ABSTRACT
This study evaluates the reliability, cost, and quality of electricity supply in Nigeria, focusing on selected service areas managed by the Power Holding Company of Nigeria (PHCN). Using a mixed-methods approach, the research involved collecting primary data through customer surveys and conducting field inspections of distribution networks. The survey provided insights into the quality of power supply, failure rates, and the associated customer inconvenience, while field observations detailed the technical state of distribution infrastructure. Descriptive statistical analysis revealed that the overall reliability and quality of the power supply are below 10% across surveyed regions, with significant seasonal variations. The findings highlight the urgent need for the redesign and re-planning of Nigeria’s electricity distribution networks. Key recommendations for enhancing both the reliability and quality of power supply are proposed.
References
- [1] G. Migisha et al. Review of concepts and determinants of grid electricity reliability. Energies (2023).
- [2] Pan et al. Reliability-constrained economic dispatch with analytical formulation of operational risk evaluation. IEEE Trans. Power Syst. (2023).
- [3] Misconel et al. Systematic comparison of high-resolution electricity system modeling approaches focusing on investment, dispatch and generation adequacy. Renew. Sustain. Energy Rev. (2022).
- [4] Li et al. A review of scenario analysis methods in planning and operation of modern power systems: Methodologies, applications, and challenges Electrs. Power Syst. Res. (2022).
- [5] Hsiang-Hua Y., Kuo-Hao C., Hsin-Wei H., Robert C., A Monte Carlo simulation-based decision support system for reliability analysis of Taiwan’s power system: Framework and empirical study,” vol. 178, pp. 252–262 (2019).
- [6] Kazemi S., “Reliability evaluation of smart distribution grids,” Alto University Publication series, (2019).
- [7] Leonardo V., David M.H., Mauro C., Ales P., An artificial intelligence system for predicting Customer default in e-commerce,” Expert Systems with Applications Journal, vol. 1, no. 4, pp. 1–21 (2018).
- [8] Srimawar S., Muhamma B.N., Andrian A., Bayu T.U., Prediction of lightning density value tower based on Adaptive Neuro-fuzzy Inference System, Archives of Electrical Engineering, vol. 70, no. 3, pp. 499–511 (2021), DOI: 10.24425/aee.2021.137570.
- [9] Alex S., Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network,” Elsevier journal Physica D: Nonlinear Phenomena, Special Issue on Machine Learning and Dynamical Systems, vol. 404, pp. 1–43 (2020).
- Olajuyin E.A., Akinyede J.A., Akinyede T., Evaluation of reliability of protective devices in power distribution network,” Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS), vol. 12, no. 1, pp. 13–18 (2021).
- Olajuyin E.A., Olubakinde E., Electrical Load forecasting in Power System, International Journal of Innovative Science and Research Technology, vol. 4, no. 8, pp. 845–847 (2019).
- Mohammed Y.S., Mahmood T.A., High impedance fault detection in radial distribution network using discrete wavelet transform technique, Archives of Electrical Engineering, vol. 70, no. 4, pp. 873–886 (2021), DOI: 10.24425/aee.2021.138267.
Download all article in PDF
