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An electric power trading framework for smart residential community in smart cities
This study proposes a multi-agent-based framework for Peer-to-Peer (P2P) power trading in a locality electricity market (LEM) for self-interested smart residential prosumers. In LEM, prosumers may sell (buy) their excess generation (demand) at a profitable market prices compared to utility prices to achieve a win–win outcome. In LEM, three agents namely locality electricity trading system (LETS), utility and prosumer act together to achieve P2P power trading in a day-ahead market. LETS computes the internal market prices employing any one of the market-clearing mechanisms and broadcasts it to the prosumers. Prosumers optimise the generation-demand schedule for the next day using residential energy management and trading system to achieve minimum electricity bill. The performance of the proposed framework is validated through different case studies on a residential locality with ten prosumers. The simulation is carried out using MATLAB parallel computation tool box and the load data is collected from the residential locality of National Institute of Technology Tiruchirappalli, India. It is evident from the simulation results that all the participants are economically benefited by P2P power trading. It is also found that the SDR mechanism in P2P outperforms and reduces the locality electricity bill by 27–68% under different operating conditions.
An electric power trading framework for smart residential community in smart cities
This study proposes a multi-agent-based framework for Peer-to-Peer (P2P) power trading in a locality electricity market (LEM) for self-interested smart residential prosumers. In LEM, prosumers may sell (buy) their excess generation (demand) at a profitable market prices compared to utility prices to achieve a win–win outcome. In LEM, three agents namely locality electricity trading system (LETS), utility and prosumer act together to achieve P2P power trading in a day-ahead market. LETS computes the internal market prices employing any one of the market-clearing mechanisms and broadcasts it to the prosumers. Prosumers optimise the generation-demand schedule for the next day using residential energy management and trading system to achieve minimum electricity bill. The performance of the proposed framework is validated through different case studies on a residential locality with ten prosumers. The simulation is carried out using MATLAB parallel computation tool box and the load data is collected from the residential locality of National Institute of Technology Tiruchirappalli, India. It is evident from the simulation results that all the participants are economically benefited by P2P power trading. It is also found that the SDR mechanism in P2P outperforms and reduces the locality electricity bill by 27–68% under different operating conditions.
An electric power trading framework for smart residential community in smart cities
Hanumantha Rao, Bokkisam (author) / Arun, Saravana Loganathan (author) / Selvan, Manickavasagam Parvathy (author)
IET Smart Cities ; 1 ; 40-51
2019-11-11
12 pages
Article (Journal)
Electronic Resource
English
residential energy management , smart grid paradigm , multiagent-based framework , energy management systems , smart residential community , locality electricity market , power markets , pricing , peer-to-peer computing , day-ahead market , internal market prices , prosumer act , bill sharing , self-interested smart residential prosumers , market-clearing mechanisms , residential locality , smart cities , peer-to-peer power trading , power system network , electric power trading framework , locality electricity bill , P2P power trading , smart power grids , trading system , LEM , profitable market prices , mid-market rate , distributed energy resources
Metadata by IET is licensed under CC BY 3.0
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