ERI: Vehicle-to-Grid applied to Demand Response

使用电动汽车（EV）以及可再生能源作为消费者的电源将减少温室气体排放。此外，电力公司鼓励消费者在高峰时段（大多数消费者使用大多数电力时）减少消费。车联网（V2 G）是一项新技术，允许电动汽车车主在连接到电网时使用电动汽车电池为家庭供电。因此，具有V2 G技术的电动汽车将对消费者具有吸引力，因为它们的电动汽车电池可以帮助减少公用事业公司在高峰时段的电力需求。人工智能算法是预测复杂特性的有力工具，在电力系统的多个领域得到了广泛的研究。拟议的研究将回答有关V2 G对消费者电力需求的影响的重要问题，使用移动的，亚拉巴马地区作为一个示例市场，以便在模拟中使用现实的财务和环境考虑。这项研究的结果将促进消费者使用V2 G来降低峰值电力需求的动机，并将提供有关使V2 G更具吸引力，促进国家繁荣的信息。该奖项的结果将通过报纸、同行评议的技术期刊文章和会议文章及报告、参加会议以及网络研讨会的演示等方式传播给学术界、社区和行业利益相关者。该项目的目标是调查V2 G在支持需求响应（DR）方面的优势和挑战，通过创建短期人工智能（AI）算法来预测未来几分钟和几小时的电动汽车负载需求状态，并创建模拟系统来验证利用电动汽车进行DR的最佳方式并评估克服挑战的方法。对亚拉巴马地区移动的现有电动汽车及其使用和功率特性的初步调查将用于验证V2 G的利弊，并为AI算法提供数据，这些算法将用于预测支持DR所需的电动汽车特性。该项目将分两个阶段开发：在第一阶段，将收集和分析该地区现有电动汽车的数据，并进行模拟;在第二阶段，人工智能算法将与一个研讨会沿着开发，该研讨会涉及从代表性不足的群体中选出的10名高中生。该研讨会将支持项目结果的传播，并将向公众澄清与电动汽车、V2 G和DR相关的一些常见误解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值进行评估而被认为值得支持。和更广泛的影响审查标准。

The use of electric vehicles (EVs) as well as renewable energy as power sources for consumers will reduce greenhouse gas emissions. Moreover, power utility companies encourage consumers to reduce their consumption during peak-hours (when most consumers use most power). Vehicle-to-Grid (V2G) is a new technology that allows EV owners to use their EV batteries to power their homes while connected to the grid. Thus, EVs with V2G technology will become attractive for consumers because their EV batteries can help to reduce their power demand from the utility company at peak-hours. Artificial Intelligence algorithms are powerful tools for forecasting complex properties and have been extensively researched for applications in several areas of Power Systems. The proposed research will answer important questions regarding the influence of V2G on consumer power demand, using the Mobile, Alabama area as an example market so that realistic financial and environmental considerations are used in the simulations. The outcomes of this research will boost the motivation for consumers to use V2G to reduce peak power demands, and will provide information on making V2G more attractive, advancing national prosperity. The results from this award will be disseminated to academic, community and industry stakeholders through newspapers, technical peer-reviewed journal articles and conference articles and reports, participation in conferences, and presentations in webinars.The goal of the project is to investigate the advantages and challenges of V2G to support Demand Response (DR), by creating short-term Artificial Intelligence (AI) algorithms to predict EV load demand statuses for a few minutes and hours ahead, and a Simulation system to verify the best way to utilize EVs for DR and evaluate the means to overcome challenges. An initial investigation of existing EVs in the Mobile, Alabama region and their usage and power characteristics will be used to verify the pros and cons of V2G and to supply data to AI algorithms that will be designed to predict the EV characteristics needed to support DR. The project will be developed in two phases: in Phase I, data about existing EVs in the region will be collected and analyzed and a simulation will be developed; in Phase II, the AI algorithms will be developed along with a workshop involving ten high-school students to be selected from underrepresenting groups. This workshop will support the dissemination of the project results, and willclarify to the general public some common misconceptions related to EVs, V2G and DR.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.