@article{35596,
  author = {Alexandre Moreira and Goran Strbac and Rodrigo Moreno and Alexandre Street and Ioannis Konstantelos},
  title = {A Five-Level MILP Model for Flexible Transmission Network Planning Under Uncertainty: A Min–Max Regret Approach},
  abstract = {<p><span>The benefits of new transmission investment significantly depend on deployment patterns of renewable electricity generation that are characterized by severe uncertainty. In this context, this paper presents a novel methodology to solve the transmission expansion planning problem under generation expansion uncertainty in a min-max regret fashion, when considering flexible network options and&nbsp;</span><span id="MathJax-Element-1-Frame" class="MathJax"><span id="MathJax-Span-1" class="math"><span><span><span id="MathJax-Span-2" class="mrow"><span id="MathJax-Span-3" class="mi">n</span><span id="MathJax-Span-4" class="mo">−</span><span id="MathJax-Span-5" class="mn">1</span></span></span></span></span></span><span>&nbsp;security criterion. To do so, we propose a five-level mixed integer linear programming (MILP) based model that comprises: (i) the optimal network investment plan (including phase shifters), (ii) the realization of generation expansion, (iii) the co-optimization of energy and reserves given transmission and generation expansions, (iv) the realization of system outages, and (v) the decision on optimal post-contingency corrective control. In order to solve the five-level model, we present a cutting plane algorithm that ultimately identifies the optimal min-max regret flexible transmission plan in a finite number of steps. The numerical studies carried out demonstrate: (a) the significant benefits associated with flexible network investment options to hedge transmission expansion plans against generation expansion uncertainty and system outages, (b) strategic planning-under-uncertainty uncovers the full benefit of flexible options which may remain undetected under deterministic, perfect information methods, and (c) the computational scalability of the proposed approach.</span></p>},
  year = {2018},
  journal = {IEEE Transactions on Power Systems},
  volume = {33},
  pages = {486 - 501},
  month = {01/2017},
  issn = {0885-8950},
  url = {http://ieeexplore.ieee.org/document/7937947/},
  doi = {10.1109/TPWRS.2017.2710637},
  language = {eng},
}