Stochastic and Chance-Constrained Conic Distribution System Expansion Planning Using Bilinear Benders Decomposition
Stochastic and Chance-Constrained Conic Distribution System Expansion Planning Using Bilinear Benders Decomposition
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DOI：
10.1109/tpwrs.2017.2751514
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发表时间：
2017-04
影响因子：
6.6
通讯作者：
H. Haghighat;Bo Zeng
中科院分区：
文献类型：
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作者：
H. Haghighat;Bo Zeng
二阶圆锥规划（SOCP）已被用于电力系统中的各种应用，如操作和扩展规划建模。在本文中，我们提出了一个两阶段的随机混合整数SOCP（MISOCP）模型的配电系统扩展规划问题，考虑不确定性，也捕捉到非线性交流潮流。为了避免由于一些极端情况而导致的昂贵的投资计划，我们进一步提出了一个可能导致成本效益解决方案的机会约束变体。为了解决计算的挑战，我们扩展了基本的Benders分解方法，并开发了一个双线性变量来计算随机和机会约束的MISOCP公式。一组数值实验来说明我们的模型和计算方法的性能。特别是，结果表明，我们的Benders分解算法在处理随机场景的数量级上大大优于专业的MISOCP求解器。
Second-order conic programming (SOCP) has been used to model various applications in power systems, such as operation and expansion planning. In this paper, we present a two-stage stochastic mixed integer SOCP (MISOCP) model for the distribution system expansion planning problem that considers uncertainty and also captures the nonlinear ac power flow. To avoid costly investment plans due to some extreme scenarios, we further present a chance-constrained variant that could lead to cost-effective solutions. To address the computational challenge, we extend the basic Benders decomposition method and develop a bilinear variant to compute stochastic and chance-constrained MISOCP formulations. A set of numerical experiments is performed to illustrate the performance of our models and computational methods. In particular, results show that our Benders decomposition algorithms drastically outperform a professional MISOCP solver in handling stochastic scenarios by orders of magnitude.