I-Corps: Artificial intelligence platform to provide customized power management solutions
I-Corps:提供定制电源管理解决方案的人工智能平台
基本信息
- 批准号:2124816
- 负责人:
- 金额:$ 5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-04-01 至 2022-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The broader impact/commercial potential of this I-Corps project is the development of an Artificial Intelligence (AI) power management system that helps businesses and residential communities reduce their electricity bills. In addition, the proposed technology may reduce the fossil fuel usage required for producing electricity and, ultimately, reduce global warming. The proposed technology may encourage more electric vehicle chargers and renewable energy generators in the customers' internal power network due to more economical operations. In addition, the technology also may alleviate the peak power burden on the utility and generators, which improves the reserve of electrical energy during the hours of a day that it is needed most. The current focus is on large companies, however, the proposed technology may be applied directly to medium and small size businesses and residential buildings as electric vehicle chargers and renewable generators are expected to be widely used.This I-Corps project is based on the development of a software package and real-time Artificial Intelligence (AI) power management system that reduces electricity usage. The proposed software has features such as comprehensive objective functions, including complex Time-Of-Use rate plans and degradation of components, and is intertwined through an iterative process with the proposed AI power management algorithm. Data is collected from the client and fed into the sizing software to determine renewable energy sources (e.g., photovoltaic panel) and battery sizes. After sizing is completed, the AI management system controls the battery's inverter in real-time. The proposed technology finds the most cost-effective strategy using artificial intelligence and optimization techniques, and the real-time decision with the savings and best resiliency outcome is released to the battery's inverter. A comprehensive sensitivity analysis to optimize the parameters of the AI management system has been completed. Preliminary results show that the proposed technology may reduce electricity bills for large-sized businesses up to 15% more than standard peak-shaving energy management methods while also increasing "resiliency under outage.”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.
I-Corps项目的更广泛影响/商业潜力是开发人工智能(AI)电源管理系统,帮助企业和住宅社区减少电费。此外,拟议中的技术可能会减少发电所需的化石燃料的使用,并最终减少全球变暖。由于更经济的运行,拟议的技术可能会鼓励更多的电动汽车充电器和可再生能源发电机进入客户的内部电网。此外,该技术还可以减轻公用事业和发电机的高峰负荷,从而在一天中最需要的时间内提高电能储备。虽然目前的重点是大企业,但随着电动汽车充电器和可再生能源发电机的广泛应用,该技术有可能直接应用于中小企业和住宅。I-Corps项目的基础是开发软件包和实时人工智能(AI)电源管理系统,以减少用电量。所提出的软件具有综合目标功能等特点,包括复杂的使用时间计划和组件退化,并通过迭代过程与所提出的人工智能电源管理算法交织在一起。从客户那里收集数据并输入尺寸软件,以确定可再生能源(例如,光伏板)和电池尺寸。定型完成后,AI管理系统实时控制蓄电池逆变器。所提出的技术利用人工智能和优化技术找到最具成本效益的策略,并将具有节省和最佳弹性结果的实时决策发布给电池逆变器。完成了人工智能管理系统参数优化的综合敏感性分析。初步结果表明,与标准的调峰能源管理方法相比,拟议中的技术可以为大型企业减少高达15%的电费,同时还可以增加“停电时的弹性”。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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Masood Shahverdi其他文献
Sensitivity Analysis of the Vehicle Model Mass for Model Predictive Control Based Power Management System of a Plug-in Hybrid Electric Vehicle
插电式混合动力汽车基于模型预测控制的电源管理系统的车辆模型质量敏感性分析
- DOI:
10.1109/itec.2018.8450164 - 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
N. Sockeel;Jian Shi;Masood Shahverdi;M. Mazzola - 通讯作者:
M. Mazzola
An Experiment-Based Methodology for Evaluating the Impacts of Full Bandwidth Load on the Hybrid Energy Storage System for Electrified Vehicles
基于实验的评估全带宽负载对电动汽车混合储能系统影响的方法
- DOI:
10.3390/sci1010003.v1 - 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
Masood Shahverdi;M. Mazzola;Matthew Doude;Quintin Grice;J. Gafford;N. Sockeel - 通讯作者:
N. Sockeel
Sensitivity analysis of the battery model for model predictive control implemented into a plug-in hybrid electric vehicle
用于插电式混合动力汽车模型预测控制的电池模型的灵敏度分析
- DOI:
10.3390/wevj9040045 - 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
N. Sockeel;Jian Shi;Masood Shahverdi;M. Mazzola - 通讯作者:
M. Mazzola
High-Fidelity Battery Model for Model Predictive Control Implemented into a Plug-In Hybrid Electric Vehicle
用于在插电式混合动力电动汽车中实施模型预测控制的高保真电池模型
- DOI:
10.3390/batteries3020013 - 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
N. Sockeel;Masood Shahverdi;M. Mazzola;Will Meadows - 通讯作者:
Will Meadows
MPC-based power management system for a plug-in hybrid electric vehicle for relaxing battery cycling
用于插电式混合动力汽车的基于 MPC 的电源管理系统,可放松电池循环
- DOI:
10.1109/itec.2016.7520222 - 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Masood Shahverdi;M. Mazzola;S. Abdelwahed;Matthew Doude;D. Zhu - 通讯作者:
D. Zhu
Masood Shahverdi的其他文献
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