed system is sufficiently generalizable to other economic conditions. Another important parameter, that can affect the final results, is the characteristics affecting the stochastic behavior of the simulated load profile. Table 8 shows the day-to-day and timestep parameters for different applications in tropical weather based on the HOMER software database, imported from the NREL. The results show that in large amounts of stochastic behavior, the renewable energy role in the power supply will increase. This case happens because of the large peaks that occur in the demand profile. This increment consequently rises the final energy costs slightly. The considered values for the stochastic behavior of the load profile were appropriate, due to the insignificant increase in costs. The electricity tariffs on national grids vary globally. For example, in 2020, electricity prices in oil and natural gas producing countries were less than $0.10/kWh. This value was approximately $0.10/kWh in India and China, roughly $0.15/kWh in the United States, and more than $0.20/kWh in the European Union countries [48] . However, the high cost of developing grid transmission lines, particularly in the sparsely populated rural areas, may increase the competitiveness of the HRES energy costs, compared to the grid electricity. The estimated cost of constructing an electricity network in developing countries is between 1000 and 8000$/km, increasing to approximately $22,000/km, if the route is difficult to build, such as in mountainous areas [49] . Generally, the cost of developing a power grid network in rural areas is seven to ten folds of the urban areas [35] . Based on the considerations mentioned above, an energy cost of less than $0.20/kWh is appropriate for an offgrid HRES. The areas with low fuel prices have a strong potential of achieving this COE goal. The proposed hybrid PV/DG/Battery system has a $0.133/kWh COE, making it an affordable option for powering health facilities in rural areas. The energy challenges of a pandemic on the standalone health care centers are analyzed for the first time. Accordingly, an attempt was made to address the limited flexibility problem of renewable energy units, facing sudden changes in demand. Furthermore, considering the fuel scarcity impact and 100% load jump, the energy can be supplied at a maximum COE of $0.230/kWh by adding 7.1 kW and 2.1 kWh of PV and battery capacity. Nonetheless, rural areas must be provided with adequate fuel for more severe shocks to the load (about 20L/day on average). The local governments must consider this issue to prepare for a COE of less than $0.20/kWh. Governments and investors can efficiently address the power supply challenges by utilizing a pre-made software based on the introduced method for HRES optimization. This challenge predictionbased method identified a cost-effective solution for powering a rural health clinic without any capacity shortages. Fig. 12 depicts the grid breakeven distance analysis for the selected area, which compares the grid extension with the proposed standalone HRES. The average grid tariff is assumed to be approximately $0.10/kWh in this simulation. Also, the cost of grid extension is assumed to be between 5000 and 1000 $/km. As a result of the HOMER grid optimization tool, it was determined that the standalone system would be affordable for the distances greater than 3.5 km, without considering pandemics. On the other side, when the pandemic effects are considered, this value reaches about 7.5 km. So, it can be concluded that the proposed HRES would be a costeffective solution even in the intensity of the pandemic because the distance between the impoverished rural areas and the nearest urban areas is usually longer than 10 km. Due to the critical nature of the power supply for rural health clinics, the current study proposes an HRES, capable of dealing with epidemic conditions. Based on the reviewed articles on the power supply of standalone health clinics, it can be concluded that the fuel constraints, Fig. 11 . Economic sensitivity analyses based on: a) PV capital cost and solar radiation variation; b) Diesel price and battery capital cost variation; c) Annual load and diesel availability variation; d) Discount rate and inflation rate variation. Analyzing the changes in optimal system configuration and COE based on different stochastic behavior. space availability for additional solar panels, and severe load shocks have not been considered in HRES optimization. So, this matter can pose a significant challenge for HRESs in essential applications in the pandemics times. The primary aim of this study is to optimize renewable hybrid systems for critical applications. By using a challenge predictionbased method, governments and investors can easily benefit from the available optimization tools, to provide an optimal solution for delivering a cost-effective energy without shortages. Without predicting a pandemic scenario, the optimized HRES includes a 5.2 kW PV, a 2.7 kW converter, a 2 kW DG, and a 5-kWh battery, resulting in a COE of $0.133/kWh. Considering the pandemic disease, the PV and battery's installation capacities reach 7.6 kW and 10 kWh, respectively, resulting in a COE of $0.141/kWh. So, the problem of severe load jumps can be solved by slightly increasing the PV/battery installation capacity and providing adequate fuel to the area. Furthermore, two modes of operation could be used in situations where the PV's installation space is limited. Firstly, a dual-axis tracking system with an LCOE of $0.0803/kWh increased the PV power output by approximately 28.8%. Secondly, vertical-axis tracking system with an LCOE of $0.0740/kWh increased the PV power output by 20.1%. On the other side, if the fuel access is restricted during pandemic years, the COE for the HRES would exceed $0.3/kWh in the extreme load jumps. Accordingly, without designing the HRES for low fuel consumption, about 21 kW PV and 80 kWh battery capacity must be installed to provide a reliable power. This hybrid system showed more than $0.325/kWh COE. While, by supplying approximately 6000 L of diesel fuel per year, the COE decreased to $0.141/kWh. This issue demonstrates the critical nature of fuel supply in the off-grid applications during the pandemics. The sensitivity analysis reveals the possible variation of the COE by − 15 to + 45% in response to the changes in the input costs and solar radiation. Additionally, it is shown that the presented HRES is more cost-effective in the countries with low inflation rates. These findings represent the proposed hybrid system's suitability for a standalone power supply in rural areas. The study's findings demonstrate the importance of considering COVID-19 pandemic scenarios when designing highly reliable standalone HRESs for health clinics' power supply. Finally, it is suggested that the recommended method would be extended to other forms of renewable hybrid energies in future studies, such as wind turbines, fuel cells, biogas generators, and combined heat and power units. 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