Electrical Load Manageability Factor Analyses by Artificial Neural Network Training

Document Type: Research paper

Authors

Department of Electrical Engineering, University of Zanjan, Zanjan, Iran

Abstract

On typical medium voltage feeder, Load side management means power energy consumption controlling at connected loads. Each load has various amount of reaction to essential parameters variation that collection of these reactions is mentioned feeder behavior to each parameter variation. Temperature, humidity, and energy pricing variation or major event happening and power utility announcing to the customers are essential parameters that are considered at recent researches. Depends on amount of improvement that each changeable parameters effect on feeder load consumption, financial assets could be managed correctly to gain proper load side management. Collecting feeder loads behavior to all mentioned parameters will gain Load Manageability Factor (LMF) that helps power utilities to manage load side consumption. Calculating this factor needs to find out each types of load with unique inherent features behavior to each parameters variation. This paper and future works will help us to catch mentioned LMF. In this paper analysis of typical commercial feeder behavior due to temperature and humidity variation with training artificial neural network will be done. Load behavior due to other essential parameters variations like energy pricing variation, major event happening, and power utility announcing to the customers, and etc will study in future works

Keywords

Main Subjects


[1]    M. Alilou, D. Nazarpour, H. Shayeghi, “Multi-Objective Optimization of demand side management and multi DG in the distribution system with demand response,” J. Oper. Autom. Power Eng., vol. 6, no. 2, pp. 230-242, 2018.

[2]    M. Kazemi-Khafri, A. Badri, A. Motie-Birjandi, “Demand response based model for optimal decision making for distribution networks,” J. Oper. Autom. Power Eng., vol. 5, no. 2, pp. 139-149, 2017.

[3]    N. Eskandari, S. Jalilzadeh, “Residential load manageability factor analyses by load sensitivity affected by temperature,” Iran. J. Elec. Electron. Eng., pp. 314-321, 2016.

[4]    M. Bartos, M. Chester, N. Johnson, B. Gorman, D. Eisenberg, I. Linkov, M. Bates. “Impacts of rising air temperatures on electric transmission ampacity and peak electricity load in the United States,” Environ. Res. Letters, lett. 11, 2016.

[5]    A. A. Salehizade, M. Rahmanian, M. Farajzadeh, and A. Ayoubi, “Analysis of temperature changes on electricity consumption in Fars Province,” Mediterranean J. Social Sci., vol. 6, no. 3s2, pp. 610-617, 2015.

[6]    P. Sullivan, J. Colman, E. Kalendra, “Predicting the Response Of Electricity Load to Climate Change,” National Renewable Energy Lab., 2015.

[7]    M. U. Fahad, N. Arbab, “Factor Affecting Short Term Load Forecasting,” J. Clean Energy Technol., vol. 2, no. 4, 2014.

[8]    Y. Yoon, D, Kang, Y. Yoon, “The Temperature Sensitivity of the Commercial Load in Korea,” J. Int. Council Electr. Eng., vol. 3, nO. 2, pp. 110-114, 2013.

[9]    N. Lu, T. Taylor, W. Jiang, C. Jin, J. C. jr, L. R. Leung, and P. C. Wong, “Climate change impacts on residential and commercial loads in the Western U. S. grid,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 480-488, 2010.

[10]  N. Lu, T. Taylor, W. Jiang, C. Jin, J. C. jr, L. R. Leung, and P. C. Wong, “the temperature sensitivity of the residential load and commercial building load,” IEEE Power Energy Soc. General Meeting Conf., 2009.

[11]  C. Crowley, F. L. Joutz, “Weather effects on electricity loads: modeling and forecasting,” Department of Economics George Washington University, 2005.

[12]  C. S. Chen, M. S. Kang, J. C. Hwang, and C. W. Huang, “Temperature effect to distribution system load profiles and feeder losses,” IEEE Trans. Power Syst., vol. 16, no. 4, pp. 916-921, 2001.

[13]  M. H. Albadi, E. F. El-Saadany, “A summary of demand response in electricity markets,” Electr. Power Syst. Res., vol.78, pp. 1989-1996, 2008.

[14]  M. Yu, and S. H. Hong, “Supply-demand balancing for power management in smart grid: A Stackelberg game approach,” Appl. Energy 164, pp. 702-710, 2016.

[15]  M. Asadi, and M. H. Moradi, “Investigation of DSM challenges in Iran with review of the international experiences,” IEEE Prime. Asia Pac. Conf. Postgraduate Res., pp. 420-423, Jan 2009.

[16]  N. Li, L. Chen, and M. A. Dahleh, “Demand Response Using Linear Supply Function Bidding,” IEEE Trans. Smart Grid, 2015.

[17]  C. Vivekananthan, Y. Mishra, G. Ledwich, and F. Li, “Demand Response for Residential Appliances via Customer Reward Scheme,” IEEE Trans. Smart Grid, vol.5, pp. 809-820, March 2014.