Novel Functionalities of Smart Home Devices for the Elastic Energy Management Algorithm

Abstract

Energy management in power systems is influenced by such factors as economic and ecological aspects. Increasing the use of electricity produced at a given time from renewable energy sources (RES) by employing the elastic energy management algorithm will allow for an increase in “green energy“ in the energy sector. At the same time, it can reduce the production of electricity from fossil fuels, which is a positive economic aspect. In addition, it will reduce the volume of energy from RES that have to be stored using expensive energy storage or sent to other parts of the grid. The model parameters proposed in the elastic energy management algorithm are discussed. In particular, attention is paid to the time shift, which allows for the acceleration or the delay in the start-up of smart appliances. The actions taken by the algorithm are aimed at maintaining a compromise between the user’s comfort and the requirements of distribution network operators. Establishing the value of the time shift parameter is based on GMDH neural networks and the regression method. In the simulation studies, the extension of selected activities related to the tasks performed in households and its impact on the user’s comfort as well as the response to the increased generation of energy from renewable energy sources have been verified by the simulation research presented in this article. The widespread use of the new functionalities of smart appliance devices together with the elastic energy management algorithm is planned for the future. Such a combination of hardware and software will enable more effective energy management in smart grids, which will be part of national power systems.

Keywords

• renewable energy sources
• energy demand control
• smart appliances
• elastic energy management algorithm
• GRASP algorithm
• GMDH neural networks