电力系统容量扩展的多尺度嵌套交叉分解算法
A Nested Cross Decomposition Algorithm for Power System Capacity Expansion with Multiscale Uncertainties
摘要：
现代电力系统已经见证了可再生能源、存储、电动车辆和各种需求响应资源的快速增长。由于新能源的多样性和不确定性，电力基础设施规划面临着更多的挑战。针对电力系统容量扩张问题，提出了一种多阶段多尺度随机混合整数规划（MM-SMIP）模型，既考虑了投资成本和长期需求随机性等粗时间尺度不确定性，又考虑了可再生能源发电量和电力需求不确定性等细时间尺度不确定性.要应用到一个真正的电力系统，所得到的模型将导致非常大规模的混合整数规划问题，这不仅遭受众所周知的灾难的维数，而且在每个阶段的大量的整数变量的计算困难。在解决这些挑战与MM-SMIP模型，我们提出了一个嵌套的交叉分解算法，由两层的分解，即，Dantzig-Wolfe分解和L形分解。该算法具有良好的计算性能下，我们的数值研究，特别是适合并行计算，这也将通过计算结果证明。
英文摘要：
Modern electric power systems have witnessed rapidly increasing penetration of renewable energy, storage, electrical vehicles and various demand response resources. The electric infrastructure planning is thus facing more challenges due to the variability and uncertainties arising from the diverse new resources. This study aims to develop a multistage and multiscale stochastic mixed integer programming (MM-SMIP) model to capture both the coarse-temporal-scale uncertainties, such as investment cost and long-run demand stochasticity, and fine-temporal-scale uncertainties, such as hourly renewable energy output and electricity demand uncertainties, for the power system capacity expansion problem. To be applied to a real power system, the resulting model will lead to extremely large-scale mixed integer programming problems, which suffer not only the well-known curse of dimensionality, but also computational difficulties with a vast number of integer variables at each stage. In addressing such challenges associated with the MM-SMIP model, we propose a nested cross decomposition algorithm that consists of two layers of decomposition, that is, the Dantzig-Wolfe decomposition and L-shaped decomposition. The algorithm exhibits promising computational performance under our numerical study, and is especially amenable to parallel computing, which will also be demonstrated through the computational results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
