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

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向电动汽车过渡。联邦政府已经设定了一个目标，到2030年，在美国销售的所有新车中有一半是零排放汽车。据预测，到2030年，美国道路上将有2640万辆电动汽车。采用电动汽车的一个问题是电力系统能否满足其高功率需求。另一个担忧是目前电动汽车的高成本，这使得该国大多数人负担不起。这个项目提供了解决这两个问题的解决方案。首先，它有助于开发先进的智能需求响应方案，该方案已被公认为有效地削减电力系统的峰值需求（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来降低成本。这种智能的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，实现电动汽车与电网之间的交易，车主可以通过在非高峰时段充电，在高峰时段将电力卖（即放电）回给电力系统，从而获得可观的额外收入。车主每年可以赚取数千美元，从而抵消了电动汽车的高成本，使他们更实惠。此外，该项目支持代表性不足的少数民族和女学生参与高水平和高质量的研究。其总体成果促进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算法，以优化管理电动汽车与电网的交互。首先，利用细胞计算网络开发了一个可扩展、可分布的分层预测框架。对电动汽车充电（网对车）和放电（车对网）的潜在交易进行了预测。其次，提出了一种基于层次结构的电动汽车可伸缩需求响应方法。层次化的需求响应体系结构覆盖了电力系统的物理层次结构，允许对需求响应进行分解，以解决电动汽车的问题，并以分布式的方式解决。使用此框架解决此优化问题所需的计算时间仅取决于分层体系结构中的层数。第三，提出了一种基于近似动态规划和强化学习相结合的自适应临界设计方法，利用电动汽车电池系统的能力对配电系统进行最优无功补偿和电压控制。这对于维持电网的安全性和可靠性至关重要，因为在未来几十年里，进入配电系统的电动汽车数量将迅速增长到数百万辆。该奖项反映了美国国家科学基金会的法定使命，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。

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 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.