Multiple Home-to-Home Energy Transactions for Peak Load Shaving

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Mahmud, Khizir
Nizami, Mohammad Sohrab Hasan
Ravishankar, Jayashri
Hossain, M Jahangir
Siano, Pierluigi
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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.

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IEEE Transactions on Industry Applications
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Mahmud, K; Nizami, MSH; Ravishankar, J; Hossain, MJ; Siano, P, Multiple Home-to-Home Energy Transactions for Peak Load Shaving, IEEE Transactions on Industry Applications, 2020, 56 (2), pp. 1074-1085