Toward the Next Generation of Multiperiod Optimal Power Flow Solvers
Toward the Next Generation of Multiperiod Optimal Power Flow Solvers
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DOI：
10.1109/tpwrs.2017.2789187
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发表时间：
2018-01
影响因子：
6.6
通讯作者：
D. Kourounis;A. Fuchs;O. Schenk
中科院分区：
文献类型：
--
作者：
D. Kourounis;A. Fuchs;O. Schenk
分布式储能设备是解决大规模可再生能源集成带来的运行挑战的一种有效方法。然而，存储设备的建模导致在感兴趣的时间段的每个细分处定义的单个最优潮流(OPF)问题的跨期耦合。由此产生的多周期最优潮流(MPOPF)问题成为难以解决的长时间段禁止、预测和规划问题。内点(IP)方法已被广泛用于求解最优潮流和最大最优潮流问题。本文针对MPOPF问题提出了一种高效的IP算法BELTISTOS。重新讨论了与Karush-Kuhn-Tucker条件相关的线性系统的结构，并提出了一种适合其结构的基于Schur补的方法。通过涉及日益复杂的电网模型的基准案例，BELTISTOS算法被证明比标准优化方法(如IPOPT、MIPS和KNITRO)的求解速度快几个数量级，使用的内存明显更少。
Distributed energy storage devices are commonly employed as an effective approach for addressing operational challenges introduced by the large scale integration of renewables. However, the modeling of storage devices results in intertemporal coupling of the individual optimal power flow (OPF) problems defined at each subdivision of the time period of interest. The resulting multiperiod optimal power flow (MPOPF) problem becomes intractable prohibiting, forecasting, and planning over long time periods. Interior point (IP) methods have been extensively employed for the solution of OPF and MPOPF problems. This work proposes an efficient IP algorithm, BELTISTOS, particularly designed for MPOPF problems. The structure of the linear system associated with the Karush-Kuhn-Tucker conditions is revisited, and a Schur-complement-based approach tailored to its structure is proposed. Through benchmark cases involving power-grid models of increasing complexity, the BELTISTOS algorithm is demonstrated to provide several orders of magnitude faster solution times than standard optimization methods, such as IPOPT, MIPS, and KNITRO, using significantly less memory.