ERI: Vehicle-to-Grid applied to Demand Response

消费者使用电动汽车和可再生能源作为电力来源，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（大多数消费者用电量最大的时候）减少用电。汽车到电网（V2G）是一项新技术，它允许电动汽车车主在连接到电网的同时使用电动汽车电池为家庭供电。因此，采用V2G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高峰时段公用事业公司的电力需求。人工智能算法是预测复杂特性的强大工具，在电力系统的多个领域得到了广泛的研究和应用。拟议的研究将回答有关V2G对消费者电力需求影响的重要问题，使用阿拉巴马州莫比尔地区作为示例市场，以便在模拟中使用现实的财务和环境考虑。这项研究的结果将提高消费者使用V2G的动机，以减少峰值电力需求，并将提供信息，使V2G更具吸引力，促进国家繁荣。该奖项的成果将通过报纸、技术同行评议的期刊文章、会议文章和报告、参加会议以及在网络研讨会上的演讲，传播给学术界、社区和行业利益相关者。该项目的目标是研究V2G支持需求响应（DR）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，以及一个仿真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。初步调查现有的电动汽车在移动,阿拉巴马州地区和它们的用法和权力特征将被用来验证的利弊V2G和提供数据,人工智能算法,将被设计来预测电动汽车特性需要支持博士项目将开发两个阶段:在第一阶段,关于该地区现有的电动汽车将数据收集和分析和仿真将发达;在第二阶段，人工智能算法将与一个研讨会一起开发，该研讨会将从代表性不足的群体中选出10名高中生。该研讨会将有助于传播项目成果，并向公众澄清一些与电动汽车、V2G和dr有关的常见误解。该奖项反映了美国国家科学基金会的法定使命，并通过基金会的知识价值和更广泛的影响评估标准进行了评估，认为值得支持。

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.