Abstract Recently, peer-to-peer (P2P) energy trading has attracted considerable attention as an approach to overcome current challenges faced by the dissemination policy of each country for renewables-based distributed energy resources. However, existing trading models do not provide solutions for the receptivity problem in trading from the perspectives of customers and related organizations. In this regard, this paper proposes a novel P2P energy trading model that can be integrated into the existing distribution system. An operator-oriented trading scheme is developed to lower barriers to allow entry into P2P trading. The operator, instead of each participant, decides the marginal/trading price and trading schedules. Unlike in the existing model, the marginal price that accommodates various electric tariffs and sales systems is derived to obtain high receptivity from customers. Furthermore, service charges comprising charges for network usage and platform service are determined to develop a feasible trading model. The proposed model achieves acceptance from the utility company and platform operator in terms of service charges. Further, a matching algorithm is formulated considering derived elements; accordingly, a constrained optimization problem is formulated, and the Lagrange multiplier method is applied to create an optimal trading schedule that maximizes social welfare while preventing imbalance in revenues between buyers and sellers. The actual data for various types of loads and distributed energy resources are applied to the IEEE 18-bus distribution system to verify the performance of the proposed P2P trading scheme.

中文翻译：

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开发面向运营商的点对点能源交易模型，以整合到现有的配电系统中

摘要 最近，点对点（P2P）能源交易作为一种克服当前各国基于可再生能源的分布式能源的传播政策所面临挑战的方法引起了相当多的关注。然而，现有的交易模型并没有从客户和相关组织的角度为交易中的接受度问题提供解决方案。对此，本文提出了一种新颖的 P2P 能源交易模型，可以集成到现有的配电系统中。开发了面向运营商的交易方案，以降低进入 P2P 交易的门槛。运营商，而不是每个参与者，决定边际/交易价格和交易时间表。与现有模型不同的是，适应各种电价和销售系统的边际价格是为了获得客户的高接受度。此外，确定包括网络使用费和平台服务费在内的服务费，以开发可行的交易模式。所提出的模式在服务费方面获得了公用事业公司和平台运营商的认可。此外，考虑派生元素制定匹配算法；因此，制定了一个约束优化问题，并应用拉格朗日乘数方法来创建一个最佳交易时间表，在防止买卖双方收入不平衡的同时，最大化社会福利。