A Profit-Oriented FDI Attack Strategy in Real-Time Peer-to-Peer Energy Trading Market

The emergence of peer-to-peer (P2P) energy trading market brings both benefits and risks to modern power system. Relying on information and communication technology (ICT), transaction in P2P energy trading market is vulnerable to false data injection attacks (FDIAs). Specifically, the malicious redistribution of short-term forecasting load data will mislead the economic dispatch (ED) in the ex-ante real-time (RT) market. Hence, the actual generation outputs deviate from the schedule, and the corrupt result of ED can affect the trading volume in RT market through a P2P framework. Accordingly, targeting on a specific agent in P2P market, we propose a strategy to inject a malicious forecasting data in order to maximize the trading profits for this agent. Moreover, since the P2P model often solved in a distributed way, which brings heavy computational burden, we conduct a preferred locational marginal price (LMP) to give a one-time solution. Eventually, we demonstrate the effectiveness of our attacking strategy based on the IEEE 9-bus test system.