电力系统保护与控制2026，Vol.54Issue(7)：92-103,12.DOI:10.19783/j.cnki.pspc.251169
内嵌输入凸神经网络小干扰稳定性约束的新能源孤岛微电网优化运行方法
Optimal operation method for renewable energy islanded microgrids with embedded input convex neural network-based small-signal stability constraints
摘要
Abstract
In a 100%renewable energy scenario,islanded microgrids lack the support of conventional generation,leading to more severe frequency and voltage fluctuations,while small-signal stability also becomes a critical challenge.To address this issue in inverter-dominated renewable islanded microgrids,an optimal operation method incorporating small-signal stability constraints based on an input convex neural network(ICNN)is proposed.First,an optimal scheduling model is established considering both inverter P-f and Q-V droop control.Information-gap decision theory(IGDT)is employed to handle uncertainties in generation and load.Then,using damping ratio sensitivity as the direction for operating point updates,sampling points are iteratively shifted to efficiently approach the stability boundary region,thereby obtaining a large number of stability boundary samples under simultaneous variations of droop control parameters.Next,the ICNN is used to learn the mapping between droop control parameters and small-signal stability indices.The nonlinear stability constraints are subsequently linearized and embedded into the scheduling model.Finally,case studies on the IEEE 33-bus islanded microgrid test system demonstrate the feasibility and accuracy of the proposed method.关键词
孤岛微电网/输入凸神经网络/信息间隙决策理论/分布式能源/小干扰稳定性Key words
islanded microgrid/input convex neural network/information gap decision theory/distributed energy resource/small-signal stability引用本文复制引用
娄轩铭,王宇,邹云阳,谢开贵..内嵌输入凸神经网络小干扰稳定性约束的新能源孤岛微电网优化运行方法[J].电力系统保护与控制,2026,54(7):92-103,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.52577084). 国家自然科学基金项目资助(52577084) （No.52577084）
