An adjustable robust optimization approach for contingency-constrained transmission expansion planning
An adjustable robust optimization approach for contingency-constrained transmission expansion planning
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DOI：
10.1109/pesgm.2015.7285747
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发表时间：
2015-07
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通讯作者：
Alexandre Moreira;A. Street;J. Arroyo
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作者：
Alexandre Moreira;A. Street;J. Arroyo
提出了一种基于广义发输电联合n-K安全准则的输电网规划方法。所提出的方法确定最佳的扩展计划，同时建模正常和应急状态下的电力系统运行。提出了一种可调整的鲁棒优化方法，以规避与传统的应急约束方法依赖于显式建模的整个应急集的易处理性问题。可调整的鲁棒模型制定为一个三层规划问题。上层问题的目标是最小化投资，运营和系统功率不平衡的成本。中层问题确定，对于一个给定的扩展计划，应急状态导致最大功率不平衡，如果有的话。最后，较低层次的问题模型的运营商的最佳反应，为一个给定的应急和投资计划，最大限度地减少系统的功率不平衡。由此产生的三层程序求解的原始-对偶算法的基础上Benders分解相结合的列和约束生成过程。所提出的方法是快速收敛到最优解，并提供了一个衡量的距离最优。仿真结果表明，所提出的方法优于传统的意外约束模型。
This paper presents a novel approach for the transmission network expansion planning under generalized joint generation and transmission n-K security criteria. The proposed methodology identifies the optimal expansion plan while modeling the power system operation under both normal and contingency states. An adjustable robust optimization approach is presented to circumvent the tractability issues associated with conventional contingency-constrained methods relying on explicitly modeling the whole contingency set. The adjustable robust model is formulated as a trilevel programming problem. The upper-level problem aims at minimizing the investment, operation, and system power imbalance costs. The middle-level problem identifies, for a given expansion plan, the contingency state leading to maximum power imbalance if any. Finally, the lower-level problem models the operator's best reaction for a given contingency and investment plan by minimizing the system power imbalance. The resulting trilevel program is solved by a primal-dual algorithm based on Benders decomposition combined with a column-and-constraint generation procedure. The proposed approach is finitely convergent to the optimal solution and provides a measure of the distance to the optimum. Simulation results show the superiority of the proposed methodology over conventional contingency-constrained models.