Modern power grids face growing frequency stability challenges as renewable energy sources displace conventional synchronous generators. Fast-acting units (FAUs)—including battery storage, demand response, and gas turbines—can stabilize frequency within seconds, but coordinating hundreds of heterogeneous resources remains computationally difficult and operationally complex.
This research proposes a neural network-based instruction dispatch framework that leverages multihead attention mechanisms to optimize AGC across distributed FAUs. The encoder-decoder architecture extracts relevant features from grid conditions, renewable output forecasts, and unit availability, then generates customized control signals for each resource. By aggregating multiple FAUs into virtual entities, the system reduces computational burden while preserving granular control capability.
A key innovation is the modified payment scheme, which includes penalty terms for handling intermittent generation and generation outages—scenarios where conventional mileage-based compensation frameworks fail. This ensures fast-acting units remain incentivized to participate reliably even during adverse conditions.
The framework accounts for real-world constraints often overlooked in academic models: communication network delays, stochastic variations in renewable output, and equipment-level FAU variability. Robustness is enhanced through L2 normalization, dropout regularization, and k-fold cross-validation, enabling the model to perform well under unforeseen operating conditions.
Validation used synthetic datasets from evolutionary optimization algorithms combined with interconnected power system simulations. Results demonstrate meaningful reductions in both frequency deviation magnitude and area control error—the standard metric for evaluating AGC performance across interconnected regions.
This work bridges the gap between theoretical optimization and practical grid operation by combining machine learning with domain knowledge of power system dynamics. As renewable penetration increases globally, data-driven dispatch frameworks capable of managing heterogeneous, fast-acting resources will become essential for maintaining grid reliability without relying on conventional synchronous generation.
