Tariff Spaces: a new concept for optimizing the use of electrical loads in smart homes /

Abstract

The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day, due to official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price which is around 17% lower than the constant price, but charges twice its value at peak hours. In addition to cost reduction, we propose a methodology to reduce user discomfort due to time shifting of controllable devices, presenting a balanced solution through the analytical analysis of a new method here called Tariff Space, derived from white tariff posts. To achieve this goal, we explore the geometric properties of the movement of devices through the Tariff Space (geometric locus of the load), over which we can define a limited region in which the cost of a load under the white tariff will be equal to or less than the constant tariff. As a trial for the efficiency of this new methodology, we collected some benchmarks (such as execution time and memory usage) against a classic multi-objective algorithm (Hierarchical) available in the language portfolio in which the project has been executed (Julia language). As a result, both methodologies achieve similar results, but the one presented in this thesis shows a significant reduction in processing time and memory usage, which could lead to the future implementation of the solution in a simple, low-cost embedded system like an ARM cortex M. In addition to scheduling household appliances based on energy cost variations, this research addresses real-time adaptation to changing energy prices, user preferences, and environmental conditions in residential settings. The proposed methodology achieves significant computational efficiency and real-time responsiveness compared to traditional multi-objective algorithms. Without demand response constraints, the main solution is approximately 10000 times faster than the classic multi-objective algorithm. With demand response constraints, an hybrid methodology that reduces overall processing time by 50% compared to the classic algorithm under the same constraints has been proposed. This emphasis on real-time adaptation enhances the practical applicability and effectiveness of our scheduling approach in dynamic residential environments.