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A motivational game-theoretic approach for peer-to-peer energy trading in the smart grid

机译:智能电网中对等能源交易的激励性博弈论方法

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Peer-to-peer trading in energy networks is expected to be exclusively conducted by the prosumers of the network with negligible influence from the grid. This raises the critical question: how can enough prosumers be encouraged to participate in peer-to-peer trading so as to make its operation sustainable and beneficial to the overall electricity network? To this end, this paper proposes how a motivational psychology framework can be used effectively to design peer-to-peer energy trading to increase user participation. To do so, first, the state-of-the-art of peer-to peer energy trading literature is discussed by following a systematic classification, and gaps in existing studies are identified. Second, a motivation psychology framework is introduced, which consists of a number of motivational models that a prosumer needs to satisfy before being convinced to participate in energy trading. Third, a game-theoretic peer-to-peer energy trading scheme is developed, its relevant properties are studied, and it is shown that the coalition among different prosumers is a stable coalition. Fourth, through numerical case studies, it is shown that the proposed model can reduce carbon emissions by 18.38% and 9.82% in a single day in Summer and Winter respectively compared to a feed-in-tariff scheme. The proposed scheme is also shown to reduce the cost of energy up to 118 phi and 87 phi per day in Summer and Winter respectively. Finally, how the outcomes of the scheme satisfy all the motivational psychology models is discussed, which subsequently shows its potential to attract users to participate in energy trading.
机译:能源网络中的对等交易预计将完全由网络的生产者进行,而对电网的影响可忽略不计。这就提出了一个关键问题:如何鼓励足够的生产者参加点对点交易,以使其运营可持续并有益于整个电力网络?为此,本文提出了如何有效利用激励心理学框架来设计对等能源交易以增加用户参与度的方法。为此,首先,按照系统的分类讨论点对点能源交易的最新技术,并找出现有研究中的空白。其次,引入了动机心理学框架,该框架由许多动机模型组成,生产者在被说服参与能源交易之前需要满足这些动机模型。第三,提出了一种博弈论的点对点能源交易方案,研究了它的相关性质,表明不同生产者之间的联盟是一个稳定的联盟。第四,通过数值案例研究表明,与上网电价补贴方案相比,该模型在夏季和冬季每天可分别减少碳排放量18.38%和9.82%。所提出的方案还显示出在夏季和冬季分别将每天的能源成本降低多达118 phi和87 phi。最后,讨论了该计划的结果如何满足所有激励心理学模型,随后显示了其吸引用户参与能源交易的潜力。

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