Abstract
Solar power generation systems are highly economic, renewable, and environmentally friendly. Still the primary concern over a solar photovoltaic power generation system (SPVGS) is the unreliable and highly fluctuating power generation due to irregular solar irradiation. The demand response under highly fluctuating power generation is a complex task. The developed model is a demand response model which uses machine learning technologies to perform power scheduling and load scheduling during fluctuating demand/power generation. A multi-layer algorithm model is developed which uses historical values as well as instantaneous values for the prediction of the demand and generation schedule. The proposed model uses basically two algorithms in different layers which include autoregressive acceleration model (ARAM) and support Vector regression (SVR) model. A hardware model is developed to test and validate the results. Successive re-approximation is performed in an adaptive manner which will update the model on each iteration, and this model is repeated until optimum results are obtained.
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Sarin, C.R., Mani, G. (2021). Demand Response of a Solar Photovoltaic Dominated Microgrid with Fluctuating Power Generation. In: Komanapalli, V.L.N., Sivakumaran, N., Hampannavar, S. (eds) Advances in Automation, Signal Processing, Instrumentation, and Control. i-CASIC 2020. Lecture Notes in Electrical Engineering, vol 700. Springer, Singapore. https://doi.org/10.1007/978-981-15-8221-9_18
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