Securing demand–response in smart grids against false pricing attacks

Abstract

Two-way communication systems in smart grids help to engage consumers in demand–response programs, which can bring many benefits but also make smart grids vulnerable to cyber attacks. In this paper, a cyber attack which aims to disturb the demand–response process by injecting false electricity price signals is considered. A real-time pricing model where the operator has incomplete knowledge of the private demand–response behaviors of the customers is proposed. The vulnerability of the power system to false pricing attacks is analyzed by a Markov decision process, and the dynamic interaction between the attacker and the defender is modeled as a zero-sum Markov game where neither player has full information of the game model. For the solution of the Markov game, a model-free multi-agent reinforcement learning method is proposed to find the Nash Equilibrium policies for both players. The proposed method is applied to the IEEE 34 Node Test Feeder, in which the effect of the defense on mitigating the impact of the attack is demonstrated and different policies of the players given various resources are analyzed. The results shows that the studied cyber attack can cause a maximum of 1.96% unsatisfied load and the proposed defense measure can reduce the unsatisfied load by 50%–70% compared with the cases without defense measures.