This paper proposes a two-stage robust optimization model for the transmission network expansion planning problem. Long-term uncertainties in the peak demand and generation capacity are modeled using confidence bounds, while the short-term variability of demand and renewable production is modeled using a set of representative days. As a distinctive feature, this work takes into account the non-convex operation of conventional generating units and storage facilities, which results in a two-stage robust optimization model with a discrete recourse problem. The resulting problem is solved using a nested column-and-constraint generation algorithm that guarantees convergence to the global optimum in a finite number of iterations. An illustrative example and a case study are used to show the performance of the proposed approach. Numerical results show that neglecting the non-convex operation of conventional generating units and storage facilities leads to suboptimal expansion decisions.

A two-stage robust optimization model for transmission network expansion planning. Solution procedure is based on a nested column-and-constraint generation algorithm. Long-term uncertainties are modeled through confidence bounds. Representative days model the daily evolution of wind production and demand. Neglecting non-convex operational constraints leads to suboptimal solutions.

针对传输网络扩展规划问题，本文提出了两阶段鲁棒优化模型。峰值需求和发电量的长期不确定性使用置信区间建模，而需求和可再生能源生产的短期可变性则使用一组代表性天数进行建模。作为一项独特功能，这项工作考虑了常规发电机组和存储设备的非凸操作，这导致了具有离散资源问题的两阶段鲁棒优化模型。使用嵌套的列和约束生成算法可以解决由此产生的问题，该算法可确保在有限数量的迭代中收敛到全局最优。一个说明性的例子和一个案例研究被用来展示所提出的方法的性能。

用于传输网络扩展规划的两阶段鲁棒优化模型。解决过程基于嵌套的列和约束生成算法。长期不确定性通过置信区间建模。代表日模拟了风能生产和需求的每日演变。忽略非凸操作约束会导致次优解决方案。