基于物理信息神经网络的含非线性频率约束线性交流最优潮流框架的电力系统前瞻调度

The increasing penetration of renewable energy re-sources degrades the frequency stability of power systems.The present work addresses this issue by proposing a look-ahead dis-patch model of power systems based on a linear alternating cur-rent optimal power flow framework with nonlinear frequency constraints.Meanwhile,the poor efficiency for solving this for-mulation is addressed by introducing a physics-informed neural network(PINN)to predict key frequency-control parameter val-ues accurately.The PINN ensures that the learned results are applicable to the original physical frequency dynamics model,and applying the predicted parameter values enables the result-ing dispatch model to be solved quickly and efficiently using readily available commercial solvers.The feasibility and advan-tages of the proposed model are demonstrated by the results of numerical computations applied to a modified IEEE 118-bus test system.