4.8 Review

Cost of power or power of cost: A US modeling perspective

期刊

RENEWABLE & SUSTAINABLE ENERGY REVIEWS
卷 77, 期 -, 页码 861-874

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2017.04.055

关键词

Integrated assessment model; Cost of power plants; Technology cost; Electricity Sector Modeling; Global Change Assessment Model (GCAM)

资金

  1. ExxonMobil Research and Engineering Company
  2. DOE by Battelle Memorial Institute [DE-AC05-76RL01830]

向作者/读者索取更多资源

Future scenarios and assessment studies used to prepare for long-term energy transitions and develop robust strategies to address climate change are highly dependent on the assessment of technology characteristics and availability. The electric power sector in the United States recently experienced a significant cost escalation: e.g., construction costs for large plants such as nuclear and coal-fired power plants doubled between 2003 and 2007. We assess the main drivers of this escalation. While many factors have affected costs, some of the most significant include cost of materials, particularly the price of metals and to a lesser extent cement, possible increases in labor quantity requirements, the aggressive worldwide competition for power plant design and construction resources, driven by high demand in Asia, market and regulatory changes, and general uncertainty about future regulations and climate policies. We recalibrate power sector technology costs in the Global Change Assessment Model (GCAM) based on an extensive literature review of recent (post-2010) studies and in the process develop a coherent and updated set of current cost and performance assumptions for all major electricity-generating technologies. While current cost and performance assumptions of electricity-generating technologies are key drivers of short-term technology deployment and technology mix in the electricity sector, medium- and long-term deployment pathways are significantly affected by assumed efficiency improvement rates and cost reductions. We develop and demonstrate a method to project efficiency and construction cost of power plants and report a sensitivity analysis to explore the importance of such assumptions in future scenarios generated by GCAM.

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