Load Management Particle Swarm Optimization in Smart Grids
The increase in energy demand worldwide has made the management of electricity consumption in homes increasingly urgent and necessary. Reducing the cost of electricity is one of the major challenges of energy demand and is directly related to energy efficiency measures. One way to reduce the energy cost of homes is to use techniques to automatically monitor and control loads through the smart grid concept. This intelligence can be implemented through low-complexity bioinspired optimization techniques, such as the genetic algorithm, ant colony optimization, flower pollination algorithm, or particle swarm optimization. Specifically, particle swarm optimization has stood out in the solution of demand response problems due to its efficiency in generating optimized solutions, presenting robust results and reduced computational efforts. In this context, the present work proposes the use of the particle swarm optimization technique to reduce electricity costs in homes, taking into account the operating restrictions of loads, defined by the consumer and the grid operator, and the application of a price-based DR program established by the concessionaire. Additionally, a complementary method is proposed to allow the use of electric vehicles to supply energy to homes at intermediate and peak tariff times in order to reduce grid energy consumption and electricity costs.