Duan Lei, Caldeira Ken
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA.
Orca Sciences LLC, Kirkland, WA 98033, USA.
iScience. 2023 Dec 7;27(1):108685. doi: 10.1016/j.isci.2023.108685. eCollection 2024 Jan 19.
Plans for decarbonized electricity systems rely on projections of highly uncertain future technology costs. We use a stylized model to investigate the influence of future cost uncertainty, as represented by different projections in the National Renewable Energy Laboratory 2021 Annual Technology Baseline dataset, on technology mixes comprising least-cost decarbonized electricity systems. Our analysis shows that given the level of future cost uncertainty as represented by these projections, it is not possible to predict with confidence which technologies will play a dominant role in future least-cost carbon emission-free energy systems. Successful efforts to reduce costs of individual technologies may or may not lead to system cost reductions and widespread deployments, depending on the success of cost-reduction efforts for competing and complementary technologies. These results suggest a portfolio approach to reducing technology costs. Reliance on uncertain cost breakthroughs risks costly outcomes. Iterative decision-making with learning can help mitigate these risks.
脱碳电力系统的规划依赖于对未来技术成本高度不确定的预测。我们使用一个简化模型来研究未来成本不确定性（由美国国家可再生能源实验室2021年年度技术基线数据集中的不同预测表示）对由成本最低的脱碳电力系统组成的技术组合的影响。我们的分析表明，鉴于这些预测所代表的未来成本不确定性水平，无法自信地预测哪些技术将在未来成本最低的无碳排放能源系统中发挥主导作用。降低单个技术成本的成功努力可能会也可能不会导致系统成本降低和广泛部署，这取决于竞争和互补技术成本降低努力的成功情况。这些结果表明了一种降低技术成本的组合方法。依赖不确定的成本突破存在产生高昂成本结果的风险。通过学习进行迭代决策有助于减轻这些风险。
