Multi-Objective Optimization of Off-Grid Hybrid Renewable Energy Systems for Sustainable Agricultural Development in Sub-Saharan Africa

Abstract

This study presents a novel multi-objective optimization (MOO) model for the design of an off-grid hybrid renewable energy system (HRES) to support sustainable agriculture and rural development in Sub-Saharan Africa (SSA). Based upon a case study selected in Linia (Chad), three system architectures are compared under different levels of the reliability requirements (LPSP = 1%, 5%, and 10%). A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to optimize the Levelized Cost of Energy (LCOE), CO₂ emissions mitigation, and social impact, referring to the Human Development Index (HDI) enhancement and the job creation (JC) opportunity, using the MATLAB R2024b environment. The calculation results show that among the three configuration schemes, the PV–Wind–Battery configuration obtains the optimal techno–economic–environmental coordination, with the lowest LCOE (0.0948 $/kWh) and the largest CO₂ emission reduction (9.58 × 10⁸ kg), and the Wind–Battery system gets the most social benefit. The method developed provides users with a decision-support method for renewable energy systems (RES) integration into rural agricultural settings, taking into consideration financial cost, environmental sustainability, and community development. This information is important for policymakers and practitioners advocating for decentralized, socially inclusive clean energy access initiatives in underserved regions.

1. Introduction

The urgency for reliable and sustainable energy solutions in rural SSA, particularly in Chad, cannot be overstated, as the absence of consistent grid access significantly impedes agricultural productivity and overall rural development [1]. Given the abundance of solar and wind resources that the region is endowed, harnessing these renewable energy sources (RES) through hybrid systems presents a promising pathway to decentralized energy generation [2,3]. Integrated wind–solar systems like these can offer reliable, renewable power at an affordable price to off-grid farms in places like Linia, Chad, while providing the energy with more bang for its buck to other customers, such as a village of around 600 people [4]. The difficulties in Chad are not limited only to access to technology and infrastructure; there are also substantial technical, social, and economic obstacles that delay energy renovation and transition [5]. The challenges created call for a full-spectrum approach addressing regional conditions and capitalizing RES’s potential to promote sustainable development [5]. The electrification levels in Chad are some of the lowest in the world with only 11.3% of people having access to electricity, and 1.3% in rural areas [4]. This shortage of low cost, reliable electricity represents a significant barrier to development, and serves to entrench poverty, limit educational advancement, and limit economic prospects for rural communities in Chad [4]. Additionally, the high price of HRES has slowed down its implementation in many developing countries [6]. Recent studies, such as [7,8,9], have explored the techno-economic performance of hybrid renewable energy systems for rural electrification in Sub-Saharan Africa. These works provide useful comparisons to the present study, which extends the analysis by integrating social indicators (HDI improvement and job creation) into the optimization framework for agricultural applications in Chad.

Though research has been conducted on individual energy sources like solar and wind, there is little or no research work done on the optimization of hybrid systems for agricultural purposes under similar conditions as those of Chad, and even much less off-grid, as witnessed in Linia [4,10,11,12,13,14,15]. This study endeavors to bridge this gap by meticulously modeling and optimizing three distinct HS configurations: PV–Battery, Wind–Battery, and PV–Wind–Battery, with a focus on evaluating their techno-economic, environmental, and social impacts, including JC and improvements in the HDI for the adjacent village of 600 residents [4]. Prior research has demonstrated the feasibility and effectiveness of HRES in similar contexts, highlighting their ability to meet power demands while minimizing environmental impact and costs [16]. Furthermore, integrating energy storage systems (ESS) becomes essential due to the intermittent nature of RES [10]. Specifically, Chad faces significant obstacles in achieving universal electricity access, as evidenced by its low electrification rate, which is approximately 10% as of 2018 [11]. This situation underscores the urgent need for innovative and sustainable energy solutions to address the energy poverty prevalent in the nation. The deployment of RES, like solar photovoltaic technology, offers a viable pathway to enhance electricity access, particularly in remote and underserved areas. The deployment of RES as distributed energy resources is especially effective in delivering essential services such as lighting, healthcare, clean water, education, communication, and irrigation in remote areas, where reliable energy access is fundamental to both household and commercial income-generating activities [17]. An alternative plan, such as diesel generators or storage medium such as batteries, is required when power is not generated by PV and wind systems [18,19,20,21].

The main goal is to optimize the three distinct hybrid systems configurations described above specifically for the agricultural farm in Linia and the neighboring village of 600 people, taking into account local resources, energy demands, and socio-economic factors. This approach provides a holistic understanding of the trade-offs and synergies associated with implementing HRES for sustainable agriculture and rural development in Chad. This goal involves the determination of optimal system layout as well as operating conditions for sustainable energy access being security, access, and sustainability. The utilization of multi-objective optimization techniques, such as PSO, will be employed to navigate the complex decision space and determine Pareto-optimal solutions that effectively reconcile conflicting objectives, such as minimizing the LCOE, maximizing CO₂ emissions avoidance, and promoting socio-economic benefits. In this study, geographical and techno-economic data is used in MATLAB to simulate the MOPSO. This research, therefore, seeks to develop a user-friendly and versatile energy planning tool, which can be used for small micro-energy systems, such as solar PV, to enable stakeholders in rural settings, such as designers, businesses, investors, local government, decision-makers in general, as well as government of Chad, to optimize and integrate such a system in a desert area of Chad in line with sustainable energy provision and rural development in Chad. The proposed research aims to address the critical need for sustainable energy solutions in rural Chad by optimizing hybrid RES for agricultural applications. Such energy accessibility is crucial to advancing economic and social progress and addressing energy poverty.

This study specifically addresses the agricultural sector in Sub-Saharan Africa by optimizing energy systems that directly power irrigation, farm lighting, and agro-processing. Agriculture is the backbone of rural economies in Chad, employing more than 80% of the population, and energy shortages directly constrain productivity. Moreover, the Sub-Saharan African context introduces unique constraints, such as low electrification rates, limited capital, and high dependence on decentralized renewable systems, which justify tailoring the methodology to these regional realities.

2. Materials and Method

2.1. Methodology Flowchart

The proposed methodology for optimizing the HRES is depicted in Figure 1. The flowchart outlines a systematic procedure beginning with the characterization of the study area and load profile, followed by system configuration and scenario development. Subsequently, mathematical modeling of the system components is performed, which serves as the basis for the optimization using the MOPSO algorithm. The evaluation framework incorporates multiple criteria, encompassing techno-economic, environmental, and social aspects. This comprehensive assessment facilitates a comparative analysis of the 3 HRES configurations. The process concludes with the derivation of final results and recommendations to guide decision-making. Key performance indicators calculated during the optimization include LCOE, avoided CO₂ emissions, JC potential, and improvements in the HDI. Such a combined analysis is beneficial in providing a comprehensive and fair assessment of energy system performance from multiple perspectives.

2.2. Study Area and Load Profile

The testbed site is situated off-grid in the region of Linia, Chad, characterized by a large energy requirement due to the intensive farming activity developed on a 17 ha farm. Being a farm operating independently from the national grid, it needs to power vital functions including irrigation and lighting using a dignified source that can stand on its own. This isolation, combined with excellent solar and wind resources, makes the site an excellent location for a HRES installation. The farm grows 1023 fruit trees, including lemon, mandarin, guava, and moringa. The principal electrical loads comprise the following:

• Four (4) irrigation pumps, each rated at 5000 W;
• Thirty-three (33) outdoor LED floodlights, rated at 50 W each;
• An energy-efficient ecological house serving domestic load requirements.

These loads collectively define the farm’s dynamic electrical load profile, which was meticulously monitored and is presented in Figure 2. This load profile is such an important data source, which helps designers in sizing and formulating the HRES based on the farm load distribution characteristic of the magnitude of the power consumed at different intervals in time. Figure 2 shows the 24 h electricity usage of the farm with a significant peak that occurs at around 15:00 during pump operation, rendering irrigation to be most intensive. Nighttime electricity usage drops considerably and the remaining is quite low due to the LED lights and household consumption. Monitoring such load variations is the key to the efficient performance of the system and to guarantee energy availability in the daily cycles.

2.3. Meteorological Data

The weather data at the study site was obtained from the Photovoltaic Geographical Information System (PVGIS), which is a widely accepted source of high-resolution solar and wind resource information [22]. The data set contains solar irradiation, wind speed, and ambient temperature, which are important parameters to affect the efficiency of the photovoltaic (PV) module, wind turbine, and battery thermal management.

Solar irradiation assessment verifies the presence of a good solar resource for the site, and hence for the successful PV power production. Temperature measurements were added to assess the temperature impact on PV efficiency and the battery’s performance. Wind speed readings indicate moderate wind state at specific times, making wind power a good counterpart to solar as a means of generation in low-sunlight months.

With these meteorological parameters, together with the farm consumption profile, it is possible to design an optimal HRES composed of PV panels, wind turbine, and a battery bank, providing the required energy with security and sustainability.

Load and resource assessments indicate that the Linia farm presents significant potential for an off-grid hybrid system. The site benefits from high solar irradiance and moderate wind speeds (Figure 3 and Figure 4), favorable for RE generation. This comprehensive evaluation underpins the development of a tailored, reliable energy solution that addresses the operational requirements of the farm and nearby communities.

Table 1. All techno-economic parameters utilized in modeling the PV system, wind turbine, battery, and inverter [4,23,24,25,26].

