Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization
Author:
Funder
National Science Foundation
Publisher
Elsevier BV
Subject
Renewable Energy, Sustainability and the Environment
Reference66 articles.
1. 20% wind energy by 2030. Increasing wind energy's contribution to U. S. electricity supply,2008
2. Technical challenges associated with the integration of wind power into power systems;Georgilakis;Renew Sustain Energy Rev,2008
3. Recent approaches of unit commitment in the presence of intermittent renewable energy resources: a review;Abujarad;Renew Sustain Energy Rev,2017
4. Toward carbon-neutral electric power systems in the New York state: a novel multi-scale bottom-up optimization framework coupled with machine learning for capacity planning at hourly resolution;Zhao;ACS Sustain Chem Eng,2022
5. Can renewable generation, energy storage and energy efficient technologies enable carbon neutral energy transition?;Zhao;Applied Energy,2020
Cited by 37 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Sustainable energy management and control for Decarbonization of complex multi-zone buildings with renewable solar and geothermal energies using machine learning, robust optimization, and predictive control;Applied Energy;2024-10
2. An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems;International Journal of Electrical Power & Energy Systems;2024-09
3. A risk assessment approach for road collapse along tunnels based on an improved entropy weight method and K-means cluster algorithm;Ain Shams Engineering Journal;2024-07
4. Time-dependent photovoltaic performance assessment on a global scale using artificial neural networks;Sustainable Energy, Grids and Networks;2024-06
5. A computational sustainable approach for energy storage systems performance evaluation based on spherical-fuzzy MCDM with considering uncertainty;Energy Reports;2024-06
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3