In an attempt to realize the most beneficial and optimal mix of electricity generation in Niger, a society''s cost of electricity (SCOE) as the levelized cost of electricity (LCOE) plus the cost of climate change and air pollution is formulated. The SCOE is used as a basis for setting the performance criteria for supply optimization to balance future electricity demand in Niger. The demand projection is derived from a system dynamics model that anticipates future changes based on its influencing factors of population growth, urbanization progress, and industrial development.
In this work, a mixed energy grid is optimized primarily on affordability while considering its sustainability. The implemented holistic approach lessens the need for energy import in the country and provides relief to energy security issues such as electricity price volatility and supply reliability. Additionally, the proposed strategy helps to guide the renewable energy transition pathway in Niger.
Per capita electricity consumption of Niger in comparison with selected West African countries in 2017
The relationship between economic development and increased energy demand [3, 6,7,8] as well as the strong correlation between urbanization and enhanced energy utilization [3, 6, 8,9,10,11] are clearly observed in the literature. These trends are also accompanied with a relative political stability and large-scale investments in the mining and oil sector of a country, which is also evident in Niger. Often, economic development reciprocates into increased energy consumption by the industrial and urban sectors. Thus, the development of a valid model that translates and anticipates quantitatively these related events would help energy policymakers to realize the development goals.
Electricity import to Niger (based on NIGELEC database) [13]
Finally, the absence of an energy mix in the power grid based on the availability and cost of generation (including the cost on the environment), which has led to an inefficient fossil dominated power generation, is another challenge to the provision of low-cost and reliable energy in Niger. Apart from an estimated 2% solar photovoltaic (PV) installation in the telecom and off-grid sectors, power generation in the country still relies entirely on fossil fuels (coal and diesel) [12, 13]. Moreover, many of NIGELEC''s diesel power plants are close to the decommissioning phase but continue to operate with high generation costs [13].
Against this backdrop, solar PV and hydropower hold the promise of becoming suitable alternative energy sources in Niger. An attractive medium to large hydropower potential in the country is estimated at about 312–450 MW [17, 18]. Until now, only a feasible potential assessment has been carried out for a few sites, such as Dyodyonga, Gambou, and Kandadji, with an estimated hydroelectric power potential of about 38, 122.5, and 125 MW, respectively [17]. Figure 3 depicts the flow rate of the Niger River in dry and wet seasons measured in Niamey [16]. Some suitable mini-hydropower sites have also been identified along four tributaries of the Niger River, namely Mekrou, Tapoa, Gorouol, and Sirba, amounting to a combined capacity of 3–8 MW [17, 18].
Niger is also endowed with a high solar PV potential. As shown in Fig. 4, almost all regions in the country have a daily average PV potential of over 4.6 kWh/kWp [19]. Thus, if properly designed and operated, power from grid-tied PV systems could provide an efficient method of harvesting the available solar power.
Photovoltaic electricity potential of Niger [19]
A potential assessment that has been carried out in Niger so far indicates that wind resource is negligible [18]. It also showed that conventional sources of energy such as diesel and coal are abundant in the country [20]. Considering these findings and the aforementioned energy-related issues, a holistic approach for finding an optimal energy mix based on the availability and cost of generation (including the cost to the environment) would be necessary and insightful. Moreover, this approach should balance the future energy demand and include a strategy to lessen the need for imported energy. Therefore, the optimal energy mix criteria should be based on the following trade-offs:
The complementary nature of solar and hydropower should be used to compensate for the lower energy output from hydropower during the dry season.
The high electricity generation cost from solar PV, which has not yet reached a level to be competitive with hydropower, and abundant conventional power sources should be balanced.
Power from conventional sources should be used to match the future energy demand but lessen their negative impact on climate change and air pollution.
Similarly to the indications given in [24, 25], all future electricity generation schemes should be designed according to the specific context of the country. Considering how poor most developing countries such as Niger are, a policy that ensures a mixed energy grid that is primarily based on affordability and has an eye on sustainability should be the main aim in designing optimal energy supply systems. Therefore, the specific objectives of the study are
To develop Niger''s future energy demand based on its influencing factors of population growth, industrialization, and urbanization progress.
To formulate and optimize a supply model for an optimal mix of conventional and renewable energy sources based on the LCOE and SCOE.
The model employed in this study consists of three main components: electricity demand projection, supply, and optimization model. The following sections give the details of these components.
Several studies were conducted for short or/and long-term electricity demand projection that can be categorized into six types: regression based [29], autoregressive integrated moving average (ARIMA) [30], artificial neural networks [31], fuzzy logic [32], support vector [33], and system dynamics models [34]. The system dynamics approach is able to handle the dynamic evolution of vital energy forecasting variables with feedback loops among each other [35] and allows the incorporation of stochastic behavior [36].
In this work, the inherent relationship between energy demand and economic and social variables is studied using the system dynamics approach. The model anticipates potential electricity demand changes in Niger based on its influencing factors. The following valid assumptions are considered in the system dynamics modeling:
Due to limited electricity consumption data for Niger (less than a couple of decades), the electricity demand projection is carried out for a short period of up to 2035.
In Niger''s context, three influencing factors that may strongly affect electricity consumption are included in the model. The first factor is the normal electricity demand due to the change in total population each year. The second influence comes from the effect of urbanization determined from the ratio of the urban population to the total population every year. Lastly, the industrialization change observed each year also influences electricity demand. Electricity demand due to industrialization can be determined from the ratio of other GDPs and industrial GDP in Niger. Other GDPs are defined in this work as GDPs related to service and agricultural economic activities.
The net change in electricity demand each year from the previous year due to the aforementioned influences can be represented by a linear function [37].
Accordingly, each year''s net electricity demand (NED) in Niger can be determined from the rate of change of total electricity demand (ED) as
The NED is the sum of the product of the previous year''s electricity demand by the normal demand rate (NDr) and the newly added electricity demand rate (ADr) due to urbanization and industrialization progress calculated by
where UF and IF are the urbanization and industrialization factors that affect the net electricity demand due to changes in previous year''s urbanization and industrialization, respectively. DTU and DTI are the urbanization and industrialization coefficients. UF and IF are represented by a functional relation of urban population (U) to the total population (P) ratio and other GDP (OG) to the industrial GDP (IG) ratio as
where the coefficients in Eq. (4) are determined from the model calibration experiment based on historical data.
On the other hand, the population and urban dynamics in each year of Eq. (3a) can be represented as
where the total births (PB), total deaths (PD), urbanization rate (UR) and immigration rate (IR) in the current year can be determined as
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