Optimizing Solar-Integrated Microgrid Design for Sustainable Rural Electrification: Insights from the LEOPARD Project

Abstract

This paper presents findings from the LEOPARD project, part of the LEAP-RE program, a joint European Union (EU) and African Union initiative to advance renewable energy solutions. The study employs a simulation-based approach to optimize solar-integrated microgrid configurations for rural electrification. The project deployed a solar-integrated pilot microgrid at the Songhai agroecological center in Benin to address key challenges, including load profile estimation, energy balancing, and diesel dependency reduction. A hybrid methodology integrating predictive modeling, real-time solar and weather data analysis, and performance simulations was employed, leading to a 65% reduction in diesel reliance and an LCOE of EUR 0.47/kWh. Quality control measures, including compliance with IEC 61215 and IEC 62485-2 standards, ensured system reliability under extreme conditions. Over 150 days, the system consistently supplied energy, preventing 10.16 tons of ${CO}_2$ emissions. Beyond the Benin pilot, the project conducted feasibility assessments in Senegal to evaluate microgrid replicability across different socio-economic and environmental conditions. These analyses highlight the scalability potential and the economic viability of expanding solar microgrids in rural areas. Additionally, this research explores innovative business models and real-time diagnostics to enhance microgrid sustainability. By providing a replicable framework, it promotes long-term energy access and regional adaptability. With a focus on community involvement and capacity building, this study supports efforts to reduce energy poverty, strengthen European–African collaboration, and advance the global clean energy agenda.

1. Introduction

Solar energy has emerged as a transformative solution to address rural electrification challenges in regions with limited or unreliable grid infrastructure. Hybrid renewable energy systems, combining solar, wind, and battery storage, have been increasingly studied as a cost-effective and scalable approach to rural electrification, particularly in developing countries [1]. This study primarily relies on simulation-based optimization to evaluate the feasibility and performance of solar-integrated microgrids. Although real-world data from the Songhai Center in Benin are incorporated for validation purposes, the core analysis is conducted through computational modeling and predictive simulations rather than physical experimentation. By leveraging photovoltaic (PV) systems, rural communities can gain access to clean, sustainable, and cost-effective electricity, fostering improved quality of life and socio-economic development [2]. Despite its potential, the widespread adoption of solar microgrids faces several significant challenges. These challenges primarily fall into four key categories:

• Accurate load profiling: Estimating energy demands in rural settings is complex due to variable consumption patterns and seasonal fluctuations. For instance, agricultural activities such as irrigation and crop processing significantly increase electricity demand during planting and harvesting seasons, while household consumption may peak in the evenings when lighting and cooking appliances are used [3].
• System scalability: expanding microgrids to accommodate growing energy needs without sacrificing efficiency remains a technical hurdle [4].
• Environmental factors: dust accumulation and thermal stress severely impact PV system performance, especially in arid and tropical climates [2].
• Economic barriers: high upfront costs and limited access to financing hinder adoption, particularly in low-income communities [5].

Global initiatives emphasize the potential of solar mini-grids to transform energy access. For instance, the World Bank reports that solar mini-grids could provide high-quality, uninterrupted renewable electricity to 380 million people in sub-Saharan Africa by 2030, positioning them as a scalable and cost-effective solution for energy needs [6].

Given the complexity of decentralized energy investments and the need for data-driven decision-making, this study introduces the PV-DEI Index, which provides a structured approach to identifying optimal investment opportunities and addressing rural electrification gaps. Drawing on data from sources such as the World Bank, World Health Organization (WHO), and International Renewable Energy Agency (IRENA), the index incorporates 52 indicators spanning environmental, social, political, and financial dimensions. The findings highlight countries such as Ethiopia, Kenya, and South Africa as favorable investment regions, while others, like Congo and Sierra Leone, require regulatory improvements. The index identifies optimal investment opportunities, addresses rural electrification gaps, and supports stakeholders in designing tailored strategies to advance sustainable energy solutions [7,8].

