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Understanding the Evolution of the U.S. Electricity Grid Taking into Account Uncertainty for Improved Management of Costs and Environmental Impacts

Understanding the Evolution of the U.S. Electricity Grid Taking into Account Uncertainty for Improved Management of Costs and Environmental Impacts
了解美国电网的演变,考虑到改进成本和环境影响的管理的不确定性
批准号:
1436469
负责人:
Eric Williams
金额:
$30.97万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-09-01 至 2021-08-31

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中文摘要
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英文摘要
Managing the evolution of the electricity grid is a critical economic and environmental challenge. Electricity generation in the U.S. produces more than $300 billion in annual revenue, but accounts for 39 percent of carbon dioxide, 69 percent of sulfur dioxide, and over 50 percent of mercury emissions. Like other infrastructures, the electric grid is long-lived which exacerbates lock-in effects: capital investments, once made, last for decades and may delay the adoption of superior new technologies. This project examines the effects that today's electricity infrastructure and policy decisions will have on the future structure of the electricity grid, using engineering and economic modeling to understand the complex relationships between infrastructure decisions, electricity policies, and technological progress. The results of this research will be valuable to both electricity grid planners and policy makers, both of whom make important long-term decisions about the U.S. electricity system. This research simulates the future grid infrastructure in the U.S., accounting for changes in electricity demand, evolving technologies, and policy interventions. The national grid is divided into 10 regions in the continental U.S., with build-out decisions in each region made by minimizing total cost of service over a 20-year horizon, constrained by policy requirements such as the Renewable Portfolio Standards and the need to meet current and future load. The goal of the project is to understand how outcomes such as levelized cost of electricity, carbon emissions, and public investments for different grid build-outs will change across policy scenarios. Grid modeling is a complex task and this work improves upon earlier efforts in two ways. First, improved search methods (genetic algorithms) are used to co-optimize minimal cost and the meeting of Renewable Portfolio Standards. Second, uncertainty is included at the core of the model, accounting for how grid build-out depends on uncertain future fuel prices, capital costs, subsidy/tax policy, and electricity demand.
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国内基金
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