4.7 Article

High-resolution modeling of the western North American power system demonstrates low-cost and low-carbon futures

期刊

ENERGY POLICY
卷 43, 期 -, 页码 436-447

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.enpol.2012.01.031

关键词

Energy modeling; Renewable energy; Carbon emissions

资金

  1. EPA STAR [FP916698]
  2. NSF DDRI [0602884]
  3. NextEra Energy Resources
  4. Karsten Family Foundation
  5. Vestas Wind LLC
  6. UC Berkeley Class
  7. CPV Consortium
  8. NSF
  9. Berkeley Nerds Fellowship
  10. Direct For Social, Behav & Economic Scie
  11. Division Of Behavioral and Cognitive Sci [0602884] Funding Source: National Science Foundation

向作者/读者索取更多资源

Decarbonizing electricity production is central to reducing greenhouse gas emissions. Exploiting intermittent renewable energy resources demands power system planning models with high temporal and spatial resolution. We use a mixed-integer linear programming model - SWITCH - to analyze least-cost generation, storage, and transmission capacity expansion for western North America under various policy and cost scenarios. Current renewable portfolio standards are shown to be insufficient to meet emission reduction targets by 2030 without new policy. With stronger carbon policy consistent with a 450 ppm climate stabilization scenario, power sector emissions can be reduced to 54% of 1990 levels by 2030 using different portfolios of existing generation technologies. Under a range of resource cost scenarios, most coal power plants would be replaced by solar, wind, gas, and/or nuclear generation, with intermittent renewable sources providing at least 17% and as much as 29% of total power by 2030. The carbon price to induce these deep carbon emission reductions is high, but, assuming carbon price revenues are reinvested in the power sector, the cost of power is found to increase by at most 20% relative to business-as-usual projections. (C) 2012 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据