A Sustainable Wind–Biogas Hybrid System for Remote Areas in Jordan: A Case Study of Mobile Hospital for a Zaatari Syrian Refugee Camp

Abstract

Access to reliable and sustainable energy in remote areas remains a pressing global challenge, significantly affecting economic development and the quality of life. This study focuses on the implementation of fully off-grid wind–biogas hybrid power systems to address this issue, with a focus on remote healthcare camp facilities. This paper investigates the performance of a hybrid renewable energy system within the context of one of Jordan’s northern remote areas, the Zaatari Syrian Refugee Camp, assessing its efficiency and environmental impact by taking the Zaatari hospital as the case study. Simulations were conducted to evaluate system components, including wind turbines, biogas generators, and diesel generators. A comprehensive evaluation was conducted, encompassing both the operational efficiency of the system and its impact on the environment. This study also considered various scenarios (SC#), including grid availability and autonomy levels, to optimize system configurations. The techno-economic assessment employed the levelized cost of energy (LCOE) as a key performance indicator, and sensitivity analyses explored the impact of diesel costs and wind power fluctuations on the system. Additionally, environmental assessment was conducted to evaluate the environmental effects of hybrid systems, with a specific focus on reducing greenhouse gas emissions. This investigation involved an examination of emissions in three different scenarios. The results indicate that the lowest LCOE that could be achieved was 0.0734 USD/kWh in SC#1 with 72.42% autonomy, whereas achieving 100% autonomy increased the LCOE to 0.1756 USD/kWh. Additionally, the results reveal that in scenarios SC#2 and SC#3, which have a higher proportion of diesel generator usage, there were elevated levels of NOx and CO₂ emissions. Conversely, in SC#1, which lacks diesel generators, emissions were notably lower. The proposed hybrid system demonstrates its potential to provide a reliable energy supply to healthcare facilities in remote regions, emphasizing both economic feasibility and environmental benefits. These findings contribute to informed decision making for sustainable energy solutions in similar contexts, promoting healthcare accessibility and environmental sustainability.

1. Introduction

Energy supply in remote areas presents a formidable challenge for numerous developing nations on a global scale. These remote regions often grapple with the stark reality of lacking access to a reliable and affordable energy source, resulting in stifled economic growth and a diminished quality of life for their inhabitants [1]. Moreover, this dearth of energy access has far-reaching consequences, profoundly impacting vital services such as healthcare, education, and communication, thereby impeding the holistic development and prosperity of these marginalized areas [2,3]. Fortunately, innovative off-grid solutions, such as microgrids and mini-grids, offer a glimmer of hope for these underserved regions. These self-contained systems can be strategically deployed to provide a dependable and cost-effective energy supply, breaking free from the constraints of traditional grid infrastructure [4,5]. Bolstering this energy autonomy are cutting-edge energy storage technologies, prominently including advanced battery systems. These technologies ensure a continuous energy supply, effectively mitigating the adverse effects of intermittent energy generation, a common challenge in remote areas [4,6,7].

Among the most promising solutions are fully off-grid power systems, which are entirely self-reliant and adept at generating and storing energy from renewable sources. These encompass solar energy, wind energy, and bioenergy, collectively forming a trifecta of sustainable energy sources. The adoption of off-grid power systems not only offers a clean and eco-friendly energy supply but also serves as a potent instrument in the global battle against climate change. By significantly reducing dependence on fossil fuels, these systems align with the imperative to reduce greenhouse gas emissions and foster a more sustainable energy landscape [8,9,10,11]. Wind–biogas hybrid systems exemplify off-grid power solutions, harnessing wind, and biogas synergies. Wind turbines capture wind energy, while biogas plants convert organic waste into electricity [12]. These systems offer a continuous 24 h power, predictable production matching demand cycles, and efficiency levels up to $30–40\%$, making them favourable for remote regions, particularly those with abundant wind and organic resources [13].

Wind energy systems are often considered more reliable than photovoltaic (PV) systems when it comes to area utilization and consistent power production. Wind turbines can be installed vertically in close proximity to one another, making efficient use of land while minimizing the impact on agriculture or other land uses [14]. Additionally, wind turbines generate power around the clock, as long as there is sufficient wind, providing a more consistent power output compared to solar panels, which are dependent on daylight hours and weather conditions. This reliability and efficient land use make wind systems an attractive option for consistent and sustainable power generation [15,16].

Numerous studies have explored the integration of wind and biogas for energy generation. One noteworthy study by Yimen et al. [17] investigated a hybrid energy system, combining photovoltaic (PV), wind, biogas, and hydro sources using The Hybrid Optimization of Multiple Energy Resources (HOMER) software. Their findings demonstrated cost-effectiveness and environmental benefits, particularly in comparison to existing systems in Africa. Sarkar et al. [18] also utilized HOMER to model a unique hybrid microgrid, integrating solar PV, wind, biomass, and vanadium redox flow battery (VRFB) storage. Their results highlighted that intelligent scheduling and control systems can eliminate power loss, adapting to the unpredictable nature of renewable energy. Additionally, Anwar et al. [19] proposed an integrated renewable energy system (IRES) incorporating PV, wind, and biogas sources, and assessed its levelized cost of energy (LCOE) and net present cost (NPC), also using HOMER software. Their analysis revealed that the NPC was particularly sensitive to load variations and less affected by changes in wind speed.

