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
基本信息
- 批准号:2340095
- 负责人:
- 金额:$ 50万
- 依托单位:
- 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:
- 关键词:
项目摘要
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(ESRS)在可靠和有弹性的电网运行中发挥着关键作用。该NSF职业项目旨在为使用可再生能源和ESRS的低碳电网运营开发新的建模和优化方法。该项目将带来变革性的变化,使电网运营商能够更有效地利用ESRS,实现可持续和高效的能源未来。这将通过ESR建模、新的配方紧凑技术和创新的优化方法来实现。智能优势包括考虑动态荷电状态(SOC)限制和可再生不确定性的新颖ESR市场参与模型;基于机器学习的创新公式紧凑方法以提高计算效率;以及基于有序优化的指数复杂性降低优化方法以有效管理电网运营中的大量ESR。该项目的更广泛影响包括为独立系统操作员(ISO)、区域传输操作员(RTO)和软件开发人员开发培训模块,以及为研究生和本科生开发培训模块,重点是在STEM学科的早期阶段吸引女性和代表性不足的学生;以及更广泛的面向K-12学生的外联活动。该项目解决了使用可再生能源和ESRS运行低碳电网的几个技术挑战,包括ESR市场参与者模型、提前调度和实时调度之间的不一致,以及ESRS独特功能(如双向放电和充电操作以及时间耦合SOC)造成的计算困难。该项目的技术部分包括建立从自我调度到完全由考虑动态SOC限制的ISO/RTO管理的各种ESR参与者模型;开发一种新的面向凸壳的基于深度学习的公式紧凑方法以获得计算效益;以及一种基于有序优化的指数复杂性降低的优化方法,以有效解决具有大量ESR的电网运行问题。由此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的现有平台中,以高效利用ESRS,从而带来经济和环境效益。该项目的成果还将促进与ESRS相关的教育和外联活动,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,并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Bing Yan其他文献
Remote Induction of Cell Autophagy by 2D MoS Nanosheets via Perturbing Cell Surface Receptors and mTOR Pathway from Outside of Cells
2D MoS2 纳米片通过扰动细胞表面受体和细胞外 mTOR 通路远程诱导细胞自噬
- DOI:
- 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Xiaofei Zhou;Jianbo Jia;Zhen Luo;Gaoxing Su;Tongtao Yue;Bing Yan - 通讯作者:
Bing Yan
Electrospinning synthesis of porous boron-doped silica nanofibers for oxidative dehydrogenation of light alkanes
静电纺丝合成多孔掺硼二氧化硅纳米纤维用于轻质烷烃氧化脱氢
- DOI:
10.1016/s1872-2067(21)63809-3 - 发表时间:
2021-10 - 期刊:
- 影响因子:16.5
- 作者:
Bing Yan;An-Hui Lu;Jian Sheng;Wen-Cui Li;Ding Ding;An-Hui Lu - 通讯作者:
An-Hui Lu
Differences in tumour characteristics of Hepatocellular Carcinoma between patients with and without Cirrhosis: A population-based study
有肝硬化和无肝硬化患者之间肝细胞癌肿瘤特征的差异:一项基于人群的研究
- DOI:
10.7150/jca.46927 - 发表时间:
2020 - 期刊:
- 影响因子:3.9
- 作者:
Bing Yan;Dou;Jian;Chi Zhang;Sheng;Xuehao Wang;Guoqing Jiang - 通讯作者:
Guoqing Jiang
Induction of inflammatory responses in human bronchial epithelial cells by Pb2+-containing model PM2.5 particles via downregulation of a novel long non-coding RNA lnc-PCK1-2:1
含 Pb2 模型 PM2.5 颗粒通过下调新型长非编码 RNA lnc-PCK1-2:1 诱导人支气管上皮细胞炎症反应
- DOI:
- 发表时间:
2019 - 期刊:
- 影响因子:11.4
- 作者:
Xiujiao Pan;Xiaoru Yuan;Xin Li;Sulian Gao;Hainan Sun;Hainan Sun;Lujian Hou;Xiaowu Peng;Yiguo Jiang;Bing Yan - 通讯作者:
Bing Yan
Point-of-interest recommendation model considering strength of user relationship for location-based social networks
基于位置的社交网络考虑用户关系强度的兴趣点推荐模型
- DOI:
10.1016/j.eswa.2022.117147 - 发表时间:
2022-04 - 期刊:
- 影响因子:8.5
- 作者:
Yuhe Zhou;Guangfei Yang;Bing Yan;Yuanfeng Cai;Zhiguo Zhu - 通讯作者:
Zhiguo Zhu
Bing Yan的其他文献
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