Components	Parameters	Values	Units
PV	Rated Power	0.330	kW
	Efficiency	20.51	%
	Capital cost	245	$/kW
	O&M cost	0.01 × Capital	$/year
	lifetime	20	Years
Wind Turbine	Rated power	2	kW
	Cut-in speed	2.5	m s−1
	Cut-out speed	25	m s−1
	Rated wind speed	5.5	m s−1
	Capital cost	3000	$/kW
	O&M cost	0.01 × Capital	$/year
	Efficiency	96	%
	lifetime	20	Years
Battery	Rate capacity	1.35	kWh
	Replacement cost	130	$/year
	Efficiency	85	%
	Capital cost	130	$
	O&M cost	0.01 × Capital	$/year
	lifetime	5	years
Inverter	Rate capacity	3	kW
	Efficiency	95	%
	Capital cost	1500	$
	O&M	0.01 × Capital	$/year
	Lifetime	10	years
Project information	Project lifetime	20	years
	Inflation rate	5	%

presents all the techno-economic parameters employed in modeling the PV system, wind turbine, battery, and inverter.

3. System Configurations

3.1. System Description

The investigated HRES is intelligent to provide electricity and receptivity to an isolated farm located in Linia/Chad using PV panels, a wind turbine (WT), and a battery energy storage system (BESS). All components are joined through a common DC bus, and power conversion from DC to AC for meeting the farm electrical load is provided through power converters. The main goal is to maximize the penetration of domestic RES with a continuous, cheap energy supply. Three (3) HRES configurations were analyzed to accommodate the farm’s seasonal and load fluctuations, and were customized to incorporate existing solar and wind resources, and to satisfy the desired energy demands. The objective of this research is to find an economically optimal configuration, by minimizing the LCOE, maximizing the RES fraction, and minimizing LPSP to improve the reliability and the economics of the system. To address that, we developed and examined the following three (3) HRESs configurations, which all used different combinations of RES and Battery storage system, to meet the farm’s energy demand.

• Scenario 1: Solar Power with Battery Storage (Figure 5)

This setup uses solar PV panels to receive an abundance of solar energy, plus a battery pack to deliver energy at night or when it is cloudy. It is ideal for the farm irrigation season working 24 h a day for irrigation pumps, security lights, and domestic use in the eco house.

• Scenario 2: Wind Power with Battery Storage (Figure 6)

This system utilizes WT to convert wind energy, supported by batteries to balance supply during low-wind intervals. It complements solar generation, particularly during cloudy periods or the off-season with reduced solar irradiance, thereby enhancing overall reliability.

• Scenario 3: Combined Solar and Wind Power with Battery Storage (Figure 7)

Integrating both PV panels and WT, this HRES leverages complementary RES, with BESS ensuring consistent energy availability regardless of weather or time. This configuration offers the highest resilience, effectively meeting the farm’s year-round energy requirements for irrigation, lighting, and domestic consumption.

Compared to existing rural energy planning tools, such as HOMER Pro and similar optimization frameworks, our model introduces key innovations. It explicitly integrates social indicators (HDI improvement and job creation) alongside techno-economic and environmental metrics. This multi-dimensional approach provides a more comprehensive decision-support tool for rural Chad, where energy interventions are expected to deliver not only cost savings but also tangible social development outcomes.

3.2. Control and Operational Strategy (OS)

The control and OS of the HRES is designed to efficiently manage power flows from PV panels, WT, and BESS to satisfy the farm’s electrical demand. The strategy prioritizes maximizing RES utilization while ensuring a reliable and cost-effective supply for the off-grid farm in Chad. Operational management is structured around four distinct scenarios that address variations in generation, storage state, and load demand. These scenarios are evaluated against techno-economic, environmental, and social criteria to ensure system sustainability and feasibility.

Case 1: Excess Generation—Battery Charging

When combined PV and WT generation exceeds the load demand, the surplus energy is stored in the battery after directly supplying the farm’s load.

Case 2: Excess Generation—Fully Charged Battery

If generation exceeds demand while the battery is fully charged, the load is supplied by PV and wind, and surplus power is curtailed to prevent system overload. This curtailed energy is accounted for in the HDI calculation, representing unrealized social and economic benefits, such as education and healthcare.

Case 3: Insufficient Generation—Battery Discharge

When PV and WT output fall short of demand, the battery discharges to bridge the deficit, maintaining uninterrupted power supply.

Case 4: Energy Deficit—Load Loss

If the combined output of PV, WT, and battery storage is insufficient to meet demand, load shedding occurs, resulting in temporary power outages for some operations. This metric is incorporated in system design to assess reliability and guide improvements in storage capacity or backup provision.

These operational scenarios are implemented through defined control sequences, detailed in the flowcharts presented in Figure 8, Figure 9 and Figure 10. The flowcharts illustrate the decision-making pathways that govern energy generation, storage management, and load supply, thereby optimizing system performance and reliability.

3.3. Operational Strategy (OS)

The OS is implemented through a comprehensive flowchart that delineates the sequential processes involved in regulating power flow and optimizing system performance. This flowchart corresponds directly to the four operational cases previously described, providing a structured framework for real-time decision-making to effectively meet the farm’s electricity demand.

The following defines the key abbreviations used throughout the flowchart:

• ${\mathrm{P}}_{\mathrm{W}\mathrm{T}}$ = Wind power.
• ${\mathrm{P}}_{\mathrm{s}}\left(\mathrm{t}\right)$ = Solar power.
• ${\mathrm{P}}_{\mathrm{L}}\left(\mathrm{t}\right)$ = Load demand at time t.
• ${\mathrm{ƞ}}_{\mathrm{I}\mathrm{n}\mathrm{v}}$ = Inverter efficiency.
• ${\mathrm{ƞ}}_{\_\mathrm{b}\mathrm{a}\mathrm{t}}$ = Battery efficiency.
• ${\mathrm{P}}_{\mathrm{c}\mathrm{h}}\left(\mathrm{t}\right)$ = Supplied power to the BESS.
• ${\mathrm{E}}_{\mathrm{c}\mathrm{h}}\left(\mathrm{t}\right)$ = Charged energy to the BESS.
• ${\mathrm{E}}_{\mathrm{d}\mathrm{u}\mathrm{m}\mathrm{p}}\left(\mathrm{t}\right)$ = Energy that can be used for deferrable load.
• ${\mathrm{P}}_{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{h}}\left(\mathrm{t}\right)$ = Power discharged from BESS.
• ${\mathrm{E}}_{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{h}}\left(\mathrm{t}\right)$ = Discharge energy of the BESS.
• ${\mathrm{E}}_{{\mathrm{b}}_{\mathrm{m}\mathrm{a}\mathrm{x}}}$ = Maximum BESS energy.
• ${\mathrm{E}}_{\mathrm{b}}$(t) = BESS battery at time t.

The next sections break down the main functions in the flowchart, explaining how the EMS works in different system conditions.

Main Function (Figure 8)

The main function outlines the overarching control strategy (CS) of the HRES as follows:

• Input Data Acquisition: The system receives solar irradiance, wind speed, ambient temperature, and load demand as inputs.
• Component Performance Modeling: The power output of PV panels, WT, BESS, and inverter efficiency are simulated.
• Energy Balance Assessment: The combined energy from PV and WT sources is compared against the load demand.
  • If RES exceeds or meets the load, the system transitions to the Excess Mode Function (Figure 9) to manage surplus energy.
  • If RES generation is insufficient, the system initiates the Discharge Function (Figure 10), whereby the battery compensates for the deficit.
• Cost Evaluation: Upon completion of the simulation horizon (typically 8760 h or one year), the LCOE is computed to evaluate economic viability.

Excess Mode Function (Figure 9)

The excess energy management function handles surplus RES generation:

• The system calculates the available battery capacity for additional energy storage.
• If the battery is fully charged, excess power is curtailed (dumped), with potential benefits evaluated for community services, such as improvements in education and healthcare (HDI improvement).
• If battery capacity is available, excess energy is stored for future use.

Discharge Function (Figure 10)

This function regulates the battery discharge process to address energy shortfalls as follows:

• The available state of charge (SoC) is verified against the energy deficit.
• If the battery capacity suffices, discharge proceeds to fulfill the load demand.
• Otherwise, load shedding occurs, and the system quantifies the consequent energy shortfall impact.

This HRES integrates PV, WT, and BESS to provide a reliable and cost-effective power supply for off-grid agricultural applications. The CS optimizes power generation, storage, and load management to ensure uninterrupted electricity availability, minimize costs, reduce environmental impacts, and foster social development. This model offers a practical solution for rural electrification in remote regions, exemplified by its applicability to off-grid farms in Chad. The flowchart serves as a robust guide for implementing an efficient and sustainable OS.

3.4. Mathematical Modeling of System Components

• PV Generator Modeling

The output PV power generator is calculated as follows (Equation (1)) [23,27]:

$$P_{PV}\left(t\right)={\sum }_{t=1}^{8760}\left(P_{{PV}_r}{N}_{PV}{PV}_{df}\right)\left({\displaystyle \frac{G\left(t\right)}{G_{base}}}\right)\left(1+K_T\left(\left(T_{am}\left(t\right)+G\left(t\right)\times \left({\displaystyle \frac{NOCT-20}{800}}\right)\times 1000\right)-T_{base}\right)\right)$$

(1)

where P_PV is the hourly PV power output (kW), P_PVr represents the rated output per PV module (kW), N_PV denotes the total number of PV panels, and PV_df is the derating factor accounting for real-world performance losses. G(t) is the hourly solar irradiance (kW/m²), and G_base = 1 kW/m² corresponds to the standard irradiance at STC (25 °C). The temperature coefficient of power, K_T, is −3.7 × 10⁻³ °C⁻¹. Ambient temperature at h t is T_am(t) (°C), T_base = 25 °C is the reference temperature, and NOCT is the nominal operating cell temperature (°C).

