Determination of zonal power demand S-curves with GA based on top-to-bottom and end-use approaches

Long-term zonal demand forecasting is a complex problem for electric distribution systems, particularly when the planner has limited data, and therefore, needs smart approaches along with proper estimations. This paper presents a novel methodology to determine saturation curves (S-curves) of demand zones which are classified based on municipality development plans. The methodology which is based on combination of top-to-bottom and bottom-to-top (also called end-use) approaches, is applied to the network of Akdeniz Distribution Company (DISCO) of Turkey. Genetic algorithm (GA) technique is utilized to determine zonal S-curves by utilizing top-to-bottom projections and expected saturation demands of the zones. Sensitivity analysis shows that the proposed method gives reliable results as long as top-to-bottom results represent the total demand of the zones satisfactorily and the planner defines the constraints reasonably.

[1]  J.E.D. Northcote-Green,et al.  Spatial electric load forecasting: A tutorial review , 1983, Proceedings of the IEEE.

[2]  H. Willis,et al.  An Improved Method of Extrapolating Distribution System Load Growth , 1984, IEEE Transactions on Power Apparatus and Systems.

[3]  Clark W. Gellings,et al.  Demand Forecasting for Electric Utilities , 1991 .

[4]  J.F. Frenzel,et al.  Genetic algorithms , 1993, IEEE Potentials.

[5]  H Lee Willis,et al.  Spatial electric load forecasting , 1996 .

[6]  K. P. Mohandas,et al.  Application of fuzzy system theory in land use based long term distribution load forecasting , 1998, Proceedings of EMPD '98. 1998 International Conference on Energy Management and Power Delivery (Cat. No.98EX137).

[7]  S. M. El-Debeiky,et al.  Long-Term Load Forecasting for Fast-Developing Utility Using a Knowledge-Based Expert System , 2002, IEEE Power Engineering Review.

[8]  C. N. Lu,et al.  A Data Mining Approach for Spatial Modeling in Small Area Load Forecast , 2002, IEEE Power Engineering Review.

[9]  C. W. Fu,et al.  Models for long-term energy forecasting , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[10]  Mostafa Al Mamun,et al.  Artificial neural networks applied to long-term electricity demand forecasting , 2004, Fourth International Conference on Hybrid Intelligent Systems (HIS'04).

[11]  T.Q.D. Khoa,et al.  Application of wavelet and neural network to long-term load forecasting , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[12]  M.B.R. Murthy,et al.  A Novel Scheme of Load Forecasting pertaining to long term Planning of a distribution system , 2005, TENCON 2005 - 2005 IEEE Region 10 Conference.