发表机构
KU Leuven; Energy Transmission Competence Hub (Etch) - EnergyVille; University of Wisconsin-Madison（荷语鲁汶大学; 能源传输能力中心（Etch）- 能源村; 威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种方法，通过LPAC近似和AC可行性检查，在IEEE 118节点系统中识别少量高价值母线拓扑，在离散负荷增长下降低发电成本最多0.147%，为系统运营商提供实用选择方案。
AI 中文摘要
输电电网正日益受到可再生能源波动性和电力需求增长的压力的影响。此类电网主要针对不同的发电机组和负荷条件而建设。电网拓扑优化提供了通过修改电网中变电站的母线拓扑来重新分配潮流的机会。然而，可行的母线配置的组合爆炸使得拓扑优化对系统运营商而言不切实际。本文提出了一种方法，用于识别一小部分高价值的母线拓扑，以在多种可再生能源-需求模式中获取拓扑优化的经济效益。该优化模型基于最优潮流公式的LPAC近似，并对应用于IEEE 118节点测试案例的最优拓扑进行交流可行性检查。在测试案例中，我们选择了两对不同的变电站（46-49和24-69），并在365个具有不同风力和负荷条件的聚类时间步长中分别优化其拓扑。对于每一对，我们识别出四个最常出现的最优拓扑，并在标准条件和拥堵条件下，以及有无离散负荷增长的情况下评估其性能。结果表明，与普通的交流最优潮流相比，从这一缩减的拓扑集合中选择可将总发电成本降低最多0.147%。此外，我们展示了拓扑优化在离散负荷增长下对所选母线接纳能力的影响。我们的发现为系统运营商提供了一种实用的方法，以选择一组最优母线拓扑用于其电网中不同的风力-负荷条件，从而在不承担实时切换决策的计算和运行风险的情况下降低发电成本。
英文摘要
Transmission grids are increasingly stressed by the fluctuating nature of renewable energy sources and by increasing electricity demand. Such grids were mainly built for a different generation fleet and load conditions. Grid topology optimization offers the possibility to redistribute power flows by modifying the busbar topology of substations in the grid. However, the combinatorial explosion of feasible busbar configurations makes topology optimization impractical for system operators. This paper proposes a methodology to identify a small subset of high-value busbar topologies to capture the economic benefit of topology optimization across a range of renewable-demand patterns. The optimization model is based on a LPAC approximation of the optimal power flow formulation, and AC-feasibility checks of the optimal topology applied to the IEEE 118-bus test case. In the test case, we select two distinct pairs of substations (46-49 and 24-69) and we optimize their topology separately over 365 clustered timesteps with different wind and load conditions. For each pair, we identify the four most recurrent optimal topologies and evaluate their performance under standard, and congested conditions, with and without a discrete load growth. Results show that selecting from this reduced set of topologies reduces total generation costs by up to 0.147% compared to a plain AC-OPF. In addition, we show the influence of topology optimization on the hosting capacity of selected busbars under discrete load growth. Our findings provide a practical methodology for system operators to select a subset of optimal busbar topologies to be used in their grid for different wind-load conditions, resulting in decreasing generation costs without the computational and operational risks of real-time switching decisions.
