Planning With Multiple Transmission and Storage Investment Options Under Uncertainty: A Nested Decomposition Approach

Paola Falugi, Ioannis Konstantelos, Goran Strbac
<span title="">2018</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gnhkf3aieraxhlw7vpy6whnksy" style="color: black;">IEEE Transactions on Power Systems</a> </i> &nbsp;
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 costeffective integration of renewables. Given the long lifetime of these network assets and their substantial capital cost, it is imperative to decide
more &raquo; ... on their deployment on a long-term costbenefit basis. However, such an analysis can result in large-scale Mixed Integer Linear Programming (MILP) problems which 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 manuscript 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 non-sequential investment state equations and sub-problem non-convexity 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 longterm uncertainty. The proposed method is shown to substantially outperform the current state-of-the-art.
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