CAREER: Computation-efficient Resolution for Low-Carbon Grids with Renewables and Energy Storage

职业：可再生能源和能源存储低碳电网的计算高效解决方案

• 批准号: 2340095
• 负责人: Bing Yan
• 金额: $ 50万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340095&HistoricalAwards=false
• 关键词: CAREER; Computation; efficient; Resolution; Low

To realize the vision of carbon-free clean power systems, renewable energy and Energy Storage Resources (ESRs) play critical roles in reliable and resilient grid operation. This NSF CAREER project aims to develop novel modeling and optimization approaches for low-carbon grid operation with renewable energy and ESRs. The project will bring transformative change to enable power grid operators to leverage ESRs more efficiently for a sustainable and efficient energy future. This will be achieved by ESR modeling, novel formulation tightening techniques, and innovative optimization methods. The intellectual merits include novel ESR market participation models considering dynamic State-of-Charge (SOC) limits and renewable uncertainties; an innovative machine learning-based formulation tightening approach to improve computational efficiency; and an Ordinal Optimization-based optimization approach for exponential complexity reduction to efficiently manage a large number of ESRs in grid operations. The broader impacts of the project include the development of a training module for Independent System Operators (ISO), Regional Transmission Operators (RTO), and software developers, and a training module for graduate and undergraduate students, focusing on engaging women and underrepresented students at an early stage in STEM disciplines; and broader outreach activities to K-12 students.The project addresses several technical challenges in low-carbon grid operation with renewable energy and ESRs including ESR market participant models, inconsistency between day-ahead scheduling and real-time dispatch, and computational difficulty caused by unique features of ESRs such as bidirectional discharge and charge operations and time-coupling SOC. The technical components of the project include the establishment of various ESR participant models from self-scheduling to being fully managed by ISOs/RTOs considering dynamic SOC limits; development of a novel convex hull-oriented deep learning-based formulation tightening approach for computational benefits; and an Ordinal Optimization-based optimization approach for exponential complexity reduction to efficiently solve grid operation problems with a large number of ESRs. The resulting models and methods with plug-and-play capabilities can be integrated into ISOs/TROs’ existing platforms developed by vendors for efficient utilization of ESRs, leading to economic and environmental benefits. The results of the project will also facilitate education and outreach activities related to ESRs for a sustainable and efficient energy future.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为了实现无碳清洁电力系统的愿景，可再生能源和储能资源 (ESR) 在可靠和有弹性的电网运行中发挥着关键作用，该 NSF 职业项目旨在开发用于低碳电网运行的新型建模和优化方法。该项目将带来变革，使电网运营商能够更有效地利用 ESR，实现可持续和高效的能源未来，这将通过 ESR 建模、新颖的配方紧缩技术和创新的优化方法来实现。包括新颖的 ESR考虑动态充电状态（SOC）限制和可再生能源不确定性的市场参与模型；基于机器学习的创新公式紧缩方法，以提高计算效率；以及基于序数优化的优化方法，以降低指数复杂性，以有效管理大量数据；该项目更广泛的影响包括为独立系统运营商（ISO）、区域输电运营商（RTO）和软件开发人员开发培训模块，以及为研究生和本科生提供培训模块，重点关注吸引女性和该项目解决了可再生能源和 ESR 低碳电网运营中的若干技术挑战，包括 ESR 市场参与者模型、日前调度与实时调度以及ESR的独特功能（例如双向放电和充电操作以及时间耦合SOC）带来的计算困难该项目的技术参与部分包括从自调度建立各种ESR模型。考虑到动态 SOC 限制，由 ISO/RTO 全面管理；开发一种新颖的基于凸包的深度学习公式紧缩方法，以实现计算效益；以及基于序数优化的优化方法，以降低指数复杂度，从而有效解决电网运行问题；所产生的具有即插即用功能的模型和方法可以集成到供应商开发的 ISO/TRO 现有平台中，以有效利用 ESR，从而产生经济和环境效益。还将促进与 ESR 相关的教育和外展活动，以实现可持续和高效的能源未来。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。