Resource adequacy refers to a power system’s ability to meet demand using available production capacity, energy storage, electricity exchanges and other flexibility [14]. A high-level installed capacity alone does not guarantee adequacy. Forced outages, scheduled maintenance, fuel supply constraints, hydrological conditions, network limitations, and the availability of imports must also be considered. Modern approach therefore recommended using several indicators that measure the frequency, duration and severity of supply shortfalls rather than relying on a single reserve margin [14]-[16].

Probabilistic indicators such as Loss of Load Expectation (LOLE), Loss of Load Probability (LOLP), and Expected Energy Not Served (EENS) remained the standard tools for assessing resource adequacy. However, calculating these indicators requires hourly demand data, unit level outage rates, maintenance schedules, hydrological profiles and electricity exchanges. When such data are unavailable, an annual analysis can identify potential supply risks but cannot provide a full probabilistic adequacy assessment.

Electricity consumption depends on household income, urbanization, industrialization, electricity prices, and access to the power grid. A study covering Côte d’Ivoire, Ghana, Nigeria, and Senegal confirm the importance of income and demographic factors in driving demand [11]. Prosumers should be considered in demand forecast, as their consumption patterns could significantly affect the future electricity needs of newly connected households [17].

In Côte d’Ivoire, the Electricity for All Program (Programme Électricité Pour Tous: PEPT) has significantly accelerated access to the grid. The total number of customers increased from 1.32 million in 2014 to 5.28 million in 2025, including 2.72 million customers connected through the PEPT program [17]. While this represents major social progress, it also creates new obligation: ensuring a reliable electricity supply for these new customers.

Electric interconnections allow countries to share reserves, combine different energy resources and reduce costs. In West Africa, they offer considerable potential, particularly by combining the use of hydropower, thermal generation, and solar energy [3][4][18]. However, their effectiveness depends on the availability of transmission and distribution infrastructure, consistent electricity trading rules and generation surplus that can be mobilized [4][19][20]. An increase in imports may simply reflect efficient system operation. However, they become a warning sign when they occur alongside declining domestic generation availability, rapid growth in electricity demand and a sustained reduction in the net export balance.

Table 1. Main variables used to analyze Côte d’Ivoire’s power system.

The complete annual data supplied by the Ivorian Electricity Company (CIE), including the units and definitions of all indicators used in this study is presented in the Appendix. it compiles technical, commercial, and financial indicators for the Ivorian electricity sector. The data source covers the period from 2014 to 2025. The variables include net domestic generation, gross domestic electricity consumption, transmission and distribution efficiency, average outage duration, energy not supplied and the numbers of customers. The dataset also contains installed capacity by generation and total plant availability. Table 1 presents the main variables used to analyze changes in the Ivorian power system, together with their observation period and their role in the study.

The average growth rate over a given period is measured using the compound annual growth rate (CAGR) as expressed in Equation (1).

where X i and X f are the initial and final values,respectively and n is the number of annual intervals.

The net electricity trade balance is calculated using Equation (2)

where N T B t is the net electricity trade balance, E x p o r t s t is the amount of electricity exported, I m p o r t s t is the amount of electricity imported and t represents the year under consideration.

The annual import dependence rate is calculated relative gross domestic electricity consumption using Equation (3)

where I D R t is the import dependence rate, I m p o r t s t is the amount of electricity imported, D o m e s t i c   c o n s u m p t i o n t is the gross domestic electricity consumption and t represents the year under consideration.

Finally, the apparent losses associated with network efficiency are estimated using Equation (4)

where L o s s e s t represents the apparent network losses, R t is the transmission or distribution network efficiency and t represents the year under consideration.

These indicators are descriptive. The import dependence rate alone does not measure the risk of an electricity shortage, since imports may result from economic dispatch decisions. It should therefore be interpreted together with domestic generation availability and the net electricity trade balance.

Linear regression is used to estimate the average annual change in each indicator and how well the trend fits the data. The Mann-Kendall test and Sen’s slope are also applied to limit the influence of extreme values and avoid relying solely on the assumption of normally distributed data [21]-[23]. The significance level is set at 5%.

