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Demand Side Load Management by Using Priority Based Load Shedding Algorithm with and without Renewable Energy Generation
ISSN: 0148-7191, e-ISSN: 2688-3627
Published October 11, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
Demand side load management (DSLM) emphasizes control of the power demanded, by reducing the peak load and control of energy utilization of the system. DSLM is introduced to improve the flexibility of the grid power usage and also to aid the utilization of Renewable Energy Generation (REG) which is intermittent. In this work, implementation of load shedding (LS) algorithm for the residential load is performed with the limit of power as constraint, considering REG and grid in three different modes of operation. Solar and Wind power are the REG considered in this work. Priority Based Load Shedding (PBLS) is performed to limit the power consumption of equipment during peak hours with the implementation of varying pricing signal. In order to implement PBLS, three residential user load data for 24 hours is considered. The users are categorized as low, medium and high priority user. The priority of the user is based on the load consumption for 24 hours. The proposed LS scheme is performed, depending on the power requirements of Home Electric Devices (HEDs) and the priority of consumer. The main objective of cost reduction (power consumption) along with minimization of user discomfort is achieved by using the PBLS algorithm. Simulation results for REG islanded mode, grid connect mode and REG aided grid mode is performed. The further cost comparison is made with and without a load schedule. The scheduling of load curve is performed using Genetic Algorithm (GA) optimization.
CitationRathinam, R. and Dayalan, S., "Demand Side Load Management by Using Priority Based Load Shedding Algorithm with and without Renewable Energy Generation," SAE Technical Paper 2019-28-0073, 2019, https://doi.org/10.4271/2019-28-0073.
Data Sets - Support Documents
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