• Wind Power Model

The power output of the wind turbine system is characterized as follows: (Equation (2)) [28,29]:

$$P_W(t)=\left\{\begin{array}{c} 0 V<V_{ci}\\ {\displaystyle \frac{N_{wt}\times P_{Wr}\times (V^3- {V_{ci}}^3)}{\left({V_r}^3-{V_{ci}}^3\right)}} { V}_{ci}<V<V_r\\ N_{wt}\times P_{Wr} V_r\le V\le V_{co} \\ 0 V_{co}\le V\end{array}\right.$$

(2)

where P_W denotes the total power generated by the wind turbines, N_wt is the number of turbines, P_Wr is the rated power per turbine, V is the wind speed at hub height, V_ci is the cut-in wind speed, V_r is the rated wind speed, and V_co is the cut-out wind speed.

The wind velocity at the wind turbine hub height can be calculated using a typical equation expressed as V_Z1 = V_Z0${\left({\displaystyle \frac{G_f\left(t\right)}{G_{STC}}}\right)}^{\propto }$, where $\propto =0.2$ is the wind shear coefficient.

• Battery Storage Model

The total hourly power output from the renewable sources is given by the following:

$$P_T\left(t\right)=P_{PV}\left(t\right)+P_{WT}\left(t\right)$$

(3)

where $P_{PV}\left(t\right)$ and $P_{WT}\left(t\right)$ represent the hourly power outputs of the photovoltaic panels and WT, respectively.

When the total generated power exceeds the load demand, i.e., ${\mathrm{P}}_{\mathrm{T}}\left(\mathrm{t}\right)>{\mathrm{P}}_{\mathrm{l}\mathrm{o}\mathrm{a}\mathrm{d}}\left(\mathrm{t}\right)$, the battery charges and its state of charge (SOC) is updated as follows [30,31]:

$${SOC}_B\left(t\right)= {SOC}_B\left(t-1\right)\left(1-\sigma \right)+(P_T\left(t\right)-P_{inv}\left(t\right)){\eta }_{BC}$$

(4)

where $\mathsf{\sigma }$ is the battery self-discharge rate, ${\eta }_{BC}$ is the battery charging efficiency, and $P_{inv}\left(t\right)$ denotes the inverter power at time t.

Conversely, when ${\mathrm{P}}_{\mathrm{T}}\left(\mathrm{t}\right)<{\mathrm{P}}_{\mathrm{l}\mathrm{o}\mathrm{a}\mathrm{d}}\left(\mathrm{t}\right)$, the battery discharges, and the SOC evolves as follows [30,31]:

$${SOC}_B\left(t\right)= {SOC}_B\left(t-1\right)\left(1-\sigma \right)-(P_{inv}\left(t\right)-P_T\left(t\right)){\eta }_{BD}$$

(5)

where ${\eta }_{BD}$ is the battery discharging efficiency.

The SOC is constrained within the battery’s operational limits as follows [23]:

$${SOC}_{Bmin}\le {SOC}_B\left(t\right)\le {SOC}_{Bmax}$$

(6)

The minimum SOC is defined by the depth of discharge (DoD) and nominal capacity as follows [23]:

$${SOC}_{Bmin}=DOD\times {SOC}_{nominal}$$

(7)

The maximum SOC corresponds to the nominal battery capacity as follows [23]:

$${SOC}_{Bmax}={SOC}_{nominal}$$

(8)

• Inverter Modeling

Inverters are essential for converting DC power from RES and BESS into AC power to meet the load demand. The instantaneous inverter power requirement, P_inv(t), is calculated by accounting for inverter efficiency as follows [32]:

$$P_{inv}\left(t\right)={\displaystyle \frac{P_L\left(t\right)}{{\eta }_{inv}}}$$

(9)

where P_L(t) is the load demand at time t (kW), and ${\eta }_{inv}$ represents the inverter efficiency.

The rated inverter capacity, $P_{inv,rated}$, is determined based on the peak load demand, adjusted for inverter efficiency, as described in [33,34]:

$$P_{inv,rated}={\displaystyle \frac{P_{L,peak}}{{\eta }_{inv}}}$$

(10)

where $P_{L,peak}$ denotes the maximum load demand (kW).

3.5. Optimization Approach

The system optimization was performed using the MOPSO algorithm, which is well-suited for solving complex, nonlinear, and multi-dimensional problems inherent in HRES design. The goal of the optimization was to determine the best configuration that meets both economic and reliability requirements.

The optimization problem was formulated with the following objectives:

-

Minimize the LCOE, ensuring economic viability;

-

Maximize the Renewable Energy Fraction, to enhance the system’s sustainability and reduce dependence on fossil-based generation;

-

Minimize the LPSP, to ensure high system reliability and availability.

The optimization was constrained by the following conditions [23,35,36]:

$$\left\{\begin{array}{c}20\%\le {SOC}_{Bat}\le 100\% \\ \\ N_x^{min}\le N_x\le N_x^{max} where x\in \left\{PV,WT,Bat\right\} \end{array}\right.$$

(11)

where ${\mathrm{S}\mathrm{O}\mathrm{C}}_{\mathrm{B}\mathrm{a}\mathrm{t}}$ denotes the state of charge of the BESS, and Nx represents the number of units of each system component (PV, WT, and BSS), constrained between specified minimum and maximum bounds.

MOPSO was selected due to its capability to efficiently navigate large solution spaces and converge toward global optima, while balancing multiple conflicting objectives. This makes it particularly effective for the techno-economic optimization of HRES.

4. System Evaluation Criteria

The performance of the proposed systems was assessed through technical, economic, environmental, and social indicators, as detailed below.

4.1. Technical Performance

To measure how well the system is capable of satisfying the energy requirements, a metric called LPSP is used, as follows:

• LPSP: Likelihood that the system will be reliable and provide all the requested energy needs of the farm without cuts or shortfall (Equation (12)) [37,38].

$$LPSP={\displaystyle \frac{\sum _{t=1}^{8760}{E}_{deficit}\left(t\right)·∆t}{\sum _{t=1}^{8760}{E}_{load}\left(t\right)·∆t}} , ∆t=1 \mathrm{h}$$

(12)

where E_deficit (t) = energy deficit at time t (kWh) and E_load (t) = total energy demand at time t (kWh).

4.2. Economic Performance

Economic viability is a critical aspect of system evaluation and is assessed through key financial metrics detailed below.

• Annual Cost: The amount spent yearly for the operation and the maintenance of system (Equation (13)) [39,40].

$$C_{ann}=C_{cap}\times CRF+C_{OM}$$

(13)

where $C_{cap}$ = initial capital cost, CRF = capital recovery factor, and $C_{OM}$ = annual operation and maintenance cost

The CRF is defined as follows (Equation (14)) [40,41]:

$$CRF={\displaystyle \frac{i_r·{(1+i_r)}^N}{{(1+i_r)}^N-1}}$$

(14)

• LCOE: A system level metric of system cost-effectiveness, and is derived by dividing the total lifetime cost by the total energy produced (Equation (15)) [34,42,43].

$$LCOE={\displaystyle \frac{C_{ann}}{\sum _{t=1}^{8760}{P}_{load}\left(t\right)}}$$

(15)

4.3. Environmental Performance

To assess the environmental benefits of the proposed systems, we quantify the reduction in greenhouse gas emissions achieved through renewable energy deployment.

• CO₂ Emissions Avoided: The amount of CO₂ emissions reduced by using RES instead of fossil fuels (Equation (16)) [23,44,45,46].

$${\mathrm{C}\mathrm{O}}_2^{\mathrm{a}\mathrm{v}\mathrm{o}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{d}}=E_{ren}\times EF$$

(16)

where $E_{ren}$ = renewable energy supplied (kWh) and EF = emission factor of greenhouse gases (kgCO₂/kWh).

Table 2. Emission Coefficients for SO_x, NO_x, and CO₂ in Power Systems [23,47,48].

Greenhouse Gas	Emission Factor Value	Unit
SOx	0.5	gSOx/kWh
NOx	0.22	gNOx/kWh
CO2	690	gCO2/kWh

summarizes the emission coefficients for SO_x, NO_x, and CO₂ in power systems, outlining the respective emission factor values for each greenhouse gas.

4.4. Social Impact

The social implications of system implementation are evaluated by examining job creation potential and improvements in the local community’s quality of life.

• Job Creation (JC): The jobs directly created by the deployment and operation of the system, from the technical to operational and managerial levels (Equation (17)) [23,49,50].

$${JC}_{total}=\sum _i\left({JC}_{i,manuf}+{JC}_{i,install}+{JC}_{i,OandM}+{JC}_{i,decom}\right)\times P_i$$

(17)

where ${JC}_{total}$ = total job creation (jobs), i = technology type (PV, Wind, Biogas, Battery),

${JC}_{i,manuf}$ = job creation factor for manufacturing per MW, ${JC}_{i,install}$ = job creation factor for installation per MW, ${JC}_{i,OandM}$ = job creation factor for operation and maintenance per MW,

${JC}_{i,decom}$ = job creation factor for decommissioning per MW, and $P_i$ = installed capacity of technology i (in MW).

The specific job creation factors are shown in

Table 3. Specific Job Creation Factors [51,52,53,54,55].

Technology	Manufacturing (Jobs/MW)	Installation (Jobs/MW)	O&M (Jobs/MW)	Decommissioning (Jobs/MW)
Solar PV	5.76	5.76	0.6	0.1
Wind	2.8	0.5	0.2	0.1
Battery	2.5	2.5	0.1	0.05

.

• Human Development Index (HDI): The impact on the local population’s quality of life, considering factors like access to energy, education, and income, as follows [23,49,50]:

$$HDI=0.0978ln\left[\sum _{t=1}^{8760}{P}_{load}\left(t\right)+min\left(\sum _{t=1}^{8760}{E}_{dump},F_{maxload}·\sum _{t=1}^{8760}{P}_{load}\left(t\right)\right)/{\eta }_{per}\right]-0.0319$$

(18)

where Edump (kWh) is the excess energy dumped, Fmax, Pload = 0.70 limits the additional load demand to 70% of the current load, and the population (Npersons) considered for HDI assessment is 600 individuals residing near the farm.

