POWER DEMAND FORECASTING AND GENERATION ADEQUACY IN NIGERIA UP TO 2040

Donatus Nwagbara; Damian Nnadi; Linus Anih

doi:10.4314/njt.v44i3.7

Authors

D. O. Nwagbara Electrical Engineering Department, University of Nigeria, Nsukka
D. B. N. Nnadi Electrical Engineering Department, University of Nigeria, Nsukka
L. U. Anih Electrical Engineering Department, University of Nigeria, Nsukka

DOI:

https://doi.org/10.4314/njt.v44i3.7

Keywords:

ANFIS, DEGA, Adequacy Assessment, Generation Expansion Plan, LCOE

Abstract

The availability and reliability of power supply in Nigeria are critical issues, undermining economic and technological development. Despite policy reforms and capital investments, inadequate generation capacity and poor power system planning continue to limit progress. This study introduces a comprehensive approach to long-term power system planning in Nigeria, combining intelligent load forecasting with generation adequacy assessment up to 2040. An Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed to forecast future electricity demand, projecting a load requirement of approximately 87,304.08 MW by 2040. A metaheuristic approach utilizing the Disparity Evolution Genetic Algorithm (DEGA) was implemented to assess system adequacy. Results revealed that the current generation capacity of 4,500MW is significantly inadequate, necessitating an additional 101,564.5 MW, or 5,071.11MW annually, to maintain acceptable reliability standards. Furthermore, using a Levelized Cost of Electricity (LCOE) model under fossil-fuel-based generation assumptions, an annual investment of $3,887,005,815.52 was determined to support the expansion. These findings underscore the critical need for accurate demand forecasting, robust adequacy assessment, and strategic investment planning to ensure long-term energy security and sustainable development in Nigeria.

References

Bishoge, O. K., Kombe, G. G., and Mvile, B. N. "Renewable energy for sustainable development in sub-Saharan African countries: Challenges and way forward". Journal of Renewable and Sustainable Energy, vol. 12, no 5, 2020.

Bamisile, O., Huang, Q., Xu, X., Hu, W., Liu, W., Liu, Z., and Chen, Z. "An approach for sustainable energy planning towards 100% electrification of Nigeria by 2030". Energy, vol. 197, pp. 117172, 2020.

Owebor, K., Diemuodeke, E. O., Briggs, T. A., and Imran, M. "Power Situation and renewable energy potentials in Nigeria–A case for integrated multi-generation technology". Renewable Energy, vol. 177, pp. 773-796., 2021.

Aruna, S. B., Suchitra, D., Rajarajeswari, R., and Fernandez, S. G. "A comprehensive review on the modern power system reliability assessment", International Journal of Renewable Energy Research (IJRER), vol. 11, no. 4, pp.1734-1747, 2021.

R. Billinton and R. N. Allan, “Basic power system reliability concepts,” Reliab. Eng. Syst. Saf., vol. 27, no. 3, pp. 365–384, (1990).

R. Billiton and R. N. Allan, Reliability assessment of large power system. Boston: Kluwer Academic Publishers, 1988.

Panda, S. K., and Ray, P. "Analysis and evaluation of two short-term load forecasting techniques". International Journal of Emerging Electric Power Systems, vol. 23, no. 2, pp. 183-196, 2022.

N. Ahmad, Y. Ghadi and M. Adnan, M. Ali, "Load Forecasting Techniques for Power System: Research Challenges and Survey," IEEE Access, vol. 10, pp. 71054-71090, 2022.

M. Madhukumar, A. Sebastian, X. Liang, M. Jamil and M. N. S. K. Shabbir, "Regression Model-Based Short-Term Load Forecasting for University Campus Load," IEEE Access, vol. 10, pp. 8891-8905, 2022,

Stamatellos, G., & Stamatelos, T. "Short-Term Load Forecasting of the Greek Electricity System". Applied Sciences, vol. 13, no. 4, pp. 2719, 2023.

Behmiri, N.B., Fezzi, C. and Ravazzolo, F., "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks". Energy, 278, pp.127831, 2023.

M. Elsaraiti, G. Ali, H. Musbah, A. Merabet and T. Little, "Time Series Analysis of Electricity Consumption Forecasting Using ARIMA Model," 2021 IEEE Green Technologies Conference (GreenTech), Denver, CO, USA, pp. 259-262, 2021.

F. V. Atabay, R. M. Pagkalinawan, S. D. Pajarillo, A. R. Villanueva and J. V. Taylar, "Multivariate Time Series Forecasting using ARIMAX, SARIMAX, and RNN-based Deep Learning Models on Electricity Consumption," 2022 3rd International Informatics and Software Engineering Conference (IISEC), Ankara, Turkey, pp. 1-6, 2022.

Lee, G.C., "Regression-Based Methods for Daily Peak Load Forecasting in South Korea", Sustainability, vol. 14, no. 7, pp. 3984, 2022.

Machado, E., Pinto, T., Guedes, V. and Morais, H., "Electrical load demand forecasting using feed-forward neural networks". Energies, vol. 14, no. 22, pp. 7644. 2021.

L.W.R. Billinton, "Reliability Assessment of Electric power systems using Monte Carlo Methods", (1994)

A. A. Kadhema, N. I. A. Wahaba, I. Arisa, J. Jasnia, A. N. A, "Computational techniques for assessing the reliability and sustainability of electrical power systems: A review," Renewable and Sustainable Energy Reviews, 80, ``75-1186, (2017).

Afshari, S.S., Enayatollahi, F., Xu, X. and Liang, X., “Machine learning-based methods in structural reliability analysis: A review”, Reliability Engineering & System Safety, vol. 219, pp.108223, 2022.

Kamruzzaman, M., Bhusal, N. and Benidris, M., “A convolutional neural network-based approach to composite power system reliability evaluation”, International Journal of Electrical Power & Energy Systems, vol. 135, pp.107468, 2022.

Abdalla, A.N., Nazir, M.S., Jiang, M., Kadhem, A.A., Wahab, N.I.A., Cao, S. and Ji, R., “Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system”, Energy Exploration & Exploitation, vol. 39, no. 1, pp.488-501, 2021