Quality control for next-generation liquefaction case histories

The Next-Generation Liquefaction (NGL) database is an open-source, global database of liquefaction and non-ground failure case-histories. The database is part of a multi-year research effort with the main goal of developing improved procedures to evaluate liquefaction susceptibility, triggering, and consequences. In NGL, a case-history is defined as the intersection of three components: (1) a site, (2) an earthquake event, and (3) post-earthquake observations. The NGL database hosts case-histories used to develop existing liquefaction models, as well as new data derived from recent earthquakes such as the 2010-2011 Canterbury earthquake sequence, the 2011 Tohoku-Oki earthquake, and the 2012 Emilia earthquake. The database also hosts lateral spread case-histories, and a substantial number of liquefaction sites characterized by the presence of co-located recording stations. All of the data present in the NGL database are reviewed by the NGL Database Working Group. The NGL formal vetting process is described for an example case-history

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject