NAIDJI Ilyes

Demand side management (DSM) is one of the main functionalities of the smart grid as it allows the consumer to adjust its energy consumption for an efficient energy management. Most of the existing DSM techniques aim at minimizing the energy cost while not considering the comfort of consumers. Therefore, maintaining a trade-off between these two conflicting objectives is still a challenging task. This paper proposes a novel DSM approach for residential consumers based on a non-cooperative game theoretic approach, where each player is encouraged to reshape its electricity consumption pattern through the dynamic pricing policy applied by the smart grid operator. The players are guided to select the best strategy that consists of scheduling their electric appliances in order to minimize the daily energy cost and their discomfort level. The Nash Equilibrium of the energy management game is achieved using Non-Sorting Genetic Algorithm NSGA-II. Simulation results show the effectiveness of...

Demand side management (DSM) is one of the main functionalities of the smart grid as it allows the consumer to adjust its energy consumption for an efficient energy management. Most of the existing DSM techniques aim at minimizing the energy cost while not considering the comfort of consumers. Therefore, maintaining a trade-off between these two conflicting objectives is still a challenging task. This paper proposes a novel DSM approach for residential consumers based on a non-cooperative game theoretic approach, where each player is encouraged to reshape its electricity consumption pattern through the dynamic pricing policy applied by the smart grid operator. The players are guided to select the best strategy that consists of scheduling their electric appliances in order to minimize the daily energy cost and their discomfort level. The Nash Equilibrium of the energy management game is achieved using Non-Sorting Genetic Algorithm NSGA-II. Simulation results show the effectiveness of the distributed non cooperative game approach for the residential energy management problem where an appreciable energy cost reduction is reached while maintaining the discomfort in an acceptable level.

Smart grids are critical cyber-physical power systems that are characterized by reliable and safe operation against physical faults and cyber attacks. Smart grid systems have to be developed for the protection from cyber-physical threats by taking the necessary countermeasures. To address this problem, this paper proposes a unified framework that can detect, identify and recon-figure the smart grid from cyber attacks as well as physical faults to recover the system operation. The proposed framework is based on sensitivity analysis which can estimate the system state in efficient way. The framework has a multi-agent-based architecture that consists of several types of agents with well-defined behaviors which cooperate to fulfill the goal of cyber-physical system (CPS) protection against cyber-physical attacks. We evaluate the performance of the proposed approach using reliability and economy metrics for a cyber-physical system aimed at monitoring a smart grid.