Demand response-based peer-to-peer energy trading among the prosumers and consumers

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...

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Autores principales:	Dharmaraj Kanakadhurga, Natarajan Prabaharan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Demand response Peer-to-peer energy trading Real time pricing Distributed generation Electric vehicle Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3943f6d9381040ca9b135108efb53132
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id	oai:doaj.org-article:3943f6d9381040ca9b135108efb53132
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spelling	oai:doaj.org-article:3943f6d9381040ca9b135108efb531322021-11-26T04:34:25ZDemand response-based peer-to-peer energy trading among the prosumers and consumers2352-484710.1016/j.egyr.2021.09.074https://doaj.org/article/3943f6d9381040ca9b135108efb531322021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721008799https://doaj.org/toc/2352-4847In 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.Dharmaraj KanakadhurgaNatarajan PrabaharanElsevierarticleDemand responsePeer-to-peer energy tradingReal time pricingDistributed generationElectric vehicleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 7825-7834 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Demand response Peer-to-peer energy trading Real time pricing Distributed generation Electric vehicle Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Demand response Peer-to-peer energy trading Real time pricing Distributed generation Electric vehicle Electrical engineering. Electronics. Nuclear engineering TK1-9971 Dharmaraj Kanakadhurga Natarajan Prabaharan Demand response-based peer-to-peer energy trading among the prosumers and consumers
description	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.
format	article
author	Dharmaraj Kanakadhurga Natarajan Prabaharan
author_facet	Dharmaraj Kanakadhurga Natarajan Prabaharan
author_sort	Dharmaraj Kanakadhurga
title	Demand response-based peer-to-peer energy trading among the prosumers and consumers
title_short	Demand response-based peer-to-peer energy trading among the prosumers and consumers
title_full	Demand response-based peer-to-peer energy trading among the prosumers and consumers
title_fullStr	Demand response-based peer-to-peer energy trading among the prosumers and consumers
title_full_unstemmed	Demand response-based peer-to-peer energy trading among the prosumers and consumers
title_sort	demand response-based peer-to-peer energy trading among the prosumers and consumers
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/3943f6d9381040ca9b135108efb53132
work_keys_str_mv	AT dharmarajkanakadhurga demandresponsebasedpeertopeerenergytradingamongtheprosumersandconsumers AT natarajanprabaharan demandresponsebasedpeertopeerenergytradingamongtheprosumersandconsumers
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Demand response-based peer-to-peer energy trading among the prosumers and consumers

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