A Distributed Demand-Side Management Framework for the Smart Grid

This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users. In particular, we use a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. We consider two practical cases: (1) a fully distributed approach, where each appliance decides autonomously its own scheduling, and (2) a hybrid approach, where each user must schedule all his appliances. We analyze numerically these two approaches, showing that they are characterized practically by the same performance level in all the considered grid scenarios. We model the proposed system using a non-cooperative game theoretical approach, and demonstrate that our game is a generalized ordinal potential one under general conditions. Furthermore, we propose a simple yet effective best response strategy that is proved to converge in a few steps to a pure Nash Equilibrium, thus demonstrating the robustness of the power scheduling plan obtained without any central coordination of the operator or the customers. Numerical results, obtained using real load profiles and appliance models, show that the system-wide peak absorption achieved in a completely distributed fashion can be reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to meet the growing energy demand

USEM: A ubiquitous smart energy management system for residential homes

With the ever-increasing worldwide demand for energy, and the limited available energy resources, there is a growing need to reduce our energy consumption whenever possible. Therefore, over the past few decades a range of technologies have been proposed to assist consumers with reducing their energy use. Most of these have focused on decreasing energy consumption in the industry, transport, and services sectors. In more recent years, however, growing attention has been given to energy use in the residential sector, which accounts for nearly 30% of total energy consumption in the developed countries. Here we present one such system, which aims to assist residential users with monitoring their energy usage and provides mechanisms for setting up and controlling their home appliances to conserve energy. We also describe a user study we have conducted to evaluate the effectiveness of this system in supporting its users with a range of tools and visualizations developed for ubiquitous devices such as mobile phones and tablets. The findings of this study have shown the potential benefits of our system, and have identified areas of improvement that need to be addressed in the future

Autonomous cycle of data analysis tasks for scheduling the use of controllable load appliances using renewable energy

International Conference on Computational Science and Computational Intelligence, 15/12/2021-17/12/2021, Estados Unidos.With the arrival of smart edifications with renewable energy generation capacities, new possibilities for optimizing the use of the energy network appear. In particular, this work defines a system that automatically generates hours of use of the controllable load appliances (washing machine, dishwasher, etc.) within these edifications, in such a way that the use of renewable energy is maximized. To achieve this, we are based on the hypothesis that depending on the climate, a prediction can be made of how much energy will be generated and, according to the behavior of the users, the energy demand required by these appliances. Following this hypothesis, we build an autonomous cycle of data analysis tasks composed of three tasks, two tasks for estimating the required load (demand) and the renewable energy produced (supply), coupled with a scheduling task to generate the plans of use of appliances. The results indicate that it is possible to carry out optimal scheduling of the use of appliances, but that they depend on the quality of the predictions of supply and demand.European CommissionAgencia Estatal de InvestigaciónJunta de Comunidades de Castilla-La Manch