Comprehensive Collaborative Control of Diverse Loads in Dedicated Transformer Area

This paper proposes a comprehensive method to manage diverse load profiles in power grids, addressing challenges associated with variable renewable energy sources and dynamic electricity demand. As renewable energy sources increase, adaptable control measures are essential for grid stability and efficiency. The approach decomposes complex load profiles into controllable, self-regulating, and uncontrollable types, analyzing their interdependencies to manage load management. A novel load forecasting model using self-organizing maps predicts and adjusts load patterns, while advanced optimization techniques like LSTM model and genetic algorithms fine-tune controllable loads within operational boundaries. Iteratively predicting load dynamics, optimizing controllable loads, and updating with real-time data achieves optimized load balancing. Implementation can improve power system resilience, reduce peak-to-valley difference by 16.19%, and enhance energy efficiency, providing a robust framework for future-proofing power grids.