Specific System Random Load Forecasts for Nigerian 330 kV 38-Bus Transmission Grid

Article Preview

Abstract:

This study presents random load forecasts for the Nigerian 330 kV 38-bus transmission grid using a complex trend analysis technique. By considering randomness of monthly load peaks and normal distribution function, yearly load mean and confidence intervals were predicted for the transmission buses from an obtained 10-year historical load population. Also using the proposed algorithm of the mentioned technique, long-term random load forecasts for the transmission system were obtained. The obtained forecasts were compared with results from an earlier prediction model created for the same grid, which comprises a Monte Carlo technique that considers the location’s predominant control variables as population and GDP growth; the maximum obtainable error was 24%. The obtained results of forecasts and comparison are applicable for determining effective transmission system planning policy in Nigeria for the forecast period.

You might also be interested in these eBooks

Info:

* - Corresponding Author

[1] A. Agbo, Ending the Power Nightmare., Tell Magazine, Lagos, (2002).

Google Scholar

[2] A. S. A. Bada, Status and Opportunities in Transmission Company of Nigeria, Presidential Task force on Power, pp.1-21, 10 January (2011).

Google Scholar

[3] A. Sambo, Matching Electricity Supply with Demand in Nigeria., in National Workshop on the Participation of State Governments in the Power Sector, Abuja, (2008).

Google Scholar

[4] G. Drayton, M. McCoy, M. Pereira, E. Cazalet, M. Johannis and D. Phillips, Transmission Expansion Planning In The Western Interconnection – The Planning Process and the Analytical Tools That Will Be Needed to do the Job, IEEE_final_draft_dwp, Portland, (2004).

DOI: 10.1109/psce.2004.1397595

Google Scholar

[5] A. K. Mishra, P. Walde, D. Rai and S. Rafiullah, Future of Coordinated Transmission Expansion & Planning Interconnected Indian Power System, International Journal of Electronic and Electrical Engineering, vol. 7, no. 5, pp.437-442, (2014).

Google Scholar

[6] W. Simpson and D. Gotham, Standard Approaches to Load Forecasting and Review of Manitoba Hydro Load Forecast for Needs For and Alternatives To (NFAT), University of Manitoba, Manitoba, (2013).

Google Scholar

[7] K. A. Cullen, Data, Forecasting Electricity Demand using Regression and Monte Carlo Simulation Under Conditions of Insufficient Data, West Virginia University, Virginia, (1999).

DOI: 10.33915/etd.974

Google Scholar

[8] R. T. Crow, R. Michael and L. S. Raymond, Forecasting Electricity Sales and Loads: A Handbook for Small Utilities., Washington, D.C.: American Public Power Association, (1981).

Google Scholar

[9] S. M. Badran and O. B. Abouelatta, Forecasting Electrical Load using ANN Combined with Multiple Regression Method, The Research Bulletin of Jordan ACM, vol. 2, no. 2, pp.52-58, (2010).

Google Scholar

[10] A. Zeblah, S. Hadjeri, E. Chatelet and Y. Massim, Efficient harmony search algorithm for multi-stages scheduling problem for power systems degradation, Springer-Verlag, vol. 92, no. 3, pp.87-97, (2010).

DOI: 10.1007/s00202-010-0165-3

Google Scholar

[11] M. O. Buygi, G. Balzer, H. M. Shanechi and M. Shahidehpour, Market-based Transmission Expansion Planning, IEEE Transaction on Power System, vol. 19, no. 4, pp.2060-2067, November (2004).

DOI: 10.1109/tpwrs.2004.836252

Google Scholar

[12] California ISO, Transmission Economic Assessment Methodology, California Independent System Operator, California, (2004).

Google Scholar

[13] BC Hydro, Load and Market Forecasting Energy Planning and Economic Development, BC Hydro, British Columbia, (2012).

Google Scholar

[14] A. O. Melodi, J. A. Momoh and O. M. Adeyanju, Probabilistic Long Term Load Forecast for Nigerian Bulk Power Transmission System Expansion Planning, in 2016 IEEE PES Power Africa Conference, Livingstone, (2016).

DOI: 10.1109/powerafrica.2016.7556621

Google Scholar

[15] D. Johnson, Jointly Distributed Random Variables, 12 May 2005. [Online]. Available: http: /cnx. org/content/m11248/latest.

Google Scholar

[16] F. James, Monte Carlo Theory and Practice, vol. 43, Geneva: The Institute of Physics, CERN, 1980, p.1147–1188.

Google Scholar

[17] Jalayer Academy, Time Series Forcasting in Excel, " 18 April 2013. [Online]. Available: https: /www. youtube. com/watch, v=gHdYEZA50KE. [Accessed 08 July 2015].

Google Scholar