These developments underscore the increasing relevance of solar-integrated microgrids for rural electrification. However, challenges persist, including accurate energy demand estimation, operational optimization, and environmental impact mitigation. To address these challenges, previous studies have explored various strategies and solutions, as summarized below:

• Load profiling and energy demand estimation: Accurate load profiling is vital for reliable microgrid operations. Sanfilippo et al. (2023) demonstrated how inaccuracies in demand estimation affected system efficiency in the Leveraging Energy Optimization for Adaptable Renewable Deployment (LEOPARD) project [3]. Falk et al. (2021) emphasized dynamic load modeling’s importance, highlighting socio-economic challenges in implementing microgrids in rural Tanzania [9].
• Operational strategies for efficiency: Optimizing energy flow and load balancing is critical for long-term microgrid performance. Arévalo et al. (2024) explored artificial intelligence (AI) to enhance energy efficiency through predictive control [10]. Ding et al. (2020) introduced temperature-dependent battery degradation models that improved reliability and reduced maintenance costs [11]. Hybrid systems combining solar PV with backup energy sources, such as diesel generators or wind energy, demonstrated increased reliability and cost efficiency [12].
• Environmental challenges and solutions: Dust accumulation and thermal stress significantly reduce PV performance in rural settings. Predictive maintenance approaches, including advanced data analytics and real-time condition monitoring, have been shown to mitigate these environmental effects, improving system efficiency and reliability [13]. Studies have shown that dust deposition can lead to efficiency losses of 10–40%, depending on environmental conditions and the frequency of cleaning [14]. Similarly, excessive heat can reduce PV module efficiency by approximately 0.4–0.5% per °C increase in temperature above the standard test condition (STC) of 25 °C [15]. Kamal et al. (2022) emphasized predictive maintenance to mitigate these effects [2]. Recent innovations, such as self-cleaning coatings and automated maintenance systems, have shown promise in maintaining PV efficiency under harsh conditions [16].
• Economic sustainability of microgrid systems: High deployment costs are a barrier in low-resource settings. Nasir et al. (2020) demonstrated the cost-effectiveness of DC microgrid architectures for rural electrification [4]. Falk et al. (2021) highlighted community engagement and public–private partnerships as successful models to reduce financial barriers [9,17].

By addressing these challenges through advanced technologies, predictive modeling, and innovative financing, recent studies have laid the foundation for scalable and sustainable solar microgrid solutions. Building on this, the LEOPARD project implemented a pilot system at the Songhai Center in Benin. This testbed serves as a demonstration site for optimizing microgrid performance, tackling environmental challenges, and enhancing economic viability, with lessons applicable to similar rural contexts. Leveraging insights from this pilot project, this study aims to systematically evaluate and optimize the key technical, economic, and environmental aspects of solar microgrid systems in rural settings.

The primary objective of this study was to evaluate and optimize solar-integrated microgrid systems for rural settings by addressing technical, economic, and environmental challenges. The specific objectives were as follows:

• Performance evaluation: assess the reliability, scalability, and adaptability of solar-integrated microgrids under varying load demands and environmental conditions, focusing on real-world deployment at the Songhai Center in Benin [3,11].
• Economic optimization: Minimize the Levelized Cost of Electricity (LCOE) through optimized system design, operational efficiency, and financing mechanisms like public–private partnerships and community solar models. Reducing costs, improving energy dispatch, and enhancing storage performance are key. Subsidy integration further lowers financial barriers [9].
• Operational optimization: implement predictive models and smart energy management strategies to improve efficiency, load balancing, and real-time decision-making [10,11].
• Scalability and replicability: provide a replicable framework adaptable to similar rural contexts, ensuring lessons learned inform future deployments across Africa and other developing regions [4].

This study aimed to bridge the energy access gap in underserved communities, contributing to long-term sustainability, economic growth, and environmental resilience. The scope focused on designing, implementing, and optimizing solar microgrids, emphasizing a pilot case study at the Songhai agroecological center in Benin [11]. This center provided a practical testbed for analyzing challenges and opportunities in rural energy solutions, including environmental stressors, scalability, and economic feasibility. To systematically assess these aspects, the case study focused on key performance areas, including:

• Case study scope: The Songhai Center integrates solar PV technology with components such as battery storage and diesel generators to evaluate the following:

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  Microgrid performance under varying solar irradiance and load conditions.

  -

  Environmental impacts on PV efficiency, such as temperature fluctuations.

  -

  Load balancing strategies to ensure reliability.

