Robust optimization and pricing of Peer-to-Peer energy trading considering battery storage

Abstract

Peer-to-peer energy trading is one of the greatest systems that helps to increase the use of renewable energy, such as photovoltaics and wind turbines, in households due to the escalating environmental issues. This study designs a peer-to-peer energy trading system under the uncertainty of renewable energy generation and peers’ demands. This study uses a cardinality-constrained robust optimization approach to tackle uncertainties, and it is solved to minimize the overall costs of energy trading, battery depreciation, and load shedding by the Alternating Direction Method of Multipliers method. This model considers peers who are equipped with distributed generation and batteries. Moreover, this local grid has a central battery storage in order to reduce load shedding and dependence on the power grid. Furthermore, the Nash bargaining theory is modeled to calculate energy prices that are traded by peers in order to boost the benefit of all peers and encourage them to use renewable energy as well as participate in peer-to-peer energy trading. Finally, based on data from Iran, the mathematical model is solved to show the effectiveness of using peer-to-peer energy trading that not only can save significant power but also make substantial profits for peers.