However, for the purposes of this study, the administrative structure of Mali is analyzed based on data from the RPGH-5, which has a slightly different structure than the RPGH-4. The study area consists of 815 municipalities, 29 of which are urban (Figure 1Figure 1) [9].

Figure 1. Study area.

Conducting this study required the use of various pieces of equipment and computer tools, including a GPS device for collecting and geolocating field data, as well as Excel, PVGIS, ArcGIS 10.3, and QGIS 3.32 for processing, analyzing, and visualizing the data.

The study draws on several sources of geographic, meteorological, satellite, and statistical data necessary for analyzing photovoltaic solar potential and identifying areas suitable for electrification. The main data used are as follows: the administrative boundaries of municipalities from the IGM vector database; data on electricity demand in rural areas, derived from INSAT electrification data; global solar irradiance data from satellite databases such as Global Solar Atlas/GIS; land-use data obtained from NASA’s Landsat satellite imagery; sunshine duration data from national meteorological service stations; data on average monthly solar radiation intensity derived from satellite data; data on the number of hours of sunshine, extracted from the German Weather Service (DWD) satellite database; and data on built-up areas in the various municipalities, provided in shapefile format by the Geographic Institute of Mali.

2.4.1. Qualitative Analysis

Assessing the geographic distribution of access to electricity is an essential step in effective electrification planning. In this context, the Analytic Hierarchy Process (AHP), combined with geographic information systems (GIS), is a relevant method for identifying and prioritizing the areas with the greatest needs for photovoltaic electrification [10]. To facilitate decision-making, selection options and evaluation criteria were defined.

Municipality Selection Options:

The selected options make it possible to distinguish municipalities based on their level of access to electrification services and, consequently, their priority level for the deployment of decentralized photovoltaic systems:

Option 1: priority municipalities, corresponding to those with no access to electrification services;Option 2: Lower-priority municipalities, comprising those that have at least one electricity service.

Selection Criteria

The selection of municipalities is based on three criteria related to the current or projected availability of electricity services:

Criterion 1: Electricity service coverage by EDM-SA;Criterion 2: Electricity service coverage by AMADER;Criterion 3: Plans for an expansion of the national power grid.

For each of these criteria, a binary score was assigned to characterize the situation in each municipality:

0: Access to electricity is not guaranteed in the municipality;1: Access to electricity is guaranteed or planned in the municipality according to the criterion in question.

Based on this classification, municipalities that lack any electrification services and are not included in grid expansion plans were identified as the highest priority areas for the deployment of decentralized photovoltaic systems. These municipalities thus constitute the set selected for the analysis of photovoltaic potential.

The location, boundaries, and spatial classification of the municipalities were determined using QGIS software, which allowed for the cross-referencing of various geographic layers and the categorization of municipalities according to their priority level. The results of this classification are presented in Table 1 and Table 2.

Table 1. Evaluation matrix for localities.

Table 2. Site selection criteria table.

Using QGIS 3.36.3 software, we identified the various municipalities that met these criteria and classified them by locality, for a total of 547 municipalities.

2.4.2. Quantitative Analysis

Estimation of Photovoltaic Energy to Be Generated:

A municipality’s electricity demand is calculated by multiplying the daily, monthly, or annual consumption of a typical person (Bji) by the total population (Pt) of that municipality. This method is based on a linear approach to energy demand grounded in individual needs. This method is particularly well-suited for rural areas where detailed consumption data are not always available. It was used by Ebhota & Tabakov (2022) to model local energy needs based on demographic parameters [11]:

B T = B j i ∗ P t (Eq1); where B T is the municipality’s electricity demand, which is 116 kWh in Mali;The planned installation area for each municipality [12]: S p = B T I S (Eq2); or: B T (in kWh): Electricity demand per municipality I S (in kWh/m²): the average global solar irradianceThe available solar potential for each municipality [13]: R P g e = S p ∗ H r ∗ T s , where H r is the average monthly irradiance (MW/km²)Electricity generation by municipality [13]: E A = S p ∗ H R ∗ η p ∗ ( 1 − λ p ) ∗ ( 1 − λ C )

or

H R : total solar irradiance (in kWh/m²/year)Solar and electricity generation potential of buildings for each municipality [13]: R P g e = S B ∗ H r ∗ T s E A = S B ∗ H R ∗ η p ∗ ( 1 − λ p ) ∗ ( 1 − λ C ) Potential availability: The availability of built-up areas is equal to 50% of the solar potential of the built-up areas for each municipality. R P g e D = 50 % ∗ R P g e E A D = 50 % ∗ E A

Figure 2Figure 2 below shows the geographical distribution, by municipality, of the various localities served and not served by the national electrification services. The area colored black consists of localities with access to electricity, totaling 268 localities. In contrast, the area colored yellow consists of localities with no access to any electrification services, totaling 547 municipalities spread across the entire national territory.

