A demand response program for isolated PV systems based on the state of charge of the battery and the prediction of demand and generation of electric power (2018)
The electrification of rural areas is limited by the difficult access, the unsustainability of energy projects and the low participation of the community in the designs. Isolated photovoltaic systems are a first-hand solution, but for its sustainability it is necessary to know information in real time to estimate the future state of the system and send signals to users. In this article, we propose a methodology that optimizes the available energy resources of a microgrid through a demand response program based on dynamic prices given by the state of charge of the battery. We propose a real-time measurement system that feeds a database and we use prediction methods such as Support Vector Machine (SVM) and Extreme Learning Machine (ELM) to calculate the demand and generation of electrical energy one day in advance. community. Simulations of results are presented to evaluate the performance of the forecast methods and the behavior of the DR program.