5. Results and Discussion

This section evaluates the technical, economic, environmental, and social performance of three off-grid HRES: PV–Battery, Wind–Battery, and PV–Wind–Battery. Each configuration is examined under three reliability levels defined by the LPSP: 1%, 5%, and 10%. The results in

Table 4. System performance under high-reliability conditions (LPSP = 1%): techno-economic and socio-environmental metrics.

System Configuration	PV (kW)	Wind (kW)	Battery (kWh)	LCOE ($/kWh)	CO2 Avoided (kg)	Jobs	HDI
PV–Battery	55.42	–	210.86	0.1238	6.2347 × 108	2	0.4360
Wind–Battery	–	2000	800	0.2821	2.9943 × 105	12	0.4685
PV–Wind–Battery	61.64	52.62	126.52	0.0948	9.5767 × 108	2	0.4416

,

Table 5. System performance under moderate reliability conditions (LPSP = 5%): techno-economic and socio-environmental metrics.

System Configuration	PV (kW)	Wind (kW)	Battery (kWh)	LCOE ($/kWh)	CO2 Avoided (kg)	Jobs	HDI
PV–Battery	70.93	-	56.92	0.0546	7.9801 × 108	2	0.4434
Wind–Battery	-	1451.9	499.10	0.2736	2.1737 × 105	8	0.4685
PV–Wind–Battery	52.49	13.74	74.33	0.0774	5.9054 × 108	2	0.4363

and

Table 6. System performance under reduced reliability conditions (LPSP = 10%): techno-economic and socio-environmental metrics.

System Configuration	PV (kW)	Wind (kW)	Battery (kWh)	LCOE ($/kWh)	CO2 Avoided (kg)	Jobs	HDI
PV–Battery	63.8	–	47.52	0.0552	7.17768 × 108	2	0.4414
Wind–Battery	–	939.73	500	0.3121	1.4069 × 105	6	0.4601
PV–Wind–Battery	59.59	10.75	48.34	0.0609	6.7041 × 108	2	0.4402

and Figure 11, Figure 12, Figure 13, Figure 14, Figure 15, Figure 16, Figure 17, Figure 18, Figure 19, Figure 20, Figure 21, Figure 22, Figure 23, Figure 24 and Figure 25 allow detailed comparison across energy generation, storage behavior, system costs, and social benefits.

5.1. System Performance Analysis Under High-Reliability Conditions (LPSP = 1%)

At high reliability (LPSP = 1%), all systems are designed to meet the entire energy demand without any shortfalls. To understand how each system performs under these conditions, we now explore their technical, economic, and social outcomes individually (Table 4).

PV–Battery System: For the PV–Battery system (55.42 kW PV capacity, 210.86 kWh battery storage), the LCOE is about 0.1238 $/kWh. This is a dependable energy source which provides an excellent CO₂ avoidance value of 6.2347 × 10⁸ kg. It creates two jobs, and increases HDI by 0.4360. The PV–Battery system is cost-effective and technically feasible, but it is not diversified as other systems.

Similar findings were reported by [7] in Ethiopia, where PV–Battery systems were found cost-effective under strong solar potential, but their dependence on extensive storage made them less attractive in resource-constrained months. This observation is also consistent with the review by [55], who highlighted that single-resource HRES often face high storage dependence and seasonal performance gaps, reinforcing the importance of hybridization for long-term sustainability in developing countries.

Wind–Battery System: The wind capacity of this system is about 2000 kW and 800 kWh of battery storage, with a high LCOE of 0.2821 $/kWh. Although it has a higher cost, it generates large social impact through 12 jobs established and an HDI improvement (0.4685). This system is particularly appropriate for situations that favor job generation and social impact. The high wind turbine capacity is a result of low wind resources in the study region, leading to higher economic cost. Thus, it is not advisable to adopt this configuration in this region since it is not efficient to exploit wind potential, and is less economically competitive than other arrangements.

This aligns with [9], who noted that single-resource systems, particularly wind-based ones, tend to underperform economically in rural SSA contexts, reinforcing the need for hybridization.

PV–Wind–Battery System: The PV–Wind–Battery system, equipped with 61.64 kW of PV, 52.62 kW of wind, and 126.52 kWh of battery, provides the minimum LCOE, 0.0948 $/kWh. This setup saves 9.5767 × 10⁸ kg CO₂ and creates two jobs and improves the HDI by 0.4416. In this study the PV–Wind–Battery system is considered as a competing source, which has a relatively balanced accuracy, cost, and convenience overall, making it cost-economical as well as environmental.

The comparison leads to the conclusion that the PV–Wind–Battery system appears balanced enough to provide not only the least cost but also effective energy management with CO₂ emissions control. The Wind–Battery system leads in the number of jobs and HDI due to its higher cost. The PV–Battery system, though cost-effective, focuses more on reliability and affordability, with less emphasis on social and environmental impacts.

Its superior cost-effectiveness under moderate reliability stems from the predictable solar resource in Chad, which aligns well with daily irrigation demand patterns. However, its dependence on large-scale batteries increases capital costs and raises sustainability concerns.

The power generation, load profile, and battery charging/discharging behaviors of the systems are shown in Figure 11 and Figure 12.

Figure 11. Hourly Power Generation, Load Profile, and Battery Charging (LPSP = 1%; Power, kW vs. Time, h) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 11. Hourly Power Generation, Load Profile, and Battery Charging (LPSP = 1%; Power, kW vs. Time, h) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 12. Hourly Battery Discharge Profiles under High Reliability (LPSP = 1%; Power, kW vs. Time, h) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 12. Hourly Battery Discharge Profiles under High Reliability (LPSP = 1%; Power, kW vs. Time, h) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 11a PV–Battery System: The PV–Battery system shows a clear peak in power generation between 10:00 and 15:00, when solar generation is at its highest. During these h, solar energy dominates, and the battery charges as solar generation exceeds the load demand. The system charges fully during the day, ensuring it has sufficient stored energy for non-sunlight h. Outside of the peak sunlight period, the system relies on the battery to meet the energy demand. The need for a large storage capacity is evident, as reflected in the charging profile, where battery charging only occurs during sunlight h, indicating a reliance on energy storage to ensure supply during the night.

Figure 11b Wind–Battery System: The Wind–Battery system displays two distinct peaks in energy generation, one in the early morning and another in the late evening. The battery charging occurs when wind generation exceeds the load demand, which typically happens during these periods. However, since the generation profile is not fully aligned with the load demand, the system relies heavily on the battery to meet energy needs during off-peak wind times. As seen in Figure 11b, this misalignment leads to inefficient energy storage and higher battery requirements, raising the economic costs of the system.

Figure 11c PV–Wind–Battery System: The PV–Wind–Battery system benefits from both solar and wind generation, providing a more stable energy supply. Solar generation dominates midday, while wind generation supports the system during early mornings and evenings. As a result, the battery charging and discharging profiles are more balanced compared to the other systems, as shown in Figure 11c. This system ensures that energy demand is met with fewer gaps and minimizes reliance on battery storage, thereby reducing stress on the battery. The diversified generation profile of the PV–Wind–Battery system contributes to its superior performance in terms of reliability and economic feasibility.

Figure 12a PV–Battery System: The PV–Battery system shows a clear peak in solar power generation between 10:00 and 15:00, when solar generation is at its highest. During these h, solar energy dominates, and the battery charges as solar generation exceeds the load demand. The battery discharges primarily during the early morning and late evening when there is no sunlight to meet the load demand. The system relies on energy storage to ensure the supply during non-sunlight h, as reflected in the discharge profile. The need for sufficient storage capacity is evident, as the battery is frequently used to meet energy needs during off-peak sunlight periods.

Figure 12b Wind–Battery System: The Wind–Battery system displays two distinct peaks in energy generation: one in the early morning and another in the late evening. Battery discharge takes place if the wind generation is not enough to meet the load demand, usually during wind off-peak h. However, as the wind generation profile does not have a full correlation with its load demand, the battery discharging is heavily leveraged to supply energy during those low wind h. This mismatch results in wasted energy storage that always uses the battery and increases the financial expenses of the system.

Figure 12c PV–Wind–Battery System: The PV–Wind–Battery system benefits from the diversity of solar–wind power and the strong stability of energy supply. Midday is dominated by solar generation, while system generation for the early morning and evening h performs utilizing wind. Battery charging profile is, therefore, more uniform when compared with the remaining systems. The system relies less on battery discharge because the combined solar and wind generation helps meet the load demand more efficiently. This system ensures that energy demand is met with fewer gaps and minimizes reliance on battery storage, reducing stress on the battery. The diversified generation profile of the PV–Wind–Battery system contributes to its superior performance in terms of reliability and economic feasibility.

Figure 13 highlights the energy contribution fractions by source type for each of the three off-grid hybrid renewable energy systems. The PV–Battery system is dominated by solar generation, the Wind–Battery system relies heavily on wind power, and the PV–Wind–Battery system displays a more balanced contribution from both solar and wind, optimizing the availability of resources.

Figure 13. Energy Contribution Fractions by Source Type under High Reliability (Contribution, % vs. System Type). (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 13. Energy Contribution Fractions by Source Type under High Reliability (Contribution, % vs. System Type). (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 13a PV–Battery: The PV–Battery system is almost entirely dependent on solar energy, with 99% of the total energy generation coming from photovoltaic (PV) power. Only 1% of the energy is supplied by the battery. This configuration demonstrates a high reliance on solar generation, with minimal use of battery storage, suggesting that the system does not significantly store energy for later use. The very low battery fraction indicates reduced storage capacity, which can reduce the system’s operational efficiency, particularly during periods with variable sunlight.