Figure 2Figure 2 below highlights an uneven spatial distribution of access to electricity nationwide. The 268 municipalities that are currently served or scheduled for service are shown in black, while the 547 municipalities that are not served are shown in yellow. This high proportion of unserved municipalities underscores the persistence of regional disparities in electrification and highlights the need to develop decentralized solutions—particularly solar photovoltaics—for off-grid areas. In other words, 67% of rural localities are not served by the two national electrification services.

Figure 2. Off-grid communities suitable for photovoltaic systems.

The map shown in Figure 3Figure 3 illustrates a positive correlation between electricity demand (orange/dawn areas) and the solar potential to be harnessed (red pie charts).

Figure 3. Estimated solar potential by municipality to meet demand.

The map in Figure 3Figure 3 reveals a proportional relationship between the energy needs of municipalities (orange areas) and their exploitable solar potential (red diagrams). This trend suggests that the localities with the greatest electricity deficits also have favorable solar resources, making photovoltaic energy a suitable solution. Thus, the higher the demand for electricity, the greater the projected solar potential, which justifies targeted investments in these areas. A total of 341397.17 MWh can be generated across the region. This relationship suggests that localities (municipalities) not served by national energy services but suitable for solar PV installations are also those where sunlight levels are sufficient to justify investments in solar photovoltaic solutions.

Consequently, the map shown in Figure 4Figure 4 provides a breakdown of projected electricity production by municipality.

Figure 4. Projected electricity generation potential by municipality.

The map shown in Figure 4Figure 4 highlights a direct correlation between high solar potential (red pie charts) and actual solar electricity generation in the municipalities (pink/beige areas). This relationship confirms that areas with high levels of sunshine have greater energy conversion capacity, resulting in higher electricity generation. High demand for electricity requires greater utilization of solar resources, thereby increasing production. This dynamic demonstrates the relevance of decentralized solutions in areas of Mali not covered by the electricity grid, where sunlight is abundant.

The solar potential on built-up areas is estimated at 84,697 ha. Taking into account the 50% available area, this potential amounts to 42348.59 ha. The map shown in Figure 5Figure 5 below provides a municipal breakdown of the available solar potential on built-up areas.

The solar potential of built-up areas is estimated at 84,697 ha across the municipalities studied. Assuming that 50% of this area is actually available and suitable for the installation of photovoltaic systems, the usable area is estimated at 42348.59 ha. This area represents considerable spatial potential for the development of photovoltaic power generation, particularly in municipalities that are not served or are inadequately served by the national power grid.

Figure 5. Solar potential on built-up areas by municipality.

Figure 6Figure 6 below shows the spatial distribution of photovoltaic solar potential across the various municipalities of Mali. It illustrates the regional disparities in potential and identifies the municipalities offering the most favorable conditions for the development of photovoltaic power generation.

This result highlights the considerable photovoltaic solar potential of built-up areas. Even when considering only 50% of the available surface area, the estimated potential reaches 311,921 × 10³ MWh, which is approximately 914 times the electricity demand to be met. This significant margin between available potential and demand shows that built-up areas could serve as a major driver for the electrification of unserved communities, subject to technical, economic, and site accessibility constraints.

Figure 7Figure 7 shows the distribution of the theoretical and technically exploitable photovoltaic solar potential at the municipal level. The theoretical potential is estimated at 6397.75 × 10⁶ kWh, and, assuming 50% of the available area is utilized, the technically exploitable potential is estimated at 3198.87 × 10⁶ kWh—approximately 914 times the electricity demand to be met.

Figure 6. Available solar potential of built-up areas.

Figure 7. Solar electricity generation potential in built-up areas.

These results demonstrate the significant photovoltaic potential available to meet the electricity needs of the municipalities studied. Despite the 50% reduction resulting from the assumption that only 50% of the areas are available, the technically exploitable potential remains very high, at 3198.87 × 10⁶ kWh—approximately 914 times the electricity demand. This significant surplus capacity underscores the value of solar photovoltaics as a sustainable solution for improving access to electricity, particularly in municipalities not connected to the national grid.