Data-Driven Voltage VAR Optimization Enabling Extreme Integration of Distributed Solar Energy
数据驱动的电压无功优化实现分布式太阳能的极致集成
基本信息
- 批准号:1929975
- 负责人:
- 金额:$ 34.7万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-08-15 至 2024-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The increasing penetration of solar energy poses significant challenges on the safe and reliable operation of power distribution systems. This project will leverage data-driven and machine learning techniques to address voltage fluctuations induced by volatile solar generation. It will significantly advance the state-of-the-art of voltage regulation, enable utility companies to address overall voltage issues, and ultimately support the large-scale solar integration in power distribution grids, thus providing higher-quality, more reliable and cleaner electricity to millions of customers across the United States. The incorporation of electrical engineering, data analytics, statistics, and optimization knowledge will foster the multidisciplinary education of graduate and undergraduate students, promote teaching and training of future workforce, and improve scientific and technological understanding through dissemination of findings to academia, industry, and the general public.Conventional voltage VAR optimization (VVO) algorithms, which are model-based, computationally intensive, offline and non-scalable, cannot meet the operation requirements of a modern power system. Important technical issues such as rapid changes of two-way power flows, coordination of new and legacy VVO devices, and lack of accurate system circuit models, will need to be resolved to accommodate a very high penetration level of solar energy. This project will develop a comprehensive data-driven VVO framework that leverages voluminous sensor and meter data to identify real-time system models, perform online prediction of nodal voltages, and orchestrate voltage control devices across different time scales to address severe voltage violations and fluctuations induced by reverse power flows and volatile renewable outputs. The new data-based VVO technique is distinguished from existing methods as it is exempt from the requirement of detailed circuit models, and can achieve high scalability and a significant speed-up of computation time without sacrificing the robustness and accuracy of VVO commands thanks to the linear superposition nature in the proposed modeling and optimization methods. The effectiveness and readily application of the developed techniques will be validated using practical distribution system models and operation data obtained from utility collaborators. The project will benefit from the PI's strong collaboration with utility companies to ensure a pathway for the successful implementation of the outcomes in the real world.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.
太阳能的日益普及对配电系统的安全可靠运行提出了重大挑战。该项目将利用数据驱动和机器学习技术来解决由不稳定的太阳能发电引起的电压波动。它将大大推进最先进的电压调节技术,使公用事业公司能够解决整体电压问题,并最终支持配电网中的大规模太阳能集成,从而为美国数百万客户提供更高质量,更可靠和更清洁的电力。电气工程、数据分析、统计和优化知识的结合将促进研究生和本科生的多学科教育,促进未来劳动力的教学和培训,并通过向学术界、工业界和公众传播研究成果来提高科学和技术的理解。传统的电压无功优化(VVO)算法,基于模型,计算密集,离线和不可扩展,不能满足现代电力系统的运行要求。重要的技术问题,如双向功率流的快速变化,新的和旧的VVO设备的协调,以及缺乏准确的系统电路模型,将需要解决,以适应非常高的太阳能渗透水平。该项目将开发一个全面的数据驱动的VVO框架,利用大量的传感器和仪表数据来识别实时系统模型,执行节点电压的在线预测,并在不同的时间尺度上协调电压控制设备,以解决由反向功率流和波动的可再生输出引起的严重电压违规和波动。新的基于数据的VVO技术是区别于现有的方法,因为它是免除详细的电路模型的要求,并可以实现高的可扩展性和显着的计算时间的加速,而不会牺牲VVO命令的鲁棒性和准确性由于线性叠加性质,在所提出的建模和优化方法。所开发的技术的有效性和易于应用将使用实际的配电系统模型和从公用事业合作者获得的操作数据进行验证。该项目将受益于PI与公用事业公司的密切合作,以确保在真实的世界中成功实施成果的途径。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。
项目成果
期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