Figure 13b Wind–Battery: The Wind–Battery case also depends almost entirely upon wind energy, where 99% of the energy is generated from the wind while the remaining 1% is provided from the battery storage. This system is highly dependent on wind, and with little help from batteries. Although it could provide a significant amount of energy on a wind generation day, the small capacity of the battery may restrict the effectiveness of the system under weak wind conditions, so the reliability and security of the system in generation are in question.

Figure 13c PV–Wind–Battery: The PV–Wind–Battery device is based on PV–Wind mix energy, in which 91% of energy is provided by PV and 9% by wind. The battery still contributes a very small fraction of less than 1%. This hybrid configuration ensures a diversified energy supply, where both solar and wind generation complement each other. The limited role of the battery reduces the need for large storage capacities, making the system more efficient and cost-effective. By leveraging both solar and wind resources, the PV–Wind–Battery system provides a more sustainable and reliable energy solution compared to the other two systems.

In comparison, while both the PV–Battery and Wind–Battery systems rely predominantly on a single energy source, the PV–Wind–Battery system optimizes energy generation from two renewable sources, offering greater efficiency and reliability, thereby reducing the dependency on large battery storage and enhancing system sustainability.

Figure 14 presents the monthly power supply composition and load satisfaction for three HRES: PV–Battery, Wind–Battery, and PV–Wind–Battery. Below is the detailed analysis, including the performance of each system across the year, with respect to power output, load satisfaction, and load losses.

Figure 14. Monthly Power Supply and Load Satisfaction under High Reliability (Energy, kWh vs. Month) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 14. Monthly Power Supply and Load Satisfaction under High Reliability (Energy, kWh vs. Month) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 14a PV–Battery: The PV–Battery system shows a total load loss of 646.5149 kWh. This system performs well in months with strong solar generation (such as January, February, and December), but experiences significant load losses in February, August, and September. During these months, solar generation is lower, and the system relies more on battery storage to meet the load demand. This increased reliance on battery output leads to higher load losses. Despite the relatively high solar output during some months, the load satisfaction is not always fully met, contributing to these energy losses. The system is best suited for areas with consistent solar availability, though the increased need for battery storage during the months of lower solar generation may present challenges, particularly when storage costs are high.

Figure 14b Wind–Battery: The Wind–Battery system shows a total load loss of 667.8248 kWh. Wind generation is highest in months like March, April, and November, effectively meeting the load demand. However, the system experiences load losses in October when wind generation is lower, leading to reliance on battery storage to meet the demand. During this low-wind power period, load losses are higher. Although the system is operating well in terms of the strong wind condition, the wind output in October is low and so it pushes the system to be not able to fully meet the load which attributes to overall energy loss. The system works best in wind-abundant areas and can face reliability challenges in locations where the wind is less reliable, meaning higher storage costs and possible load losses.

Figure 14c PV–Wind–Battery: In the PV–Wind–Battery system, the total load loss is 658.7977 kWh. This system is a mix of sun and wind caught, which will offer more stable output than the others. Nevertheless, some load losses do continue during certain months, especially when solar or wind generation is particularly low. For example, July, August, and September might be higher loss periods on average when solar and wind generation may be limited and more battery storage may prove necessary. The ability to trade off between solar and wind eliminates an additional amount of load loss in comparison to the other system, although some loss is still incurred during these non-renewable months. The PV–Wind–Battery has the highest reliability of the three, with the best mean overall load satisfaction, but it still experiences low satisfaction months when the resources are at a low level.

The PV–Battery system, which has cumulative load shedding 646.5149 kWh, suffered higher load shedding in months such as June and July due to decreased solar generation and more battery operation. For the Wind–Battery system, there is a 667.8248 kWh higher load loss being caused by low wind generation in summer (e.g., June, July) and more requirement on storage and increased loss. The total load loss of the PV–Wind–Battery system is 658.7977 kWh. If a more balanced generation of solar and wind took place, it would still bear losses in some months where generation would not be sufficient.

Figure 15 presents the annualized cost breakdown by component for each of the three off-grid hybrid renewable energy systems. Below is the detailed analysis, which includes the total annualized cost, along with the costs of individual components such as wind, solar, battery, and inverter for each system.

Figure 15. Annualized Cost Breakdown by Component under High Reliability (Cost, USD/year vs. System Type) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 15. Annualized Cost Breakdown by Component under High Reliability (Cost, USD/year vs. System Type) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 15a PV–Battery: The PV–Battery system has a total annualized cost of $12,757, with the largest portion of this cost allocated to the battery ($8440.10). Since there is no wind generation in this system, the wind cost is $0. The solar cost is $2175.60, and the inverter cost is $2141.50. This system is viable for areas with strong solar potential, though the reliance on large battery storage may become costly in regions where storage prices are high. The higher battery cost indicates that this system may not be the most cost-effective option in regions with significant storage requirements, highlighting the trade-off between storage cost and system reliability.

Figure 15b Wind–Battery: The Wind–Battery system records the maximum total annualized cost of $122,310. A large cost of this is the wind generation ($88,146), since the system is very dependent on wind energy facilities. The battery is also expensive; at $32,021.00, the system relies on large amounts of stored energy to meet the variation in wind power. No solar generation so the solar cost is $0. The inverter pricing is still $2141.50. This system is best suited for regions with ample wind resources, but may not be economically viable in locations with lower wind potential due its high upfront cost for both wind and storage infrastructure. The large capital costs of wind and battery components make the system less cost-effective where abundant wind resources are not available.

Figure 15c PV–Wind–Battery: The PV–Wind–Battery system results as the least expensive with a cumulative cost over the year of $11,945. This system is a combination of investment in solar generation and wind generation, having a solar price of $2419.90 and a price of wind equal to $2319.00. This brings the battery cost down to 5064.60 and ensures that the approach to energy storage is more balanced than in the other systems. The inverter amount of $2141.50 does not change. The system integrates power objectives of solar energy, and wind generator, which reduces the demand for energy-storage devices of battery specifically and is the most economical and realistic way to save energy and lighting. This system would provide a steady supply of energy at lower costs since it is harnessing the two renewable sources, which will be quite effective and less costly solution than both the Wind–Battery and PV–Battery system.

On the other hand, for large wind penetration and big energy storage, Wind–Battery has the highest TAC (at $122,310), which practically makes it less viable for regions with less wind potential; however, with a relatively larger energy independence and job generation. The PV–Battery system is cheaper than the Wind–Battery system and depends largely on expensive battery storage, so it is appropriate for areas with plentiful solar resources but difficult in the high storage cost areas. The PV–Wind–Battery system, with an annual cost of $11,945, provides the most optimal and cost-effective scenario due to the complementary operation of solar and wind resources, and decreases the use of expensive battery storage to ensure economic feasibility in the long-run. The balanced allocation of costs across components makes this system the most sustainable and efficient choice for most regions.

5.2. System Performance Analysis Under Moderate Reliability Conditions (LPSP = 5%)

At moderate reliability (LPSP = 5%), all systems are designed to meet most of the energy demand with a slightly higher chance of shortfalls. To understand how each system performs under these conditions, we now explore their technical, economic, and social outcomes individually (Table 5).

PV–Battery System: The PV–Battery configuration, with 70.93 kW of PV capacity and 56.92 kWh of battery storage, has an LCOE of 0.0546 $/kWh. This system offers an even more cost-effective solution with a CO₂ avoidance of 7.9801 × 10⁸ kg. It generates two jobs and provides an HDI improvement of 0.4434. The PV–Battery system becomes significantly more cost-effective and environmentally beneficial compared to the high-reliability scenario, maintaining a focus on affordability and environmental impact.

Wind–Battery System: With a wind capacity of 1451.9 kW and 499.1 kWh of battery storage, the Wind–Battery system has a higher LCOE of 0.2736 $/kWh. This strategy may have high costs, but it has a significant social return (8 jobs created and an increase in HDI = 0.4685). This one is rigged when it comes to employment and the HDI. However, the CO₂ avoidance is much lower, 2.1737 × 10⁵ kg, which is less environmentally efficient under medium reliability. The high cost of this turbine still makes it impractical, especially with the low wind potential in the region.

PV–Wind–Battery System: The PV–Wind–Battery system with 52.49 kW of PV, 13.74 kW of wind, and 74.33 kWh of storage can be operated at an LCOE of 0.0774 $/kWh. This configuration saves 5.9054 × 10⁸ kg of CO₂ and creates two jobs, and also an HDI increase by 0.4363. Although the PV–Wind–Battery system also provides a reasonable decision associated with cost-effective usage of the energy management level and environmental advantages, it does not achieve the lowest LCOE. That distinction belongs to the PV–Battery system. The PV–Wind–Battery system experiences a slight decrease in CO₂ avoidance and HDI improvement compared to the high-reliability scenario.

The comparison indicates that the PV–Battery system provides the most cost-effective and environmentally favorable solution under moderate reliability conditions, with a marked improvement in CO₂ avoidance and affordability. The Wind–Battery system, while offering strong social benefits, especially in job creation and HDI improvement, faces inefficiencies in environmental impact and remains economically expensive. The PV–Wind–Battery system still presents a balanced approach, combining cost-effectiveness with environmental benefits, though its social outcomes are slightly reduced compared to the high-reliability conditions.

Comparable outcomes were observed in Namibia by [8], who showed that mixed-resource hybrid systems reduce storage dependency and achieve better long-term sustainability compared to single-resource configurations. This result also aligns with [52], who catalogued trends across remote tropical regions, including Sub-Saharan Africa, and found that hybrid configurations consistently outperform single-resource systems in addressing resource intermittency and ensuring long-term reliability.

Although wind potential is modest in Linia, the large turbine capacities required increase costs. Nevertheless, this configuration maximizes local employment, since wind system installation and maintenance are labor-intensive.

The power generation, load profile, and battery charging/discharging behaviors of the systems under moderate reliability are shown in Figure 16 and Figure 17.

