考虑极端场景的新能源电力系统输电网架智能扩展规划方法OA

In recent years,extreme weather disasters occur frequently,leading to an increased probability of power grid equipment failure and exacerbating the operation risks of the power system.Based on deep reinforcement learning algorithms,an expansion planning method for transmission networks is proposed,considering both the voltage support strength of power system with renewable energy and the economic efficiency of expansion under extreme scenarios.Firstly,considering the impact of extreme disasters on the power grid,typical extreme scenarios for the power grid have been constructed.Secondly,the network planning problem is transformed into a sequential decision-making process by using Markov chains.The short-circuit ratio margin index and the comprehensive cost index of line expansion are taken as the objective functions,resulting in a network expansion planning model.Furthermore,a solving method for expansion planning model based on the multi-gate mixture-of-expert model and twin-delayed deep deterministic policy gradient reinforcement learning algorithm is proposed.Finally,in a modified IEEE RTS-24 system with integrated wind and photovoltaic power,as well as a DC transmission system case from a certain region in Northwest China,extreme operation scenarios of the system are simulated,and network expansion planning schemes considering various extreme scenarios are solved to verify the effectiveness and robustness of the proposed method.