In rural and remote regions, the viability of hybrid power systems has been extensively explored. For instance, Vendoti et al. [12] conducted a study that encompassed the integration of PV, wind, biomass, biogas, and fuel cells within a single system. Their research underscored the system’s reliability, with a potential cost of energy (COE) as low as 0.214 USD/kWh under optimal conditions. In another context, Li et al. [20] investigated an off-grid hybrid renewable energy system tailored for remote rural electrification in a West Chinese village (Leopard Beach). Employing simulations, optimization techniques, and sensitivity analyses, the researchers evaluated various combinations of PV panels, wind turbines, and biogas generators. Their results unveiled a dependable and cost-effective hybrid power solution, combining solar, wind, and biomass components, which not only facilitated sustainable rural electrification but also yielded environmental benefits. Furthermore, Das et al. [21] conducted a study to assess the feasibility of a self-sufficient hybrid power generation system for a remote community in Bangladesh (Katakhali village). This proposed system seamlessly integrated diverse renewable energy sources, including biogas generators, PV modules, wind turbines, and diesel generators. Notably, the system demonstrated a COE of 0.28 USD/kWh, a total net present cost (NPC) of 612,280 USD, and an impressive renewable fraction of $60\%$. Beyond economic advantages, it exhibited the potential to curtail ${CO}_2$ emissions by approximately $59.6\%$ annually compared to diesel-based systems and by $40.5\%$ relative to grid electricity.

After conducting a comprehensive survey of the existing literature, it became evident that most of the studies centered around hybrid power systems primarily emphasized their feasibility under controlled and uniform conditions. While such analyses provide valuable insights from an economic standpoint, they often fall short in capturing the real-world dynamics and operational intricacies of these systems. To bridge this gap, the present study embarks on a rigorous exploration of the practical performance of a hybrid wind–biogas power system deployed in remote northern areas of Jordan. The main novelty and motivation of the current work can be summarised as follows:

• The current investigation focuses on using hybrid power systems to supply energy to healthcare facilities situated in refugee camps in remote regions.
• The current study performs a multifaceted assessment, addressing both the system’s operational efficiency and its environmental footprint.
• The present study focuses on examining different energy-supplying scenarios for the health facility, which in turn helps in understanding the nature of the types of energy systems suitable for remote areas.
• The current study also considers the environmental sustainability of hybrid systems employed in remote areas.

The mentioned points will, in turn, enrich the knowledge about dealing with the energy-providing issues in refugee’s camps, especially when they are characterized by high energy demand.

To address the main objectives of the current work, this paper begins with the introduction section, providing an overview of the case study and related studies found in the literature. Then, the methodology section includes a description of the study area, mathematical models for each system with the power plant, and an economic analysis of the applied scenarios. Following this, the results and discussion section is presented to address the main findings of this study and to comment on the results of each scenario. Finally, the conclusion section summarizes the key findings of this study and the results obtained after applying the suggested scenarios.

2. Materials and Methods

2.1. Demographic Profile of the Case Study Area

This research centres its attention on the hospital at the Zaatari Syrian Refugee Camp, a geographic focal point depicted in Figure 1, strategically located just $8$ miles from the Syrian border at coordinates (32°17′44.4″ N 36°19′25.5″ E). Distinguished as the largest refugee camp in the Middle East and the world’s second-largest of its kind [22], the camp sprawls across an expansive terrain, spanning approximately $5.5$ square kilometers. Its inception dates back to $2012$, conceived as a response to the Syrian internal conflict, and it presently shelters a formidable population of around $\mathrm{80,000}$ residents. Notably, the Zaatari camp has been strategically positioned within an area that serves as a pivotal groundwater reservoir for Jordan, further underscoring its regional significance [23].

In $2012$, a pivotal development unfolded within Zaatari—the establishment of an electrical grid, undertaken in collaboration with the Irbid District Electricity Company. This visionary undertaking was meticulously designed to extend essential energy services throughout the camp, serving as a lifeline for various stakeholders, including the base camp, non-governmental organizations (NGOs), and the illumination of thoroughfares within the camp’s expanse. In its initial incarnation, electrical connections to individual shelters and enterprises were informally assembled by the resourceful refugees themselves, albeit at a steep cost to both health and security, as documented in [25]. Considering these pressing safety concerns and the mounting electricity expenditures incurred by The United Nations High Commissioner for Refugees (UNHCR), a pivotal juncture occurred in $2015$ when the entire network underwent a transformative overhaul. This overhaul resulted in the provision of dedicated electrical connections to each household, significantly enhancing safety and reliability.

Notably, October $2017$ stands as a watershed moment in the camp’s energy landscape with the completion of a $12.9 \mathrm{M}\mathrm{W}$ solar plant, an epochal achievement that bestowed upon it the distinction of housing the world’s largest renewable energy infrastructure within a refugee camp [26]. Financed through the generous support of the Government of the Czech Republic, this solar marvel not only bestows a bounty of renewable energy upon the entire camp’s populace but also extends its benevolent reach to empower over $50$ organizations and essential operational facilities through the local grid.

