Abstract:

Abstract Modern electrical systems' efficient and reliable distribution of power and energy depend heavily on intelligent grid management. To optimize grid operations, enhanced control and management systems are needed given the increased integration of renewable energy sources and the rising demand for sustainable energy options. In order to effectively monitor, regulate, and optimize power and energy systems, this article suggests an intelligent grid management strategy that integrates smart grid technology, cutting-edge analytics, and control algorithms. The suggested intelligent grid management system makes use of real-time data collection from smart metres, sensors, and other grid equipment to facilitate situational awareness and decision-making. In order to analyze the gathered data and extract insights for grid operation and planning, advanced analytics techniques, including as machine learning and optimization algorithms, are utilized. Utilizingthis information will improve power production, load scheduling, energy storage use, and system stability. In order to encourage energy users to modify their energy consumption habits in response to price signals and grid circumstances, the intelligent grid management system also integrates demand response methods. Demand-side management promotes grid stability and resilience by balancing supply and demand, lowering peak loads. In addition, the suggested approach incorporates distributed energy resources (DERs) into the grid management procedure, including solar panels, wind turbines, and energy storage devices. To maximize their impact on grid performance overall and improve grid resiliency during emergencies, these DERs are coordinated and controlled in a decentralized way. Case studies and simulation results provide as proof of the efficacy of the intelligent grid management strategy. The grid efficiency, energy costs, grid reliability, and integration of renewable energy sources are all improved by the system.

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