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• W3204057384 abstract "In recent years, smart consumers along with Distributed Generation (DGs) (Photovoltaic (PV) and wind) and Electric Vehicles (EV) are considered as prosumers. The prosumers trade the available excess power to the consumers for minimizing their electricity cost. Each appliance in the smart home can be scheduled by using Demand Response (DR) implementation based on the Real-Time Pricing (RTP). The implementation of peer-to-peer (P2P) energy trading in the smart home further minimizes the electricity cost of the consumer due to the energy trading from prosumers instead of the grid. This article deals with the impact of DR-based P2P energy trading among prosumers and consumers. In this work, two stages of scheduling are proposed to minimize the electricity cost of the consumers. The first stage represents the scheduling of each appliance in a smart home based on the RTP using the Binary Particle Swarm Optimization (BPSO) algorithm. The second stage represents the P2P energy trading among prosumers and consumers based on the DR implementation. The simulation results are proved that the reduction in electricity cost is achieved by implementing energy trading in the smart home. Also, the burden on the utility during the peak hour is reduced by implementing DR-based P2P energy trading." @default.
• W3204057384 created "2021-10-11" @default.
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• W3204057384 date "2021-11-01" @default.
• W3204057384 modified "2023-10-02" @default.
• W3204057384 title "Demand response-based peer-to-peer energy trading among the prosumers and consumers" @default.
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• W3204057384 doi "https://doi.org/10.1016/j.egyr.2021.09.074" @default.
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