Collaborative Research: CISE-MSI: DP: IIS RI: Research Capacity Expansion via Development of AI Based Algorithms for Optimal Management of Electric Vehicle Transactions with Grid

合作研究：CISE-MSI：DP：IIS RI：通过开发基于人工智能的算法来扩展研究能力，以实现电动汽车与电网交易的优化管理

• 批准号: 2318612
• 负责人: Ganesh Venayagamoorthy
• 金额: $ 30万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318612&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; DP

Pressing challenges such as climate change and the necessity to reduce carbon emissions require the transition from gasoline-powered vehicles to electric vehicles. The Federal Government has set a goal to make half of all new vehicles sold in the U.S. in 2030 zero-emissions vehicles. It is projected that there will be 26.4 million electric vehicles on U.S. roads in 2030. One concern regarding the adoption of electric vehicles is the ability of power systems to accommodate their high-power demand. Another concern is the present high costs of electric vehicles, which make them unaffordable for most of the country’s population. This project contributes a solution to address both the concerns. First, it contributes to developing advanced intelligent demand response programs, which have been recognized as being effective in shaving peak demand of power systems (including the demand by electric vehicles), thereby reducing the system operation cost and cutting costs by deferring equipment upgrade and investment. Such intelligent demand response programs can potentially save billions of dollars annually. Second, the project develops intelligent algorithms that enable transactions between electric vehicles and power grids, where the vehicle owners can make considerable additional income by charging during off-peak hours and selling (i.e., discharging) power back to the power system during peak hours. The owners can earn thousands of dollars per year, thereby offsetting the high costs of electric vehicles and making them more affordable. Furthermore, the project supports underrepresented minorities and female students participating in high-level and high-quality research. Its overall outcomes increase sustainable development and economic competitiveness of the United States.The emphasis of this project is to advance artificial intelligence and machine learning algorithms for optimal management of electric vehicles interactions with the electric power grid. First, a hierarchical forecasting framework that is scalable and distributable is developed using cellular computational networks. Electric vehicle charging (Grid-to-Vehicle) and discharging (Vehicle-to-Grid) potential transactions are forecasted. Secondly, a hierarchical architecture-based methodology for scalable demand response with electric vehicles is developed. The hierarchical demand response architecture overlaying the physical hierarchy of the power system allows for decomposing the demand response to tackle the electric vehicle’s problem and solve it in a distributed manner. The computational time required to solve this optimization problem using this framework is only dependent on the number of levels in the hierarchical architecture. Thirdly, an adaptive critic design approach based on combined concepts of approximate dynamic programming and reinforcement learning is created for utilizing the capabilities of the electric vehicle battery systems for optimal reactive power compensation and voltage control on the distribution system. This is essential to maintain grid security and reliability as the number of electric vehicles penetrating the electric power distribution system rapidly grows to millions over the next few decades.This project is jointly funded by the CISE MSI Research Expansion and the Established Program to Stimulate Competitive Research (EPSCoR).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.

气候变化和减少碳排放的必要性等紧迫挑战要求从汽油动力汽车转向电动汽车，联邦政府设定了一个目标，即到 2030 年，美国销售的新车中有一半是零排放汽车。预计到 2030 年，美国道路上将有 2640 万辆电动汽车。电动汽车的采用的一个问题是电力系统能否满足其高功率需求，另一个问题是目前的高成本。该项目为解决这两个问题提供了解决方案，首先，它有助于开发先进的智能需求响应计划，该计划已被认为可以有效减少电动汽车的高峰需求。电力系统（包括电动汽车的需求），从而降低系统运营成本，并通过推迟设备升级和投资来削减成本。其次，该项目开发了支持交易的智能算法。电动汽车与电力之间电网，车主可以通过在非高峰时段充电并在高峰时段将电力卖回（即放电）给电力系统来获得可观的额外收入，从而抵消高额成本。此外，该项目支持代表性不足的少数族裔和女学生参与高水平和高质量的研究，其总体成果提高了美国的可持续发展和经济竞争力。该项目的重点是。推进人工智能和机器学习首先，使用蜂窝计算网络开发可扩展和分布式的分层预测框架。 ）其次，开发了一种基于分层架构的电动汽车可扩展需求响应方法。分层需求响应架构覆盖电力系统的物理层次结构，可以分解需求响应以解决电力问题。使用该框架解决该优化问题所需的计算时间仅取决于分层架构中的级别数。第三，基于近似动态规划的组合概念的自适应关键设计方法。创建强化学习是为了利用电动汽车电池系统的功能来实现配电系统的最佳无功功率补偿和电压控制，这对于维护电网安全性和可靠性至关重要，因为电动汽车的数量迅速渗透到配电系统中。在接下来的几十年里将增长到数百万。这个项目是由 CISE MSI 研究扩展和既定计划共同资助，反映了刺激竞争性研究 (EPSCoR)。该奖项是 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。