The study area consists of Mali’s 703 municipalities, which constitute the study area (Figure 1Figure 1) [9].

Figure 1. Study area.

Devicesused:

GPS: To determine the coordinates of several points in the municipalities concerned.

Laptop: For data analysis and report writing.

Software:PVSyst: Software used for photovoltaic modeling. It provides access to the database based on image processing from the Meteosat Second Generation satellite. It was therefore used in this research to process satellite images of the various municipalities concerned.Microsoftoffice: The company’s proprietary office suite.Excel: Used to organize, analyze, visualize, and manipulate data using its spreadsheet, formula, and charting features.PVGIS: Software used in photovoltaic modeling. It provides access to a database built from satellite image processing.ArcGIS10.3andQGIS3.32.3: This Geographic Information System software was used for digitization (map digitization), file conversion, data editing and modeling, visualization of created spatial data, spatial information management, and spatial analysis.

These devices and software enabled us to generate the necessary information in these municipalities to analyze the data and generate maps.

2.3.1. Geospatial Data on Mali’s Administrative Boundaries

In this study, geospatial data on Mali’s administrative boundaries, provided by the Geographic Institute of Mali (IGM) in shapefile (SHP) format, were used to precisely delimit the various territorial entities analyzed. The different categories of data used are as follows:

Regionalboundaries:

Provided by the Geographic Institute of Mali (IGM) in shapefile (SHP) format. The regional boundaries delimit the eight major administrative subdivisions of Mali under the former administrative division, to which the district of Bamako is added.

Circleboundaries:

Provided by the Geographic Institute of Mali (IGM) in shapefile format (SHP). The circles represent an intermediate subdivision between regions and municipalities. The 49 circles included in this study allow for a more refined territorial analysis that takes into account local administrative and geographic realities, while facilitating decentralized management and sectoral planning [10].

Municipalboundaries:

Provided by the Geographic Institute of Mali (IGM) in shapefile (SHP) format. The 703 municipalities, 29 of which have urban status, constitute the basic administrative level directly concerned by local development policies. Geospatial data on municipal boundaries were used to identify target areas. These data are vector-based and reflect Mali’s former administrative divisions. They were collected from the databases of the Mali Geographic Institute (IGM). This vector file contains various geographical information on the 703 municipalities, the eight (8) regions plus the district of Bamako, and the 49 circles of Mali’s former administrative division [10]. It provides all the necessary information on the latitude, longitude, boundaries, and surface area of all the files covered by the study.

Demographicstatistics:

In this study, municipal demographic statistics are taken from the Fourth General Population and Housing Census (RGPH 4) conducted in Mali by the National Institute of Statistics (INSTAT) and the Central Census Bureau (BCR). These data provide detailed information on the population by municipality, including an average annual population growth rate of 3.6% between 2009 and 2022 [10]. They were used to assess basic service needs, particularly electricity, taking into account local demographic dynamics.

2.3.2. Collection of Data on Power Plants

As part of this study, energy data was collected from several national institutions, including the National Energy Directorate (DNE) and the Malian Agency for the Development of Domestic Energy and Rural Electrification (AMADER). This information, covering the period 2009-2023, includes the location of power plants, the situation of the various localities covered by electrification services, their installed capacity, and their energy source (thermal, solar, hybrid, or hydraulic). It has made it possible to take stock of national energy coverage and identify areas that are still unserved.

2.3.3. Collection of National Energy Data

National energy data was collected from the National Energy Directorate (DNE) and the Malian Agency for the Development of Domestic Energy and Rural Electrification (AMADER). The main energy data collected are as follows:

Datafilesonlocalitieswithaccesstoelectricityservices:

a) LocalitiesservedbytheEDMinterconnectednetwork:

The data in Table 1 comes from the National Energy Directorate (DNE), which is the governmental agency body responsible for planning and monitoring national energy policies (year, 2022).

Table 1. BT subscribers and meter usage density by region (RI).

b) LocalitiesservedbyEDM’sisolatedcenters:

The data in Table 2 comes from the National Energy Directorate (DNE), which is the governmental agency body responsible for planning and monitoring national energy policies.

Table 2. BT subscribers and meter density by region (isolated EDM centers).

c) ServedbyAMADER:

With regard to the areas served by AMADER, the data was collected from the Rural Electrification Directorate (DER), specifically the Project Monitoring Service in 2022, and is summarized in Table 3. This data reflects operational expertise in the field, given AMADER’s role in the development of mini-grids and autonomous photovoltaic systems in rural areas.

Table 3. BT subscribers and average household size by region (AMADER power plants).

This is both qualitative and quantitative.

2.4.1. Qualitative Analysis: Territorial Distribution of Access to Electricity

Assessing the geographic distribution of access to electricity is essential for effective planning, and the criteria-based binary classification method is an effective way to identify such areas [11]. The “criterion-based binary classification method” (or “multi-criteria binary analysis”) was used. This is a method in which each site is evaluated based on specific criteria (EDM, AMADER, network coverage), assigning it only values of 0 or 1, and then classifying the sites according to these results. However, we have established options and criteria for effective decision-making.

Optionsforselectingmunicipalities:

The choice of options makes it possible to distinguish between municipalities with higher priority and those with lower priority for the deployment of decentralized PV systems:

Option 1: Priority municipalities (have no access to any electrification services).Option 2: Lower priority municipalities (have access to at least one electrification service).

Selectioncriteriabylocality:

The selection criterion is applied by assigning a binary score of 0 or 1 to the localities concerned.

Criterion1: Electricity service coverage by EDM-SA

Criterion2: Electricity service coverage by AMADER

Criterion3: National network expansion plan

0: Access to electricity is not guaranteed in the municipality.

1: Access to electricity is guaranteed in the municipality.

The municipalities suitable for PV installation were selected in each locality A, B, C, and D and form a single locality that we have designated E (high-priority municipalities) with a score of 1 for each criterion represented in Table 4.

Table 4. Selection of suitable localities.

Using QGIS 3.36.3 software, we located, delimited, and classified the municipalities according to locality Table 4.

Using QGIS 3.36.3 software, we located the different municipalities that met these criteria and classified them according to location, for a total of 435 municipalities.

2.4.2. Quantitative Analysis

This enabled us to calculate: The rates of access to electricity, photovoltaic coverage, and installed capacity for each municipality, expressed as a percentage [12]:

Access rate ( % ) = Number of subscribers × Number of people per meter Total population of the locality ∗ 100 ,

(1)

Territorial coverage rate ( % ) = Number of localities served Total number of localities ∗ 100 , (2) Installed capacity rate ( % ) = Installed capacity per service Total number of localities ∗ 100 , (3)

3.1.1. Municipalities Served by Energie Du Mali

Figure 2Figure 2 below shows the distribution of the different municipalities served by Mali’s interconnected energy network.

Figure 2. The EDM’s interconnected network (RI).

According to the figure, the interconnected network (RI) covers 35 municipalities in green, mainly in the south and center of the country. The isolated centers (CI) serve 33 municipalities in purple spread across the country. Two (2) municipalities, in red, benefit from interconnection with Côte d’Ivoire.

A total of 70 urban municipalities are served by EDM, representing approximately 9.96% of the municipal territory.

3.1.2. Municipalities Included in the EDM-SA Expansion Plan

Figure 3Figure 3 shows the various municipalities included in the electricity expansion plan by Energie du Mali.

Figure 3. Municipalities covered by the EDM expansion plan.

According to Figure 3Figure 3, a total of 81 municipalities are covered by the EDM expansion plan, representing 11.52% of the territory.

3.1.3. Municipalities Served by AMADER

Figure 4Figure 4 below shows the distribution of the different municipalities served by AMADER.

Figure 4. Municipalities served by AMADER.

According to Figure 4Figure 4, a total of 117 municipalities colored in black are served by AMADER, representing 16.64% of the municipal territory.

3.1.4. Municipalities Served by Electrification Services

Table 5 shows the geographical distribution of all municipalities served by electrification services.

Table 5. Territorial coverage rate by electrification service.

Localitieswithoutelectricitysupply:

Figure 5Figure 5 below shows the geographic distribution of the various communities served and not served by the national power grid.

Figure 5. Territorial distribution map.

In Figure 5Figure 5 above, the area shaded in black represents the various municipalities that have access to a reliable electricity supply, totaling 268 municipalities. In contrast, the yellow-shaded area represents the various municipalities that do not have access to a reliable electricity supply, totaling 435 municipalities. However, the map shown in Figure 6Figure 6 below clearly illustrates the distribution of the various municipalities in Mali that are not connected to the electricity grid.

Figure 6. Municipalities not covered.

Figure 6Figure 6 above shows the geographic distribution of the various municipalities that are not served by the national power grid. The area shaded in black corresponds to the 435 municipalities that do not have access to a reliable electricity supply.

Figure 7Figure 7 below shows the rate of access to electricity by locality through the interconnected network (RI). It can be seen that the district of Bamako dominates with access rates of 98.26% and 89.07%, depending on whether the population of the localities served alone or the total population is considered. On the other hand, regions such as Gao, Timbuktu, and Kidal have zero rates and therefore do not benefit from access to electricity through the interconnected network (RI).

Figure 8Figure 8 shows the rate of access to electricity through isolated centers (CI). It can be seen that the regions of Mopti and Timbuktu lead the way with access rates of 39.81% - 8.14% and 38.94% - 11.76%, depending on whether only the population served or the total population is considered. In contrast, the district of Bamako has a zero rate through EDM’s isolated centers.

Figure 9Figure 9 below shows the rate of access to electricity in Mali through AMADER. It can be seen that the regions of Kayes and Sikasso have high rates of 26.9% and 8.1% and 28.9% and 5.2%, respectively, depending on whether only the population served or the total population is considered.

Figure 10Figure 10 below shows the national access rate through the various electrification services, where we can see that Bamako has high rates of 98.26% and 89%. In contrast, the Mopti region has the lowest access rate at 12.64% and 10.34%.

Figure 7. Electricity access rates across RI.

Figure 8. Access rates through CI.

The national average is 59.06% for the population served and 26.11% for the total population.