Metodologia para gerenciamento, previsão e manejo de carga aplicada a consumidores residenciais com geração distribuída

Abstract

Over the past few years, the terms economy and energy efficiency have become increasingly popular with residential consumers. The possibility of generating savings in the electricity bill at the end of the month through photovoltaic generation has created interest in several low voltage (BT) consumers. In addition to this, there is still a white tariff, where the consumer pays a lower tariff according to the time of day. This work aims to build a methodology for managing, forecasting and handling the load of single-phase residential consumers that have distributed generation with storage (battery bank) and that are framed in the white tariff modality. A server was developed in conjunction with a database for storing and handling user data. The stored data are voltage and current, coming from photovoltaic generation, the utility network and the battery bank, obtained in a period of 5 minutes between measurements. An internet of things (IOT) microcontroller was used in conjunction with an electronic circuit to employ the proposed methodology. Through the user's load curve, the current energy requirement and the level of energy in the batteries, the system is responsible for carrying out the user's intelligent load management, acting in a more economically advantageous way. Case studies were performed, varying the user's load curve and analyzing the system's responses during the day analyzed, and it was observed that the system worked according to the proposed.