To surmount the escalating water requirements, a substantial number of groundwater wells have been meticulously excavated, both within the confines of the Zaatari camp and its neighbouring environs. Notably, in $2016$, the camp witnessed the inauguration of three water wells, nestled within its bounds, collectively boasting an impressive daily water yield of $3600 {\mathrm{m}}^3$ [27]. Furthermore, a state-of-the-art wastewater treatment facility, with a commendable capacity of $3000 {\mathrm{m}}^3/\mathrm{d}\mathrm{a}\mathrm{y}$, was erected to uphold the camp’s commitment to environmental stewardship and sanitation [27]. Complementing these endeavors is an ongoing initiative to establish a piped water distribution system, alongside a piped sewerage network, which will seamlessly link the collection system to the wastewater treatment plant. These ambitious undertakings collectively stand as a testament to the unwavering dedication to addressing the water and sanitation needs of the camp’s burgeoning population [27].

Moreover, recognizing the intensifying energy demands within the camp, a milestone was reached in $2019$ with the establishment of a pilot biogas production unit [28]. This innovative unit is poised to harness the latent energy potential residing within the camp’s organic solid waste and the sewage sludge processed by the wastewater treatment plant. Figure 2 offers a compelling visualization of the daily flow rates pertaining to both wastewater and biogas production, underscoring the dual benefits of sustainable waste management and renewable energy generation that this initiative promises to deliver throughout the year.

Within the Zaatari camp, eight medical facilities provide a comprehensive range of healthcare services to refugees, including primary care and selective secondary and tertiary healthcare. These services encompass health consultations, antenatal care, community health initiatives, nutrition support programs, screening and treatment, health promotion, and mental health support. However, budget constraints and increasing healthcare demands, particularly for non-communicable disease (NCD) services, have presented significant challenges. In response, mobile hospitals have become a primary focus in refugee camps due to their capacity to offer comprehensive and adaptable healthcare solutions [18]. These mobile medical units are equipped to provide a wide spectrum of healthcare services, including surgical procedures, emergency care, and specialized treatments. However, mobile hospitals require a consistent and reliable energy supply to operate various types of critical medical equipment, such as ventilators, diagnostic devices, surgical tools, and life support systems. Consequently, mobile hospitals have higher energy requirements than small clinics.

Table 1. Electricity consumption devices and estimated daily usage in a mobile hospital [29,30].

Equipment	$\mathbf{Power} (\mathbf{W}$)	$\mathbf{Hours} \mathbf{Used} \mathbf{per} \mathbf{Day} (\mathbf{h}$)	Energy Consumption (Wh/Day)
Lights	$11–25$	$8–12$	$88–175$
Mobile phone charger	$5–20$	$8–12$	$40–240$
Fan (DC, AC)	$30–100$	$8–12$	$300–1200$
Water pump	$100–200$	$6$	$600–1200$
Computer	$150–200$	$8$	$675–1000$
Portable electrical heater	$1000–1500$	$3$	$2500–3500$
Printer (ink, laser)	$65–1000$	$2$	$130–2000$
Small waste autoclave	$600–6000$	$1–2$	$600–6000$
Sterilizer (steam)	$500–1560$	$4$	$2000–6200$
Suction	$24–50$	$10–14$	$240–700$
Pulse Oximetry	$24–50$	$3$	$72–150$
RO water purifier	$260–570$	$7$	$1820–2590$
Magnetic resonance imaging (MRI)	$6000–7000$	$4$	$\mathrm{24,000}–\mathrm{28,000}$
Computed tomography (CT)	$2500–3500$	$4$	$\mathrm{10,000}–\mathrm{14,000}$
X-ray machine (dental)	$2000–5000$	$3–5$	$6000–\mathrm{25,000}$
X-ray machine	$3000–6000$	$2–6$	$6000–\mathrm{36,000}$
Mechanical ventilator	$600–700$	$3–6$	$1800–4200$
Ultrasound scanner	$2000$	$4–6$	$2000–\mathrm{12,000}$
Electrocardiogram (ECG)	$500–800$	$3–4$	$1500–2400$
Nebulizer	$180–250$	$3–5$	$540–1250$
Refrigeration unit	$400–500$	$4–8$	$1600–4000$
Microscope	$30$	$2$	$60$
Centrifuge	$600$	$1.5–3$	$900–1800$
Spectrophotometer	$63$	$1$	$63$
Blood chemistry analyzer	$45–100$	$2$	$90–200$
Hematology analyzer	$230–500$	$2$	$460–1000$

provides a list of the major types of medical and non-medical equipment commonly used in mobile hospitals, along with estimated daily operation hours and energy demands. This information is used to create a typical load profile, as shown in Figure 3, which illustrates the total electrical energy consumption of the mobile hospital. This load profile is instrumental in determining the sizing requirements for the hybrid power plant utilizing wind and biogas energy sources.

2.2. System Description

Figure 4 illustrates the schematic layout of the envisaged hybrid power plant, designed to cater to the energy requirements of the Zaatari camp. This integrated facility comprises an assortment of biogas and diesel generators, a wind turbine, and a sophisticated control unit. The subsequent subsections provide comprehensive descriptions of each constituent within the hybrid power system.

