In order to maximize the benefits of industrial smart grids in terms of energy optimization, this research presents the Dynamic Energy Optimization System for Industrial Smart Grids (DEOS-ISG). DEOS-ISG is an adaptive and efficient energy management system that combines three important algorithms: Predictive Modeling for Energy Demand (PMED), Adaptive Demand Response and Load Shifting (ADRLS), and Integration of Distributed Energy Resources (IDER). PMED is centered on forecasting energy needs, which enables forethought in strategy and improvement. With ADRLS, load shifting and demand response may be adjusted dynamically in response to changes in energy costs and grid circumstances. In order to lessen our dependency on the grid and boost sustainability, IDER maximizes DERs. Data-driven decision making is facilitated by the mathematical underpinnings of these techniques, which include regression analysis, time series forecasting, reinforcement learning, and mixed-integer linear programming.
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Shreenidhi et al. (2024) studied this question.