ABSTRACT The rise of large‐scale renewables has exacerbated frequency instability, revealing the limits of conventional frequency regulation frameworks in controlling deviations and incentivising fast‐acting units (FAUs). Although the mileage‐based payment framework promotes FAUs’ participation in automatic generation control (AGC) services, efficient instruction dispatch (ID) across an increasing number of heterogeneous AGC units remains challenging. The existing mileage‐based payment framework lacks validation under intermittent generation or generation outages scenarios. This work proposes a data‐driven ID framework with a modified payment scheme, adding a penalty term for refining mileage calculation to handle intermittent generation or generation outages during AGC operation. The framework uses a multihead attention‐based encoder–decoder model, where the encoder extracts latent features and the decoder predicts unit‐specific instructions. Attention mechanism improves accuracy by prioritising critical features, whereas L2 normalisation, dropout and k‐fold cross‐validation enhance models' robustness under unforeseen scenarios. The model aggregates the flexibility of multiple FAUs into a single entity, termed the FAU aggregator. Trained on a synthetic dataset generated from the evolutionary optimisation‐based ID framework and validation on an interconnected system accounting for disturbance due to intermittent renewable energy sources' output, FAU variability and stochastic communication effects. The results demonstrate a reduction in both frequency deviation and area control error in comparison with other ID frameworks.
Roy et al. (Thu,) studied this question.
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