2.2.1. Wind Turbine

For the estimation of wind turbine power generation, the TRNSYS^® software V.15 leveraged the $Type 90$ model. This particular model offers a highly accurate prediction of power production by leveraging a meticulously crafted power-to-wind-speed relationship, conveniently stored in an external data file. Additionally, the model accounts for fluctuations in air density and the influence of elevated wind speeds. The underlying equations driving this model are elaborated upon in the subsequent section. Equation (1) below defines the power output of the wind turbine, denoted as $P$ in $\mathrm{k}\mathrm{i}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{a}\mathrm{t}\mathrm{t}\mathrm{s} \left(\mathrm{k}\mathrm{W}\right)$ [31].

$$P=\frac{1}{2}\rho A_R{U}_0{ }^{3}4a(1-a{)}^2$$

(1)

In Equation (1), $\rho$ represents the air density ($\mathrm{k}\mathrm{g}/{\mathrm{m}}^3$), $A_R$ signifies the rotor surface area in $square meters$, $U_0$ corresponds to the wind speed ($\mathrm{m}/\mathrm{s}$), and the variable $a$ is defined as the axial induction factor.

To determine the most suitable wind turbine model for integration into the hybrid power plant, an assessment of four distinct commercial wind turbines was conducted using a TRNSYS^® simulation, as outlined in Figure 5 and

Table 2. Symbols corresponding to the studied wind turbine models.

Turbine Model	Symbol
Vestas V44/600	T1
Enercon E40 600/46	T2
Nordex N43/600	T3
REpower 48/600	T4

. All selected wind turbines have the capacity to generate up to $600 \mathrm{k}\mathrm{W}$ of power based on the prevailing wind speeds. It should be noted that TRNSYS^® was chosen for this part of the study for several reasons. These include its extensive library of components for wind turbine systems, its robust numerical solver that ensures accurate results, and its ability to integrate with other software tools, enhancing its utility in multidisciplinary system configurations.

Figure 6 illustrates the power output characteristics of each turbine as a function of wind speed. Notably, it is evident from the figure that all turbines exhibit identical power curves within a wind speed range of $1–12 \mathrm{m}/\mathrm{s}$. However, as wind speeds surpass $12 \mathrm{m}/\mathrm{s}$, discrepancies emerge among the power curves, with certain turbines sustaining a consistent power output, while others exhibit fluctuations. To gauge the available wind energy resources within the Zaatari camp location, the wind speed data for Mafraq city were employed in the TRNSYS^® simulations. Figure 7 displays the hourly wind speeds throughout the year for Mafraq, along with data for two additional cities, Amman and Aqaba. The yearly average wind speed for Mafraq city stands at approximately $8.2 \mathrm{m}/\mathrm{s}$, equivalent to an estimated $220 \mathrm{k}\mathrm{W}$ of power potential.

2.2.2. Biogas and Diesel Generators

In this study, we utilize the electricity generated by the biogas generator to fulfill the hospital’s essential energy requirements. To achieve this, we estimate the hourly energy output achievable from the biogas generated within the wastewater treatment plant, assuming an average calorific value of $0.22 \mathrm{M}\mathrm{J}/{\mathrm{m}}^3$ [34]. Additionally, we assume a continuous and consistent biogas production rate on an hourly basis throughout each day. Furthermore, we consider the biogas generator to have an efficiency of $35\%$ [34]. To ensure an uninterrupted power supply, we integrate a diesel generator as a backup system to bridge any energy shortfalls during periods of deficiency. The quantity of diesel ($V_{diesel}$) consumed in ($\mathrm{L}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}/\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}$) can be approximated using Equation (2) [35], where $E_{DG}$ is the hourly demand covered by the diesel generator in $\mathrm{k}\mathrm{W}\mathrm{h}$.

$$V_{diesel}={0.244\times E}_{DG}+7$$

(2)

2.3. Energy Dispatch Strategy

As previously mentioned, the biogas generator is designated to meet the base energy demand, while wind energy is harnessed to satisfy additional requirements whenever possible. However, in scenarios where wind energy proves insufficient to fulfill the demand, we have outlined three distinctive approaches:

Scenario 1 (SC#1): In this setup, a reliable grid connection is consistently available and can be utilized to compensate for any energy deficit. The grid serves as a fallback option, ensuring uninterrupted power supply even during periods of low wind energy production.

Scenario 2 (SC#2): In situations where grid access is characterized by occasional unpredictable outages, our system is designed with a $95\%$ autonomy target. This means that it aims to be self-sufficient for $95\%$ of the time. Should wind energy fall short and the grid is either unavailable or only covers $5\%$ of the demand, a diesel generator is deployed to bridge the energy gap.

Scenario 3 (SC#3): In the absence of a grid connection characterized by a fully off-grid ($100\%$ autonomy) configuration, the diesel generator becomes the primary means to meet the entire energy demand.

