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、分布稳健的优化(DRO)方法，以解决具有可再生能源整合的能源系统中出现的不确定性下的现实决策问题。具体地说，拟议的技术实施了DRO模式，以优化配备屋顶太阳能光伏(PV)系统的居民区的电池存储单元操作。该模型旨在最小化不确定用电量、光伏发电和电价下的长期电力成本。该技术将被集成到智能家居系统中，该系统将自动控制最优电池存储单元的操作和购电决策，并将在嵌入式控制器单元中实施，以实现实时决策。该项目的目的是确定拟议的DRO模型的可行性并验证其实际性能，特别是在能源系统领域。该i-Corps项目基于使用分布式稳健优化(DRO)范例为住宅光伏(PV)电池系统运行开发新的模型和算法。所提出的方案自动控制电池存储单元的操作，以最优地确定何时存储过剩的光伏发电量或何时释放存储的光伏发电以满足家庭能源需求。对于不确定的电价、光伏发电和能源消耗，现有系统不切实际地假设了已知的概率描述。在大多数现实生活中，这种描述是永远不会出现的。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缺陷，然后针对针对该集合中的所有分布表现最好的电池存储操作进行优化，安全地预测潜在的不利结果。拟议的方案可能会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操作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。