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http://dx.doi.org/10.5370/KIEE.2015.64.6.852

A Stochastic Pplanning Method for Semand-side Management Program based on Load Forecasting with the Volatility of Temperature  

Wi, Young-Min (Dept. of Electrical and Electronic Engineering, Gwangju University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.64, no.6, 2015 , pp. 852-856 More about this Journal
Abstract
Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.
Keywords
Demand-side management; Load forecasting; Temperature stochastic modeling;
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  • Reference
1 W.K. Härdle, B. López-Cabrera, and M. Ritter, “Forecast based pricing of weather derivatives,” SFB 649 Discussion Paper Series 2012, 2012.
2 Loughran, David S. and Jonathan Kulick, “Demand-side management and energy efficiency in the United States,” The Energy Journal, vol. 25, no. 1, pp.19-43, 2004.
3 P. Samadi, H. Mohseniab-Rad, R. Schober, and V. Wong, “Advanced demand side management for the future smart grid using mechanism design,” IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1170-1180, 2012.   DOI   ScienceOn
4 Power demand management portal https://www.kdrm.or.kr
5 M. Mraoua and D. Bari, “Temperature stochastic modeling and weather derivatives pricing: empirical study with Moroccan data,” Africa Statistika, vol. 2, no. 1, pp. 22-43, 2007.