SBIR Phase I: Agile Model Reduction for Topology Optimization Software

SBIR 第一阶段：拓扑优化软件的敏捷模型缩减

• 批准号: 1648071
• 负责人: Stanton Cady
• 金额: $ 22.5万
• 依托单位: NewGrid, Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1648071&HistoricalAwards=false
• 关键词: SBIR; Phase; Agile; Model; Reduction

The broader impact/commercial potential of this project is a substantial increase in the efficiency and reliability of the power grid, which would result in lower electricity rates to consumers and facilitate the integration of renewable energy into the grid. This project will enable the use of topology optimization in the operation of the power transmission grid and allow operators to adapt the grid configuration to address changes in system conditions in real time. These adaptive reconfigurations increase the ability of the system to transfer power across the network in the directions that matter for economic, reliability, or environmental reasons. The economic benefits of topology optimization represent a 50% reduction in the cost of grid congestion, which translates to $1-4 billion/year production cost savings in the US. In addition, topology optimization would consistently reduce, or entirely remove, the otherwise frequent overloads on transmission facilities, thereby increasing the reliability of the grid. Topology optimization would also facilitate grid operations with large amounts of variable renewable resources, such as wind and solar, by relieving their curtailment by about 40%. Given the increase in renewable energy in the generation mix, topology optimization is expected to become even more effective and important in the future. This Small Business Innovation Research (SBIR) Phase I project will develop fast and adaptive power system model reduction software fully integrated with topology optimization software. The technical challenge arises from the fact that finding a power flow solution for a large full nodal model of a power system requires the model to be reduced to an equivalent smaller model, to avoid numerical issues with the full model. For typical analyses, this reduction only needs to be performed once; however, for topology optimization, in the course of finding a good topology configuration, hundreds of different topologies need to be analyzed. Thus, the main technical hurdle preventing topology optimization from being used in online operations decision-support is the current state-of-the-art model reduction computation speed. As such, this project?s objective is to lower the computation time of the model reduction component by at least 10 times compared to existing capabilities in commercial software, when used as part of topology optimization routines. Model reduction calculations will be designed to take advantage of problem-specific attributes of the topology optimization routine, which will enable reduced computation time, by, for example, making use of partial recalculations and decomposition of the problem to support parallelization.

该项目更广泛的影响/商业潜力是大幅提高电网的效率和可靠性，从而降低消费者的电费，并促进可再生能源融入电网。该项目将在输电网的运行中使用拓扑优化，并允许运营商调整电网配置，以应对真实的系统条件的变化。这些自适应重新配置提高了系统在经济、可靠性或环境原因方面重要的方向上跨网络传输功率的能力。拓扑优化的经济效益表现为电网拥塞成本降低50%，这意味着美国每年可节省10 - 40亿美元的生产成本。此外，拓扑优化将持续减少或完全消除输电设施上的频繁过载，从而提高电网的可靠性。拓扑优化还将通过减少约40%的限电来促进大量可变可再生资源（如风能和太阳能）的电网运营。鉴于可再生能源在发电组合中的增加，预计拓扑优化在未来将变得更加有效和重要。该小型企业创新研究（SBIR）第一阶段项目将开发快速和自适应的电力系统模型简化软件，并与拓扑优化软件完全集成。技术挑战来自于这样的事实，即为电力系统的大型全节点模型找到潮流解需要将模型简化为等效的较小模型，以避免全模型的数值问题。对于典型的分析，这种简化只需要执行一次;然而，对于拓扑优化，在寻找良好的拓扑配置的过程中，需要分析数百种不同的拓扑。因此，主要的技术障碍，防止拓扑优化被用于在线操作决策支持是当前最先进的模型简化计算速度。因此，该项目？的目标是降低模型简化组件的计算时间至少10倍相比，现有的商业软件的能力，当用作拓扑优化例程的一部分。模型简化计算将被设计为利用拓扑优化例程的问题特定属性，这将能够减少计算时间，例如，通过利用部分重新计算和问题分解来支持并行化。