The linear trend is estimated using Equation (5):

where X t is the value of indicator in year t , α is the intercept or estimated value of the indicator when t = 0 , β is the regression slope which represents the average annual change in indicator, ε t is the error term which captures the variation not explained by the linear trend.

Sen’s slope is calculated using Equation (6):

where β S e n is theSen’s slope, X j and X i are the values at time i and j respectively.

Because the Mann-Kendall test assumes that successive observations are independent, each series was checked for fist-order correlation. The trend was first removed using Sen’s slope, as shown in Equation (7)

where X ′ t is the detrended value in year t.

Lag-1 correlation was then calculated from the ranks of the detrended observations. Its significance was assessed using the approximate 95% limits given by Equation (8).

where r 1 , c r i t represents the critical autocorrelation limits and n is the sample size.

For the 12 annual observations used in this study, the limits were approximatively ±0.566. An autocorrelation coefficient outside these limits was considered statistically significant.

When significant serial correlation was detected, the Hamed-Rao modified Mann-Kendall test was applied. This method adjusts the variance of the Mann-Kendall statistic using Equation (9):

where V a r * ( S ) is the corrected variance, V a r ( S ) is the original Mann-Kendall variance and ρ k is the significant autocorrelation at lag k and m the maximum lag considered.

For the original Mann-Kendall test, exact two-sided p-values were used for series without tied observations. For series containing ties, the variance was adjusted for tied values and p-values were calculated using the normal approximation with continuity correction.

Relationship between pairs of variables are measured using Pearson and Spearman correlations coefficients. Because two variables may appear strongly, partial correlation is also calculated after removing the linear effect of the year. These coefficients describe statistical associations and should not be interpreted as evidence causality.

Three main demand scenarios are developed using 2025 as the base year. The moderate scenario assumes an annual growth rate of 5%, representing a slowdown in electricity demand. The trend scenario uses 6.5%, which is close to the arithmetic mean of the annual growth rates observed over the study period. The accelerated scenario assumes 8%, a rate consistent with the recent growth reported in the annual review. A 10% growth rate is included only as a high-growth sensitivity case.

Electricity consumption for each year is projected using Equation (10)

where C t is the projected electricity consumption in year t, C 2025 is the electricity consumption recorded in the base 2025, g is the assumed annual growth rate.

A linear extrapolation is also provided as a benchmark, together with a 95% prediction interval. The difference between the linear projection and the compound-growth scenarios reflects uncertainty related to the choice of the mathematical model. In other words, the results depend on whether future demand is assumed to increase by a constant amount of energy each year or by a constant annual percentage.

Since only 12 annual observations were available, ARIMA modeling was not considered reliable. The lack of hourly load, generation, outages, hydrology, and electricity trade data also prevented a probabilistic adequacy assessment using indicators such as LOLE and EENS [14][24]-[26].

Gross domestic electricity consumption nearly doubled between 2014 and 2025, rising from 7333 to 14,635 GWh, with a compound annual growth rate (CAGR) of 6.48%. On the same period, net domestic generation increased from 8166 to 14,563 GWh, corresponding to a CAGR of 5.40%. As shown in Figure 1, gross demand grew slightly faster than generation, gradually reducing the margin between domestic supply and consumption.

The number of customers quadrupled from 1.32 million to 5.28 million, with a CAGR of 13.47%. This suggests that many new customers are still consuming relatively little electricity. However, demand could continue to rise as households acquire more electrical appliances and productive uses expand. Table 2 summarizes these changes, including the strong growth in consumption, customers number, and imports between 2014 and 2025. Gross domestic consumption and net domestic production are based on different accounting scopes. Gross domestic consumption is calculated based on gross production before deducting auxiliary consumption by power plants, whereas net production excludes this internal use.

Table 2. Trends in the main ındicators of Côte d’Ivoire’s power system, 2014-2025.

As shown in Figure 2 and Table 3, electricity consumption increased steadily between 2014 and 2025. The linear trend explains 95% of its annual variation ( R 2 = 0.956 ). Significant serial dependence was detected after removing the trend ( r 1 = 0.698 ). However, the increase remained statistically significant after the Hamed Rao correction ( p = 0.001 ). Sen’s Slope indicates that consumption increased by approximately 625 GWh per year.

