Local Energy Trading Markets with Prosumers Considering Fairness and Security

Abstract

The emergence of renewable energy resources such as solar energy from individuals’ solar panels enables individuals to generate energy not only to meet their own needs but also to sell to their neighbours. With microgrid technology, individuals can engage in P2P energy trading in a local energy market without a need for a third party. However, P2P energy trading also raises new challenges on security and fairness. The P2P energy trading involves bi-directional network communication, making it vulnerable to cyberattacks. P2P energy trading also involves individuals with different preferences and load demands, hampering the fair distribution of energy and benefits. This dissertation investigates possible security threats, such as false data injection attacks, in the local P2P energy trading markets and their impacts on the markets. Moreover, this thesis proposes machine learning based techniques to detect false data injection attacks in the local P2P energy trading markets. Regarding the fairness challenge, the dissertation investigates energy sharing in multi-unit buildings and then proposes new fair energy sharing models.