Deep Learning-Based Method for Operation Dispatch Strategy Generation of Virtual Power Plants
2025
Jie Li | Wenteng Liang | Yuheng Liu | Nan Zhou | Tao Qian | Qinran Hu
Centralized and distributed optimization methods used by traditional virtual power plants (VPPs) in power system dispatching face issues such as high computational complexity, difficulties in privacy protection, and slow iterative convergence. There is an urgent need to propose an accurate and efficient acceleration method for generating VPP operational dispatching strategies. This paper proposes a deep learning-based acceleration method for generating VPP operational dispatching strategies. By using the equivalent projection method to solve the operation feasible region of the VPP, the objective function and constraints of the VPP are transformed into constraints of coordination variables and submitted to the system dispatching center for optimization, thereby avoiding the slow convergence problem of iterative computation methods. The Kolmogorov&ndash:Arnold Network (KAN) is employed to predict the batch operation feasible regions of the VPP, addressing the inefficiency of individually calculating feasible regions. Tests on a 13,659-node system show that the proposed method reduces solution time by 64.40% while increasing the objective function value by only 4.74%, verifying its accuracy and speed.
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