Domestic generation also increased significantly by approximately 555 GWh per year. No significant serial dependance was found in this series ( r 1 = 0.698 ) and its adjusted p-value remained below 0.001. Because consumption increased faster than domestic generation, the margin between national supply and demand gradually narrowed.

Table 3. Trend analysis results for the main ındicators of Côte d’Ivoire’s power system, 2014-2025.

Figure 2. Statistical trends in electricity consumption, imports, net trade balance, and customer numbers, 2014-2025.

The number of customers rose from 1.32 million in 2014 to 5.28 million in 2025 and remained on a significant upward trend after correction ( p = 0.001 ).

Electricity imports, increasing by approximately 41 GWh per year ( p < 0.001 ), while the net trade balance declined by approximately 101 GWh annually ( p = 0.024 ). Côte d’Ivoire remained a net exporter of electricity, but the gap between exports and imports gradually narrowed.

Distribution efficiency improved by about 0.8 percentage point per year over the full period ( p < 0.001 ), despite a slight decline after 2022. Average outage duration also decreased, but this trend was not statistically significant after correction ( p = 0.322 ).

As shown in Figure 3, electricity consumption, domestic generation, imports and customers numbers generally increased together between 2014 and 2025. The particularly strong correlation between consumption and costumer growth reflects the effect of expanded electricity access on domestic demand. Domestic generation also increased with consumption, but at a slightly slower rate, which gradually reduced the margin between supply and demand.

Electricity consumption showed a strong correlation with imports (Pearson, r = 0.939 , p < 0.001 ); Spearman ( ρ = 0.965 ). This relationship remained positive and statistically significant after controlling for the common time trend (partial r = 0.782 , p = 0.0044 ). Thus, years with unusually high consumption were generally associated with higher electricity imports. This finding highlights the growing role of regional trade in meeting domestic demand. However, imports also depend on the availability of production capacity, hydrological conditions, gas supplies, and regional electricity prices.

The net trade balance showed a negative correlation with both consumption and imports. After controlling for the time effect, the relationship between consumption and the net balance remained negative but was not statistically significant (partial r = − 0.545 , p = 0.083 ). In contrast, the relationship between imports and the net balance remained significant (partial r = − 0.678 , p = 0.022 ). However, this result should be interpreted with caution, because the net trade balance is defined as exports minus imports. The negative association with imports is therefore partly mechanical and does not establish a causal link.

Distribution losses, expressed as a percentage, generally decreased as consumption, generation, imports, and customer numbers increased, reflecting improvement in network performance over much of the period. However, the slight decline in distribution efficiency observed after 2022 suggests that this improvement has not continued consistently in recent years. The average duration of outages showed only a weak to moderate correlation with the other indicators, confirming that service interruptions also depend on equipment failures, maintenance, network configuration, and the effectiveness of restoration efforts.

Figure 3. Spearman correlation matrix for the main indicators of Côte d’Ivoire’s power system, 2014-2025.

As shown in Figure 4 and Table 4, Côte d’Ivoire’s installed generation capacity increased from 2229 MW in 2020 to 3224 MW in 2025, a gain of 995 MW, or 44.6%. This expansion was driven mainly by thermal power generation, which rose from 1350 to 2166 MW following the commissioning of new units, the expansion of the Atinkou power plant, and additional Aggreko capacity. Hydropower increased more modestly from 879 to 991 MW, while solar entered the generation mix in 2023 and reached 67 MW in 2025, supports by the Boundiali solar power plant.

In 2025, thermal plants accounted for 67.2% of installed capacity, compared with 30.7% for hydropower and 2.1% for solar power. Although thermal generation offers adjustable capacity, this structure makes the power grid vulnerable to constraints on natural gas supply, fuel costs, and power plant outages.

Despite the expansion of the generation fleet, the total availability of power plants fell from 98% in 2021 to 79% in 2025. A simplified estimate based on multiplying installed capacity by the annual availability rate gives an indicative value available capacity decreased approximately from 2645 MW in 2023 to 2547 MW in 2025, even as nominal capacity increased by 317 MW. This simplified calculation does not constitute an estimate of available capacity during peak periods, as annual availability does not take into account the timing or simultaneous occurrence of power plant outages. It simply illustrates how lower availability can reduce the expected benefits of new generation capacity.

