用元模型神经过程实现电网拓扑变化下的快速概率潮流计算
Meta-model Neural Process for Probabilistic Power Flow under Varying N-1 System Topologies
- 基于上下文集的拓扑表示与函数条件分布学习，实现拓扑自适应
- 9节点和118节点系统最大相对误差分别低至1.11%和0.77%
- 适合高比例新能源和电动车接入场景的电网规划与运行人员
概率潮流（PPF）问题对于量化不确定注入引起的节点电压分布至关重要。传统方法假设拓扑固定，一旦拓扑改变（如单线故障），需重新求解，导致计算负担重且不便利。本文提出一种基于元模型神经过程（MMNP）的拓扑自适应新方法，可应对多变的N-1拓扑情况。通过基于上下文集的拓扑表示与函数条件分布学习技术，该方法显著提升模型对拓扑变化的鲁棒性，避免在新配置下重新训练。在IEEE 9-bus和IEEE 118-bus系统上的仿真验证表明，最大%L1相对误差分别为1.11%和0.77%。该方法填补了高电网波动时代下概率潮流方法的关键空白。
原文摘要 · Abstract (English)
The probabilistic power flow (PPF) problem is essential to quantifying the distribution of the nodal voltages due to uncertain injections. The conventional PPF problem considers a fixed topology, and the solutions to such a PPF problem are associated with this topology. A change in the topology might alter the power flow patterns and thus require the PPF problem to be solved again. The previous PPF model and its solutions are no longer valid for the new topology. This practice incurs both inconvenience and computation burdens as more contingencies are foreseen due to high renewables and a large share of electric vehicles. This paper presents a novel topology-adaptive approach, based on the meta-model Neural Process (MMNP), for finding the solutions to PPF problems under varying N-1 topologies, particularly with one-line failures. By leveraging context set-based topology representation and conditional distribution over function learning techniques, the proposed MMNP enhances the robustness of PPF models to topology variations, mitigating the need for retraining PPF models on a new configuration. Simulations on an IEEE 9-bus system and IEEE 118-bus system validate the model's performance. The maximum %L1-relative error norm was observed as 1.11% and 0.77% in 9-bus and 118-bus, respectively. This adaptive approach fills a critical gap in PPF methodology in an era of increasing grid volatility.
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