Planning With Multiple Transmission and Storage Investment Options Under Uncertainty: A Nested Decomposition Approach
Planning With Multiple Transmission and Storage Investment Options Under Uncertainty: A Nested Decomposition Approach
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DOI：
10.1109/tpwrs.2017.2774367
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发表时间：
2018-07
影响因子：
6.6
通讯作者：
P. Falugi;I. Konstantelos;G. Strbac
中科院分区：
文献类型：
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作者：
P. Falugi;I. Konstantelos;G. Strbac
实现世界各国政府制定的雄心勃勃的气候变化减缓目标，必然会导致前所未有的网络投资，以适应低碳能源。除了投资传统输电线路之外，储能等新技术还可以提高运营灵活性，并有助于经济高效地整合可再生能源。鉴于这些网络资产的使用寿命长，资本成本高，因此必须在长期成本效益的基础上决定其部署。然而，这样的分析可能导致包含数千个连续和二进制变量的大规模混合整数线性规划问题。由于需要容纳多个候选资产并考虑可能发生的各种外部系统开发场景，复杂性严重加剧。在本文中，我们提出了一种新的，高效的，高度概括性的框架，解决大规模的规划问题的不确定性，通过使用时间分解方案的基础上的原则嵌套折弯机。由于存在的非序贯投资状态方程和子问题的非凸性所带来的挑战，突出和解决。通过IEEE 118总线测试系统，涉及规划的多个传输和存储资产下的长期不确定性的案例研究表明，所提出的方法的大量计算增益。所提出的方法被示出为基本上优于现有技术。
Achieving the ambitious climate change mitigation objectives set by governments worldwide is bound to lead to unprecedented amounts of network investment to accommodate low-carbon sources of energy. Beyond investing in conventional transmission lines, new technologies, such as energy storage, can improve operational flexibility and assist with the cost-effective integration of renewables. Given the long lifetime of these network assets and their substantial capital cost, it is imperative to decide on their deployment on a long-term cost-benefit basis. However, such an analysis can result in large-scale mixed integer linear programming problems that contain many thousands of continuous and binary variables. Complexity is severely exacerbated by the need to accommodate multiple candidate assets and consider a wide range of exogenous system development scenarios that may occur. In this paper, we propose a novel, efficient, and highly generalizable framework for solving large-scale planning problems under uncertainty by using a temporal decomposition scheme based on the principles of Nested Benders. The challenges that arise due to the presence of nonsequential investment state equations and subproblem nonconvexity are highlighted and tackled. The substantial computational gains of the proposed method are demonstrated via a case study on the IEEE 118 bus test system that involve planning of multiple transmission and storage assets under long-term uncertainty. The proposed method is shown to substantially outperform the current state of the art.