Aging hydropower facilities, prolonged outages, maintenance needs, gas supply constraints, and the unavailability of thermal power plants may explain this deterioration. These findings show that creating new capacity alone is not enough to guarantee supply security. Refurbishing existing assets, shorter outage periods, effective maintenance, and reliable fuel supply are equally important. System adequacy should therefore be assessed using the capacity that can be delivered during peak demand rather than on installed capacity alone.

Table 4. Installed capacity by generation technology and total plant availability, 2020-2025.

As shown in Figure 5, Côte d’Ivoire’s cross-border electricity trade changed significantly between 2014 and 2025. Exports peaked at 1655 GWh in 2016 and remained relatively high through 2020, when they reached 1333 GWh. They then declined to 730 GWh in 2024 before recovering slightly to 797 GWh in 2025.

Imports followed the opposite pattern. After remaining below 100 GWh per year through 2019, they rose from 149 GWh in 2020 to 267 GWh in 2022, 382 GWh in 2024, and finally to 708 GWh in 2025, an increase of nearly 4.8 times over five years. Although these purchases represented only 4.84% of domestic electricity consumption in 2025, their rapid growth is an important signal of supply security.

Consequently, the net trade balance fell from 1184 GWh in 2020 to 706 GWh in 2022, 348 GWh in 2024 and only 90 GWh in 2025. Côte d’Ivoire therefore remained a net exporter, but with a very narrow margin. Imports accounted for nearly 89% of exports in 2025, compared with just 11% in 2020.

Regional interconnections offer valuable flexibility by supplying electricity during power plant outages, peak demand periods, or when lower-cost energy is available from neighboring countries. However, the simultaneous increase in imports, the decline in the export surplus, reduction in domestic plant availability and sustained demand growth suggest that the grid’s internal margins are tightening. If this trend continues, Côte d’Ivoire could temporarily become a net importer during years of high consumption or in the event of major production constraints.

As shown in Figure 6, plant availability and electricity imports generally moved in opposite directions between 2020 and 2025. The Pearson correlation was moderately strong and negative ( r = − 0.695 ), indicating that lower availability tended to coincide with greater reliance on external supply. For example, imports totaled approximately 195 GWh when availability reached 98% in 2021 but increased to 382 GWh in 2024 and 708 GWh in 2025 as availability fell to 81% and 79%, respectively. However, this relationship was not statistically significant at the 5% level ( p = 0.125 ), largely because the analysis covered only six annual observations. The result should therefore be viewed as a preliminary signal rather than solid evidence of a systematic relationship. The data also reveal some variability: availability was relatively low in 2020, at 86%, but imports nevertheless remained limited to 149 GWh. In addition, the sharp rise recorded in 2025 likely had a strong influence on the estimated correlation.

From an operational perspective, electricity purchases can compensate for unavailability of domestic generation when local resources are insufficient to fully meet demand. Nevertheless, their level also depends on consumption growth, regional electricity prices, the generation capacity of neighboring countries, interconnection constraints, and grid operation strategies. An increase in imports cannot therefore be attributed solely to declining plant availability.

A more reliable assessment would require monthly, daily, or hourly data. Such information would help determine whether imports increase during generating unit outages or peak demand periods while accounting for domestic production, market prices, and contractual exchanges.

As shown in Figure 7, transmission losses remained relatively low and stable, generally ranging between 5% and 8%. In 2025, they fell to 5%, corresponding to a transmission efficiency of 95%, the highest level recorded since 2019.

Distribution performance improved significantly between 2014 and 2022, with efficiency rising from 82.4% to 91.3% and losses decreasing from 17.6% to 8.7%. However, this progress subsequently slowed: efficiency fell to 89.38% in 2025, while losses increased to 10.62%. This recent deterioration could be attributed to rapid network expansion, customer growth, overloaded feeders, the length of distribution lines, load imbalance, aging equipment and non-technical losses. The available data does not allow the contribution of each factor to be measured separately.

Table 5 shows that the network’s overall efficiency remained nearly unchanged at 84.54% in 2025, meaning that approximately 15.5% of available energy did not reach end users. Improved transmission performance largely offset lower distribution efficiency, showing that loss-reduction efforts should focus on this part of the network.

