.png){kind=icon}
返回
Using robust optimization to inform US deep decarbonization planning
.png){kind=icon}
OA
AI
DOI：10.1016/j.esr.2022.100892.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
US energy system development consistent with the Paris Agreement will depend in part on future fuel prices and technology costs, which are highly uncertain. Energy system optimization models (ESOMs) represent a critical tool to examine clean energy futures under different assumptions. While many approaches exist to examine future sensitivity and uncertainty in such models, most assume that uncertainty is resolved prior to the model run. Policy makers, however, must take action before uncertainty is resolved. Robust optimization represents a method that explicitly considers future uncertainty within a single model run, yielding a near-term hedging strategy that is robust to uncertainty. This work focuses on extending and applying robust optimization methods to Temoa, an open source ESOM, to derive insights about low carbon pathways in the United States. A robust strategy that explicitly considers future uncertainty has expected savings in total system cost of 12% and an 8% reduction in the standard deviation of expected costs relative to a strategy that ignores uncertainty. The robust technology deployment strategy also entails more diversified technology mixes across the energy sectors modeled.
Keyword:
Robust optimization
Energy system planning
Parametric uncertainty
Energy modeling
Monte Carlo analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.9
论文数:
2.0K
被引数:
9.3K
机构
引用论文
Strategies against shocks in power systems - An analysis for the case of Europe应对电力系统冲击的策略 -- 以欧洲为例的分析.png)
ENERGY ECONOMICS
ENERGY ECONOMICSIF14.2
PREAI
分享收藏Improving the representation of energy efficiency in an energy system optimization modelAPPLIED ENERGYOAAI
APPLIED ENERGYIF11
OAAI分享收藏分享收藏Modeling for insight using Tools for Energy Model Optimization and Analysis (Temoa)ENERGY ECONOMICS
ENERGY ECONOMICSIF14.2
PREAI
分享收藏
查看更多内容