A zero-sum Markov defender-attacker game for modeling false pricing in smart grids and its solution by multi-agent reinforcement learning

Daogui Tang (1) , Yiping Fang (2) , Enrico Zio (3)

1 Université Paris-Saclay
2 LGI - Laboratoire Génie Industriel - EA 2606
3 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec

Consumers in smart grids are expected to engage demand-response programs by two-way communication. This makes smart grids vulnerable to cyber attacks. In this paper, we study the false pricing attacks and model the interaction between attackers and defenders using a zero-sum Markov game, where neither player has full knowledge of the game model. A multi-agent reinforcement learning method is used to solve the Markov game and find the Nash Equilibrium policies for both players. An application to a simple radial power distribution system is worked out. The results show that the proposed algorithm can help the players find mixed strategies to maximize their long-term return.

Mots clés: smart grids; demand-response; false pricing attack; game theory; multi-agent reinforcement learning

Domaines: Energie électrique

Daogui Tang, Yiping Fang, Enrico Zio. A zero-sum Markov defender-attacker game for modeling false pricing in smart grids and its solution by multi-agent reinforcement learning. 29th European Safety and Reliability Conference (ESREL2019), Sep 2019, Hannover, Germany. ⟨10.3850/978-981-11-2724-3-0743-cd⟩. ⟨hal-02303650⟩