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

I-Corps项目更广泛的影响/商业潜力是开发一种数据驱动的分布式稳健优化（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）系统的住宅小区的电池存储单元运行。该模型的目的是在不确定用电量、光伏发电和电价的情况下，使长期电力成本最小化。该技术将集成到智能家居系统中，自动控制最佳电池存储单元的运行和电力购买决策，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模型的可行性并核实其实际性能，特别是在能源系统领域。这个I-Corps项目是基于使用分布式鲁棒优化（DRO）范例开发住宅光伏（PV）电池系统运行的新模型和算法。提出的方案自动控制蓄电池储能单元的运行，以最佳地确定何时存储多余的光伏发电量或何时释放存储的电量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，先前的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，这种描述是不可用的。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，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，从而解决了这一根本缺陷。所提出的方案可以减轻对数据的过拟合，并在样本外情况下产生高质量的电池操作。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。

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.