A Microgrid Model with an Integrated Forecasting and Intelligent Load Management Module

The development of the modern electric power industry is aimed at applying artificial intelligence technologies to manage the smart grid with distributed generation plants and electricity storage units. Part of the smart grid are clusters (microgrids) that combine consumers of electricity, networks, electricity storage units, and distributed generation plants. The paper presents a description of a microgrid model with a built-in forecasting and intelligent load management module for multiple facilities simultaneously, including those with distributed generation. Decisions are made a day in advance, thus shaping the strategy of the generation profile and management of current-using equipment items. The above timing is due to the information available to the intelligent system: a forecast of demand and price of electricity from the centralized electric power system for each hour of the next day. We describe special aspects of switching during peak times to additional power sources along with distribution by microgrids. The forecast is based on two approaches: the method of least squares and machine learning using built-in Python 3 options and external open-source components.