Figure 16. Hourly Power Generation, Load Profile, and Battery Charging (LPSP = 5%; Power, kW vs. Time, h) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 16. Hourly Power Generation, Load Profile, and Battery Charging (LPSP = 5%; Power, kW vs. Time, h) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 17. Hourly Battery Discharge Profiles under Moderate Reliability (LPSP = 5%; Power, kW vs. Time, h). (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 17. Hourly Battery Discharge Profiles under Moderate Reliability (LPSP = 5%; Power, kW vs. Time, h). (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 16a PV–Battery System: The PV–Battery system in Figure 16a shows a similar pattern to the high-reliability case, but with moderate reliability conditions. Solar generation peaks between 10:00 and 15:00, and during this time, solar energy dominates. The system charges the battery during these h as solar generation exceeds the load demand. Outside the peak sunlight period, when solar power is insufficient to meet the load, the system relies heavily on the battery to ensure energy supply. The battery charging primarily happens during daylight h, and the system discharges the battery during non-sunlight h. As indicated by the total load loss of 3.3805 × 10³ kWh, this system still has relatively high energy losses, which are typical when battery storage is crucial for balancing demand during non-sunlight h.

Figure 16b Wind–Battery System: The Wind–Battery system in Figure 16b exhibits two primary peaks in wind power generation: one in the early morning and another in the evening. These peaks cause the system to charge the battery when wind power exceeds the load demand. However, the wind generation profile does not perfectly align with the load demand, leading to inefficient energy storage. The battery discharges frequently, especially when wind generation is low at midday, thus ensuring energy supply during those periods. This system results in 3.3832 × 10³ kWh of load loss, which is slightly higher than the PV–Battery system due to the misalignment between wind generation and demand, leading to more frequent battery discharging and energy losses.

Figure 16c PV–Wind–Battery System: The PV–Wind–Battery system in Figure 16c benefits from both solar and wind generation. Solar generation dominates at midday, while wind generation helps support the system during early mornings and evenings. The battery charging and discharging profiles are more balanced compared to the other systems. As a result, the need for battery storage is reduced, and energy demand is met with fewer gaps. This system, with a total load loss of 3.3862 ×10³ kWh, exhibits the least loss among the three, showing that the combined generation from solar and wind leads to better alignment with the load demand and more efficient energy use.

Figure 17a PV–Battery System: In Figure 17a, the PV–Battery system’s performance under moderate reliability is reflected in its higher frequency of battery discharges during non-sunlight h. The solar generation still peaks between 10:00 and 15:00, but the system frequently relies on the battery to meet the load demand outside of these peak h. The total load loss remains 3.3805 × 10³ KWh, which is indicative of the same behavior as in Figure 16, where the system heavily relies on battery storage due to a mismatch between solar generation and load demand during off-peak h.

Figure 17b Wind–Battery System: In Figure 17b, the Wind–Battery system continues to show two primary peaks in wind generation during early morning and late evening h. Similar to Figure 16, the wind generation does not match the load demand well, especially in the afternoon when wind generation is low. As a result, the system frequently discharges the battery, leading to higher energy losses. The total load loss in this case is 3.3832 × 10³ kWh, consistent with Figure 16, indicating that misalignment between wind power generation and the load profile causes inefficiencies in energy storage and higher reliance on battery discharging.

Figure 17c PV–Wind–Battery System: In Figure 17c, the PV–Wind–Battery system benefits from both solar and wind generation, which leads to a more balanced battery discharging profile. Solar energy dominates at midday, while wind energy supports the system during the early mornings and evenings. The battery is used less frequently compared to the other two systems, as the combined solar and wind generation better aligns with the load demand. The total load loss of 3.3862 × 10³ kWh in this system is the lowest, indicating superior efficiency compared to the other systems. This reduced reliance on battery storage reflects the improved performance of the PV–Wind–Battery system under moderate reliability.

Under high reliability (Figure 11 and Figure 12), the systems exhibit more efficient energy generation and battery usage, as the generation profiles are well-aligned with the load demands. This alignment results in lower load losses and less frequent battery discharges, which in turn minimizes the need for energy storage and reduces operational costs. In this aspect, the PV–Wind–Battery system shows the most potential, including solar and wind in the generation mix and thus has the most stable energy supply. Its capability to satisfy energy demand with less storage means less stress on batteries, less frequent changing batteries, and leaner economic viability.

Under moderate reliability, on the other hand, (Figure 16 and Figure 17), the systems experience increased load losses and battery discharging occurred because of the mismatching between the available generated power and the load demand, especially in low-wind or off-peak solar conditions. This implies an increased utilization of batteries storage and thus, increasing energy losses and O and M costs. The PV–Battery hybrid system is in fact more efficient than the PV–Wind–Battery hybrid under moderate reliability. The PV–Battery system benefits from the more predictable nature of solar generation, which better aligns with the load during moderate reliability, while the PV–Wind–Battery system’s performance becomes less efficient due to the misalignment between solar and wind generation. Despite the inefficiencies, the Wind–Battery system continues to struggle with inefficient charging and discharging cycles. As a result, all systems show increased load losses and higher battery usage under moderate conditions, leading to higher economic costs and system inefficiency. This reflects the challenges of integrating renewable energy sources when generation is less reliable.

Figure 18 highlights the energy contribution fractions by source type for each of the three off-grid HRES under moderate reliability conditions. The PV–Battery system is dominated by solar generation, with 99% of the energy coming from photovoltaic (PV) power and less than 1% from battery storage. The Wind–Battery system relies heavily on wind power, with 99% of energy coming from wind generation and minimal battery use. The PV–Wind–Battery system displays a more balanced contribution from both solar and wind, with 97% of energy coming from PV and 3% from wind, and the battery still contributes less than 1%, optimizing the availability of resources.

Figure 18. Energy Contribution Fractions by Source Type under Moderate Reliability (Contribution, % vs. System Type) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 18. Energy Contribution Fractions by Source Type under Moderate Reliability (Contribution, % vs. System Type) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 19 presents the energy supply composition and load satisfaction for the three hybrid renewable energy systems under moderate reliability conditions. The PV–Battery system (Figure 19a) continues to rely almost entirely on solar generation, with solar power consistently providing the majority of the energy. However, due to lower solar generation in certain months (e.g., during winter), the system must rely more on battery storage to meet the load. Load losses occur in months with lower solar availability, such as February, June, and November, when the system cannot fully satisfy the load demand, leading to higher reliance on the battery and increased load losses.

Figure 19. Monthly Power Supply and Load Satisfaction under Moderate Reliability (Energy, kWh vs. Month) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 19. Monthly Power Supply and Load Satisfaction under Moderate Reliability (Energy, kWh vs. Month) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

The Wind–Battery system shows a similar pattern, but with wind power being the dominant energy source. Wind generation is highest in months like March, April, and November, effectively meeting the load demand. However, periods of low wind generation, particularly in summer months (e.g., July and August), result in increased reliance on battery storage, leading to higher load losses. Battery output is higher in these months due to wind’s intermittent nature, and the system experiences load dissatisfaction when wind generation is insufficient.

The PV–Wind–Battery system benefits from both solar and wind generation, contributing a more balanced energy mix. While solar power dominates in summer months, wind generation supports the system in spring and fall, reducing the need for battery storage. The system experiences fewer load losses compared to the PV–Battery and Wind–Battery systems, as the combined generation from solar and wind optimizes energy availability. However, despite its better performance, some load losses still occur during months of insufficient generation, particularly in summer months like July and August, when both solar and wind output are low.

Figure 19 presents the monthly power supply composition and load satisfaction for three HRES: PV–Battery, Wind–Battery, and PV–Wind–Battery under a moderate reliability constraint. Below is the detailed analysis, including the performance of each system across the year, with respect to power output, load satisfaction, and load losses.

Figure 19a PV–Battery: The PV–Battery system in Figure 19 shows a total load loss of 3380.5 kWh under the moderate reliability constraint. The system performs well during months with high solar generation, such as January, March, and December. However, it experiences significant load losses during summer months (June, July, and August), when solar generation is lower, and the system relies more heavily on battery storage to meet demand. The total load loss is substantial due to the increased reliance on battery output when solar energy is insufficient, particularly during months with suboptimal solar conditions. This suggests that while the system works effectively in areas with strong solar resources, its efficiency diminishes when there is reduced sunlight, leading to higher energy storage costs.

Figure 19b Wind–Battery: The Wind–Battery system in Figure 19 shows a total load loss of 3383.2 kWh. Wind generation is highest in February, March, April, and December, effectively meeting the load demand during these months. However, the system faces increased load losses during the summer months (June, August, and October), when wind generation is lower, requiring more battery storage to meet demand. Despite performing well in areas with strong wind resources, the Wind–Battery system still experiences significant load losses during periods of low wind, leading to a higher reliance on storage, especially when wind availability is inconsistent.

Figure 19c PV–Wind–Battery: The PV–Wind–Battery system in Figure 19 shows a total load loss of 3386.2 kWh, which is the highest of the three systems under moderate reliability. This hybrid system benefits from both solar and wind generation, ensuring a more stable output compared to the individual systems. While the load satisfaction improves due to the combined generation sources, losses still occur during months of low solar or wind generation (e.g., May, June, July, August, and October). Despite being a hybrid system, it still faces higher load losses compared to the other systems, especially during periods with insufficient generation from both renewable sources. However, the system’s overall performance remains more reliable than the others due to the combination of resources.

Figure 20 presents the annualized cost breakdown by component for each of the three off-grid HRES under moderate reliability conditions. Below is the detailed analysis, including the total annualized cost and costs of individual components such as wind, solar, battery, and inverter for each system.

