Abstract
Market grounded pricing of electrical energy provides consumers with the liberty of lowering their electricity bill by shifting the load to lower price slots. First part of the work presented in this paper is the outcome of application of DSM techniques to residential consumers. The proposed algorithm schedules the charging time for household inverter battery for the next day considering day ahead pricing, hence lowering the peak demand on utility and generating savings to consumers. Second part of the paper proposes an optimizing algorithm for charging PHEVs parked in a parking garage at a work place. Based on power demand and duration of stay, PHEVs are prioritized and charged. Part of the demand is met by solar PV panels and rest from utility mains. MATLAB tools have been used for performance evaluation and algorithm implementation.
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Parvathy, S., Patne, N.R., Safni Usman, T. (2021). Optimal Battery Charging Forecasting Algorithms for Domestic Applications and Electric Vehicles by Comprehending Sustainable Energy. In: Singh, A.K., Tripathy, M. (eds) Control Applications in Modern Power System. Lecture Notes in Electrical Engineering, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-15-8815-0_3
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