Game Theoretic Smart Residential Buildings Energy Management System Under False Data Injection Attack

Abstract

A game-theory-based optimal and cyber-attack resilient energy scheduling in multiple smart buildings (SBs) framework considering false data injection (FDI) attack has been proposed in this work. The proposed resilient scheduling is based on the consumers’ past behavior, and import and export power between the SBs and grids. An optimal resilient energy scheduling framework is designed considering renewable energy sources (RESs), combined heat and power (CHP) generators, battery storage systems (BSSs), various smart home (SH) appliances, and FDI attack. An iterative algorithm is used to solve a game-theory-based mixed integer quadratic constrained program (MIQCP) problem in the general algebraic model system (GAMS) environment with a CPLEX solver. For identifying the FDI attack and making a resilient scheduling against possible attacks, the proposed technique uses the difference between the actual and forecasted bills as well as maximum change in demand. The impacts of FDI attack which can be detrimental can be avoided, however, there may be a small difference between energy cost without considering FDI attack and energy cost with resilient scheduling.