Figure 20. Annualized Cost Breakdown by Component under Moderate Reliability (Cost, USD/year vs. System Type) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 20. Annualized Cost Breakdown by Component under Moderate Reliability (Cost, USD/year vs. System Type) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 20a PV–Battery: The PV–Battery system under moderate reliability conditions shows a total annualized cost of $7204.3. The largest portion of the cost is allocated to the solar component ($2784.6), followed by the battery cost ($2278.2). The inverter cost remains constant at $2141.5, and there is no wind generation, hence the wind cost is $0. This system remains viable for regions with strong solar potential but, like in the high-reliability scenario (Figure 15), the reliance on the battery for energy storage makes it expensive, especially in areas where storage costs are high. The relatively lower battery cost compared to the high-reliability scenario suggests that with moderate reliability, the system becomes somewhat more affordable, but the trade-off between battery storage cost and solar generation remains.

Figure 20b Wind–Battery: The Wind–Battery system under moderate reliability conditions incurs a total annualized cost of $86,108.00, with a large portion allocated to wind generation ($63,989.00). The battery cost is also substantial at $19,977.00, reflecting the system’s heavy reliance on energy storage to manage the variability in wind generation. The inverter cost remains at $2141.5, while the solar cost is $0. This system is most effective in regions with strong wind resources, though it is economically infeasible in regions where wind generation potential is low, due to the high upfront costs for wind infrastructure and storage.

Figure 20c PV–Wind–Battery: The PV–Wind–Battery system under moderate reliability conditions has a total annualized cost of $7782.9. The cost is more evenly distributed across its components, with solar generation costing $2060.6, wind generation costing $605.5, and battery storage costing $2975.3. The inverter cost is $2141.5. This system balances its investment across solar, wind, and battery components, making it a more cost-effective and viable option compared to the Wind–Battery system. The hybrid nature of the system reduces the reliance on costly energy storage, making it a more economically sustainable option than the Wind–Battery system, while still providing reliable energy from both solar and wind resources.

5.3. System Performance Analysis Under Reduced Reliability Conditions (LPSP = 10%)

At reduced reliability (LPSP = 10%), all systems are designed to meet a higher risk of energy shortfalls. To understand how each system performs under these conditions, we now explore their technical, economic, and social outcomes individually (Table 6).

PV–Battery System: The PV–Battery configuration, with 63.8 kW of PV capacity and 47.52 kWh of battery storage, has an LCOE of 0.0552 $/kWh. This system offers a highly cost-effective solution with a CO₂ avoidance of 7.1776 × 10⁸ kg. It generates two jobs and provides an HDI improvement of 0.4414. The PV–Battery system remains focused on affordability and environmental impact, with slightly reduced battery storage compared to previous configurations. Even with decreased reliability, the system remains as a robust alternative in terms of both cost-effectiveness and CO₂ reduction.

Wind–Battery System: With 939.73 kW of wind and 500 kWh of battery, the Wind–Battery system has an LCOE of 0.3121 $/kWh which is higher than others. It is, on the one hand, faced with a higher economic cost, but on the other, featuring the rather conspicuous social benefit, namely, the generation of 6 jobs and HDI advancement by a factor of 0.4601. The CO₂ emission saved is 140,690 kg, which is quite low. This system is particularly focused on social results, and in job generation, but it still is inefficient with respect to CO₂ avoidance and can be economically costly. Its high price is disadvantageous when reliability is reduced, since cheaper alternatives, such as PV–Battery, are favored.

PV–Wind–Battery System: The PV–Wind–Battery system, consisting of 59.59 kW PV, 10.75 kW wind, and 48.34 kWh battery storage, has an LCOE of 0.0609 $/kWh. This setup saves 6.7041 × 10⁸ kg of CO₂ and creates two jobs, increasing the HDI by 0.4402. Though the PV–Wind–Battery is still a balanced system with lower overall cost and better environmental benefits, it has a small compromise in CO2 avoidance and HDI enhancement compared to the more reliable system. Also, its LCOE is higher than that of PV–Battery, but it is cost-effective and provides good environmental benefits.

It is observed from the comparison that with low reliability, the PV–Battery is the most economical and clean solution with the lowest LCOE which is equal to 0.0552 $/kWh and a high CO₂ avoidance (7.1776 × 10⁸ kg). It is still a good choice and cheaper for those concerned with environmental impact and economic efficiency. The Wind–Battery system scores highly in social aspect with high job creation (6 jobs) and HDI increase (0.4601) but lags in efficiently avoiding CO₂ emissions (1.4069 × 10⁵ kg) and in economic perspective as well, being costly with 0.3121 $/kWh value of LCOE. The PV–Wind–Battery system continues to offer a balanced approach, with a moderate LCOE of 0.0609 $/kWh and good environmental benefits (6.7041 × 10⁸ kg CO₂ avoided), but shows slight reductions in CO₂ avoidance and HDI improvement compared to more reliable conditions.

This configuration benefits from resource complementarity, with solar dominance during the day and wind contribution in the evenings. This reduces battery stress, lowers total costs, and ensures a more balanced trade-off across economic, environmental, and social dimensions.

The power generation, load profile, and battery charging/discharging behaviors of the systems under reduced reliability are shown in Figure 21 and Figure 22.

Figure 21. Hourly Power Generation, Load Profile, and Battery Charging (LPSP = 10%; Power, kW vs. Time, h) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 21. Hourly Power Generation, Load Profile, and Battery Charging (LPSP = 10%; Power, kW vs. Time, h) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 22. Hourly Battery Discharge Profiles under Reduced Reliability (LPSP = 10%; Power, kW vs. Time, h) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 22. Hourly Battery Discharge Profiles under Reduced Reliability (LPSP = 10%; Power, kW vs. Time, h) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 21a PV–Battery System: The PV–Battery system shows a consistent pattern of solar power peaking between 10:00 and 15:00, as seen in earlier reliability levels. During these peak sunlight h, solar generation exceeds the load, allowing battery charging. However, under reduced reliability, energy management becomes less efficient. The battery charges only briefly during daylight h, and the system lacks sufficient stored energy to meet nighttime demand. Compared to high and moderate reliability, the charging durations are shorter and less consistent, suggesting the system is unable to fully charge the battery during available solar h. This system results in a total load loss of 6.6899 × 10³ kWh, indicating a noticeable increase in energy loss due to reduced charging and discharging efficiency.

Figure 21b Wind–Battery System: The Wind–Battery system exhibits two primary peaks in wind generation, early morning and late evening, similar to the patterns seen under higher reliability conditions. However, the wind generation under reduced reliability is more variable and often fails to meet the load demand efficiently. The system charges the battery intermittently and discharges it frequently when generation is insufficient, especially at midday. The total load loss in this system is 6.6816 × 10³ kWh, slightly lower than the PV–Battery system, but still significant, reflecting the inefficiency of energy storage due to the misalignment between wind generation and load demand.

Figure 21c PV–Wind–Battery System: The PV–Wind–Battery system benefits from both solar and wind generation, with solar generation dominating midday and wind generation supporting the system during early mornings and evenings. Despite the reduced reliability, the system shows better alignment between generation and load compared to the PV–Battery and Wind–Battery systems. Battery charging and discharging are more balanced, although still less efficient than in higher reliability conditions. This system experiences a total load loss of 6.6762 × 10³ kWh, the lowest among the three, showing that the combination of solar and wind generation helps reduce the impact of reduced reliability.

Figure 22a PV–Battery System: Under reduced reliability, the PV–Battery system experiences an increase in battery discharges, as solar generation cannot meet the entire load during non-sunlight h. The system relies heavily on the battery to supply energy during early mornings and evenings. Due to shorter charging durations and lower solar generation, the battery frequently discharges, leading to higher risks of load shortfalls. The total load loss remains 6.6899 × 10³ kWh, indicating an increase in energy losses as the system becomes more dependent on stored energy to meet the load during off-peak h.

Figure 22b Wind–Battery System: The Wind–Battery system shows continuous and irregular battery discharges throughout the period, especially during times of low wind generation. The mismatch between wind power generation and load demand leads to inefficient storage management, requiring frequent battery discharges. This inefficient use of the battery results in significant energy losses, with a total load loss of 6.6816 × 10³ kWh. The discharging profile is jagged and irregular, reflecting the challenges the system faces in maintaining energy balance and the increased reliance on battery storage.

Figure 22c PV–Wind–Battery System: The PV–Wind–Battery system maintains a more balanced battery discharging profile compared to the other two systems. Solar and wind generation together provide a more stable energy supply, and the battery discharges less frequently as the combined generation aligns better with the load demand. Despite the reduced reliability, this system performs the best in managing energy supply and storage. The total load loss of 6.6762 × 10³ kWh is the lowest, indicating superior efficiency in energy use and battery management compared to the PV–Battery and Wind–Battery systems.

Under reduced reliability, all systems experience higher load losses and more frequent battery discharges due to the reduced availability and variability of renewable energy generation. The total load losses increase as follows: PV–Battery = 6.6899 × 10³ kWh, Wind–Battery = 6.6816 × 10³ kWh, and PV–Wind–Battery = 6.6762 × 10³ kWh, reflecting the challenges of balancing supply and demand. The PV–Wind–Battery system still outperforms the other two systems, with the lowest load loss, thanks to the complementary nature of solar and wind generation. However, even this system shows increased energy losses under reduced reliability, emphasizing the difficulties of relying on renewable energy alone in less favorable conditions. The Wind–Battery system and PV–Battery system exhibit greater inefficiencies, with higher energy losses and more frequent reliance on battery storage, highlighting the need for hybrid generation sources to improve energy management and system resilience.

Figure 23 highlights the energy contribution fractions by source type for each of the three off-grid HRES under reduced reliability conditions. The PV–Battery system remains dominated by solar generation, with 99% of the energy supplied by PV and a minimal contribution from the battery (<1%). The Wind–Battery system continues to rely almost entirely on wind power (99%), with a small battery fraction (<1%), but the increased variability of wind generation results in more frequent battery discharges. The PV–Wind–Battery system benefits from a more balanced mix, with 97% of energy from PV, 2% from wind, and <1% from the battery, reflecting the optimization of both solar and wind resources despite reduced reliability.

