Open Access Open Access  Restricted Access Subscription or Fee Access

A Pricing Model for the Nigerian Electricity Transmission Using Artificial Neural Network

B. O. Ogbonna, C. O. Ahiakwo, D. C. Idoniboyeobu, S. Orike

Abstract


For the restructuring of Nigeria’s power sector to be effective, efforts must be made to evolve an excellent pricing methodology that will economically be able to accommodate disintegrated units like generating, transmitting and distributing/retailing units. This work is aimed at developing an improved electricity transmission pricing scheme for the Nigerian Network using Artificial Neural Network. A model based on artificial neural network for improved electricity transmission pricing for the Nigerian Network was developed in order to forecast the transmission price for a longer period of time. The performance of the neural network model was evaluated by applying the actual transmission pricing data from Transmission Company of Nigeria to predict the prices. Raw data was collected from Transmission Company of Nigeria, Port-Harcourt district. The data was trained, tested and validated on the MATLAB/Simulink environment. Forecast results revealed that the model performed very well with a mean absolute percentage error of 0.09%, an average mean error of 0.5 and a regression value of 0.99. It was concluded that the improved and modified transmission use of system pricing is the best pricing method which will be acceptable to consumers and also ensure recovery of transmission cost in Nigeria. It was recommended that Artificial Intelligent-based techniques (ANN) in particular, must be implemented for long-term improved electricity transmission pricing forecast for the Transmission Company of Nigeria.


Keywords


Long Run Marginal Cost, Short Run Marginal Cost, Transmission Pricing, Transmission Capacity.

Full Text:

PDF

References


Ahiakwo, C.O. Chukwu, U.C and Dike, D.O. (2008). Optimal Transmission Line Pricing Algorithm for a Restructured Power System. IEEE Transactions on Power Systems. Vol. 978, no.1, PP.4244-4250

Anderson, D., & McNeil, G. (2006). Artificial neural networks technology: A DACS State-of the Art Report, Data & Analysis Center for Software, 1-35

Anireh, V.I., & Osegi, E.N. (2016). A Modified Activation Function with Improved Run-Times for Neural Networks. Advances in Multidisciplinary and Scientific Research Journal 3 (02).

Araneda, J.C. (2002). Foundations of Pricing and Investment in Electricity Transmission. A Thesis Submitted to the University of Manchester Institute of Science and Technology for the degree of Master of Philosophy.

Desai, K. and Dutta, G. (2013). A dynamic pricing approach to electricity prices in the Indian context. International Journal of Revenue Management, Vol.7 No.3-4. p.268.

Galkin I. (2005). Crash introduction to artificial neural networks.

Green, R. (2000). England and Wales - A competitive Electricity Market, University of California Energy Institute (UCEI). PWP-060 paper.

Hans-Joachim Bodenhofer and Norbert Wohlgemut (2001). Power transmission pricing: issues and international experience. International Energy Symposium Ossiach.

Happ, H., (1994). Cost of Wheeling Methodologies. IEEE Transactions on Power System, Vol, 9. No. 1, pp.147-156.

Illic, M. (1997). Toward Regional Transmission Provision and its Pricing in New England. Utility Policy, Vol. 6, No. 3, pp.245-256.

Komane T (2001). Short term peak load forecasting using neural networks, Department of Electrical Engineering, University of Cape, 2001

Lima, J. Allocation of Transmission fixed charges: an Overview (1996). IEEE Transactions on Power Systems, Vol.11, No. 3, pp. 1409-1418

Mohammed, S. (2002) Market Operations in Electric Power System. Pp. 374,

MYTO document (2008).

MYTO document (2012).

Murali, M. Kumari, M.S. Sydulu, M. (2014). A Review of Transmission Pricing Methods in Restructured Electricity Market and Case Studies. International Electrical Engineering Journal (IEEJ) Vol. 5 No. 1, pp. 1186-1197.

Ogbonna, B.O, C.O.Ahiakwo, D.C. Idoniboyeobu, S. Orike (2019). Improved Electricity transmission pricing for the Nigerian Network. Journal of Newviews in Engineering and Technology (JNET). Vol 1,

Saheed L.B. (2013) Evaluating the Methodology of Setting Electricity Prices in Nigeria. International Association for Energy Economics. Fourth quarter, pp. 31-32.

Singh A, Chen H, Canizares A.C, “ANN-based short term load forecasting inelectricity markets”, Proceedings of the IEEE power engineering society transmission and distribution conference, 2001, pp2:411-415

Stergiou C and Siganos D, (2004). Neural Networks found on http://www.doc.ic.ac.uk/~nd/surprise 96/journals /vol4/cs11/report.html.


Refbacks

  • There are currently no refbacks.