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