Figure 23. Energy Contribution Fractions by Source Type under Reduced Reliability (Contribution, % vs. System Type) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 23. Energy Contribution Fractions by Source Type under Reduced Reliability (Contribution, % vs. System Type) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 24 presents the monthly power supply composition and load satisfaction for three HRES: PV–Battery, Wind–Battery, and PV–Wind–Battery under a reduced reliability constraint. Below is the detailed analysis, including the performance of each system across the year, with respect to power output, load satisfaction, and load losses.

Figure 24. Monthly Power Supply and Load Satisfaction under Reduced Reliability (Energy, kWh vs. Month) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 24. Monthly Power Supply and Load Satisfaction under Reduced Reliability (Energy, kWh vs. Month) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 24a PV–Battery: The PV–Battery system shows a total load loss of 6689.9 kWh under the reduced reliability constraint. This system performs well in months with strong solar generation (such as January, March, and December). However, during the summer months (June, July, and August), when solar generation is lower, the system experiences an increase in load losses. The increased reliance on battery storage during these months, due to insufficient solar energy, leads to higher load losses. The system also performs poorly in low-solar generation seasons like April and October when it is very dependent on battery storage with the highest energy waste.

Figure 24b Wind–Battery: The Wind–Battery system presents a total load loss of 6681.6 kWh under the loosened reliability restriction. Its peak wind production is in February, March, April, and December, all months when a low rate of wind generation is required to meet the load without any shortage. But in the summer, when wind generation levels are low, the system incurs more load losses because utility-scale batteries are relied upon more extensively. The system performs best in areas of high wind but has difficulties in serving load; either the load is not met or a diesel generator has to be operated to provide supply. This causes greater energy losses during the summer—especially if wind generation is deficient.

Figure 24c PV–Wind–Battery: The total load loss of the PV–Wind–Battery system was 6676.2 kWh at the relaxation of the constraint of reliability. This combined solar and wind plant draws on the advantages of each: As both the sun and wind are not constant, this solution contributes to a more balanced overall power production. But it continues to suffer steep load losses in months in which there is little solar or wind generation (like June, July, and August). In these months, the system has to depend more on battery storage to serve the load demand which in return leads to higher load losses. The system remains exposed during a lack of generation from either solar or wind, however, and especially when both are low. During these months, the system must rely heavily on battery storage to meet the load demand, resulting in higher load losses. Despite its hybrid nature, the system is still vulnerable during periods with insufficient generation from either solar or wind, particularly when both resources are low.

Under the reduced reliability constraint, the PV–Battery system has a total load loss of 6689.9 kWh, the highest of the three systems, experiencing higher losses primarily during the summer months when solar generation is lower, leading to greater battery use. The Wind–Battery system shows a total load loss of 6681.6 kWh, slightly lower than the PV–Battery system, but still faces losses during the summer months when wind generation decreases. The PV–Wind–Battery system has the lowest total load loss at 6676.2 kWh, despite benefiting from both solar and wind resources. However, it still faces higher losses during months with insufficient generation from either resource.

Figure 25 presents the annualized cost breakdown by component for each of the three off-grid HRES under reduced reliability conditions. Below is the detailed analysis, which includes the total annualized cost, along with the costs of individual components such as wind, solar, battery, and inverter for each system.

Figure 25. Annualized Cost Breakdown by Component under Reduced Reliability (Cost, USD/year vs. System Type) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 25. Annualized Cost Breakdown by Component under Reduced Reliability (Cost, USD/year vs. System Type) (a) PV–Battery, (b) Wind–Battery, and (c) PV–Wind–Battery.

Figure 25a PV–Battery: The PV–Battery system under reduced reliability conditions shows a total annualized cost of $6548.2. The solar cost is $2504.6, and the battery cost is $1902.2, while the inverter cost remains at $2141.5. As with the moderate reliability scenario, this system remains viable for regions with strong solar potential. However, due to reduced reliability, the system’s battery costs remain significant, and the overall system remains more expensive compared to hybrid systems like PV–Wind–Battery, which benefits from a combination of renewable sources to reduce storage requirements.

Figure 25b Wind–Battery: The Wind–Battery system under reduced reliability incurs a total annualized cost of $63,571.0, with wind generation accounting for $41,417.0 of this cost. The battery cost is $20,013.0, and the inverter cost is $2141.5. The lack of solar generation results in a $0 solar cost. This system remains most suitable for regions with strong wind resources, but the significant reliance on wind infrastructure and energy storage makes it economically unfeasible in areas with lower wind potential, just like in the moderate reliability scenario.

Figure 25c PV–Wind–Battery: The PV–Wind–Battery system under reduced reliability shows a total annualized cost of $6889.6. The solar cost is $2339.3, the wind cost is $473.8, and the battery cost is $1935.0. The inverter cost remains at $2141.5. Despite the reduced reliability condition, this hybrid system benefits from both solar and wind generation, minimizing the need for large battery storage. The overall costs are more balanced, with a lower reliance on expensive battery storage compared to the other systems, making this system the most cost-effective option for regions with varied renewable resources.

The comparison of costs across reliability scenarios shows that the PV–Battery system has a total annualized cost of $6548.2 under reduced reliability, slightly lower than under moderate reliability ($7204.3), reflecting a reduced need for storage but still significant reliance on battery costs. The Wind–Battery system experiences a substantial decrease in annualized costs under reduced reliability ($63,571.0) compared to moderate reliability ($86,108.0), as reduced reliability likely reduces wind generation, thus lowering infrastructure costs, but it still requires considerable energy storage. The PV–Wind–Battery system has a total annualized cost of $6889.6 under reduced reliability, slightly lower than under moderate reliability ($7782.9), with reduced reliability decreasing the system’s reliance on battery storage, making it the most balanced and cost-effective solution across all reliability levels.

5.4. Performance Degradation in Wind and PV–Wind–Battery Systems Under Reduced Reliability

This section explores how reduced reliability conditions impact the efficiency and performance of both Wind–Battery and PV–Wind–Battery systems, highlighting the challenges associated with the variability of RES and their effects on economic, environmental, and social outcomes.

As the system reliability decreases, the performance of both the Wind–Battery system and the PV–Wind–Battery system deteriorates because of the increased variability and uncertainty of renewable energy source. Wind resource utilization is less efficient due to the fluctuations in the wind energy availability for the Wind–Battery system. Wind power production is naturally variable, and it becomes more variable as the system becomes less reliable. Hence, the wind turbine capacities need to be greater, as does the extra energy storage needed to be able to cope with the enhanced unpredictability. This results in an increased LCOE of 0.3121 $/kWh thus making the system less economically robust under lower reliability circumstances. In addition, the use of backup storage and the impossibility of exploiting completely the available wind resource makes the system less capable to reduce CO₂ emissions, therefore having a lower environmental impact than more steady ones, like PV–Battery.

Likewise, the PV–Wind–Battery system performance degrades with lower reliability of wind and solar resources. Wind’s share of the contribution decreases as wind generation becomes more intermittent, and solar’s share rises. But sun power could be a bit hit or miss, especially in times when wind and sun resources are low. Given that battery storage is used more often to fill the energy gap, LCOE increases to 0.0609 $/kWh, while the environmental benefits of the system, namely the CO₂ avoidance, and its social effects, namely the HDI improvement, are both decreased. The system that was supposed to take advantage of the strengths of solar and wind, in other words, does not work quite as well when one of the two is weak. Thus, the entire system efficiency decreases and the operating costs increase, as well as a decrease in CO₂ avoidance and social external benefits. These findings illustrate the difficulties of controlling a number of energy sources when reliability is low, eventually resulting in the rising of costs and a performance reduction for the overall system, from the environmental and the social perspective.

This study has certain limitations. The analysis assumes a closed system, without considering potential interactions with neighboring farms, grid extensions, or diesel backup options. Furthermore, the variability of wind resources in Chad means that wind-related results should be interpreted with caution. Future work should integrate resource-sharing schemes, demand-side management, and hybridization with alternative storage technologies such as biogas to address these constraints.

6. Conclusions and Future Work

This work has assessed the reliability of three off-grid HRES for agricultural applications in Linia, Chad. The PV–Wind–Battery configuration, combining solar and wind, proved to be the most reliable and economically favorable option. It achieved the lowest LCOE and the largest reductions in CO₂ emissions. Its mixed generation profile reduced the need for extensive battery storage, yielding a more balanced and sustainable energy resource compared to the PV–Battery and Wind–Battery alternatives, where storage dependency was higher and less cost-effective at lower reliability levels. Results indicate that single RES configurations may be insufficient in areas with limited resource potential. While wind-based systems offer social benefits through job creation, they were not financially viable due to under-utilization of the wind resource. Under strong solar conditions, the PV–Battery system provided reliable performance but incurred higher storage costs and seasonal energy losses. Overall, the findings highlight the importance of hybrid systems in areas with intermittent resources, ensuring greater energy security, reduced load shedding, and lower emissions.

Future work will explore the integration of biogas and renewable power forecasting to develop adaptive systems and further optimize energy supply.

In conclusion, this study demonstrates that multi-objective optimization of HRES can simultaneously address technical, economic, environmental, and social challenges in rural Sub-Saharan Africa. The PV–Wind–Battery system emerges as the most balanced option, while PV–Battery is cost-optimal under moderate reliability. These findings are particularly relevant for agricultural electrification in Chad, where irrigation and rural livelihoods depend on affordable, sustainable, and job-creating energy systems. Policymakers and planners can adapt the proposed methodology as a versatile energy planning tool to guide rural electrification strategies across the Sub-Saharan region.