Efficient and Scalable Methods for Multi-Stage Transmission Expansion under Uncertainty

不确定性下多级传输扩展的高效且可扩展的方法

• 批准号: 1710974
• 负责人: Mort Webster
• 金额: $ 31.47万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1710974&HistoricalAwards=false
• 关键词: Efficient; Scalable; Methods; Multi; Stage

A critical component of the electric power system is the underlying network of high voltage transmission lines that connect geographically dispersed generation and load. The transmission network achieves two important objectives: reducing the cost of energy by providing access to low-cost generation and maintaining reliability by enabling many alternative generation sources and transmission routes to serve load centers. Although utilities, and more recently regional transmission operators (RTOs), have long engaged in transmission planning, the current context requires planning for larger regions and over longer time horizons. The uncertainties to which any new transmission lines should be robust to changes in include the types and locations of generation as well as variations in the spatial and temporal distribution of load, both driven by rapid changes in technologies, market factors, and regulations. The potential sunk cost of transmission lines that do not anticipate future conditions, as well as the expected benefit of properly located transmission additions, can be valued in the millions of dollars. The transmission planning tools commonly used by utilities and RTOs are well suited for near-term tactical planning when uncertainties are manageable, but are not appropriate to long-term planning when the range of possible future conditions becomes large. This project develops new methods for transmission planning over several decades and across a wide range of possible futures.Specifically, the research team will develop two alternative algorithms for solving multi-stage stochastic transmission expansion problems: (i) Multi-stage schemes for smoothed nonconvex problems, and (ii) Monte-Carlo and Importance Sampling-based Q-Learning schemes. In the context of both schemes, convergence properties will be analyzed and error bounds will be developed. Furthermore, both sets of schemes will be implemented within a high performance computing environment (such as a network of computing nodes) with an emphasis on asynchronous implementations. As part of the project, the team will collaborate with the planning group at PJM Interconnection and apply the methods developed to their network, consisting of approximately 16,000 buses. The application of stochastic analysis will help to identify long-term congestion issues that should be anticipated, and provide an initial list of candidate lines that would be robust to the large set of future scenarios. More broadly, the development and adoption of these methods will enable planners for regional power systems across the nation to identify crucial additions to the infrastructure that can reduce energy costs, maintain reliability of energy supply, and enable and enhance the ability of new generation technologies to reduce environmental impacts. The project will also make contributions to education at the graduate and undergraduate levels. Finally, this project will occur within the Penn State Initiative for Sustainable Electric Power Systems, which organizes workshops and collaborations with the power industry and other academic institutions.

电力系统的一个关键组成部分是连接分散在不同地理位置的发电和负荷的高压输电线路的底层网络。输电网络实现了两个重要目标：通过提供获得低成本发电的途径来降低能源成本，以及通过使许多替代发电源和输电线路能够服务于负荷中心来保持可靠性。虽然公用事业公司和最近的地区性输电运营商(RTO)长期从事输电规划，但当前的背景要求规划更大的区域和更长的时间范围。任何新的输电线路都应该对变化保持稳健，这些不确定性包括发电的类型和位置以及负荷的时空分布的变化，这两者都是由技术、市场因素和法规的快速变化驱动的。无法预测未来状况的输电线路的潜在沉没成本，以及正确定位的输电附加设施的预期收益，可能价值数百万美元。当不确定性可控时，公用事业公司和RTO通常使用的输电规划工具非常适合于近期战术规划，但当未来可能出现的情况范围变得很大时，则不适合于长期规划。研究团队将开发两种可供选择的算法来解决多阶段随机输电扩展问题：(I)多阶段平滑非凸问题的多阶段算法；(Ii)基于蒙特卡罗和重要性抽样的Q-学习算法。在这两种方案的背景下，将分析收敛特性并得出误差界。此外，这两组方案都将在高性能计算环境(如计算节点网络)中实现，重点是异步实现。作为该项目的一部分，该团队将与PJM互联的规划小组合作，并将开发的方法应用于他们的网络，该网络由大约16,000辆公交车组成。随机分析的应用将有助于识别应预见的长期拥堵问题，并提供一份候选线路的初始列表，这些候选线路将对未来的大量情况保持稳健。更广泛地说，这些方法的开发和采用将使全国区域电力系统的规划者能够确定基础设施的关键补充，以降低能源成本，保持能源供应的可靠性，并启用和增强新一代技术减少环境影响的能力。该项目还将为研究生和本科生的教育做出贡献。最后，该项目将在宾夕法尼亚州立大学可持续电力系统倡议范围内开展，该倡议组织研讨会并与电力行业和其他学术机构合作。

项目成果

Approximate Latent Factor Algorithm for Scenario Selection and Weighting in Transmission Expansion Planning

输电扩容规划场景选择和加权的近似潜因子算法

• DOI: 10.1109/tpwrs.2019.2942925
• 发表时间: 2020
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Bukenberger, Jesse P.;Webster, Mort D.
• 通讯作者: Webster, Mort D.