4.6 Article

Federating Smart Cluster Energy Grids for Peer-to-Peer Energy Sharing and Trading

Journal

IEEE ACCESS
Volume 8, Issue -, Pages 102419-102435

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2998747

Keywords

Smart grids; Biological system modeling; Peer-to-peer computing; Industries; Energy resources; Renewable energy sources; Optimization; Energy sharing; cluster federation; peer-to-peer networks; smart grids; fish industries

Funding

  1. EU INTERREG piSCES Project: Smart Cluster Energy Grid Systems for Fish Industries funded via the European Regional Development Fund through the Ireland Wales Cooperation Program

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With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project.

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