{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T12:03:22Z","timestamp":1784894602505,"version":"3.55.0"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006318","name":"University Carlos III of Madrid","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006318","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Operations Research"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.cor.2026.107584","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T16:38:07Z","timestamp":1781541487000},"page":"107584","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Risk-averse wind farms placement via quantile constraint learning"],"prefix":"10.1016","volume":"194","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1880-9727","authenticated-orcid":false,"given":"Wenxiu","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Alc\u00e1ntara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1663-1061","authenticated-orcid":false,"given":"Carlos","family":"Ruiz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.cor.2026.107584_b1","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.ejor.2024.06.038","article-title":"Optimal day-ahead offering strategy for large producers based on market price response learning","volume":"319","author":"Alc\u00e1ntara","year":"2024","journal-title":"European J. Oper. Res."},{"key":"10.1016\/j.cor.2026.107584_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127876","article-title":"A quantile neural network framework for two-stage stochastic optimization","volume":"284","author":"Alc\u00e1ntara","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.cor.2026.107584_b3","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.enconman.2017.10.082","article-title":"Deep assessment of wind speed distribution models: A case study of four sites in Algeria","volume":"155","author":"Aries","year":"2018","journal-title":"Energy Convers. Manage."},{"key":"10.1016\/j.cor.2026.107584_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.esr.2025.101977","article-title":"Optimizing wind farm portfolios: A risk-mitigation approach to strengthen renewable integration and market resilience","volume":"62","author":"Arrieta-Prieto","year":"2025","journal-title":"Energy Strat. Rev."},{"key":"10.1016\/j.cor.2026.107584_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2023.109552","article-title":"Data-driven optimization for wind farm siting","volume":"155","author":"Arrieta-Prieto","year":"2024","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.cor.2026.107584_b6","series-title":"Handbook of Image and Video Processing (Second Edition)","first-page":"21","article-title":"2.1 - Basic gray-level image processing","author":"Bovik","year":"2005"},{"key":"10.1016\/j.cor.2026.107584_b7","series-title":"Grid connection optimization","author":"Bundesverband WindEnergie (BWE)","year":"2021"},{"key":"10.1016\/j.cor.2026.107584_b8","article-title":"A unified view of piecewise linear neural network verification","volume":"31","author":"Bunel","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cor.2026.107584_b9","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.renene.2016.08.008","article-title":"Optimal siting and sizing of wind farms","volume":"101","author":"Cetinay","year":"2017","journal-title":"Renew. Energy"},{"issue":"1","key":"10.1016\/j.cor.2026.107584_b10","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rser.2011.08.002","article-title":"A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria","volume":"16","author":"Chellali","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.cor.2026.107584_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2020.106741","article-title":"Data-driven optimal voltage regulation using input convex neural networks","volume":"189","author":"Chen","year":"2020","journal-title":"Electr. Power Syst. Res."},{"issue":"1","key":"10.1016\/j.cor.2026.107584_b12","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/TPWRS.2018.2867209","article-title":"Data-driven power system operation: Exploring the balance between cost and risk","volume":"34","author":"Cremer","year":"2018","journal-title":"IEEE Trans. Power Syst."},{"issue":"3","key":"10.1016\/j.cor.2026.107584_b13","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/TEC.2013.2259627","article-title":"Design and analysis of an MPPT technique for small-scale wind energy conversion systems","volume":"28","author":"Dalala","year":"2013","journal-title":"IEEE Trans. Energy Convers."},{"key":"10.1016\/j.cor.2026.107584_b14","series-title":"2012 North American Power Symposium","first-page":"1","article-title":"A framework to determine the probability density function for the output power of wind farms","author":"Dhople","year":"2012"},{"key":"10.1016\/j.cor.2026.107584_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2023.113990","article-title":"Brazilian wind energy generation potential using mixtures of Weibull distributions","volume":"189","author":"dos Santos","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.cor.2026.107584_b16","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.compchemeng.2012.06.006","article-title":"Process synthesis of biodiesel production plant using artificial neural networks as the surrogate models","volume":"46","author":"Fahmi","year":"2012","journal-title":"Comput. Chem. Eng."},{"issue":"1","key":"10.1016\/j.cor.2026.107584_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2023.04.041","article-title":"Optimization with constraint learning: A framework and survey","volume":"314","author":"Fajemisin","year":"2024","journal-title":"European J. Oper. Res."},{"key":"10.1016\/j.cor.2026.107584_b18","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.cor.2018.04.006","article-title":"Machine learning meets mathematical optimization to predict the optimal production of offshore wind parks","volume":"106","author":"Fischetti","year":"2019","journal-title":"Comput. Oper. Res."