In instances where there is an energy surplus generated by the system, the excess is channeled back into the grid, accumulating as credit for the hospital. This credit can subsequently offset grid usage in SC#1 and SC#2, reducing operational costs. However, in the fully off-grid SC#3 scenario, any surplus energy is dissipated as heat.

2.4. Techno-Economic Model

Energy performance is a crucial factor when examining a renewable energy system (RES). It is important to consider economic factors while analyzing the technical performance to ultimately select the optimal configuration that leads to the highest energy savings for a given investment. The main key performance indicator (KPI) employed in this analysis is the levelized cost of energy (LCOE). This metric is typically used to determine the total cost per unit of energy, considering all cost elements throughout the entire lifespan of the system. This includes expenses such as operation and maintenance, and any other related costs. The LCOE of the RES can be estimated using the following approach [36]:

$$LCOE=\frac{C_i+\sum _{t=1}^{LT}\frac{M_{t }}{{(1+\mathrm{r})}^{\mathrm{t}}}+\sum _{t=1}^{LT}\frac{F_{cost}}{{(1+\mathrm{r})}^{\mathrm{t}}}+\sum _{t=1}^{LT}\frac{B_{grid}}{{(1+\mathrm{r})}^{\mathrm{t}}}}{\sum _{t=1}^{LT}\frac{\sum _{h=1}^{8760}{D}^h}{{(1+\mathrm{r})}^{\mathrm{t}}}}$$

(3)

where $C_i$ represents the capital cost of the wind, BG, and DG systems; $\mathrm{L}\mathrm{T}$ represents the system’s expected lifespan; $M_t$ is the system’s annual maintenance cost; and $D^h$ is the hospital’s demand. Further, $r$, $F_{cost}$, and $B_{grid}$ represent the annual discount rate, diesel cost, and grid bill, respectively. As mentioned earlier, during periods of deficiency, the utility grid provides the remaining shortfall at a tariff ($GT$) of 0.16 USD/kWh [37].

RES fraction ($F_{RES}$) is utilized to evaluate the ability of the wind–BG system to meet the energy needs of the hospital. This is determined by computing the percentage of demand fulfilled by the wind–BG system (${ D}_{met}^h$), which can be computed using Equation (4) [38]:

$$F_{RES}=\frac{\sum _{h=1}^{8760}{ D}_{met}^h}{\sum _{h=1}^{8760}{D}^h}$$

(4)

The economic parameters used in this study are displayed in Table 3, which shows the detailed cost of each component within the hybrid power system.

Table 3. The costs of the wind–biogas system, its lifespan, and the economic parameters used in this work.

Item	Unit	Value	Reference
Capital cost of the wind system	USD/kW	$1325$	[39]
Capital cost of the DG		$200$	[39]
Capital cost of the BG		$1500$	[35]
Yearly maintenance cost of the wind plant		$18$	[39]
Yearly maintenance cost of the DG	USD/h	$0.01$	[35]
Yearly maintenance cost of the BG		$0.1$	[35]
Wind lifespan	Year	$30$	[39]
DG and BG lifespan		$10$	[39]
Diesel cost	$\mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{L}$	Figure 8
Discount rate	$\%$	$8.5$	[40]

Table 3. The costs of the wind–biogas system, its lifespan, and the economic parameters used in this work.

Item	Unit	Value	Reference
Capital cost of the wind system	USD/kW	$1325$	[39]
Capital cost of the DG		$200$	[39]
Capital cost of the BG		$1500$	[35]
Yearly maintenance cost of the wind plant		$18$	[39]
Yearly maintenance cost of the DG	USD/h	$0.01$	[35]
Yearly maintenance cost of the BG		$0.1$	[35]
Wind lifespan	Year	$30$	[39]
DG and BG lifespan		$10$	[39]
Diesel cost	$\mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{L}$	Figure 8
Discount rate	$\%$	$8.5$	[40]

Figure 8. Monthly diesel costs in Jordan for the year 2022 [41].

Figure 8. Monthly diesel costs in Jordan for the year 2022 [41].

2.5. Optimization Problem

In order to obtain the best solution, a nonlinear solving algorithm is used in this work. This algorithm considers several variables that control the hybrid system, where the relationship between the capacities of the system’s components is nonlinear. To evaluate the optimal capacities of these components, the generalized reduced gradient (GRG) algorithm is utilized, which can handle nonlinear inequality constraints. A bearing of search is made; for example, the dynamic requirements remain accurate even with small adjustments. If some of the dynamic requirements are not fully met due to the nonlinearity of the limitation functions, the Newton–Raphson technique is adopted to return to the requirement boundary [42]. The RESs are modeled and optimized using Microsoft Excel, validated in [36].

The primary objective of the optimization process is to achieve the lowest LCOE as it is a critical metric used to evaluate the cost-effectiveness of a power generation system over its lifetime. In this optimization, the key variables under consideration are the capacities of the DG and the wind turbine, which play a pivotal role in shaping the system’s performance and economic viability. The specific optimization constraints vary across the three cases:

• In SC#1, no constraints are imposed;
• In SC#2, a stringent constraint is introduced, targeting a 95% autonomy rate;
• SC#3 represents the most self-reliant and off-grid configuration, characterized by a 100% autonomy constraint. In this scenario, the system is required to be fully self-sufficient, with no reliance on the grid. This necessitates careful capacity planning for both the DG and the wind turbine to ensure an uninterrupted energy supply.

