CAREER: Computation-efficient Algorithms for Grid-scale Energy Storage Control, Bidding, and Integration Analysis

储能是未来低碳电力系统的基石，可以减少碳排放，提高电力系统抵御极端事件的可靠性。NSF CAREER项目旨在开发新的计算工具，帮助储能电网集成，其总体目标是在可持续电力系统中提供负担得起的可靠电力供应。该项目将带来变革，使电力系统运营商和储能业主能够更准确地投标和调度各种现有和新兴的储能技术。 这将通过开发一种新的计算框架来实现，该框架将基于模型的优化与机器学习相结合，从而实现可靠的性能和计算效率。该项目的智力优势包括为复杂的储能模型开发新的控制算法，并研究将现有和新兴储能技术整合到电力市场的方法。该项目的更广泛影响包括开发大学课程和将数据科学纳入能源脱碳和气候变化教育的K-12推广计划，以及开发推广社区太阳能+储能部署的推广计划，重点关注纽约市的弱势社区。该项目同时解决了储能电网集成的几个技术挑战，包括多阶段不确定性，非线性和非凸存储模型，以及在大量网络存储资源上的计算可扩展性。该项目将i）开发一个完全开源的分析算法，无需专门针对储能的专有商业求解器，以极高的计算速度解决非线性随机动态规划; ii）开发新的市场模型和定价方案，灵感来自动态规划的机会价值函数，以经济地管理电网调度中的储能荷电状态; iii）将联合收割机机器学习与动态规划结合成两阶段学习模型，以更有效地分析和管理参与电力市场的大量存储资源。该项目的成果将有利于电力系统运营商和储能业主开发储能资源的能源管理系统软件，更准确地反映储能运行特性和未来的不确定性，该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的学术价值和更广泛的影响审查标准。

Energy storage is a cornerstone in future low-carbon power systems for reducing carbon emissions and enhancing power system reliability against extreme events. This NSF CAREER project aims to develop new computation tools aiding grid integration of energy storage with the overarching goal to provide affordable and reliable electricity supply in sustainable power systems. The project will bring transformative change to enable power system operators and storage owners to more accurately bid and dispatch a variety of existing and emerging storage technologies. This will be achieved by developing a novel computation framework combining model-based optimization with machine learning, achieving both reliable performance and computation efficiency. The intellectual merits of the project include developing novel control algorithms for complex storage energy models and investigating approaches to integrate existing and emerging storage technologies into electricity markets. The broader impacts of the project include developing university curricula and K-12 outreach programs on incorporating data science into energy decarbonization and climate change education, and developing an outreach program to promote community solar plus storage deployments with a focus on disadvantaged neighborhoods in New York City.The project simultaneously addresses several technical challenges in energy storage grid integration including multi-stage uncertainties, nonlinear and nonconvex storage models, and computation scalability over a large number of networked storage resources. The project will i) develop a fully open-source analytical algorithm without proprietary commercial solvers tailored for energy storage to solve nonlinear stochastic dynamic programming with extreme computation speed; ii) develop new market models and pricing schemes inspired by the opportunity value function from dynamic programming to economically manage storage state-of-charge in grid dispatch; iii) combine machine learning with dynamic programming into a two-stage learning model to more efficiently analyze and manage a large number of storage resources participating in electricity markets. The results of this project will benefit power system operators and storage owners to develop energy management system software for storage resources that more accurately reflect the storage operating characteristics and future uncertainties, and aid education and outreach activities related to energy storage deployments for energy sustainability and resiliency.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.