1.四川大学电气工程学院,四川省成都市610065
2.国家电力调度控制中心,北京市 100031
3.电网安全全国重点实验室(中国电力科学研究院有限公司),北京市 100192
曾琦(1977—),女,博士,副教授,主要研究方向:电力系统稳定与控制、柔性直流输电。E-mail:zengqi-hk@163.com
刘子琦(2001—),男,硕士研究生,主要研究方向:电力系统静态分析、电网规划。E-mail:2023223035163@stu.scu.edu.cn
杨良(1989—),男,博士,高级工程师,主要研究方向:电网运行调度控制。E-mail:liangyang@sgcc.com.cn
高仕林(1994—),男,通信作者,博士,副研究员,硕士生导师,主要研究方向:电力系统暂态建模仿真、稳定分析、数字孪生。E-mail:gaoshilin@scu.edu.cn
收稿:2025-03-10,
修回:2025-06-23,
网络首发:2025-08-11,
纸质出版:2026-01-25
移动端阅览
曾琦,刘子琦,杨良等.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.
ZENG Qi,LIU Ziqi,YANG Liang,et al.Intelligent Expansion Planning Method for Transmission Network of Power System with Renewable Energy Considering Extreme Scenarios[J].Automation of Electric Power Systems,2026,50(2):71-82.
曾琦,刘子琦,杨良等.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. DOI: 10.7500/AEPS20250310007.
ZENG Qi,LIU Ziqi,YANG Liang,et al.Intelligent Expansion Planning Method for Transmission Network of Power System with Renewable Energy Considering Extreme Scenarios[J].Automation of Electric Power Systems,2026,50(2):71-82. DOI: 10.7500/AEPS20250310007.
近年来
极端气象灾害频发,电网设备故障概率增加,电力系统运行风险加剧。基于深度强化学习算法,文中提出了一种考虑极端场景下新能源电力系统电压支撑强度和扩建经济性的输电网架扩展规划方法。首先,考虑极端灾害对电网的影响,构建了电网典型极端运行场景。其次,利用马尔可夫链将网架规划问题转化为序列决策过程,将短路比裕度指标和线路扩建综合成本指标作为目标函数,得到网架扩展规划模型。进一步,提出了基于多门控专家混合模型-双延迟深度确定性策略梯度强化学习算法的扩展规划模型求解方法。最后,在含风电和光伏并网的改进IEEE RTS-24系统以及中国西北某地区直流送出系统算例中,模拟了系统极端运行场景,并求解考虑不同极端场景的网架扩展规划方案,验证了所提方法的有效性与鲁棒性。
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.
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