• Broader implications: The findings provide a replicable framework for rural and off-grid energy solutions, offering

  -

  Insights into system optimization, including load estimation and quality control;

  -

  Recommendations for economic sustainability via innovative financing models.

• Contribution to global energy goals: By addressing rural electrification challenges, this study aligns with global efforts to

  -

  Reduce energy poverty in underserved regions;

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  Promote low-carbon solutions to mitigate climate change;

  -

  Empower communities through sustainable energy systems.

The insights gained from this study contribute not only to advancing solar microgrid deployment in rural regions but also to shaping policies and investment strategies for a more sustainable and equitable energy future.

2. Methodology

2.1. Project Scope and Demonstrator Location

This study evaluated the replicability and performance of solar-integrated microgrid systems in rural settings. The Songhai Center in Benin, chosen as the flagship demonstration site for the LEOPARD project, serves as an agroecological hub. Its diverse energy demands and realistic environmental conditions provide an ideal setting for assessing solar-integrated microgrid performance and scalability. To ensure that the demonstration site provided meaningful insights, several critical factors were considered in the selection process:

• Geographical location: the center’s location offered abundant solar energy potential [3].
• Diverse energy demands: mixed-use energy loads facilitated testing under real-world conditions.
• Environmental challenges: thermal stress and dust accumulation provided conditions reflective of microgrid challenges in West Africa [11].

Figure 1 illustrates the high solar energy potential in the region, showcasing monthly solar irradiation data over an 11-year period (2013–2024).

As shown in Figure 1, the region experiences peak monthly solar irradiation of approximately 150 kWh/m² and a minimum of around 80 kWh/m² during less favorable months. This consistent availability of solar energy throughout the year provides a strong foundation for deploying solar microgrids, ensuring reliable energy generation and reduced dependency on diesel generators. Key insights from the solar irradiation data include the following:

• Seasonal stability: The steady variation in solar energy ensures predictable PV system performance, allowing for effective system sizing and design optimization. This stability supports long-term operational reliability, even during less favorable months.
• Energy storage opportunities: High solar irradiation during peak months allows for surplus energy to be stored, which can be utilized during periods of lower irradiation. This facilitates load balancing and ensures an uninterrupted energy supply, reducing the need for diesel backup systems.
• Environmental benefits: By leveraging the region’s abundant solar resource, diesel generator usage can be minimized, significantly reducing greenhouse gas emissions and aligning with sustainability goals.

These attributes position the Songhai Center as an ideal location for demonstrating scalable and replicable solar microgrid solutions, particularly for rural electrification projects in regions with similar climatic and geographical conditions.

2.2. Technical Framework

The technical framework integrated advanced modeling tools, predictive systems, and real-time data analysis to optimize solar microgrid design and operation.

2.2.1. Modeling and Predictive Tools for Optimization

The LEOPARD project’s Microgrid Modeling Optimization and Performance Assessment (MEMOGRID) and Levelized Cost of Electricity (LCOE) tools optimize microgrid sizing and economic viability [19]. Key features include the following:

• Modeling components such as PV arrays, inverters, batteries, and diesel generators.
• Sensitivity analyses for parameters like grid length, population size, and renewable energy share.
• Optimization using advanced meta-heuristics via the OptQuest solver to improve efficiency.

The MEMOGRID tool follows a structured approach to microgrid optimization, integrating data collection, simulation, and decision support.

• Simulation and modeling: MEMOGRID evaluates real-world constraints and decision variables to analyze system behavior under different conditions. The optimization process employs meta-heuristic algorithms through OptQuest, allowing efficient search and decision-making in complex, multi-variable energy models. This approach ensures accurate feasibility assessments and enhances system resilience.
• Optimization and Decision Support: The tool focuses on multiple key aspects of microgrid operations:

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  Resource allocation: determines optimal PV, battery, and diesel generator configurations.

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  Load balancing: ensures stable energy distribution under varying demand conditions.

  -

  Predictive analytics: forecasts energy generation and consumption trends to minimize lost energy.

• Outputs include the optimal system design (PV and battery size), diesel usage percentage, yearly diesel input, lost energy, and LCOE calculations.

