Dynamic Aggregation Strategy of Virtual Power Plants Considering the Adjustable Ability Recovery Process and Dependencies of Distributed Resources

For the aggregation process of distributed resources into a Virtual Power Plant (VPP) to respond to grid dispatching, this paper proposes a dynamic aggregation strategy considering the adjustable ability recovery process and dependencies of distributed resources (DRs). By analyzing the benefits of the recovery process of DRs and the dependencies of adjustable abilities among DRs, the economic efficiency and reliability of flexible resource aggregation are effectively improved. Firstly, an economic optimization-based aggregation strategy considering the recovery process is proposed. Predictive control is used to solve the economic benefits of each DR during the power response recovery process under optimal operation. Aggregation is then performed using the optimal benefits of each DR. After that, a dependency-based predictive screening strategy is proposed. The Gaussian copula function is used to establish the dependencies among DRs, predict the adjustable power of each DR under different dispatch states, and screen the optimal economic aggregation results, effectively improving the reliability of the response. Finally, the effectiveness of the proposed method is verified using actual data from 247 charging stations in Shenzhen.