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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484721008799 |
| _version_ | 1819097999165358080 |
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| author | Dharmaraj Kanakadhurga Natarajan Prabaharan |
| author_facet | Dharmaraj Kanakadhurga Natarajan Prabaharan |
| author_sort | Dharmaraj Kanakadhurga |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-22T00:24:00Z |
| format | Article |
| id | doaj.art-3943f6d9381040ca9b135108efb53132 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-12-22T00:24:00Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-3943f6d9381040ca9b135108efb531322022-12-21T18:45:06ZengElsevierEnergy Reports2352-48472021-11-01778257834Demand response-based peer-to-peer energy trading among the prosumers and consumersDharmaraj Kanakadhurga0Natarajan Prabaharan1SASTRA Deemed University, Thanjavur, 613401, TamilNadu, IndiaCorresponding author.; SASTRA Deemed University, Thanjavur, 613401, TamilNadu, IndiaIn 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.http://www.sciencedirect.com/science/article/pii/S2352484721008799Demand responsePeer-to-peer energy tradingReal time pricingDistributed generationElectric vehicle |
| spellingShingle | Dharmaraj Kanakadhurga Natarajan Prabaharan Demand response-based peer-to-peer energy trading among the prosumers and consumers Energy Reports Demand response Peer-to-peer energy trading Real time pricing Distributed generation Electric vehicle |
| title | 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_short | 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 |
| topic | Demand response Peer-to-peer energy trading Real time pricing Distributed generation Electric vehicle |
| url | http://www.sciencedirect.com/science/article/pii/S2352484721008799 |
| work_keys_str_mv | AT dharmarajkanakadhurga demandresponsebasedpeertopeerenergytradingamongtheprosumersandconsumers AT natarajanprabaharan demandresponsebasedpeertopeerenergytradingamongtheprosumersandconsumers |