Optimal Scheme with Load Forecasting for Demand Side Management (DSM) in Residential Areas

Mohamed AboGaleela; Magdy El-Marsafawy; Mohamed El-Sobki

doi:10.4236/epe.2013.54B171

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Energy and Power Engineering > Vol.5 No.4B, July 2013

Optimal Scheme with Load Forecasting for Demand Side Management (DSM) in Residential Areas ()

Mohamed AboGaleela, Magdy El-Marsafawy, Mohamed El-Sobki

Electrical power and Machines department, Faculty of engineering Cairo University, Giza Egypt.

DOI: 10.4236/epe.2013.54B171 PDF HTML 3,486 Downloads 4,876 Views Citations

Abstract

Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main Target of this paper is to show the relation between DSM and Load Forecasting. Moreover, it highlights on the effect of applying DSM on Forecasted demands and how this affects the planning strategies for utility companies. This target will be clearly illustrated through applying the developed algorithm in this paper on an existing residential compound in Cairo-Egypt.

Keywords

Component; Demand Side Management(DSM); Load factor(L.F.); Short Term Load Forecatsing(STLF); Long Term Load Forecasting(LTLF); Artificial Neural Network(ANN)

Share and Cite:

M. AboGaleela, M. El-Marsafawy and M. El-Sobki, "Optimal Scheme with Load Forecasting for Demand Side Management (DSM) in Residential Areas," Energy and Power Engineering, Vol. 5 No. 4B, 2013, pp. 889-896. doi: 10.4236/epe.2013.54B171.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	H. Attia, “Mathematical Formulation of the Demand Side Management (DSM) Problem and its Optimal Solution,” Proceedings of the 14th International Middle East Power Systems Conference (MEPCON’10), Cairo University, Egypt, December 19-21, 2010.
[2]	Electric Power Research Institute, ―Demand-side Management: Utility Options for the Future,‖ EPRI Reports, CU. 3028.10.89, 2006.
[3]	N. Kanoksing and W. Tayati, “Economics of Demand Side Manage-ment and Hybrid Renewable Energy System for a Remote Village Electrification in Northern Thailand, AUPEC'07, Curtin University of Technology, Perth, Australia, Dec 2007.
[4]	R. Achnata, “Long Term Electric Load Forecasting using Neural Networks and Support Vector Machines,” IJCST, Vol. 3, No. 1, 2012.
[5]	R. Holmukhe, “Artificial Neural Network based short term load forecasting technique for Indian power system energy management,” College of Engineering Bhanti dyapeetb Diversity, Pune43, 2007.
[6]	E. Feinberg, Applied Math & Statistics Stony Brook University NSF workshop, November 3-4, 2003.
[7]	M. Peng, N. F. Hubele and G. G. Karady, “Advancement in the Application of Neural Networks for Short-Term Load Forecasting,” IEEE Transactions on Power Systems, Vol. 7, 1992, pp. 250-257. doi:10.1109/59.141711
[8]	M. Abogaleela, M. Elsobki and M. ElMarsafawy, “A Two Level Optimal DSM Load Shifting Formulation Using Genetics Algorithm, Case study: Residential load, Power Africa 2012.IEEE PES, South Africa.
[9]	Smart Meter “Iskra” Tool Owned by “TAQA Power” Company.