Forecasting and managing the microgrid community using artificial intelligence

A characteristic feature of the modern electric power industry in recent decades is the sharp increase in electricity consumption. This can be explained by technological, social, economic, and other reasons. Therefore, the forecasting of electricity consumption is important for many processes, including the planned operation of generating equipment and managing and optimizing the operating modes of energy systems. It is also a significant aspect in the operation of industrial enterprises, since breaches can result in fines. One of the urgent tasks in the electricity market today is the forecasting of electricity consumption for a certain period. The article presents a description of a microgrid model with a built-in block for predicting power consumption, as well as intelligent load control for several objects at the same time, including those with distributed generation. Decisions are made the previous day, in order to forme strategies or the generation profile and control of power receivers. This timing is dictated by the information available to the intelligent system. This information includes forecast of demand and electricity prices of the centralized energy system for every hour of the next day. The process of switching at peak time to additional sources of electricity, distribution over microgrids is also described. The forecast was implemented using the Holt-Winters model from the statsmodels library (Python 3). The model uses the ideas of exponential smoothing, but is more complex and can be applied to series containing trend and seasonality. The trained model predicts with 95.21% accuracy.