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.

这个I-Corps项目的更广泛的影响/商业潜力是开发一种数据驱动的分布式鲁棒优化（DRO）方法，以解决可再生能源集成能源系统中出现的不确定性下的现实决策问题。具体而言，所提出的技术实现了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操作。该模型的目的是在不确定的用电量，光伏发电量和电价的长期电力成本最小化。该技术将被集成到智能家居系统中，自动控制最佳电池存储单元操作和电力购买决策，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。这个I-Corps项目是基于住宅光伏（PV）电池系统操作的新模型和算法的开发，使用分布式鲁棒优化（DRO）范式。所提出的方案自动控制电池存储单元的操作，以最佳地确定何时存储PV发电的过量量或释放所存储的量以满足家庭能量需求。先前的系统不切实际地假设了不确定的电价、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，这种描述永远不会出现。决策者只能访问可用于推断潜在概率描述的历史数据。DRO方案通过首先构建一组与可用信息一致的合理分布来解决这一根本性缺点，然后优化电池存储操作，使其在集合中的所有分布中表现最佳，安全地预测潜在的不利结果。建议的方案可以减轻数据的过度拟合，并在样本外的情况下产生高质量的电池操作。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。