In all scenarios, the optimization strives to strike the optimal balance between DG and wind turbine capacities, considering the unique constraints and requirements of each scenario to minimize the LCOE effectively. The estimation of the autonomy of the system takes into account the hourly energy production from wind, biogas, and diesel generator as well as the hourly demand of the hospital. Additionally, for calculating the LCOE, the model accounts for the capital cost of the system components, operation and maintenance costs, diesel cost, and grid electricity bill.

3. Results and Discussion

3.1. Sizing the Wind/BG/DG System for the Refugee Camp Hospital

When designing an energy system to meet the demands of a hospital, two critical factors come to the forefront: reliability and cost-effectiveness. The system must be reliable, ensuring that it can consistently meet the energy demands of the hospital. Simultaneously, it must maintain competitive energy costs to manage the operational expenses efficiently. This becomes particularly paramount when the system relies on renewable energy sources, such as wind energy, which are known for their significant variability and unpredictability.

The sizing of the hybrid system is mainly driven by the LCOE minimization goal with autonomy constraints depending on the analyzed scenario. In the currently developed model, the following points are considered:

• The match between the supply (hourly energy production from wind, biogas, and diesel generator) and demand.
• The capital cost of the system components, operation and maintenance costs, diesel cost, and grid electricity bill.
• The lifespan of the system.
• The annual discount rates.

So, in SC#1 the only critical factor used to size the system is the LCOE, whereas in SC#2 and SC#3 both LCOE and the autonomy of the system are critical for the sizing.

To mitigate the challenges associated with the variability and unpredictability of wind energy, our approach involves harnessing abundant resources and implementing a robust backup system. As elucidated in the methodology section, the Zaatari refugee camp boasts a wastewater treatment plant that currently, and regrettably, releases biogas into the atmosphere without utilization. This untapped resource not only represents a missed opportunity but also contributes to additional environmental concerns. By incorporating this readily available biogas resource into our energy system, we not only enhance its reliability but also reduce its environmental impact, making our solution even more sustainable.

The biogas generated offers a continuous and reliable power source, capable of supplying approximately $59 \mathrm{k}\mathrm{W}$ of power. Given the frequent power outages experienced in the camp, the necessity of a backup system is evident. Diesel generators are a common backup option, but they come with associated costs, prompting us to seek alternative power sources. The camp’s geographical location presents a promising wind energy potential, making it a viable candidate to meet the hospital’s energy needs.

In our analysis, we begin by examining the impact of the diesel generator (DG) capacity and the wind system capacity (utilizing turbine T2) on two critical aspects: the autonomy of the proposed energy system (comprising wind, biogas, diesel generator, and the grid) and the LCOE, as illustrated in Figure 9. Figure 9a reveals that a system equipped with a single turbine ($0.6 \mathrm{M}\mathrm{W}$ capacity) and a $59.1 \mathrm{k}\mathrm{W}$ biogas generator (BG) can fulfill nearly $72\%$ of the demand, achieving the lowest LCOE at approximately $0.07 \mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{k}\mathrm{W}\mathrm{h}$, as demonstrated in Figure 9b.

Introducing a $150 \mathrm{k}\mathrm{W}$ diesel generator significantly enhances the system’s autonomy, achieving nearly $97\%$ coverage with only $3\%$ reliance on the grid. However, this addition nearly doubles the LCOE. Furthermore, incorporating two turbines dramatically extends the system autonomy without the need for a diesel generator. Interestingly, further increases in the number of turbines do not substantially improve the autonomy but, instead, result in a significant rise in the LCOE. The primary reason behind this phenomenon is the mismatch between demand and the wind turbine’s supply. Consequently, sizing the hospital’s energy system necessitates careful consideration of both technical feasibility and economic viability.

Table 4. Optimal wind/biogas/diesel generator system configurations and corresponding techno-economic parameters in each scenario.

Parameter	$\mathit{S}\mathit{C}\#1$	$\mathit{S}\mathit{C}\#2$	$\mathit{S}\mathit{C}\#3$
Turbine model	T2	T2	T2
Wind capacity ($\mathrm{k}\mathrm{W}$)	$600$	$600$	$600$
Diesel generator capacity ($\mathrm{k}\mathrm{W}$)	$0.00$	$104.81$	$331.84$
Biogas generator capacity ($\mathrm{k}\mathrm{W}$)	$59.10$	$59.10$	$59.10$
LCOE ($\mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{k}\mathrm{W}\mathrm{h}$)	$0.0734$	$0.1563$	$0.1756$
RES fraction ($\mathrm{\%}$)	$72.42$	$72.42$	$72.42$
Autonomy ($\mathrm{\%}$)	$72.42$	$95.00$	$100.00$

outlines the optimal wind, biogas, and diesel generator system configurations across three scenarios: the optimal system with the lowest LCOE in $SC\#1$, the optimal system with the lowest LCOE and $95\%$ autonomy in $SC\#2$, and, finally, the optimal system with the lowest LCOE and $100\%$ autonomy in $SC\#3$.

