Utilização da meta-heurística PSO para otimização multiobjetivo de um Smart Home Controller

Abstract

Energy efficiency is a top concern for companies, governments, investors and other energy market participants in recent decades. Thus, in pursuit of the objectives of efficient and sustainable energy supply, reduction of gas emissions, energy savings and achieving satisfactory levels of safety and quality in energy supply, Smart Grid technologies are attracting increasing attention due to its inherent ability to achieve a sustainable energy management system. From this context, this dissertation is in price-based demand response scenario, in which, by to advertised price, residential consumers can regulate the operations of their handsets. User satisfaction is one of the most relevant aspects that should be considered along with energy bill reduction power. This dissertation presents a Smart Home Controller (SHC), which uses a population-based metaheuristic optimization technique. The proposal presents the mathematical formulation of the problem through a multiobjective function that seeks to achieve a tradeoff between the total cost of electricity consumption and the optimization of level of comfort of the user, besides ensuring that the total consumption does not exceed the considered maximum demand. The solution to the described optimization problem is performed through the implementation of the Particle Swarm Optimization algorithm (PSO), making use of two tariffs, flat tariff and the White Tariff, the Time Of Use tariff used in Brazil to residential users. The implementation and simulations results show that SHC can provide a reduction in electricity costs while considering satisfactory levels to user comfort offered.