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.

管理电网的演变是一项关键的经济和环境挑战。美国发电的年收入超过3000亿美元，但占二氧化碳，二氧化硫69％的39％，占汞排放的50％以上。像其他基础设施一样，电网是长期的，这加剧了锁定效果：一旦进行，一旦进行，持续了数十年，并且可能会推迟采用优质新技术。该项目研究了当今的电力基础设施和政策决策将对电网的未来结构产生的影响，利用工程和经济建模来了解基础设施决策，电力政策和技术进步之间的复杂关系。这项研究的结果对于电网规划师和政策制定者来说都是有价值的，他们俩都对美国的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，这是电力需求，不断发展的技术和政策干预措施的变化。国家电网分为美国大陆的10个区域，每个地区的建设决定是通过最大程度地减少20年期限内服务成本的决定，受到政策要求的限制，例如可再生能源投资组合标准，并且需要满足当前和未来的负担。该项目的目的是了解电力，碳排放和公共投资等结果如何在政策方案之间发生变化。网格建模是一项复杂的任务，这项工作以两种方式改善了早期的努力。首先，改进的搜索方法（遗传算法）用于将最低成本和可再生投资组合标准的达到最低限制。其次，该模型的核心包括不确定性，这考虑了网格积聚如何取决于未确定的未来燃料价格，资本成本，补贴/税收政策和电力需求。