| 引用本文: | 娄轩铭,王 宇,邹云阳,等.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(07):92-103. |
| LOU Xuanming,WANG Yu,ZOU Yunyang,et al.Optimal operation method for renewable energy islanded microgrids with embedded input convex neural network-based small-signal stability constraints[J].Power System Protection and Control,2026,54(07):92-103 |
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| 摘要: |
| 100%可再生能源场景下的孤岛微电网缺乏传统电源的支撑，其频率和电压波动更为剧烈，小干扰稳定性也面临挑战。针对逆变器主导的新能源孤岛微电网，提出一种内嵌输入凸神经网络(input convex neural network, ICNN)小干扰稳定性约束的新能源孤岛微电网优化调度方法。首先，建立考虑逆变器有功-频率、无功-电压双下垂控制的优化调度模型，利用信息间隙决策理论(information-gap decision theory, IGDT)处理源荷的不确定性。然后，以阻尼比灵敏度为运行点更新方向并移动采样点，迅速采样到稳定边界区域附近，得到双下垂控制参数同时变化的大量稳定边界样本。接着利用ICNN学习下垂控制参数映射的小干扰稳定性指标，并对非线性稳定条件进行线性化处理，将其内嵌于调度模型。最后，基于IEEE33节点系统形成的孤岛微电网验证了所提方法的可行性和准确性。 |
| 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 |
| DOI：10.19783/j.cnki.pspc.251169 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(52577084) |
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| Optimal operation method for renewable energy islanded microgrids with embedded input convex neural network-based small-signal stability constraints |
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LOU Xuanming1, WANG Yu1, ZOU Yunyang2, XIE Kaigui1
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1. State Key Laboratory of Power Transmission Equipment Technology (Chongqing University), Chongqing 400044, China;
2. School of Electrical and Electronic Engineering (Nanyang Technological University), Singapore 639798, Singapore
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| 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. |
| Key words: islanded microgrid input convex neural network information gap decision theory distributed energy resource small-signal stability |