The optimization process identified turbine T2 as the most suitable choice for the specific location under study. This selection was based on technical specifications, including cut-in and rated speeds, that align more favorably with the local wind resources. Consequently, this choice maximizes the utilization of wind energy while simultaneously reducing the LCOE, as illustrated in Figure 10.

Table 4 provides a comprehensive overview of the LCOE across the three considered scenarios. In SC#1, where the grid tariff is $0.16 \mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{k}\mathrm{W}\mathrm{h}$, the LCOE is at its lowest, presenting an appealing option. However, SC#1 also involves a significant reliance on the grid, covering approximately $38\%$ of the demand. As depicted in Figure 11a,b, this dependency varies among months. This reliance raises concerns due to the camp’s frequent power outages, emphasizing the importance of pursuing a higher autonomy system by introducing an additional backup source. In this case, the diesel generator serves as the backup in both SC#2 and SC#3. This augmentation increases the autonomy but concurrently elevates the electricity costs due to the added capital cost of the DG and the expense of diesel fuel.

In SC#2, the DG covers approximately $23\%$ of the demand, with $5\%$ covered by the grid, where this dependency varies, as demonstrated in Figure 11c,d. Meanwhile, in SC#3, the DG covers $28\%$ of the demand, which also varies depending on the available wind resources, as depicted in Figure 11e,f. Notably, the system generates a substantial surplus of energy, primarily from the wind turbine, as indicated in Figure 12. This surplus arises from the mismatch between supply and demand, and is considered wasted energy within the current power grid regulations.

Presently, any surplus energy exported to the grid accumulates as a balance for the user, offsetting energy consumption from the grid throughout the year. However, Figure 12 underscores the significant surplus credit, which often exceeds grid energy consumption by a considerable margin. Consequently, this surplus is regarded as waste, as it is essentially given to the utility grid for free. Importantly, this excess energy is consistent across the three scenarios. However, since only $5\%$ of demand is covered by the grid in $SC\#2$, and none in $SC\#3$, these scenarios accumulate the largest energy credits, signifying the highest energy waste. This surplus energy credit offers potential for various utilization strategies. For instance, transitioning to an electrical heating system could enhance the techno-economic feasibility of the entire system.

3.2. Sensitivity Analysis

The continuous fluctuation in fossil fuel prices exerts a notable impact on the economic dynamics of the proposed system, particularly in cases where a diesel generator is employed as a backup. It is, therefore, essential to visualize the system’s sensitivity to variations in diesel costs, as depicted in Figure 13. Evidently, an escalation in diesel costs results in a corresponding increase in the LCOE. Notably, SC#2 exhibits a lower sensitivity due to its reduced dependency on the diesel generator in comparison to SC#3.

Furthermore, given the inherent unpredictability of wind energy, a thorough examination of the impact of wind power fluctuations on system autonomy and LCOE is imperative, as showcased in Figure 14. It is evident that SC#1 is the most sensitive to changes in wind energy, as it relies solely on wind power to meet demand and lacks a DG backup system. Consequently, any decline in wind energy availability results in a heightened reliance on grid energy and, consequently, increased electricity costs. This is further exacerbated by a reduction in energy credits, primarily driven by decreased wind energy generation. In contrast, SC#3 exhibits the lowest sensitivity, thanks to its backup DG system that can cater to peak demand without grid reliance. In this scenario, the LCOE experiences only a marginal increase, typically below a $60\%$ drop in wind energy, primarily due to heightened diesel expenses.

Lastly, we delve into the essential diesel generator capacity required to sustain $95\%$ autonomy (as per SC#2) under varying levels of wind energy production drops, as illustrated in Figure 15. The analysis reveals that, to accommodate a $40\%$ decline in wind energy production while upholding $95\%$ autonomy, the DG should be oversized by approximately $24\%$. This oversizing results in a subsequent $28\%$ increase in the LCOE.

In a scenario where wind energy production experiences an $80\%$ reduction, the DG’s oversizing requirement escalates to approximately $68\%$, leading to a corresponding $99\%$ rise in the LCOE. This underscores the delicate balance between DG capacity and maintaining autonomy levels, with higher oversizing demands significantly impacting the overall economic feasibility of the system.

To provide a better understanding of the reliability of the current system, a brief comparison between it and other systems of similar size is presented in

Table 5. A comparison between the current study results and other studies in the literature.

Study	Location	Configuration	LCOE (USD/kWh)	RES Fraction (%)	Autonomy (%)
Current study	Jordan	Wind–Biogas/Diesel	0.175	72	100
[35]	India	PV–Wind–Diesel	0.210	64	100
[8]	Turkey	PV–Wind–Diesel	0.383	55	100
[30]	Iran	PV–Wind–Battery	0.521	100	100
[7]	Nigeria	PV–Wind–Diesel	0.117	84	100

. It is noticeable from the data that the current system exhibits a competitive LCOE value compared to other systems, particularly those in Turkey and Iran. The cost-effectiveness of biogas production in comparison to the relatively high price of diesel fuel accounts for this difference in LCOE. However, in Nigeria, for example, diesel prices are considered low, and, consequently, this results in a lower LCOE value.

