Distributed Proximal Policy Optimization With Embedded Dual Rules for Power Systems Considering Wind and Photovoltaic Forecasting

Most renewable energy power systems are created to provide more resilient, reliable, economical, sustainable and secure power support services for loads. However, owing to the inherent forecasting errors of wind and photovoltaic (PV) power, existing optimal dispatch decisions based on forecasting errors have biases. To address this issue, this paper proposes the distributed proximal policy optimization (DPPO) model with embedded dual rules for optimal power dispatch that considers wind and PV power forecasting error correction. The proposed model embeds forecasting and error correction information into the DPPO state space. Moreover, considering the physical characteristics and operational security constraints of the power grid, power balance and flow constraints are embedded in the DPPO network in a regular form. Finally, by integrating the established rules, wind and PV forecasting, and error correction information, the proposed model achieves optimal dispatch decisions through the calculation of state and execution of prescribed actions. The proposed method is applied and tested on a modified IEEE-30 bus system using actual data from a provincial power grid. The numerical results demonstrate that the proposed method effectively addresses optimal dispatch decisions caused by wind and PV forecasting errors. Compared with three other advanced methods, the proposed approach has significant advantages in promoting wind power accommodation, reducing operating costs, and enhancing the adaptability of optimal dispatch to uncertainty.