I-Corps: Data-Driven Robust Optimization Technology for Battery Storage System Management

I-Corps：数据驱动的电池存储系统管理鲁棒优化技术

• 批准号: 2222450
• 负责人: Grani Adiwena Hanasusanto
• 金额: $ 5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222450&HistoricalAwards=false
• 关键词: Corps; Data; Driven; Robust; Optimization

The broader impact/commercial potential of this I-Corps project is the development of a data-driven, distributionally robust optimization (DRO) methodology to address real-life decision problems under uncertainty arising in energy systems with renewable integration. Specifically, the proposed technology implements the DRO model to optimize battery storage unit operations in residential areas equipped with rooftop solar photovoltaic (PV) systems. The model is designed to minimize the long-run electricity costs under uncertain electricity usage, photovoltaic engery generation, and electricity prices. The technology will be integrated into an intelligent home system that automatically controls the optimal battery storage unit operation and electricity purchase decisions, and will be implemented in the embedded controller unit to enable real-time decisions. The aim of the project is to establish the feasibility and verify the real-world performance of the proposed DRO model, particularly in the field of energy systems. This I-Corps project is based on the development of new models and algorithms for residential photovoltaic (PV)-battery system operations using the distributionally robust optimization (DRO) paradigm. The proposed scheme automatically controls the operation of the battery storage unit to optimally determine when to store the excess amount of PV generation or to discharge the stored amount to satisfy the household energy demand. Prior systems unrealistically assumed a known probabilistic description for the uncertain electricity prices, PV generation, and energy consumption. In most real-life situations, this description is never available. The decision-makers only have access to historical data that may be used to infer the underlying probabilistic description. The DRO scheme addresses this fundamental shortcoming by first constructing a set of plausible distributions consistent with the available information and then optimizes for battery storage operations that perform best for all distributions in the set, safely anticipating potentially adverse outcomes. The proposed scheme may mitigate overfitting to the data and yield high-quality battery operations in out-of-sample circumstances.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.

该 I-Corps 项目更广泛的影响/商业潜力是开发数据驱动的分布式稳健优化 (DRO) 方法，以解决可再生能源整合能源系统中出现的不确定性下的现实决策问题。具体来说，所提出的技术实施了 DRO 模型，以优化配备屋顶太阳能光伏 (PV) 系统的住宅区的电池存储单元运行。该模型旨在最小化用电、光伏发电和电价不确定情况下的长期电力成本。该技术将集成到智能家居系统中，自动控制最佳的电池存储单元运行和购电决策，并将在嵌入式控制器单元中实现，以实现实时决策。该项目的目的是建立可行性并验证所提出的 DRO 模型的实际性能，特别是在能源系统领域。该 I-Corps 项目基于使用分布式鲁棒优化 (DRO) 范式开发住宅光伏 (PV) 电池系统运行的新模型和算法。该方案自动控制电池存储单元的运行，以最佳地确定何时存储多余的光伏发电量或释放所存储的电量以满足家庭能源需求。先前的系统不切实际地假设了对不确定的电价、光伏发电和能源消耗的已知概率描述。在大多数现实生活中，这种描述永远不可用。决策者只能访问可用于推断潜在概率描述的历史数据。 DRO 方案解决了这一基本缺点，首先构建一组与可用信息一致的合理分布，然后优化电池存储操作，使该组中的所有分布表现最佳，安全地预测潜在的不利结果。拟议的方案可以减轻对数据的过度拟合，并在样本外的情况下产生高质量的电池运行。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。