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

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）采用的增长为公用事业和电力系统创造了及时的机会，以增加收入并建立可持续的负荷增长。然而，不受控制的EV与电网集成，特别是在诸如微电网的较小规模的电力系统中，带来了重大问题，例如，电力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。这种方法增加了电动汽车车主的参与度，并将提高车辆与微电网（V2 M）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。拟议的框架将使人们了解未来网络基础设施设计的特点和要求，特别是在综合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的使命，以促进科学的进步和促进繁荣和福利。该项目的总体目标是为V2 M集成开发一个（近）实时低计算成本框架。该研究结合了负载预测和博弈论的概念，推进了V2 M技术，根据双方的需求在微电网中使用电动汽车的灵活能源容量。由于微电网中各种分布式能源的耦合导致其负荷曲线波动性大，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）设计一种创新的机器学习算法，用于微电网的短期负荷预测，包括强大的数据预处理，特征提取和选择算法，然后是加权高级长短期记忆（WA-LSTM）模型。所选特征具有高相关性和最小冗余性，显著降低了所提出的WA-LSTM预测负载分布的计算成本; 2）开发合作游戏以捕获EV及其相应ICS之间的交互，该ICS考虑车辆参数和约束条件概述EV的充电分布。以满足微电网需求为全局目标，提出了一种新的惩罚分配策略和放松约束条件的Nash讨价还价博弈模型，使联盟中所有参与者的收益最大化; 3）使用硬件在环（HIL）在实验测试平台中评估所提出的方法该奖项反映了NSF的法定使命，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。

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.