Results of the summarizing process for the arXiv paper: 2211.02674v1

In a modern power system that relies increasingly on renewable energy sources, accurate wind power forecasting is essential for effective power grid dispatching plans due to the volatile nature of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy and the problem of data islands. To address these challenges, this study proposes a novel approach called federated deep reinforcement learning (FedDRL) that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting. The paper first utilizes the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to enhance prediction accuracy. Then, it integrates the DDPG forecasting model into the framework of federated learning. FedDRL enables the acquisition of an accurate prediction model in a decentralized manner by sharing model parameters instead of sharing private data, thereby avoiding sensitive privacy issues. Simulation results demonstrate that FedDRL outperforms traditional prediction methods in terms of forecasting accuracy. Moreover, while ensuring superior forecasting performance, FedDRL effectively safeguards data privacy and alleviates communication pressure compared to conventional centralized forecasting approaches. Additionally, a simulation with different federated learning parameters confirms the robustness of the proposed scheme. Overall, this research presents an innovative solution for wind power forecasting that addresses data privacy concerns through federated deep reinforcement learning. By combining these techniques, it achieves accurate predictions while protecting sensitive information and promoting decentralized collaboration in modern power systems with renewable energy sources.

• Accurate wind power forecasting is essential for effective power grid dispatching plans in a modern power system that relies on renewable energy sources
• Traditional centralized forecasting methods raise concerns regarding data privacy and data islands
• The study proposes a novel approach called federated deep reinforcement learning (FedDRL) for ultra-short-term wind power forecasting
• FedDRL combines federated learning and deep reinforcement learning (DRL)
• The deep deterministic policy gradient (DDPG) algorithm is used as the basic forecasting model to enhance prediction accuracy
• FedDRL enables the acquisition of an accurate prediction model in a decentralized manner by sharing model parameters instead of sharing private data, avoiding sensitive privacy issues
• Simulation results show that FedDRL outperforms traditional prediction methods in terms of forecasting accuracy
• FedDRL effectively safeguards data privacy and alleviates communication pressure compared to conventional centralized forecasting approaches while ensuring superior forecasting performance
• Simulation with different federated learning parameters confirms the robustness of the proposed scheme
• The research presents an innovative solution for wind power forecasting through federated deep reinforcement learning, addressing data privacy concerns and promoting decentralized collaboration in modern power systems with renewable energy sources.

Accurate Wind Power Forecasting with Federated Deep Reinforcement Learning

The modern power system is increasingly relying on renewable energy sources, making accurate wind power forecasting essential for effective power grid dispatching plans. Traditional centralized forecasting methods, however, raise concerns regarding data privacy and the problem of data islands. To address these challenges, this study proposes a novel approach called federated deep reinforcement learning (FedDRL) that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting.

Deep Deterministic Policy Gradient Algorithm

The paper first utilizes the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to enhance prediction accuracy. DDPG is an actor-critic method based on the policy gradient theorem which can be used to solve continuous control problems in reinforcement learning. It uses two neural networks: an actor network which takes observations from the environment and outputs actions; and a critic network which evaluates how good those actions are by predicting future rewards given state-action pairs. By combining these techniques, DDPG can accurately forecast wind power output in ultra-short time horizons while protecting sensitive information and promoting decentralized collaboration in modern power systems with renewable energy sources.

Federated Learning Integration

To further improve accuracy of predictions while ensuring superior performance, FedDRL integrates the DDPG forecasting model into the framework of federated learning. This approach enables acquisition of an accurate prediction model in a decentralized manner by sharing model parameters instead of sharing private data, thereby avoiding sensitive privacy issues while still achieving accurate forecasts.

Simulation Results

Simulation results demonstrate that FedDRL outperforms traditional prediction methods in terms of forecasting accuracy compared to conventional centralized approaches such as artificial neural networks or support vector machines. Additionally, simulations with different federated learning parameters confirm its robustness against varying conditions such as different communication bandwidths or number of participants involved in training process without compromising accuracy or privacy protection features provided by FedDRL scheme .

Conclusion

Overall, this research presents an innovative solution for wind power forecasting that addresses data privacy concerns through federated deep reinforcement learning. By combining these techniques it achieves accurate predictions while protecting sensitive information and promoting decentralized collaboration in modern power systems with renewable energy sources