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

电力系统的一个关键组成部分是连接地理上分散的发电和负荷的高压输电线路的基础网络。 输电网络实现了两个重要目标：通过提供低成本发电来降低能源成本，并通过使许多替代发电源和输电线路为负荷中心服务来保持可靠性。 虽然公用事业公司，以及最近的区域输电运营商（RTO），长期从事输电规划，目前的情况下，需要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具有鲁棒性的不确定性，包括发电的类型和位置以及负荷的空间和时间分布的变化，这两者都是由技术，市场因素和法规的快速变化驱动的。 没有预见到未来条件的输电线路的潜在沉没成本，以及适当定位的输电增加的预期收益，可以以数百万美元计。 当不确定性可控时，公用事业和RTO常用的输电规划工具非常适合于短期战术规划，但当未来可能条件的范围变大时，不适合于长期规划。 该项目开发了未来几十年输电规划的新方法。具体而言，研究团队将开发两种解决多阶段随机输电扩展问题的替代算法：（i）平滑非凸问题的多阶段方案，以及（ii）基于蒙特-卡罗和重要性抽样的Q学习方案。在这两种方案的背景下，收敛性能进行了分析，并将开发的误差范围。此外，这两组方案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 Interconnection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确定应预期的长期拥堵问题，并提供一个候选线路的初始列表，该列表将对大量未来场景具有鲁棒性。 更广泛地说，这些方法的开发和采用将使全国各地的区域电力系统规划者能够确定基础设施的重要补充，这些基础设施可以降低能源成本，保持能源供应的可靠性，并使新一代技术能够减少环境影响。 该项目还将对研究生和本科生教育作出贡献。 最后，该项目将在宾州州立大学可持续电力系统倡议范围内进行，该倡议组织研讨会，并与电力行业和其他学术机构合作。

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.