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The energy output of photovoltaic (PV) power plants depends on theenvironment and thus fluctuates over time. As a result, PV power can causeinstability in the power grid, in particular when increasingly used. Limitingthe rate of change of the power output is a common way to mitigate thesefluctuations, often with the help of large batteries. A reactive controllerthat uses these batteries to compensate ramps works in practice, but causesstress on the battery due to a high energy throughput. In this paper, wepresent a deep learning approach that uses images of the sky to compensatepower fluctuations predictively and reduces battery stress. In particular, weshow that the optimal control policy can be computed using information that isonly available in hindsight. Based on this, we use imitation learning to traina neural network that approximates this hindsight-optimal policy, but uses onlycurrently available sky images and sensor data. We evaluate our method on alarge dataset of measurements and images from a real power plant and show thatthe trained policy reduces stress on the battery.

Learning to Compensate Photovoltaic Power Fluctuations from Images of the Sky by Imitating an Optimal Policy