Privacy-Driven Electricity Group Demand Response in Smart Cities Using Particle Swarm Optimization

In the smart cities of the future, digital connectivity will become the cornerstone for implementing intelligent management of electric power from the side of demand. In particular, consumers will connect via communication networks and exchange data messages or share information. Utilization of information will allow consumers to manage their electricity consumption in a more efficient and economical way. However, connectivity and information exchange come at a cost of reduced privacy. In particular, third parties connected to the power grid are able to monitor the consumption signals and make inferences about the consumers' behavior. In this a paper, an intelligent methodology for enhancing privacy in smart power systems in smart cities is presented. The methodology fuses the demand patterns of several consumers, which are connected to the power grid, and provides a new consumption pattern. The new pattern, which hides individual consumer characteristics, is obtained as the solution to an optimization problem whose solution is computed by particle swarm optimization. Testing of the methodology is performed on a set of real consumption patterns, while benchmarked against genetic algorithm. Results exhibit the efficiency of the proposed intelligent methodology, and its superiority over the benchmarked algorithm.