This article proposes a new technique to manage the domestic peak load demand through peer-to-peer energy transaction among multiple homes. In this process, the houses willing to sell energy are identified as the Parent, and the houses that require energy are identified as a Child. The parents having energy resources such as photovoltaics, battery storage and electric vehicles will utilize their resources to meet their peak power demand and sell the extra energy to a child. A mixed integer linear programming optimization is used to find the parent–child matching based on their energy availability, power demand, and distances. After selecting the parent–child match, the power demand of a child is forecasted using two different techniques, i.e., autoregressive moving average and artificial neural networks, to identify to child's need in a day ahead of the actual operation. The proposed algorithm calculates the available energy of a parent to sell in real-time and the required energy of a child in a day-ahead, while ensuring to minimize the peak load demand. The proposed method, as confirmed by the presented analysis using data of a real Australian power distribution network, is able to significantly minimize the peak load demand, which in-turn is expected to minimize the electricity costs. The method also facilitates two agreed prosumers to transact energy between themselves without the involvement of a third party.

中文翻译：

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用于调峰的多个家庭到家庭能源交易

本文提出了一种新技术，通过多个家庭之间的点对点能源交易来管理国内高峰负荷需求。在这个过程中，愿意出售能源的房屋被识别为父，需要能源的房屋被识别为子。拥有光伏、电池存储和电动汽车等能源资源的父母将利用他们的资源满足峰值电力需求，并将多余的能源出售给孩子。混合整数线性规划优化用于根据能源可用性、电力需求和距离找到父子匹配。选择亲子匹配后，使用两种不同的技术预测孩子的电力需求，即自回归移动平均和人工神经网络，以识别孩子的 s 需要在实际操作前一天。所提出的算法实时计算父母可出售的可用能量以及前一天孩子所需的能量，同时确保最大限度地减少峰值负载需求。所提出的方法，正如所呈现的使用澳大利亚真实配电网络数据的分析所证实的那样，能够显着降低峰值负荷需求，进而有望最大限度地降低电力成本。该方法还有助于两个商定的产消者在没有第三方参与的情况下在他们之间进行能源交易。正如使用真实澳大利亚配电网络数据进行的分析所证实的那样，能够显着降低峰值负载需求，从而有望最大限度地降低电力成本。该方法还有助于两个商定的产消者在没有第三方参与的情况下在他们之间进行能源交易。正如所呈现的使用澳大利亚真实配电网络数据的分析所证实的那样，能够显着降低峰值负荷需求，进而有望最大限度地降低电力成本。该方法还有助于两个商定的产消者在没有第三方参与的情况下在他们之间进行能源交易。