CRII: OAC: A (near) Real-time Framework for Smart Integration of Electric Vehicles to Microgrids

CRII：OAC：电动汽车与微电网智能集成的（近）实时框架

• 批准号: 2153438
• 负责人: Zoleikha Biron
• 金额: $ 17.5万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153438&HistoricalAwards=false
• 关键词: CRII; OAC; near; Real; time

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The growth of electric vehicle (EV) adoption creates a timely opportunity for utilities and power systems to boost revenue and build sustainable load growth. However, uncontrolled EV to power grid integration, particularly in smaller-scale power systems such as microgrids, brings significant problems, e.g., power flow fluctuation and unacceptable load peaks reducing power network reliability and power quality. In this project we develop a framework to use the flexible energy capacity of EVs to provide services for microgrids. This method increases EV owners’ engagements and will enhance social welfare for all shareholders of the vehicle to microgrid (V2M) connection including EV owners, intelligent charging stations (ICSs), and microgrids. Considering the vast amount of data required for managing a large number of EVs in a microgrid, the project will employ advanced machine learning techniques and analytical approaches to reduce the computational complexity of the problem. The proposed framework will provide an understanding of the characteristics and requirements for future cyberinfrastructure design, particularly in integrated large-scale ecosystems. This work represents a broad, novel contribution to literature, education, outreach, and diversity; as such, the project aligns with NSF’s mission to promote the progress of science and to advance prosperity and welfare. The overall aim of the project is to develop a (near) real-time low computational cost framework for V2M integration. The research incorporates concepts of load prediction and game theory advancing V2M technology to use the flexible energy capacity of EVs in microgrids according to the needs of both. Traditional clustering and forecasting methods cannot be directly applied to microgrids due to their high volatility load profile caused by coupling various distributed energy resources. The technical contribution of the project is threefold: 1) to design an innovative machine learning-enabled algorithm for short-term load forecasting in microgrids that consists of robust data preprocessing, feature extraction, and a selection algorithm, followed by a weighted advanced long short-term memory (WA-LSTM) model. The selected features have high relevance and minimum redundancy reducing the computational cost significantly for the proposed WA-LSTM to predict the load profile; 2) to develop a cooperative game to capture interactions among EVs and their corresponding ICSs that outlines (dis)charging profiles for EVs considering vehicle parameters and constraints. A Nash bargaining game with relaxed constraints and a new penalty distribution policy is proposed to maximize the profits of all players in the coalition while satisfying the microgrids' requirements as the global goal; 3) to evaluate the proposed methods in experimental testbeds using hardware in the loop (HIL) setups to provide both analytic and experimental evidence to demonstrate the effectiveness of the proposed solutions.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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。电动汽车采用（EV）的增长为公用事业和电力系统提供了及时的机会，以提高收入并建立可持续的负载增长。但是，不受控制的电动电动机与电网集成，特别是在诸如微电网等较小规模的电力系统中，带来了重大问题，例如，功率流波动和不可接受的负载峰会降低电力网络的可靠性和功率质量。在这个项目中，我们开发了一个框架，以使用电动汽车的灵活能源能力为微电网提供服务。这种方法增加了电动汽车所有者的交战，并将增强车辆所有股东的社会福利（V2M）连接，包括电动汽车所有者，智能充电站（ICS）和微电网。考虑到管理微电网中大量电动汽车所需的大量数据，该项目将采用先进的机器学习技术和分析方法来降低问题的计算复杂性。拟议的框架将提供对未来网络基础设施设计的特征和要求，特别是在集成的大规模生态系统中。这项工作代表了对文学，教育，外展和多样性的广泛，新颖的贡献。因此，该项目符合NSF促进科学进步并促进繁荣和福利的使命。该项目的总体目的是为V2M集成开发一个（近）实时的低计算成本框架。该研究结合了负载预测和游戏理论的概念，推进V2M技术，根据两者的需求在微电网中使用电动汽车的柔性能量。传统的聚类和预测方法不能直接应用于微电网，因为它们的高波动率负载曲线是由各种分布式能源耦合而引起的。该项目的技术贡献是三倍：1）设计一种创新的机器学习算法，用于在微电网中进行短期负载预测，其中包括强大的数据预处理，功能提取和选择算法，然后使用加权的高级短期内存（WA-LSTM）模型。所选功能具有很高的相关性和最小冗余，可显着降低所提出的WA-LSTM的计算成本，以预测负载曲线； 2）开发一个协调的游戏，以捕获电动汽车之间的交互及其相应的ICS，这些ICS概述了考虑使用车辆参数和约束的电动汽车的（DIS）充电配置文件。提出了一个带有轻松限制和新的罚款分配政策的NASH谈判游戏，以最大程度地提高联盟中所有参与者的利润，同时满足微电网的要求作为全球目标； 3）用循环（HIL）设置中的硬件在实验测试台上评估所提出的方法，以提供分析性和实验证据，以证明拟议解决方案的有效性。本奖奖反映了NSF的法定任务，并被认为是通过评估基金会的知识和更广泛的影响来通过评估来获得支持的珍贵的，这是珍贵的。