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过渡到电动汽车。联邦政府制定了一个目标，即到2030年，在美国销售的所有新车中，有一半是零排放车辆。预计到2030年，美国道路上将有2640万辆电动汽车。关于采用电动汽车的一个问题是电力系统适应其高功率需求的能力。另一个令人担忧的问题是目前电动汽车的高成本，这使得该国大多数人口负担不起。该项目为解决这两个问题提供了一个解决方案。首先，它有助于开发先进的智能需求响应程序，这已被公认为是有效的削峰电力系统的需求（包括电动汽车的需求），从而降低系统运行成本，并通过推迟设备升级和投资削减成本。这种智能需求响应程序每年可以节省数十亿美元。其次，该项目开发智能算法，实现电动汽车和电网之间的交易，车主可以通过在非高峰时段充电和销售（即，放电）在高峰时间期间将电力返回到电力系统。车主每年可以赚取数千美元，从而抵消电动汽车的高成本，使其更实惠。此外，该项目还支持代表性不足的少数民族和女生参与高水平和高质量的研究。该项目的重点是推进人工智能和机器学习算法，以优化电动汽车与电网的交互管理。首先，一个层次化的预测框架，是可扩展的和分布式的开发使用细胞计算网络。预测了电动汽车充电（电网到车辆）和放电（车辆到电网）的潜在交易。其次，提出了一种基于层次结构的电动汽车可扩展需求响应方法。覆盖电力系统的物理层级的分层需求响应架构允许分解需求响应以解决电动车辆的问题并以分布式方式解决它。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，基于近似动态规划和强化学习相结合的概念的自适应批判设计方法，利用电动汽车电池系统的能力，对配电系统进行最佳无功补偿和电压控制。随着未来几十年内进入配电系统的电动汽车数量迅速增长至数百万辆，这对维持电网安全性和可靠性至关重要。该项目由CISE MSI研究扩展和刺激竞争研究的既定计划（EPSCoR）共同资助。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和更广泛的影响审查标准进行评估，被认为值得支持。