Energy Efficiency in Periods of Load Shedding and Detrimental Effects of Energy Dependence in the City of Maroua, Cameroon


During the years 2021 and 2022, the city of Maroua experienced repeated power blackouts. However, this locality has significant photovoltaic energy potential. Nevertheless, the evaluation of the electrical performance showed the dependence of the population on these fluctuations, which could be bypassed or suppressed. In most cases, the blackout occurs during high energy demand. In this paper, a method for evaluating electrical efficiency is proposed and its credibility has been demonstrated on the one hand, and on the other hand, a renewable energy production system is proposed. The Homer software has made possible the analysis of the proposed system and its impact on the environment has also been carried out. The techno-economic study of the system has proved that a solar photovoltaic farm associated with an energy storage system, with a capacity of 47 MW, can meet the energy demand of the town of Maroua. This alternative is profitable for this locality which lives in a precarious situation and a continuous need.

Share and Cite:

Bello-Pierre, N. , Nisso, N. , Kaoga, D. ,  , K. and Tchakounté, H. (2023) Energy Efficiency in Periods of Load Shedding and Detrimental Effects of Energy Dependence in the City of Maroua, Cameroon. Smart Grid and Renewable Energy, 14, 61-71. doi: 10.4236/sgre.2023.144004.

1. Introduction

The survey of electric power consumers in Maroua is the result of an investigation following several complaints. The use of diesel fuel is a source of significant losses for the government, which spends a lot of funds on the purchase of diesel fuel. In addition, diesel fuel is an important source of greenhouse gas production. Although the Lagdo dam produces energy, it does not meet the needs of all localities, especially Maroua, which has a high demography. This is why the energy company in Cameroon, ENEO, proceeds to temporary power outages, and many blackouts are also observed. However, based on statistical analysis, load shedding is beneficial to the provider while the consumer is penalised. The locality of Maroua has the potential for photovoltaic energy. Therefore, it is important to address these issues in this work, to avoid dependence on suffering and load shedding, and to propose an alternative [1] . Indeed, analysis of the sample meters showed that load shedding has perverse effects on the customer: it obliges the customer to consume almost the same amount at an imposed period and waste is recorded each time the power is restored in the absence of the customer. Appliances that remained connected are running all night while the offices are empty of occupants. From the studies and the collection of statistical data made from consumers and the electric energy provider in Cameroon named ENEO and based on energy efficiency, the data are as follows: 44.8% of customers have higher bills during load shedding while 1.5% of customers pay the amount. 53.7% of customers experience a slight decrease in the bill. This category of customers is made up of businesses where there is a constant human presence and an autonomous power source is available.

Load shedding is becoming a permanent reality in Cameroon [2] [3] [4] despite the increase in energy supply. Demand seems to be growing too fast [5] . Beyond the multiple harms that load shedding causes to customers, its overall impact is so immense and diverse. Curiously, there is little or no change in electricity bills. This means in effect that the energy supplier loses nothing by carrying out load shedding. On the contrary, it gains a great deal by avoiding the obligation to manage evening peaks. This seems to justify the systematic use of load shedding since only customers seem to suffer. This study is based on the observation that the amount of the monthly electric energy bill does not change despite load shedding. The first step is to collect data from the bills of a sample of subscribers and to proceed with the analysis of the sample in order to proceed with a comparative analysis. Since the observation made is very likely, the second part of the study will be devoted to the search for reasons that would justify this constant in energy consumption despite the load shedding whose monthly duration is close to half time. From the elements that explain the phenomenon, we will be able to identify tips to reduce the unnecessary consumption of electrical energy [6] in the context of load shedding.

It is obvious to make decisions on the choice of sources through two scenarios. The efficiency of each model is obtained through the study of economic [7] [8] [9] , technical and environmental [10] aspects.

- Scenario 1:

Depend on the energy company and expect satisfaction

- Scenario 2:

Seek an alternative to the national energy supplier, in order to satisfy the energy demand in households.

2. Materials and Methods

To confirm or refute the first research hypothesis, the bills of the sampled customers should be collected. Alternatively, collect initial and final meter readings from these customers for a 90-day period during the permanent supply period [11] and the same 90 days during the load shedding period.

A collection form (Table 1) designed for the purpose was used by the team members.

Let be I P 0 k and I P 1 k the start index (0) and the final index (1) taken during the permanent supply (P) on a meter k. We will note E P k the energy consumed during the permanent supply period.

E P k = I P 1 k I P 0 k (1)

The load shedding is evaluated using a rate defined by Equation (2).

τ d = T 0 T (2)

where T is the load shedding period.

