基于Stackelberg博弈与改进深度神经网络的多源调频协调策略研究OA北大核心

With the increase of new energy penetration in the power grid,the traditional frequency regulation of thermal power units can no longer meet the power quality demand.Aiming at the problem of large area control error in traditional automatic generation control systems in multi-source scenario,a multi-source frequency regulation strategy based on the Stackelberg game and improved deep neural network(S-DNN)is proposed.Firstly,an improved multilevel deep neural network(DNN)is proposed,which consists of a DNN layer,natural gradient boosting layer,and least squares support vector machine layer to sequentially and progressively complete the prediction,evaluation,and execution of actions,and output the total frequency regulation power command.This multilevel total frequency regulation power output model considers the dynamic impact of new energy penetration on the frequency regulation system,fully learns more features from historical information and real-time state,and improves the accuracy of frequency regulation instructions.Then,based on Stackelberg game theory,it considers the characteristics and synergy of multi-source frequency regulation,optimizes the power allocation among frequency regulation sources,and improves the economy of the system's secondary frequency regulation.Finally,the effectiveness of the proposed multi-source frequency regulation strategy is verified by case analysis.Compared with the traditional frequency regulation method,the proposed S-DNN multi-source frequency regulation strategy can effectively reduce the area control error and frequency deviation,and reduce the frequency regulation cost.