},{"issue":"7","key":"10.1016\/j.cor.2026.107584_b19","doi-asserted-by":"crossref","DOI":"10.3390\/en15072698","article-title":"A new wind speed scenario generation method based on principal component and R-Vine copula theories","volume":"15","author":"Goh","year":"2022","journal-title":"Energies"},{"key":"10.1016\/j.cor.2026.107584_b20","series-title":"2018 Power Systems Computation Conference","first-page":"1","article-title":"Data-driven security-constrained AC-OPF for operations and markets","author":"Halilba\u0161i\u0107","year":"2018"},{"key":"10.1016\/j.cor.2026.107584_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2019.109387","article-title":"Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China","volume":"115","author":"Han","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.cor.2026.107584_b22","series-title":"Quantitative Applications in the Social Sciences","article-title":"Quantile Regression","author":"Hao","year":"2007"},{"issue":"8","key":"10.1016\/j.cor.2026.107584_b23","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1002\/we.397","article-title":"General statistics of geographically dispersed wind power","volume":"13","author":"Hasche","year":"2010","journal-title":"Wind. Energy"},{"key":"10.1016\/j.cor.2026.107584_b24","series-title":"ERA5 hourly data on single levels from 1940 to present","author":"Hersbach","year":"2023"},{"key":"10.1016\/j.cor.2026.107584_b25","series-title":"Corporate sourcing of renewables","author":"International Energy Agency (IEA)","year":"2019"},{"key":"10.1016\/j.cor.2026.107584_b26","series-title":"Renewables 2024: Analysis and forecasts to 2030","author":"International Energy Agency (IEA)","year":"2024"},{"key":"10.1016\/j.cor.2026.107584_b27","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.energy.2015.03.126","article-title":"Determination of extreme wind values using the Gumbel distribution","volume":"86","author":"Kang","year":"2015","journal-title":"Energy"},{"key":"10.1016\/j.cor.2026.107584_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.116873","article-title":"A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series","volume":"295","author":"Katikas","year":"2021","journal-title":"Appl. Energy"},{"key":"10.1016\/j.cor.2026.107584_b29","series-title":"2014 International Conference on Probabilistic Methods Applied To Power Systems","first-page":"1","article-title":"Optimizing wind farm locations to reduce variability and increase generation","author":"Lowery","year":"2014"},{"issue":"2","key":"10.1016\/j.cor.2026.107584_b30","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1287\/opre.2021.0707","article-title":"Mixed-integer optimization with constraint learning","volume":"73","author":"Maragno","year":"2025","journal-title":"Oper. Res."},{"issue":"3","key":"10.1016\/j.cor.2026.107584_b31","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/j.apenergy.2009.09.022","article-title":"A methodology to generate statistically dependent wind speed scenarios","volume":"87","author":"Morales","year":"2010","journal-title":"Appl. Energy"},{"key":"10.1016\/j.cor.2026.107584_b32","series-title":"System advisor model (SAM) financial models: Utility-scale power purchase agreement","author":"National Renewable Energy Laboratory (NREL)","year":"2023"},{"issue":"6","key":"10.1016\/j.cor.2026.107584_b33","doi-asserted-by":"crossref","first-page":"2806","DOI":"10.1016\/j.enpol.2010.01.012","article-title":"Optimizing transmission from distant wind farms","volume":"38","author":"Pattanariyankool","year":"2010","journal-title":"Energy Policy"},{"issue":"2","key":"10.1016\/j.cor.2026.107584_b34","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.ejor.2024.04.026","article-title":"An efficient solver for large-scale onshore wind farm siting including cable routing","volume":"317","author":"Pedersen","year":"2024","journal-title":"European J. Oper. Res."},{"issue":"6","key":"10.1016\/j.cor.2026.107584_b35","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1127\/metz\/2020\/1041","article-title":"Spectral characteristics and spatial smoothing of wind power\u2013a case study of the faroe islands","volume":"29","author":"Poulsen","year":"2020","journal-title":"Meteorol. Z."},{"issue":"6","key":"10.1016\/j.cor.2026.107584_b36","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.5194\/wes-7-2335-2022","article-title":"Optimization of wind farm portfolio to minimize the overall power fluctuations at selective frequencies\u2013a case study of the Faroe Islands","volume":"7","author":"Poulsen","year":"2022","journal-title":"Wind. Energy Sci."},{"issue":"11","key":"10.1016\/j.cor.2026.107584_b37","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1002\/we.1657","article-title":"Dampening variations in wind power generation\u2014the effect of optimizing geographic location of generating sites","volume":"17","author":"Reichenberg","year":"2014","journal-title":"Wind. Energy"},{"key":"10.1016\/j.cor.2026.107584_b38","series-title":"Evaluating robustness of neural networks with mixed integer programming","author":"Tjeng","year":"2017"},{"key":"10.1016\/j.cor.2026.107584_b39","series-title":"Model Building in Mathematical Programming","author":"Williams","year":"2013"},{"issue":"8","key":"10.1016\/j.cor.2026.107584_b40","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1016\/j.engstruct.2006.01.001","article-title":"Probability distributions of extreme wind speed and its occurrence interval","volume":"28","author":"Xiao","year":"2006","journal-title":"Eng. Struct."},{"issue":"3","key":"10.1016\/j.cor.2026.107584_b41","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1109\/TSTE.2019.2920255","article-title":"Investigating the wind power smoothing effect using set pair analysis","volume":"11","author":"Yang","year":"2019","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"10.1016\/j.cor.2026.107584_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.125369","article-title":"Weather-informed probabilistic forecasting and scenario generation in power systems","volume":"384","author":"Zhang","year":"2025","journal-title":"Appl. Energy"},{"key":"10.1016\/j.cor.2026.107584_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2023.148421","article-title":"Accelerating operation optimization of complex chemical processes: A novel framework integrating artificial neural network and mixed-integer linear programming","volume":"481","author":"Zhou","year":"2024","journal-title":"Chem. Eng. J."}],"container-title":["Computers &amp; Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0305054826002029?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0305054826002029?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T11:36:57Z","timestamp":1784893017000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0305054826002029"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":43,"alternative-id":["S0305054826002029"],"URL":"https:\/\/doi.org\/10.1016\/j.cor.2026.107584","relation":{},"ISSN":["0305-0548"],"issn-type":[{"value":"0305-0548","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Risk-averse wind farms placement via quantile constraint learning","name":"articletitle","label":"Article Title"},{"value":"Computers & Operations Research","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cor.2026.107584","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"107584"}}