Let be I D 0 k and I D 1 k the start index (0) and final index (1) readings during the load shedding season (D) on the same k meter. We will also note E D k is the energy consumed during the load shedding period. The Data collection sheet is presented in Table 1

E D k = I D 1 k I D 0 k (3)

The difference k between the two variables represents the difference between the bill for permanent supply and the bill for interrupted supply or load shedding.

k = E P k E D k = ( I P 1 k I P 0 k ) ( I D 1 k I D 0 k ) (4)

k = ( I P 1 k + I D 0 k ) ( I D 1 k + I P 0 k ) (5)

k can be positive, zero or negative. Ideally, it should be positive and τ E P k where τ is the load shedding ratio; it is the ratio of the sum of the load shedding durations t D over a period of time T. In this case, T represents 90 days.

τ = t D T (6)

Table 1. Data collection sheet.

If k is close to zero, it is because load shedding only harms the customers, not the supplier. The case k where is negative is a catastrophic scenario. This would mean that the customer pays more when they are offloaded.

k can take different values depending on the customer’s behavior. To assess the overall impact of k , it must be evaluated on the sample of N counters or customers counted. Let ∂ be the overall difference between what the supplier charges in during load shedding and outside, so an estimate can be obtained by the following expressions:

= k = 1 N k = k = 1 N E P k k = 1 N E D k (7)

Equation (4) and Equation (5) allow us to write:

k G = k = 1 N ( I P 1 k + I D 0 k ) k = 1 N ( I P 0 k + I D 1 k ) (8)

k G represents the overall difference.

This data can be inserted into an application so that the arithmetic operations related to Equations (1), (2), (3) and (5) are calculated automatically.

3. Results

The results obtained are extracted from the analysis of the customers using an on-site survey before being proven by an extrapolation of the electrical energy demand. The data collected is then implemented in HOMER for a technical, economic and environmental study.

3.1. Proposed System

The model of the system proposed as an alternative is presented in Figure 1. This system is formed with a photovoltaic source, a storage battery and an inverter.

3.2. Distribution According to the Sign of the Index k

After collecting the data (87 subscribers) and entering it into the table, the index is calculated and ranked in ascending order of index value k (Table 2). The customers can be divided into 3 groups according to Table 3.

Figure 1. Model of proposed system.

Table 2. Index values.

Table 3. Distribution according to the sign of the index k .

Figure 2 shows the fraction of energy consumed in households during load shedding periods. A fraction of 83.83% represents the dependence on energy during load shedding periods.

Figure 3 shows the behavior of households or inhabitants for one day. Two attitudes are presented: the behavior of households in normal times and in times of load shedding. This figure shows a high energy demand during load shedding.

The observation of customers classified in the different groups shows that customer behaviour towards load shedding varies according to whether they are:

1) Public utility;

2) Residential utility;

3) Manufacturing company;

4) Commercial utility.

It can also be seen that while some customers resign themselves to load shedding, others change their working habits and a certain minority acquire an alternative source of electricity, either for minimal service or for all loads.

3.3. Alternative against Energy Dependency

Figure 4 shows the possibility of energy production from a photovoltaic source. This photovoltaic generator associated with an energy storage system is capable of producing sufficient energy for the town of Maroua with a surplus of energy.

When we look at the different data provided during the year in Figure 5, we can see that the monthly power profile clearly shows that the photovoltaic energy deposit is sufficient to produce electricity. These values are provided by the Homer software that extracts the NASA meteorological data [11] for the locality of Maroua. The analysis of these data guarantees the feasibility and the economic and environmental impact.

Figure 6 shows the daily power profile as a function of sunlight availability in the city of Maroua.

The power profiles presented in Figure 7 show us that the proposed system meets the energy demand. It is obvious from Figure 4, Figure 5 and Figure 7 that a photovoltaic source is an alternative with good energy efficiency.

Many solutions against load shedding are given in these last decades [12] [13] [14] . The use of a hybrid photovoltaic and wind power source is proposed as a solution [15] [16] . In the same way, integration of wind power plants into the electrical grid can be also a solution. When considering all of these works, the issue of load shedding is not considered as an important parameter of investigation. That is the reason why a specific area is selected to evaluate the energy efficiency.

Figure 2. Fraction of energy for a month.

Figure 3. Households behavior.

Figure 4. Power generated by the proposed system.

Figure 5. Daily power profile during a year.

Figure 6. Daily power profile in Maroua locality.

Figure 7. Power profile for Maroua locality.