Service reliability improved in 2025 compared with 2024, but the available data do not establish a statistically significant long-term trend (p = 0.322). The average duration of outages fell from 26.2 hours in 2024 to 18.6 hours, a reduction of approximately 29%. During the same period, the SAIDI fell from 17.6 to 10 hours, while the SAIFI decreased from 22 to 18 interruptions. Energy not supplied also dropped 22.5%, from 37,723 to 29,227 MWh. Nevertheless, these values remain above their 2020 levels; therefore, several additional years of data are needed to confirm lasting improvement.

Approximately 59% of the energy not supplied in 2025 was associated with the medium-voltage network. Reinforcing heavily loaded feeders, automating switching operations, improving fault location, strengthening preventive maintenance, and clearing vegetation near power lines could therefore reduce outages duration and accelerate service restoration.

Finally, year-to-year comparisons should be interpreted with caution. The average duration of outages, the SAIDI and SAIFI indices, and energy not supplied values are fully comparable only when calculated for the same service area and according to consistent rules for counting customers and interruptions [27]. The 2025 results indicate significant short-term progress, but a longer and methodically consistent dataset is required to establish a sustained trend.

Figure 7. Trends in network losses and average outage duration, 2014-2025.

Table 5. Network efficiency and service reliability ındicators, 2020-2025.

Figure 8 and Table 6 present historical domestic electricity consumption through 2025 and three possible growth scenarios for 2026-2030. Demand increases under every scenario, while the gap between projections becomes larger over time.

In the moderate scenario, consumption grows by 5% per year and reaches 18,678 GWh in 2030, 4043 GWh or 27.6% above the 2025 level. The trend scenario extends the historical average growth rate of 6.5%, resulting in 20,051 GW, a 37% increase. This trajectory may serve as a reference to planning if customer connections, household electricity use, industrial activity and electrification continue to develop at rates like those observed recently.

The accelerated scenario assumes 8% annual growth and brings demand to 21,504 GWh by 2030, nearly 47% higher than in 2025. Such a trajectory could result from faster industrialization, the expansion of mining, wider electrification, or higher consumption among newly connected households. A 10% sensitivity case produces 23,570 GWh, representing an increase of more than 61%. Although this is not considered a central projection, it provides a useful stress case for assessing system resilience under stronger growth.

The difference between the moderate and accelerated scenarios increases from 439 GWh in 2026 to 2826 GWh in 2030. Including the high-growth sensitivity case increases the gap to 4892 GWh at the end of the projection period. This uncertainty underscores the need for flexible decision-making regarding generation, network expansion, and energy procurement.

A linear extrapolation gives a lower 2030 estimate of 16,944 GWh, with a 95% prediction interval of 15,379 to 18,510 GWh. This value is 3107 GWh below the 6.5% trend scenario. This difference reflects the modeling approach: the linear model adds an approximately constant amount each year, whereas compound growth applies a fixed percentage to an increasingly larger consumption base.

These results should be interpreted as planning scenarios rather than precise forecasts. Economic conditions, energy efficiency, electricity prices, industrial projects, air conditioning use, electric mobility, and production activities could affect future demand. Moreover, the values represent annual energy requirements, not the additional generation capacity needed. System planning must also account for network losses, plant auxiliary consumption, outages, regional exchanges, peak demand, load profile, and reserve margins.

Table 6. Projected electricity consumption by scenario, 2026-2030 (GWh).

The first major finding is that an increase in installed capacity does not necessarily an equivalent improvement in security of supply. Between 2020 and 2025, Côte d’Ivoire added nearly 1000 MW of generation capacity, while the total plant availability decline by seven percentage points. A generation unit that is out of service, constrained by fuel shortages or affect by network limitations contributes little or nothing to meeting demand. Modern methods for assessing resource adequacy therefore consider outages, weather conditions, energy constraints, and the actual availability of interconnections, rather than relying solely on nominal capacity [14][15].

Côte d’Ivoire should combine investments in new energy sources with better use of its existing infrastructure. Rehabilitating aging hydropower facilities, reducing thermal plants outages, securing natural gas supplies, and improving maintenance coordination could deliver benefits more quickly than building another large power plant.

