考虑操作和不确定性的大型输电系统FACTS设备安装
Operations- and Uncertainty-Aware Installation of FACTS Devices in a Large Transmission System
AI总结：
本文研究了在大型输电系统中安装FACTS设备的规划问题，考虑了操作和不确定性因素，通过高效启发式算法解决非线性、非凸和多场景优化问题，以提高系统可行性和降低长期发电成本。
摘要：
分散式电力市场和可再生能源的进一步整合要求扩展现有输电基础设施以适应电力流动的波动性。然而，在许多国家，这种扩展受到政治和环境问题的严重限制。此外，高可再生能源整合需要额外的无功功率支持，这迫使输电系统运营商创造性地利用现有电网，例如利用新型技术，如灵活交流输电系统（FACTS）设备。我们制定了、分析并解决了在现有大型输电电网中安装两种类型FACTS设备（串联电容器和静止无功补偿器）的挑战性投资规划问题。我们考虑了电力流动的交流特性、多个负载场景的概率建模、FACTS设备在容量限制内的灵活性，以及长期实践中资本支出与运营支出（CAPEX vs OPEX）之间的权衡。证明了适当安装设备可以做到两方面：扩展或改进系统的可行性域以及降低长期发电成本（使更便宜的发电成为可能）。非线性、非凸和多场景感知优化通过一种高效的启发式算法解决，该算法由一系列二次规划问题组成，通过CPLEX求解，结合每个场景的精确AC功率流（PF）解决，以在迭代过程中维持可行的操作状态。该方法的效率和可扩展性在IEEE 30节点模型和波兰2736节点模型上进行了说明。
英文摘要：
Decentralized electricity markets and more integration of renewables demand expansion of the existing transmission infrastructure to accommodate inflected variabilities in power flows. However, such expansion is severely limited in many countries because of political and environmental issues. Furthermore, high renewables integration requires additional reactive power support, which forces the transmission system operators to utilize the existing grid creatively, e.g., take advantage of new technologies, such as flexible alternating current transmission system (FACTS) devices. We formulate, analyze and solve the challenging investment planning problem of installation in an existing large-scale transmission grid multiple FACTS devices of two types (series capacitors and static VAR compensators.) We account for details of AC character of the power flows, probabilistic modeling of multiple-load scenarios, FACTS devices flexibility in terms of their adjustments within the capacity constraints, and long term practical tradeoffs between capital vs operational expenditures (CAPEX vs OPEX). It is demonstrated that proper installation of the devices allows to do both - extend or improve feasibility domain for the system and also decrease long term power generation cost (make cheaper generation available). Nonlinear, nonconvex, and multiple-scenario-aware optimization is resolved through an efficient heuristic algorithm consisting of a sequence of quadratic programmings solved by CPLEX combined with exact AC PF resolution for each scenario for maintaining feasible operational states during iterations. Efficiency and scalability of the approach is illustrated on the IEEE 30-bus model and the 2736-bus Polish model from Matpower.
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