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: Rochester Institute of Tech
• 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
• 起止时间: 2014-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1436469&HistoricalAwards=false
• 关键词: Understanding; Evolution; U.S.; Electricity; Grid

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.

管理电网的发展是一项重大的经济和环境挑战。美国的发电年收入超过3000亿美元，但占二氧化碳排放量的39%，二氧化硫的69%和汞排放量的50%以上。与其他基础设施一样，电网的寿命很长，这加剧了锁定效应：资本投资一旦投入，将持续数十年，并可能推迟采用上级新技术。本项目通过工程学和经济学建模，研究当前的电力基础设施和政策决策对未来电网结构的影响，了解基础设施决策、电力政策和技术进步之间的复杂关系。这项研究的结果将对电网规划者和政策制定者都有价值，他们都对美国电力系统做出重要的长期决策。这项研究模拟了美国未来的电网基础设施，电力需求的变化，不断发展的技术和政策干预的会计。国家电网在美国大陆分为10个地区，每个地区的扩建决策是通过在20年的时间范围内最大限度地减少服务总成本来做出的，受到政策要求（如可再生能源组合标准）以及满足当前和未来负荷的需要的约束。该项目的目标是了解不同电网建设的平准化电力成本、碳排放和公共投资等结果将如何在政策情景中发生变化。网格建模是一项复杂的任务，这项工作在两个方面改进了早期的工作。首先，改进的搜索方法（遗传算法）被用来共同优化最小成本和可再生能源组合标准的会议。其次，不确定性是模型的核心，考虑到电网建设如何取决于不确定的未来燃料价格，资本成本，补贴/税收政策和电力需求。