Solar generation doubled in 2025 but still accounted for only 1% of net national generation and about 2% of installed capacity. Thermal plants remained dominant, supplying 73% of the country’s electricity. Although these facilities offer dispatchable output, their importance increases vulnerability to natural gas supply constraints and fuel price volatility. Hydropower offers a flexible domestic resource, but its contribution depends on hydrological conditions and plant availability.

Long-term studies suggest that Côte d’Ivoire can expand its solar photovoltaic generation while continuing to pursue its economic and climate goals [7]. Better coordination and electricity exchanges within the West African Power Pool could also support the integration of variable renewable energy [5]-[8]. However, further diversification will require flexible reserves, economically justified storage, better weather forecasting, modernized distribution systems, and stronger transmission infrastructure. In practice, the contribution of solar capacity supply security depends not only on its nominal rating but also on its location and production profile.

Reducing distribution losses provides an indirect but immediately available source of energy. Every percentage point avoided represents electricity that no longer needs to be generated, imported, or transmitted. With annual consumption above 14 TWh, a one-point improvement could save more than 140 GWh, depending on the parameters of the calculation. The decline in distribution efficiency observed since 2022 therefore requires a detailed assessment to distinguish between technical losses and non-technical causes, such as unauthorized connections, metering errors, and billing issues.

Technical measures may include upgrading overloaded conductors, improving phase balance, locating transformers closer to load centers, and installing reactive power compensation devices. Commercial actions should focus on detecting irregular connections, improving metering accuracy and deploying smart meters in priority areas.

These measures should be guided by detailed loss mapping at substations, transformers and medium- or low-voltage feeder level. This approach would focus investments on areas with the greatest energy and financial benefits and make it easier to track long-term results. In many cases, reducing losses can generate equivalent energy savings more quickly and at a lower cost than building new generation capacity.

The sharp increase in imports in 2025 could be explained by the normal use of regional interconnections to address temporary supply constraints. It demonstrates the practical value of the West African Power Pool (WAPP) in supplementing domestic generation, reducing the risk of shortages, and providing access to lower-cost electricity. Regional power exchanges can therefore help balance the system without requiring immediate investment in new capacity.

However, total import capacity cannot be considered permanently available. Its contribution depends on surplus generation in neighboring countries, interconnections availability, and transmission constraints along regional corridors. Dry conditions, peak demand, or major equipment failures can affect several countries simultaneously, reducing their ability to support one another.

System planning must therefore consider at least two operating conditions: a normal interconnection scenario, in which imports are available within contractual and technical limits and a stress scenario in which regional exchanges are partially or completely unavailable. Intermediate cases could include the loss of a major interconnection or simultaneous generation shortages in several countries.

This approach would provide a more realistic estimate of how regional trade contributes to security of supply and would help determine the export volumes that Côte d’Ivoire can maintain without compromising domestic demand. Interconnections should complement sufficient and reliable domestic production rather than permanently replace it.

According to the trend scenario, domestic electricity consumption reaches 20.1 TWh by 2030, approximately 37% above 2025 level. To meet this demand, simply adding a few generation units will not be enough. Planning must coordinate investments in generation, transmission lines, substations, distribution networks, system control, and flexibility resources. Without an integrated approach, new plants could be constraints by network limitations or fail to support peak demand effectively.

The accelerated scenario reaches 21.5 TWh, while 10% growth sensitivity case approaches 23.6 TWh. Although the latter is not considered the most likely outcome, it provides a useful stress test for the power system. Even the moderate scenario rises to 18.7 TWh, requiring more than 4 TWh of additional energy within five years. Each trajectory therefore requires significant and timely investments, given the long construction periods for power plants, power transmission lines and substations.

The lower linear extrapolation also shows that these projections are uncertain. Future consumption will depend on the select modeling approach and changes in economic activity, industrial development, electricity rates and final consumption patterns. These results should therefore be treated as a range of possible scenarios rather than a single forecast.

The projections are consistent with studies anticipating strong electricity demand growth in Côte d’Ivoire [9][28]. However, this analysis does not determine an optimal generation mix or minimize total system costs, as TIMES, PyPSA, and regional optimization models generally do. Its purpose is to extend recent trends, quantify the possible scale of future needs and provide a practical basis for testing the robustness of investment decisions.