电力系统自动化2026，Vol.50Issue(2)：71-82,12.DOI:10.7500/AEPS20250310007
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
Intelligent Expansion Planning Method for Transmission Network of Power System with Renewable Energy Considering Extreme Scenarios
摘要
Abstract
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.关键词
新能源/输电网/强化学习/电压支撑强度/极端灾害/短路比裕度/网架扩展规划Key words
renewable energy/transmission network/reinforcement learning/voltage support strength/extreme disaster/short-circuit ratio margin/network expansion planning引用本文复制引用
曾琦,刘子琦,杨良,高仕林,周旭,习工伟,程奕..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[J].电力系统自动化,2026,50(2):71-82,12.基金项目
国家电网有限公司科技项目(5200-202455396A-3-3-ZX). This work is supported by State Grid Corporation of China(No.5200-202455396A-3-3-ZX). （5200-202455396A-3-3-ZX）
