Artificial intelligence in energy management of microgrid

Fuel scarcity and escalating carbon emissions pose a dual challenge to conventional energy sources. Photovoltaic (PV) solar and wind energy systems serve as renewable substitutes for traditional energy systems due to their eco-friendliness, versatility, and green nature. Furthermore, regulations adopted by numerous governments encouraging the installation of PV and wind power systems have reduced the cost of electricity production. The development of renewable energy sources, such as solar, wind, fuel cells, and biomass, has enabled the current electric power industry to advance technologically. A smart grid is a digital technology that integrates various microgrids with the grid and monitors them with appropriate management and control to reduce or eliminate power quality concerns. The likelihood of individual microgrid stability criteria being met is increased by interconnected microgrids. Therefore, self-sustaining smart grid technology is necessary to decrease carbon emissions and provide an energy management solution. The use of passive or active filters is one method to enhance the grid's power quality. Despite their higher cost, active filters are preferred due to their size and performance advantages. Utility grid-interfaced PV and wind energy system installations as active filters have gained popularity as a significant research topic worldwide since the turn of the millennium. The active filtering capabilities of smart grid systems connected with microgrids have received significant attention in the literature. Instantaneous reactive power theory (IRPT), synchronous reference frame theory (SRFT), and the improved linear sinusoidal tracer (ILST) are some of the methods used for managing shunt active filters (SAFs). These algorithms can reduce current harmonics on the source side and offer reactive power compensation when utilized to manage the voltage source inverter (VSI) in grid-connected systems.A "smart microgrid" refers to an intelligent electricity distribution system that connects loads, distributed energy resources, and storage within clearly defined electrical boundaries, functioning as a single, controllable entity with respect to the main grid, according to India's Model Smart Grid Regulations. Microgrids (MGs) are small-scale power plants that can operate in both grid-connected and island modes, boasting high levels of energy security, dependability, storage capacity, and economic efficiency for demand-side and load-side control. The electricity sector can self-recover by assessing and addressing issues, as they are capable of local control via automated methods. To reduce costs and return excess energy to local microgrids, the electric industry has been revolutionized through the use of solar and wind renewable energy sources (RESs) and smart grid technologies. Smart microgrids have the potential to integrate with the grid, but they are also self-sufficient and can service a local community without relying on centralized power systems. Based on comparable electrical system research, future generations will require intelligent controller-based smart grid systems, with many microgrids integrating with the grid without affecting the quality of electricity.A smart microgrid system comprises one or more interconnected smart microgrids, facilitated by a robust controller that can be integrated into or operated independently from the main grid. This comprehensive system encompasses diverse renewable and non-renewable energy sources, along with load centers catering to residential, commercial, and industrial sectors. Real-time management of electric power systems necessitates data analytics and artificial intelligence (AI) techniques for statistical analysis of consumer energy usage data and weather forecasts pertaining to various RESs. Furthermore, the AI-based controller incorporates modules such as tariff control and power flow management while processing customer power data.