  In a modern power system with an increasing proportion of renewable energy,
wind power prediction is crucial to the arrangement of power grid dispatching
plans due to the volatility of wind power. However, traditional centralized
forecasting methods raise concerns regarding data privacy-preserving and data
islands problem. To handle the data privacy and openness, we propose a
forecasting scheme that combines federated learning and deep reinforcement
learning (DRL) for ultra-short-term wind power forecasting, called federated
deep reinforcement learning (FedDRL). Firstly, this paper uses the deep
deterministic policy gradient (DDPG) algorithm as the basic forecasting model
to improve prediction accuracy. Secondly, we integrate the DDPG forecasting
model into the framework of federated learning. The designed FedDRL can obtain
an accurate prediction model in a decentralized way by sharing model parameters
instead of sharing private data which can avoid sensitive privacy issues. The
simulation results show that the proposed FedDRL outperforms the traditional
prediction methods in terms of forecasting accuracy. More importantly, while
ensuring the forecasting performance, FedDRL can effectively protect the data
privacy and relieve the communication pressure compared with the traditional
centralized forecasting method. In addition, a simulation with different
federated learning parameters is conducted to confirm the robustness of the
proposed scheme.
