Robust Optimization Method for Consumer-Oriented Load Management under Real-Time Pricing Integrity Attack

Future active consumers will play an important role in energy markets. With smart meters and associated communication infrastructures, active consumers can adapt their load demand in response to real-time pricing and optimize their use of energy. The increased reliance on cyber assets and communications makes these systems prone to cyberattacks. When an attacker manipulates the real-time prices over communication channels, the consumers may make incorrect decisions and they may suffer losses. This paper focuses on consumer-level load management under false data injection (FDI) attack on price of electricity, which is known as real-time pricing integrity attack. First, the impact of such attack of the price-responsive demand is investigated and the economic loss of error of total demand is quantified. Then, robust optimization method is proposed to handle the real-time pricing attack as uncertainty to obtain the optimal energy consumption plan based on the preference of consumers. Finally, a case study is conducted to verify the effectiveness of the proposed method. A comparison with a deterministic method is provided.