4. Conclusion

Ultimately, it can be said that load shedding continues because the energy supplier gains more than it loses. Since peak load management involves a source of fuel expenditure, the customer and the national economy appear to be sacrificed economically. However, the use of a photovoltaic source with energy storage can satisfy the locality of Maroua for a capacity of 47 MW. Photovoltaic systems are not responsible for the production of carbon dioxide, unlike the use of thermal power plants. The maintenance of these systems is easy. The city of Maroua has a large amount of sunshine that can be exploited. This is why Scenario 1 is an alternative to be adopted.

Ethical Approval

The Manuscript is not submitted to any other journal, and it is not published in any previous paper.

Availability of Data and Materials

The data used during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.


[1] Djidimbélé, R., Ngoussandou, B.-P., Kidmo, D.K., et al. (2022) Optimal Sizing of Hybrid Systems for Power Loss Reduction and Voltage Improvement Using PSO Algorithm: Case Study of Guissia Rural Grid. Energy Reports, 8, 86-95.
[2] Hermann, D.T., Donatien, N., Armel, T.K.F. and René, T. (2021) A Feasibility Study of an On-Grid PV/Wind/Battery/Diesel for Residential Buildings under Various Climates in Cameroon. Energy Technology, 9, Article ID: 2100615.
[3] Kitmo, Tchaya, G.B. and Djongyang, N. (2022) Optimization of Hybrid Grid-Tie Wind Solar Power System for Large-Scale Energy Supply in Cameroon. International Journal of Energy and Environmental Engineering, 1-13.
[4] Kidmo, D.K., Danwe, R., Doka, S.Y. and Djongyang, N. (2015) Statistical Analysis of Wind Speed Distribution Based on Six Weibull Methods for Wind Power Evaluation in Garoua, Cameroon. Rеvuе des Energies Renouvelables, 18, 105-125.
[5] Dieudonné, N.T., Armel, T.K.F., Vidal, A.K.C. and René, T. (2022) Prediction of Electrical Energy Consumption in Cameroon through Econometric Models. Electric Power Systems Research, 210, Article ID: 108102.
[6] Kitmo, Tchaya, G.B. and Djongyang, N. (2021) Optimization of the Photovoltaic Systems on the North Cameroon Interconnected Electrical Grid. International Journal of Energy and Environmental Engineering, 13, 305-317.
[7] Talla Konchou, F.A., Djeudjo Temene, H., Tchinda, R. and Njomo, D. (2021) Techno-Economic and Environmental Design of an Optimal Hybrid Energy System for a Community Multimedia Centre in Cameroon. SN Applied Sciences, 3, Article No. 127.
[8] Ibrahim, I.D., Hamam, Y., Alayli, Y., et al. (2021) A Review on Africa Energy Supply through Renewable Energy Production: Nigeria, Cameroon, Ghana and South Africa as a Case Study. Energy Strategy Reviews, 38, Article ID: 100740.
[9] Kitmo, Djidimbélé, R., Kidmo, D.K., et al. (2021) Optimization of the Power Flow of Photovoltaic Generators in Electrical Networks by MPPT Algorithm and Parallel Active Filters. Energy Reports, 7, 491-505.
[10] Kitmo, Tchaya, G.B., Kidmo, D.K., et al. (2021) Optimization of the Smart Grids Connected Using an Improved P&O MPPT Algorithm and Parallel Active Filters. Journal of Solar Energy Research, 6, 814-828.
[11] Gormo, V.G., Kidmo, D.K., Ngoussandou, B.P., et al. (2021) Wind Power as an Alternative to Sustain the Energy Needs in Garoua and Guider, North Region of Cameroon. Energy Reports, 7, 814-829.
[12] Balan, G., Arumugam, S., Muthusamy, S., et al. (2022) An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance. International Transactions on Electrical Energy Systems, 2022, Article ID: 8548172.
[13] Alphonse, S., Jacques, B., Kitmo, et al. (2021) Optimization PV/Batteries System: Application in Wouro Kessoum Village Ngaoundere Cameroon. Journal of Power and Energy Engineering, 9, 50-59.
[14] Giri, N.C. and Mohanty, R.C. (2022) Agrivoltaic System: Experimental Analysis for Enhancing Land Productivity and Revenue of Farmers. Energy for Sustainable Development, 70, 54-61.
[15] Giri, N.C., Misha, S.P. and Mohanty, R.C. (2020) Performance Parameters, Optimization, and Recommendation in Large Scale On-Grid SPV Power Plant, Odisha, India. International Journal of Modern Agriculture, 9, 159-167.
[16] Yaouba, Bajaj, M., Welba, C., et al. (2022) An Experimental and Case Study on the Evaluation of the Partial Shading Impact on PV Module Performance Operating Under the Sudano-Sahelian Climate of Cameroon. Frontiers in Energy Research, 10, Article 924285.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.