An Online Feedback-Based Linearized Power Flow Model for Unbalanced Distribution Networks
不平衡配电网基于在线反馈的线性潮流模型
- DOI:10.1109/tpwrs.2021.3133257
- 发表时间:2022
- 期刊:
- 影响因子:6.6
- 作者:Cheng, Rui;Wang, Zhaoyu;Guo, Yifei
- 通讯作者:Guo, Yifei
Cooperative Peak Shaving and Voltage Regulation in Unbalanced Distribution Feeders
- DOI:10.1109/tpwrs.2021.3069781
- 发表时间:2021-11
- 期刊:
- 影响因子:6.6
- 作者:Yifei Guo;Qianzhi Zhang;Zhaoyu Wang
- 通讯作者:Yifei Guo;Qianzhi Zhang;Zhaoyu Wang
Outage Detection in Partially Observable Distribution Systems Using Smart Meters and Generative Adversarial Networks
- DOI:10.1109/tsg.2020.3008770
- 发表时间:2019-12
- 期刊:
- 影响因子:9.6
- 作者:Yuxuan Yuan;K. Dehghanpour;Fankun Bu;Zhaoyu Wang
- 通讯作者:Yuxuan Yuan;K. Dehghanpour;Fankun Bu;Zhaoyu Wang
A Data-Driven Customer Segmentation Strategy Based on Contribution to System Peak Demand
- DOI:10.1109/tpwrs.2020.2979943
- 发表时间:2018-10
- 期刊:
- 影响因子:6.6
- 作者:Yuxuan Yuan;K. Dehghanpour;Fankun Bu;Zhaoyu Wang
- 通讯作者:Yuxuan Yuan;K. Dehghanpour;Fankun Bu;Zhaoyu Wang
Parameter Reduction of Composite Load Model Using Active Subspace Method
利用主动子空间法对复合载荷模型进行参数约简
- DOI:10.1109/tpwrs.2021.3078671
- 发表时间:2021
- 期刊:
- 影响因子:6.6
- 作者:Ma, Zixiao;Cui, Bai;Wang, Zhaoyu;Zhao, Dongbo
- 通讯作者:Zhao, Dongbo
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Zhaoyu Wang其他文献
Atomically Thin, Ionic–Covalent Organic Nanosheets for Stable, High‐Performance Carbon Dioxide Electroreduction
原子薄的离子共价有机纳米片,用于稳定、高性能二氧化碳电还原
- DOI:
10.1002/adma.202110496 - 发表时间:
2022-08 - 期刊:
- 影响因子:29.4
- 作者:
Yun Song;Jun‐Jie Zhang;Yubing Dou;Zhaohua Zhu;Jianjun Su;Libei Huang;Weihua Guo;Xiaohu Cao;Le Cheng;Zonglong Zhu;Zhenhua Zhang;Xiaoyan Zhong;Dengtao Yang;Zhaoyu Wang;Ben Zhong Tang;Boris I. Yakobson;Ruquan Ye - 通讯作者:
Ruquan Ye
The effect and mechanism of closed double equal channel angular pressing deformation on He+ irradiation damage of low activation steel
闭式双等通道角挤压变形对低活化钢He辐照损伤的影响及机理
- DOI:
10.1016/j.fusengdes.2022.113358 - 发表时间:
2023-02 - 期刊:
- 影响因子:1.7
- 作者:
Ping Li;Jiren Dai;Lusheng Wang;Yufeng Zhou;Zhaoyu Wang;Kemin Xue - 通讯作者:
Kemin Xue
Experimental Study on Thermal Conductivity of Sand Solidified by Microbially Induced Calcium Carbonate Precipitation
微生物诱导碳酸钙沉淀固化砂导热系数实验研究
- DOI:
10.1088/1755-1315/304/5/052069 - 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Jinhua Ding;Zhaoyu Wang;N. Zhang;P. Jiang;M. Peng;Y. Jin;Qi Li - 通讯作者:
Qi Li
Distribution Network Outage Data Analysis and Repair Time Prediction Using Deep Learning
使用深度学习进行配电网停电数据分析和修复时间预测
- DOI:
10.1109/pmaps.2018.8440354 - 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
Anmar I. Arif;Zhaoyu Wang - 通讯作者:
Zhaoyu Wang
A Linear Solution Method of Generalized Robust Chance Constrained Real-Time Dispatch
广义鲁棒机会约束实时调度的线性求解方法
- DOI:
10.1109/tpwrs.2018.2865184 - 发表时间:
2018-01 - 期刊:
- 影响因子:6.6
- 作者:
Anping Zhou;Ming Yang;Zhaoyu Wang;Peng Li - 通讯作者:
Peng Li
Zhaoyu Wang的其他文献
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{{ truncateString('Zhaoyu Wang', 18)}}的其他基金
CAREER: Learning Smart Meter Data to Enhance Distribution Grid Modeling and Observability
职业:学习智能电表数据以增强配电网建模和可观测性
- 批准号:
2042314 - 财政年份:2021
- 资助金额:
$ 34.7万 - 项目类别:
Continuing Grant
EAGER: SSDIM: Simulated and Synthetic Data Generation for Interdependent Natural Gas and Electrical Power Systems Based on Graph Theory and Machine Learning
EAGER:SSDIM:基于图论和机器学习的相互依赖的天然气和电力系统的模拟和综合数据生成
- 批准号:
1745451 - 财政年份:2017
- 资助金额:
$ 34.7万 - 项目类别:
Standard Grant
Data-driven modeling, monitoring and mitigation of cascading outages in transmission and distribution systems
输配电系统级联停电的数据驱动建模、监控和缓解
- 批准号:
1609080 - 财政年份:2016
- 资助金额:
$ 34.7万 - 项目类别:
Standard Grant
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