With the increasing penetration of renewable generating units, especially in remote areas not well connected with load demand, there are growing interests to co-optimize generation and transmission expansion planning (GTEP) in power systems. Due to the volatility in renewable generation, a planner needs to include the operating decisions into the planning model to guarantee feasibility. However, solving the GTEP problem with hourly operating decisions throughout the planning horizon is computationally intractable. Therefore, we propose several spatial and temporal simplifications to the problem. Built on the generation expansion planning (GEP) formulation of Lara et al. (2018), we propose a mixed-integer linear programming formulation for the GTEP problem. Three different formulations, i.e., a big-M formulation, a hull formulation, and an alternative big-M formulation, are reported for transmission expansion. We theoretically compare the tightness of the LP relaxations of the three formulations. The proposed MILP GTEP model typically involves millions or tens of millions of variables, which makes the model not directly solvable by the commercial solvers. To address this computational challenge, we propose a nested Benders decomposition algorithm and a tailored Benders decomposition algorithm that exploit the structure of the GTEP problem. Using a case study from Electric Reliability Council of Texas (ERCOT), we are able to show that the proposed tailored Benders decomposition outperforms the nested Benders decomposition. The coordination in the optimal generation and transmission expansion decisions from the ERCOT study implies that there is an additional value in solving GEP and TEP simultaneously.

随着可再生发电机组的日益普及，尤其是在与负载需求联系不紧密的偏远地区，人们越来越关注电力系统中的发电和输电扩展规划（GTEP）的协同优化。由于可再生能源发电的波动性，规划者需要将运营决策纳入规划模型以保证可行性。然而，在整个规划范围内通过每小时运行决策来解决 GTEP 问题在计算上是难以处理的。因此，我们对这个问题提出了几种空间和时间的简化。建立在 Lara 等人的生成扩展规划 (GEP) 公式之上。(2018)，我们为 GTEP 问题提出了一个混合整数线性规划公式。三种不同的配方，即大M配方，船体配方，和替代的 big-M 公式，据报道用于传输扩展。我们在理论上比较了三种配方的 LP 松弛的紧密度。所提出的 MILP GTEP 模型通常涉及数百万或数千万个变量，这使得商业求解器无法直接求解该模型。为了解决这个计算挑战，我们提出了一种嵌套 Benders 分解算法和一种利用 GTEP 问题结构的定制 Benders 分解算法。使用德克萨斯电力可靠性委员会 (ERCOT) 的案例研究，我们能够证明所提出的定制 Benders 分解优于嵌套 Benders 分解。