article · Energy Strategy Reviews
This research examines the operational and economic impact of peer-to-peer energy trading among smart homes. The study models four distinct prosumer households equipped with photovoltaic systems, energy storage, and flexible demand technologies such as electric vehicles. An energy management system establishes a dynamic marketplace linking peer-to-peer market operators with distribution system operators, adjusting trading platforms to price shifts across different time intervals. Deep learning mathematical modelling is applied to find optimal trading solutions that reduce costs. In addition to examining energy volumes exchanged across varied housing scenarios and timeframes, the work explores two-way energy transfer for electric vehicles and the contribution of solar and storage assets. An input and output interface validates system performance and demonstrates the economic advantages to participating users beforehand.
Rising power demand and climate concerns require better ways to integrate decentralised renewable power into modern grids. Facilitating direct energy trades between smart homes equipped with solar generation, batteries, and electric cars helps lower household costs and improves grid flexibility. This approach provides practical insights into how neighbourhood-level trading mechanisms can balance local supply and demand efficiently.
The work could enable software tools for neighbourhood energy markets, microgrid management, and smart home automation. Likely users include distribution system operators, energy aggregators, and prosumer households with renewable assets and electric vehicles. Given that the abstract describes case-study models, deep learning optimisation, and validation via an input and output interface, the technology appears to be at an applied, tested prototype stage rather than ready for immediate commercial deployment.
AI-generated from the published abstract. Always read the original work before citing.
The increase in demand due to scientific innovation and economic expansion raises challenges linked to environmental impact and sustainable market development. This study analyzes the role played by Peer-to-Peer (P2P) energy trading in system operation. Several case studies with four distinct prosumer models, each fitted with diverse Energy Storage Systems (ESS), photovoltaic (PV) systems, and responsive demand technologies such as Electric Vehicles (EV), are examined. An economic analysis is conducted using the developed interface. An energy management system establishes a dynamic market framework, enabling energy trading via P2P market operators and distribution system operators. The implemented trading platform is chosen depending on pricing variations across different time intervals for home models. The P2P energy trading system employs mathematical modeling via deep learning to achieve an optimal solution that minimizes costs. The study uses other domestic models and optimization techniques to explore the amount of energy traded across households and timeframes. Additionally, it investigates the accomplishment of two-way energy transfer in electric cars, the importance of energy storage and photovoltaic systems, and various housing scenarios concerning peer-to-peer energy trading. Additionally, the system's advantages are demonstrated to participants beforehand by validating and presenting data through an input and output system that accepts these instances.
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DOI: 10.1016/j.esr.2023.101288
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