Figure 2 illustrates the structured workflow of the MEMOGRID tool, which integrates data collection, optimization requirements, and system evaluation into a comprehensive framework for microgrid planning. The process begins with input data collection, which includes the following:

• Environmental factors: temperature and solar irradiation data.
• Load characteristics: energy demand profiles from household, commercial, and public sector consumers.
• Technical specifications: PV panel efficiency, battery capacity, and inverter performance.
• Economic considerations: investment and maintenance costs, lifespan, and financial discount rates.

The optimization model refines system parameters to enhance efficiency and economic feasibility. Key areas of optimization include the following:

• Optimization requirements: constraints such as maximum uncovered demand and the acceptable percentage of diesel usage.
• Predictive modeling: simulation of different microgrid configurations under real-world scenarios.
• Iterative optimization: refining system parameters to improve performance.

Final outputs include the following:

• Optimal PV and battery sizing to meet energy demand.
• Diesel percentage and total yearly diesel consumption.
• Levelized Cost of Electricity (LCOE) and lost energy assessments.

This framework enables an efficient, data-driven approach to microgrid planning, ensuring optimal energy management with minimal fossil fuel reliance.

2.2.2. Load Profile Estimation

Accurate load profiling is critical for microgrid success. The Load Estimation and Network Identification (LENI) tool provides the following:

• Identification of population clusters and energy demands using geospatial data.
• Network length estimations for specific rural sites [4].
• Application of clustering and pathfinding algorithms to group buildings and determine optimal network layouts.

Figure 3 demonstrates the working principle of the LENI tool, which integrates geospatial data sources such as Google Open Building and Open Street Map to estimate population clusters, network lengths, and energy demand in rural areas. LENI applies clustering algorithms to identify population concentrations and pathfinding algorithms to determine optimal network routes along existing roads. Population estimates are further refined through comparisons with the provided data and geographical validation checks.

2.2.3. Solar Irradiation Data from EU PVGIS

The EU PVGIS tool provides high-resolution solar irradiation data for precise modeling and optimization [18]. These data enhance the following:

• Accurate PV array performance modeling under varying solar conditions.
• Sensitivity analyses for optimal microgrid sizing and energy production.

2.2.4. Economic Feasibility and LCOE

Economic feasibility was assessed using the LCOE equation. A multi-objective sizing optimization approach, balancing cost and carbon emissions, was proposed to improve the sustainability of microgrid investments [20].

$$\mathrm{LCOE}={\displaystyle \frac{{\sum }_{t=0}^T\left({\mathrm{CAPEX}}_t+{\mathrm{OPEX}}_t\right)/{(1+r)}^t}{{\sum }_{t=0}^T\left(\mathrm{Energy}{\mathrm{Output}}_t\ast {(1-d)}^t\right)/{(1+r)}^t}}$$

(1)

where

• ${\mathrm{CAPEX}}_t$: capital expenditures.
• ${\mathrm{OPEX}}_t$: operational expenditures.
• $\mathrm{Energy}{\mathrm{Output}}_t$: annual energy output.
• r: discount rate.
• d: degradation rate.
• T: project lifespan.

Capital expenditure (CAPEX): The total CAPEX consists of the initial costs required for setting up the microgrid system, including:

$$\mathrm{CAPEX}=C_{\mathrm{PV}}+C_{\mathrm{BESS}}+C_{\mathrm{inverter}}+C_{\mathrm{installation}}$$

(2)

where

• ${\mathrm{C}}_{\mathrm{PV}}$ is the cost of photovoltaic modules (EUR per kW);
• ${\mathrm{C}}_{\mathrm{BESS}}$ is the cost of battery energy storage systems (EUR per kWh);
• ${\mathrm{C}}_{\mathrm{inverter}}$ represents the cost of inverters and other power electronics;
• ${\mathrm{C}}_{\mathrm{installation}}$ includes labor, grid connection, and site preparation costs.

Operational expenditure (OPEX): OPEX accounts for the recurring annual costs of maintaining and operating the microgrid:

$$\mathrm{OPEX}=C_{\mathrm{maintenance}}+C_{\mathrm{replacement}}+C_{\mathrm{fuel}}$$

(3)

where

• ${\mathrm{C}}_{\mathrm{maintenance}}$ includes routine servicing, monitoring, and predictive maintenance.
• ${\mathrm{C}}_{\mathrm{replacement}}$ accounts for periodic replacement of batteries and inverters based on lifespan.
• ${\mathrm{C}}_{\mathrm{fuel}}$ applies to diesel backup systems if integrated.

