Abstract
In this paper, a day-ahead load management system is proposed using the household power demand and photovoltaic (PV) power generation prediction. The prediction is made using an artificial neural network. A power demand management algorithm is developed to process these predicted values considering the boundary conditions of battery storage to flatten the peaks in a load curve. The proposed system is tested in a real power distribution network under realistic load pattern, and power demand and PV power generation uncertainties. The study found that strategic use of battery energy storage and PV, and a time-ahead prediction of power demand can substantially reduce the peaks and improve the load factor.
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Mahmud, K., Peng, W., Morsalin, S., Ravishankar, J. (2020). A Day-Ahead Power Demand Prediction for Distribution-Side Peak Load Management. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_27
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DOI: https://doi.org/10.1007/978-981-13-7564-4_27
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