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With the increasing proportion of renewable energy in the generation side, itbecomes more difficult to accurately predict the power generation and adapt tothe large deviations between the optimal dispatch scheme and the day-aheadscheduling in the process of real-time dispatch. Therefore, it is necessary toconduct look-ahead dispatches to revise the operation plan according to thereal-time status of the power grid and reliable ultra-short-term prediction.Application of traditional model-driven methods is often limited by the scaleof the power system and cannot meet the computational time requirements ofreal-time dispatch. Data-driven methods can provide strong onlinedecision-making support abilities when facing large-scale systems, while it islimited by the quantity and quality of the training dataset. This paperproposes a model-informed reinforcement learning approach for look-ahead ACoptimal power flow. The reinforcement learning model is first formulated basedon the domain knowledge of economic dispatch, and then the physics-informedneural network is constructed to enhance the reliability and efficiency. Atlast, the case study based on the SG 126-bus system validates the accuracy andefficiency of the proposed approach.

Look-Ahead AC Optimal Power Flow: A Model-Informed Reinforcement Learning Approach