These cost parameters are evaluated over the project lifespan, applying a discount rate to determine present values. The inclusion of predictive maintenance and smart monitoring systems is expected to reduce long-term OPEX by minimizing unexpected failures and optimizing performance [21,22].

This detailed breakdown ensured that the LCOE estimation was realistic, taking into account technical, economic, and operational variables that influenced microgrid sustainability.

2.2.5. CO₂ Emission Reduction

The CO₂ emission savings were calculated by comparing the energy generated by the solar microgrid to the equivalent energy production using diesel generators. Recent research on low-emission microgrids in remote communities emphasizes the potential of hybrid renewable systems in significantly reducing carbon footprints [23]. The methodology involves multiplying the solar energy output by the average CO₂ emission factor of diesel generators. The general formula used was as follows:

$${\mathrm{CO}}_2\mathrm{Avoided}\left(\mathrm{kg}\right)=E_s\times E{F}_d$$

(4)

where

• ${\mathrm{CO}}_2\mathrm{Avoided}\left(\mathrm{kg}\right)$: the total amount of CO₂ emissions avoided by using solar energy instead of diesel generators;
• $E_s$: solar energy produced by the microgrid (in kWh);
• $E{F}_d$: CO₂ emission factor of diesel generators (in kg CO₂/kWh), typically ranging from 0.27 to 0.35 kg CO₂/kWh, depending on generator efficiency and fuel type.

The emission factor was selected based on standard references and represented the average CO₂ emissions per unit of energy produced by diesel generators [24]. This straightforward approach provided an estimate of the greenhouse gas emissions avoided by replacing diesel-based energy production with solar energy.

2.2.6. Integration of GIS, Microgrid Optimization, and Economic Assessment

The methodology for microgrid planning and optimization follows a two-phase approach, integrating GIS-based site identification, technical system optimization, and economic feasibility analysis.

• Phase 1—LENI Tool: The Load Estimation and Network Identification (LENI) tool utilizes GIS-based clustering and pathfinding algorithms to identify potential microgrid sites by estimating network lengths and population clusters.
• Phase 2—MEMOGRID and LCOE Tools: The MEMOGRID tool optimizes system sizing and operational strategies based on environmental, technical, and economic inputs, ensuring a minimal LCOE. The LCOE tool then evaluates financial viability by integrating cost and operational data to determine the feasibility of identified sites.

This structured workflow enables efficient microgrid deployment by ensuring optimal design, cost-effectiveness, and sustainability. Figure 4 illustrates the step-by-step process.

These calculations highlight the environmental benefits of the system, particularly in reducing reliance on fossil fuels and contributing to sustainability goals.

3. System Design and Optimization

3.1. PV System Configuration

The PV system design aimed to maximize energy production and ensure efficient energy flow within the microgrid. Recent studies have highlighted the benefits of integrating multiple renewable resources, such as wind and solar, to enhance system resilience and energy reliability in residential microgrids [25]. This involves careful technology selection, optimized panel placement, and the integration of complementary components, as depicted in Figure 5.

• Optimal panel placement and inclination: The layout included three PV areas (Figure 5), with panels positioned in unshaded regions to maximize solar irradiance capture. Tilt angles between 15° and 20°, based on EU PVGIS data, were optimized for West African solar conditions [2].
• Technology selection: Key components were integrated to ensure efficient energy generation and storage:
  • PV modules: JAsolar 455 Wc monocrystalline panels with 20% efficiency and high-temperature performance.
  • Charge controllers: Victron MPPT RS 450/100-Tr controllers (3 units) for optimal power point tracking.
  • Battery storage: BYD Lithium Batteries (15.4 kWh each) offering 95% efficiency and a 6000-cycle lifespan.
  • Inverters: Victron Quattro 48V/10kVA (3 units) and Fronius SYMO grid inverters with 98.1% efficiency.
• Container Design and Housing: The system was housed in a 20 ft container to ensure modularity, protection, and ease of deployment. Features included removable frames for rapid setup of PV arrays (

  Table A1. Container dimensions and validation metrics for LEOPARD prototype.