3.3. Environmental Assessment

Growing concerns about global warming stemming from the emissions of greenhouse gases from fossil fuel-based energy sources have spurred significant research into cleaner and more sustainable energy alternatives, such as wind energy and biofuels, for various applications. The environmental advantages of incorporating hybrid renewable energy (RE) systems in poultry farming are particularly noteworthy with regard to greenhouse gas emissions. The quantity of pollutant emissions was computed in the current study for each scenario. The amounts of CO₂ and NOx were considered, as these two emissions are among the most produced emissions in energy power plants [43]. Figure 16 provides an overview of the annual amount of NO_x and CO₂ emissions in each scenario. The amount of emissions was calculated based on the number of kilograms per each kWh of produced energy. As grid energy comes from natural gas power plants, the amount of CO₂ and NO_x emissions is 0.372 kg/kWh and 7.1 g/kWh, respectively [44]. For diesel electricity generation, the CO₂ and NO_x emissions are 14.4 g/kWh and 1.27 kg/kWh, respectively [45]. For biogas generators, the CO₂ and NO_x emissions are 6.2 g/kWh and 0.289 kg/kWh, respectively [46]. As depicted in Figure 16, SC#3 includes the highest amount of NO_x and CO₂ emissions, which, of course, refers to the high share of the diesel generators in the power production process. Diesel generators primarily rely on demand loads, resulting in a substantial fuel consumption and its associated adverse environmental impacts. However, SC#2 has fewer emissions than SC#3 due to the contribution of the grid power. Replacing the diesel-only system with a grid-connected system (SC#1) reduces greenhouse gas emissions by 20% compared to SC#3, but simultaneously creates a low autonomy issue, as discussed earlier. Overall, having wind energy in all scenarios will contribute to a reduction in emissions by an average value of 30–40%. This reduction in emissions, when it is compared to some other systems in [12,15,47], was found to be acceptable, especially for SC#3 which relies on biogas and diesel fuels.

3.4. Future Work and Recommendations

Future work will focus on wind–biogas hybrid power plants in remote areas, addressing critical aspects such as the integration of reliable energy storage systems and enhancing their efficiency and reliability. This is essential to overcome the intermittent nature of wind and biogas energy sources, thereby ensuring a consistent power supply to remote communities, even in the absence of primary sources. Additionally, research and development endeavors should be targeted at reducing the initial installation costs of these hybrid systems, thereby increasing accessibility, particularly in economically disadvantaged regions. The challenge of localized biogas production in remote areas, arising from limited organic waste availability, underscores the need to develop innovative approaches for biogas feedstock generation and transport. Furthermore, comprehensive community engagement and educational programs are necessary to ensure that the local population comprehends the benefits of such systems and actively participates in promoting sustainability. Ultimately, while wind–biogas hybrid power plants offer immense potential for remote areas, it is important to recognize that their success hinges on technological advancements, cost reduction strategies, and robust community involvement, all of which are among the limitations inherent to these systems.

4. Conclusions

This study offers a holistic assessment of fully off-grid wind–biogas hybrid power systems, with a specific focus on their application in remote healthcare facilities. The key findings emphasize the system’s efficacy in meeting the critical energy demands of mobile hospitals, ensuring uninterrupted healthcare services. The integration of wind turbines, biogas generators, and, where needed, diesel generators showcases high efficiency and sustainability, making these systems a robust solution for remote energy challenges. The techno-economic analysis reveals their cost-effectiveness, particularly when considering grid availability and autonomy levels. The sensitivity analyses underscore the significance of diesel costs and wind power variability, highlighting the importance of strategic planning and real-time monitoring.

Three distinct primary scenarios have undergone examination regarding the levelized cost of energy (LCOE) value. In Scenario 1 (SC#1), which is regarded as the most cost-effective option, sole reliance on grid power is assumed. In Scenario 2 (SC#2), the grid’s contribution is reduced to just 5%, with the remaining power requirements being met by the hybrid system. In Scenario 3 (SC#3), the entire load is met by the hybrid power system. Considering the specific context of this case study, which pertains to a mobile hospital, it was determined that SC#3 represents the most sustainable scenario. This is because it ensures a continuous power supply without the risk of disconnection from the grid. In contrast, SC#2 and SC#1 exhibit varying degrees of risk when it comes to potential grid disconnection, with SC#1 having the highest risk and SC#2 having a lower risk.

Environmental analyses have been carried out to assess the hybrid system’s environmental impact, particularly in the reduction of greenhouse gas emissions. By studying the emission outcomes for the three applied scenarios. It was found that SC#2 and SC#3 have the highest NO_x and CO₂ emission levels, where the diesel generators share is the highest. However, SC#1 has fewer emissions due to the absence of the diesel generators.

Future research in this domain should prioritize the collection of real-time operational data from deployed wind–biogas hybrid systems to validate their performance. Exploring advanced energy storage solutions and smart grid integration can further enhance system resilience and optimize energy distribution. Additionally, assessing the environmental impact, engaging local communities, and shaping supportive policy frameworks are essential steps toward realizing the potential of these systems in improving healthcare services, sustainability, and well-being in remote areas.