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    Distributed Algorithms for Peer-to-Peer Energy Trading

    Jogunola, Olamide (2019) Distributed Algorithms for Peer-to-Peer Energy Trading. Doctoral thesis (PhD), Manchester Metropolitan University.


    Available under License Creative Commons Attribution Non-commercial No Derivatives.

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    A S the proliferation of the ’sharing economy’ increases, its phenomenon is actively extending to the power grid, where energy consumers are motivated to use, produce, trade or share energy with the main grid and themselves. To optimise the potential of this changing era in smart grid, considering the complexity requirements of the individual distributed connected components, a distributed coordination algorithm is required to manage the large influx of energy as well as the altruistic goal of diverse energy producers. Furthermore, a trading platform is actively needed to implement these distributed algorithms to match the prosumers, coordinate their resources and maximise their utilities for increased profits and cost savings. This research investigates distributed algorithms for peer-to-peer energy trading and sharing (P2P-ETS) to facilitate discovery, communication and utility maximisation of peers who are trading energy in a P2P fashion. To begin, a four-layer system architectural model is proposed to categorise the key elements and technologies associated with the P2P-ETS. Then, constrained by as few assumptions as possible, while showing promising performance and key metrics, three distributed algorithms are developed to facilitate discovery, peer’s matching, data routing, energy transfer, and utility maximisation of the trading entities. These algorithms utilise only local information to solve the problem with promising results, complementing their presentation with simulations that demonstrate their effectiveness over imperfect communication links. Finally, based on these distributed algorithms, a software platform is developed to support the pairing of prosumers on the P2P-ETS platform. A case study based on real microgrid data is used to verify the performance of the platform which demonstrate increase in local energy consumption. Simulation results show that the developed platform is able to balance local generation and consumption and increase cost savings of 45% for prosumers that trade energy among themselves compared to trading with the power grid. This savings however varies depending on the participants on the platform.

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