  Specification	Details	Unit
  Container’s Sizing
  Length	6060	mm
  Width	2440	mm
  Height	2590	mm
  Features
  Container type	Standard 20 ft container	–
  Building frame	Removable and dismantled (roof arrangement)	–
  LEOPARD Prototype Validation
  Target commissioning time	3–5	h
  Tare weight	2.3	t
  Transportation	Trucks	–

  ).
  This design choice played a crucial role in system optimization by enhancing both scalability and operational efficiency. The container

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  Provided a controlled environment for protecting PV system components against harsh environmental conditions, ensuring long-term reliability;

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  Enabled a plug-and-play approach, allowing for quick installation and deployment in remote or off-grid locations;

  -

  Facilitated replicability and scalability, as standardized modular units could be easily transported and integrated into various microgrid configurations.

  Additionally, the compact and standardized form factor allowed for efficient logistics and deployment, reducing setup time and labor costs. The connection between container sizing and system efficiency is particularly relevant in replicability studies, where transportability and ease of integration are key factors in designing scalable microgrid solutions.
• Environmental impact mitigation: Strategies addressed dust accumulation, temperature effects, and shading:

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  Regular cleaning and potential use of self-cleaning coatings to minimize dust impact [26].

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  Temperature management to maintain panel and battery efficiency.

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  Shading minimization through optimized spatial design.

3.2. System Optimization Strategies

Optimization focused on improving efficiency, balancing energy flow, and addressing solar variability with battery integration and diesel backup (Figure 6). Recent research highlights the importance of advanced energy management strategies for microgrid systems, particularly in campus-scale implementations, where predictive control and real-time monitoring play a crucial role in optimizing energy efficiency [27]. Advanced tools like MEMOGRID and LENI, alongside real-time monitoring, support these strategies.

Figure 6 demonstrates how PV, battery, and diesel systems were integrated with real-time energy flow prioritization. This approach ensured efficient energy management by leveraging renewable energy first, followed by stored energy, and finally relying on diesel backup during critical demand periods.

• Algorithms for efficiency improvement: Predictive control and machine learning models optimized energy flow and balance loads:

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  Dynamic distribution of energy among PV, batteries, and diesel generators (Figure 6).

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  Proactive energy storage management based on solar irradiance predictions [10].

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  Energy loss minimization through load redistribution [28].

  Theenergy flow diagram (Figure 6) illustrates the implementation of these control strategies. Predictive control and real-time monitoring ensured efficient distribution of energy sources, reducing dependence on diesel and maximizing renewable energy utilization.
• Battery management: BYD batteries were central to energy reliability:

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  Optimized charge/discharge cycles extended battery lifespan [11].

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  Peak shaving reduced diesel reliance during high-demand periods.

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  State of Charge (SOC) management maintained optimal battery operation (20–80% SOC range).

  The energy flow diagram highlights the central role of batteries in storing excess PV energy and reducing diesel reliance. Battery SOC management ensures system stability and prolongs component lifespan.
• Diesel backup integration: Diesel engines provided supplementary power when solar generation was insufficient:

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  Hybrid energy management prioritized renewables, minimizing diesel runtime [28].

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  Predictive activation reduced fuel consumption by triggering diesel engines only during critical SOC thresholds.

  As shown in Figure 6, diesel backup was only activated when both PV generation and battery storage were insufficient, minimizing runtime and fuel consumption.

3.3. Energy Balancing and Operational Considerations

Balancing energy supply and demand addressed seasonal and daily solar variability:

• Real-time monitoring: Sensors continuously monitored solar irradiance and system performance, informing operational adjustments [28].
• Load prioritization: Critical loads were prioritized during low-generation periods, ensuring uninterrupted supply.
• Hybrid energy integration: Combining PV, battery, and diesel systems ensured reliability and reduced emissions.

The integration of optimized components and advanced strategies supported scalable, efficient, and sustainable energy solutions for rural microgrids like the Songhai Center.

4. Replicability

The replicability of solar microgrid solutions is crucial for scaling sustainable energy access in resource-constrained regions [7,8]. This section evaluates the feasibility of replicating microgrid systems in Benin and Senegal, focusing on social, technical, and economic factors such as CAPEX, operating expenditure (OPEX), and LCOE variations. Using tools like LENI and sensitivity analyses, potential replication sites and the impact of subsidies are identified. The findings provide a framework for expanding renewable energy microgrids to foster energy sustainability and equitable access.

