Data-Driven Voltage VAR Optimization Enabling Extreme Integration of Distributed Solar Energy

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。它将显著推进电压调节技术的发展，使公用事业公司能够解决整体电压问题，并最终支持配电网络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。电子工程、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、计算量大、离线且不可扩展，已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，新的和传统的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，以解决由反向潮流和不稳定的可再生输出引起的严重电压违规和波动。基于数据的VVO技术与现有方法的不同之处在于，它不需要详细的电路模型，并且由于所提出的建模和优化方法中的线性叠加特性，在不牺牲VVO命令的鲁棒性和准确性的情况下，可以实现高可扩展性和显著的计算时间加快。将通过实际的配电系统模型和从公用事业合作者处获得的运行数据来验证所开发技术的有效性和易于应用。该项目将受益于PI与公用事业公司的紧密合作，以确保在现实世界中成功实施成果的途径。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.