Resilient Transmission Hardening Planning in a High Renewable Penetration Era
Resilient Transmission Hardening Planning in a High Renewable Penetration Era
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DOI：
10.1109/tpwrs.2018.2872893
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发表时间：
2019-03
影响因子：
6.6
通讯作者：
A. Bagheri;Chaoyue Zhao;F. Qiu;Jianhui Wang
中科院分区：
文献类型：
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作者：
A. Bagheri;Chaoyue Zhao;F. Qiu;Jianhui Wang
强化传输系统中的部件是一种提高系统对自然灾害可能造成的干扰的恢复能力的做法。在可再生能源普及率非常高的电力系统中，可再生能源的不确定性和变异性将使系统硬化变得更加复杂。本文在可再生能源高渗透率注入概率潮流的背景下，研究输电线路硬化规划问题。我们假设可再生能源的概率信息是不完全和模糊的，并提出了一种数据驱动的方法来逼近可再生能源的不确定性集。然后，我们将$N-1$安全标准扩展到多个同时发生的意外情况，并寻求为最坏的情况准备一个强化计划。建立了考虑风电出力分布和输电线路意外事故的两阶段数据驱动随机模型。然后，我们应用列和约束生成方法来求解所提出的模型。为了验证该方法的有效性，我们在24节点和118节点的测试系统上进行了实验。数值结果表明，数据驱动方法能够有效地解决不确定性歧义问题，并能产生有效的加固计划，提高系统的抗风险能力。
Hardening components in transmission systems is a practice to improve system resilience against possible disturbances caused by natural disasters. In a power system with a very high penetration of renewable energy, the system hardening will be further complicated by the uncertainty and variability of renewable energy. In this paper, we study the transmission line hardening planing problem in the context of probabilistic power flows injected by the high penetration of renewable energy. We assume that the probabilistic information of renewable energy is incomplete and ambiguous and propose a data-driven approach to approximate the renewable uncertainty sets. We then extend the $N-1$ security criteria to multiple simultaneous contingencies and seek to prepare a hardening plan for the worst-case scenarios. A two-stage data-driven stochastic model is formulated by considering the joint worst-case wind output distribution and transmission line contingencies. Then, we apply the Column-and-Constraints generation method to solve the proposed model. To test the effectiveness of the proposed approach, we conduct experiments on 24-bus and 118-bus test systems. We numerically show that the data-driven approach can effectively address the uncertainty ambiguity and the proposed approach can produce effective hardening plans that improve the system resilience.