• Relevant input data: 

  Table 1. Component CAPEX and OPEX for Benin and Senegal.

  Component	Benin CAPEX (EUR)	Senegal CAPEX (EUR)	OPEX (EUR/kWh/Year)
  PV	397.28	450.00	0.198
  Battery	385.97	385.97	0.198
  Diesel generator	224.40	300.00	0.110

  summarizes the CAPEX and OPEX costs for photovoltaic (PV) systems, batteries, and diesel generators in Benin and Senegal.

  -

  Benin:

  *

  Social data: included building distribution by customer type, population density estimates, and clustering analyses.

  *

  Technical data: CAPEX for PV systems (EUR 397.28/kW), batteries (EUR 385.97/kWh), and diesel generators (EUR 224.40/kW) tailored to local conditions.

  *

  Economic data: operational costs were EUR 0.198/kWh annually for renewable systems and EUR 0.107/kWh for diesel fuel, with an 8% discount rate.

  -

  Senegal:

  *

  Social data: cluster-specific load profiles for villages ranging from 200 to 1000 households.

  *

  Technical data: higher CAPEX for PV systems (EUR 450/kW) and diesel generators (EUR 300/kW). Battery CAPEX remained the same as in Benin (EUR 385.97/kWh).

  *

  Economic data: operational costs were similar to those in Benin, with an annual cost of EUR 0.198/kWh for renewable systems. However, higher diesel dependency increased annual expenses.

  The battery prices in both Benin and Senegal remained the same because they were sourced from a standardized procurement process, ensuring uniform pricing across different locations. However, the CAPEX for PV systems and diesel generators differed due to local market conditions, import taxes, and supply chain logistics. Similarly, the OPEX for PV and batteries appeared identical due to comparable maintenance needs and degradation rates over time. While PV arrays require periodic cleaning and inverter maintenance, battery systems undergo capacity degradation and replacement cycles, leading to similar long-term operational costs. This assumption was validated through cost modeling and real-world microgrid operation data.
• Geographical replicability: The LENI tool identified potential replication sites in Benin and Senegal (Figure 7). Key findings included the following:

  -

  Benin: Without subsidies, 144 sites were economically viable for replication. A 30% CAPEX subsidy increased this to 196 sites.

  -

  Senegal: Without subsidies, 43 sites were viable. A 30% subsidy increased this to 296 sites, while a 40% subsidy further raised the total to 321 sites.

• Economic replicability: Economic analyses revealed how microgrid feasibility varied across different regions due to cost dynamics and infrastructural constraints.

  -

  Benin: the LCOE ranged from EUR 0.40 to EUR 0.47/kWh, primarily influenced by grid length and population size. Figure 8 shows that the LCOE decreased slightly as population size increased, though variability remained due to site-specific conditions. Grid length had a minor impact on the LCOE, indicating that shorter distances between settlements led to more consistent costs.

  -

  Senegal: the LCOE averaged EUR 0.48/kWh, affected by a higher CAPEX and diesel dependency. Figure 9 demonstrates that the LCOE increased with grid length, as longer networks required higher infrastructure investment. Population size did not significantly reduce the LCOE, likely due to higher initial costs and operational constraints.

  Comparison and key insights: Figure 8 and Figure 9 illustrate different cost trends in Benin and Senegal:

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  In Benin, the LCOE remained relatively stable with respect to grid length, whereas in Senegal, longer grid networks resulted in higher costs.

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  Higher population density in Benin led to a lower LCOE, while in Senegal, diesel dependency limited cost reductions even with larger population sizes.

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  Senegal exhibited greater cost variability, likely due to a higher CAPEX and reliance on fossil fuel backup systems.

  -

  Benin’s LCOE remained below Senegal’s average (EUR 0.48/kWh), suggesting that microgrid replication may be more cost-effective in Benin under similar conditions.

  This analysis highlights how geographical, infrastructural, and economic factors shape microgrid feasibility, emphasizing the need for region-specific optimization strategies.
• Sensitivity analysis: As shown in Figure 10, the sensitivity analysis evaluated the impact of CAPEX subsidies and diesel share on economic feasibility:

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  Benin: Increasing the diesel share from 14% to 25% added 51 viable sites, expanding the total to 195. A 30% CAPEX subsidy slightly increased this to 196 sites.

