低压电网节点级高效分位数解析的接入容量评估
Efficient Quantile-Resolved Hosting Capacity Assessment on Nodal Level for Low-Voltage Grids
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中文总结 AI 辅助
本文提出一种高效计算低压电网节点级分位数解析接入容量分布的方法，利用多元正态分布和线性化潮流模型，在保持高准确性的同时大幅降低计算时间，适用于配电系统实时管理。
中文摘要 AI 辅助
接入容量——网络在不违反运行限制的情况下所能容纳的最大额外容量——是配电系统规划和运行中的关键指标。关于电网加固和灵活性管理部署的决策不仅需要最坏情况下的接入容量，还需要理解在不同负荷和发电模式可能性下接入容量的分布。分位数解析的接入容量分布通过将接入容量表示为可接受的运行限制超限可能性的函数来提供这一视角。蒙特卡洛采样是计算此类分布的既定方法，但需要大量的计算资源。近似方法在评估实际网络时要么牺牲准确性，要么牺牲捕获不确定性相关性的能力，要么牺牲可扩展性。本文介绍了一种计算分位数解析接入容量分布的高效计算方法。该方法将负荷样本表示为多元正态分布，并通过线性化潮流模型进行传播。这允许利用每个接入容量分位数的重新参数化的交流最优潮流问题。在现实低压网络上与基于蒙特卡洛的方法进行基准测试表明，所提方法实现了相当的准确性，平均偏差约为3%，同时将计算时间减少了几个数量级。对于示例网络，计算时间分别从11分钟减少到2秒，从38小时减少到50秒。该方法的可扩展性也适用于配电系统运营商实践中遇到的15分钟周期的重新计算，例如实时电网管理。
英文摘要
Hosting capacity - the maximum additional capacity a network can accommodate without violating operational limits - is a key metric in distribution system planning and operation. Decisions on grid reinforcements and the deployment of flexibility management require not only the worst-case HC but also an understanding of the distribution of HC under different likelihoods in load and generation patterns. Quantile-resolved HC distributions provide this view by expressing HC as a function of an acceptable operational limit exceedance likelihood. Monte Carlo sampling is the established approach for computing such distributions but demands large computational resources. Approximations sacrifice either accuracy, the ability to capture uncertainty correlations, or scalability when assessing real-world networks. This paper introduces a computationally efficient method for calculating distributions for quantile-resolved HC. It uses a representation of load samples as multivariate normal distribution, propagated through a linearized power flow model. This allows for leveraging a re-parametrized AC-OPF problem for each hosting capacity quantile. Benchmarking against Monte Carlo-based methods on realistic LV networks demonstrates that the proposed method achieves comparable accuracy with a mean deviation of approx. 3%, while reducing computational time by orders of magnitude. For the exemplary networks the computational time decreases from 11 min to 2 s, and 38 h to 50 s, respectively. The method's scalability is also suitable for recalculation in 15-minute cycles encountered in DSO practice for e.g., real-time grid management.
发表机构
- Siemens AG（西门子股份公司）
- Technical University of Darmstadt（达姆施塔特工业大学）
机构由 AI 辅助整理，请以论文原文为准。
