Load Management Particle Swarm Optimization in Smart Grids
The growing demand for electricity around the world has made it increasingly urgent and necessary to manage electricity consumption in homes. Reducing the cost of electricity is one of the great challenges of energy demand and is directly related to energy efficiency measures. One way to reduce the energy cost of homes is to use automatic load monitoring and control techniques through the smart grid concept. This intelligence can be implemented through low-complexity bioinspired optimization techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Flower Pollination Algorithm (FPA) or Particle Swarm Optimization (PSO). In this context, this work proposes the use of the PSO technique to reduce the cost of electricity in a home, taking into account the load operation restrictions, defined by the consumer and also by the smart grid operator, and a price-based demand response program defined by the utility.