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  Senegal: A 30% subsidy raised the number of viable sites from 43 to 296, while a 40% subsidy added 25 more sites, totaling 321.

5. Conclusions

The LEOPARD project has demonstrated the potential of solar-integrated microgrids in addressing rural electrification challenges through simulation-driven optimization techniques. The study utilized predictive computational models rather than experimental testing to evaluate energy performance, economic feasibility, and system scalability.

• Performance optimization:

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  Integration of solar PV systems achieved energy conversion efficiencies exceeding 95%.

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  The system supplied between 50% and 80% of daily energy demands, with an average output of 184 kWh/day under optimal conditions.

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  Energy storage maintained an efficiency of 88%, ensuring consistent power availability during periods of low solar irradiance.

• Economic feasibility:

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  The Levelized Cost of Electricity (LCOE) was reduced to EUR 0.47/kWh, significantly lower than diesel-based alternatives.

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  Operational costs were lowered by decreasing diesel dependency by 65%, resulting in annual savings of EUR 0.198/kWh compared to diesel operations.

• Environmental sustainability:

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  The reduction in diesel usage prevented 10.16 tons of CO₂ emissions within the first 150 operational days, supporting regional sustainability objectives.

  -

  Annual CO₂ savings are projected to exceed 25 tons with continued maintenance and optimization.

This study provided a structured feasibility analysis for solar-integrated microgrids, bridging the gap between theoretical modeling and real-world deployment. Unlike prior studies, it integrated cost analysis, replicability assessments, and predictive analytics to optimize performance and economic feasibility. The findings serve as a reference for future research, supporting scalable and cost-effective rural electrification strategies.

5.1. Broader Impacts

• Community empowerment: The project provides stable, affordable electricity, enabling enhanced access to education, healthcare, and economic opportunities, thus fostering socio-economic development.
• Alignment with global goals: The initiative aligns with the Paris Agreement’s climate action targets, promoting renewable energy adoption in Sub-Saharan Africa while fostering European–African collaboration.
• Scalability and replicability: The system’s modular and adaptable design offers a scalable blueprint for similar rural settings. Its architecture allows for flexible expansion by integrating additional PV arrays, battery storage, and hybrid energy sources based on site-specific requirements. Additionally, predictive analytics and geospatial modeling tools assist in identifying suitable locations, ensuring technical feasibility. This framework has already demonstrated viability, with 296 sites in Senegal and 196 in Benin identified as potential replication candidates under subsidized conditions.

5.2. Challenges and Recommendations

• Technical challenges:

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  Enhance load profile accuracy using dynamic modeling to address demand variability.

  -

  Mitigate environmental impacts, such as dust accumulation and thermal stress, through hydrophobic and self-cleaning coatings (e.g., TiO₂-based nanocoatings) that reduce dust deposition and thermally insulating materials that minimize heat-induced performance losses [29]. Additionally, predictive maintenance strategies utilizing infrared thermography and AI-driven fault detection enhance system longevity and efficiency [30,31]. Recent studies highlight the role of predictive maintenance in reducing PV inefficiencies, ensuring early fault detection and proactive maintenance scheduling [32].

• Economic barriers: Expand access to subsidies and financial incentives, such as a 30% CAPEX subsidy, to enhance economic feasibility for additional deployment sites.
• Scalability: Develop flexible system architectures to accommodate diverse energy demands, environmental conditions, and hybrid integration with other renewable sources like wind energy.

5.3. Future Directions

• Technology integration:

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  Incorporate IoT-based monitoring for real-time diagnostics and system performance optimization.

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  Explore hybrid systems integrating wind energy and other renewable sources to diversify energy generation.

• Policy and collaboration:

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  Strengthen European–African partnerships to drive innovation and adoption of renewable energy technologies.

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  Advocate for supportive policies, including CAPEX subsidies and carbon credits, to accelerate widespread adoption.

• Research opportunities:

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  Conduct longitudinal studies to assess long-term system performance and socio-economic benefits.

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  Investigate novel materials and technologies to improve efficiency and durability in challenging environmental conditions.