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.

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。电动汽车（EV）普及率的增长为公用事业和电力系统创造了一个及时的机会，可以增加收入并实现可持续的负荷增长。然而，不受控制的电动汽车与电网的融合，特别是在微电网等规模较小的电力系统中，带来了显著的问题，如潮流波动和不可接受的负荷峰值，降低了电网的可靠性和电能质量。在这个项目中，我们开发了一个框架，利用电动汽车的灵活能源容量为微电网提供服务。这种方法提高了电动汽车车主的参与度，并将提高车辆与微电网（V2M）连接的所有股东的社会福利，包括电动汽车车主、智能充电站（ics）和微电网。考虑到在微电网中管理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的理解，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、推广和多样性的广泛而新颖的贡献；因此，该项目与美国国家科学基金会促进科学进步和促进繁荣与福利的使命是一致的。该项目的总体目标是为V2M集成开发一个（接近）实时的低计算成本框架。本研究结合负荷预测和博弈论的概念，推进V2M技术，根据两者的需求，在微电网中利用电动汽车的灵活能量容量。由于各种分布式能源的耦合导致微电网负荷曲线波动较大，传统的聚类和预测方法不能直接应用于微电网。该项目的技术贡献有三个方面：1)设计了一种创新的机器学习算法，用于微电网的短期负荷预测，该算法由鲁棒数据预处理、特征提取和选择算法组成，然后是加权高级长短期记忆（WA-LSTM）模型。选取的特征相关度高、冗余度最小，大大降低了所提出的WA-LSTM预测负荷分布的计算成本；2)开发一种合作博弈，捕捉电动汽车及其相应的ics之间的相互作用，在考虑车辆参数和约束的情况下，勾勒出电动汽车的充电轮廓。在满足微电网需求为全局目标的前提下，提出了一种约束放宽的纳什议价博弈和一种新的惩罚分配策略；3)利用硬件在环（HIL）装置在实验测试台上评估所提出的方法，以提供分析和实验证据来证明所提出解决方案的有效性。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。