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Demand response management: SMGs can use advanced algorithms to adjust the power consumption of connected devices in response to changes in demand, helping to balance the supply and demand of power.
Energy storage management: SMGs can use energy storage systems to store excess energy generated by renewable sources, and release it as needed to meet demand.
Distributed energy resource management: SMGs can use advanced algorithms to optimize the operation of distributed energy resources, to ensure the most efficient use of available resources.
Load balancing: SMGs use advanced algorithms to balance the load across different distributed energy resources and energy storage systems to ensure a stable, reliable power supply.
Real-time monitoring: SMGs use sensors and monitoring systems to collect real-time data on the status of the grid, allowing operators to make informed decisions about how to manage the system.
Energy theft threatens the economic viability and sustainability of smart microgrids. Theft of energy includes tampering, bypassing, and unlawful connections. Energy theft, including smart microgrids, costs the global energy industry billions of dollars. The dispersed architecture and distributed energy supplies of smart microgrids make them more vulnerable to electricity theft than conventional power grids5. Smart microgrids can analyze sensor and meter data to identify trends of energy theft.
Energy storage systems (ESS) are essential for microgrid systems because they store and distribute electrical power to stabilize load and renewable energy generation, improve power quality, and ensure system reliability. ESSs are classified by storage and response as electrical, mechanical, chemical, electrochemical, or thermal.
This paper proposes a practical solution to improve the efficiency and security of energy management in smart microgrids. This paper presents a prototype of an intelligent microgrid energy management system. In order to optimize energy consumption and reduce costs, the system considers the uncertainty of renewable energy sources and the possibility of energy theft. The prototype employs machine learning algorithms and sensors to monitor and predict energy production and consumption and detect any unauthorized energy usage.
The proposed method accounts for the Earth''s radius, a crucial factor in accurately forecasting energy distribution across various locations. This consideration is necessary for optimizing the microgrid''s load distribution to improve energy efficiency while also accounting for the unique geographical features of the area it serves.
The incorporation of time-of-use pricing principles as part of the energy management system represents an innovative way to incentivize efficient energy consumption patterns among consumers.
Developing an intuitive mobile app that empowers consumers to participate in and control their energy usage actively is a significant innovation. This enhances user engagement and promotes more sustainable energy practices.
The paper''s overarching novelty lies in its comprehensive approach to improving the dependability and sustainability of smart microgrid energy management. Addressing electricity theft issues and optimizing energy usage contributes to a more resilient and efficient energy infrastructure.
The monitoring system constantly analyzes its current state and makes necessary adjustments to guarantee the SMG equipment always functions smoothly and safely. The combination of software and hardware in this SMG guarantees the system''s continuous, real-time operation.
IoT provides real-time data and insights into the performance and operation of SMGs. This enables more efficient control of the microgrid and improves its performance and dependability. IoT monitors microgrids in several ways28:
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