International Journal of Electrical and Electronics Research ISSN 2348-6988 (online) Vol. 10, Issue 1, pp: (54-71), Month: January - March 2022, Available at: www.researchpublish.com
IMPROVING THE POWER TRANSFER CAPACITY OF 11KVA DISTRIBUTION POWER NETWORK USING ARTIFICIAL NEURAL BASED DEMAND SIDE MANAGEMENT Okechukwu Cletus1, J. Eke .2, Odeh A.A3, J.C. Iyidiobi4 1,2,3,4
Department of Electrical and Electronics Engineering Faculty of Engineering
Enugu State University of Science & Technology, Enugu State, Nigeria
Abstract: This research presented a improving the power transfer capacity of 11KVA distribution network using artificial neural based demand side management technique. The study was embarked on to address the problem of low profit margin experienced in the distribution companies and also the issues of user dissatisfaction on the quality of power supplied. This was addressed using artificial neural network to develop prediction model using data collected from EEDC and then train. The model was implemented with Matlab and deployed for load forecasting and demand response. The result showed that the load forecast accuracy is 94% while the cost estimated accuracy is 97.6%. The implication of this result showed that the model will accurately provides information for better demand response. Keyword: Power transfer capacity, artificial neural network, demand side management technique, 11KVA distribution network, Load forecasting, prediction model.
I. INTRODUCTION Electricity is one of the most essential components of the modern human life. It is the main driving forces of the modern world today even though it is taken for granted by some people. On one hand, there are almost 1.3 billion people still not having access to electricity and on the other hand, the demand for electricity is expected to increase significantly over the coming years. Since electricity plays a vital role in the human being society, conservation and appropriate energy management strategies for the grids is a must [1]. Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern (e.g., day-ahead forecasted load) can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems [2]. Smart meter is one of the most important devices implemented in the smart grid (SG). With smart meters, electrical data such as voltage and frequency are measured and real-time energy consumption information is recorded. Smart meter supports bidirectional communications between the meter and the central system. Also, the smart meter has the built in ability to disconnect and reconnect certain loads remotely, which can be used to monitor and control the user’s devices
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