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

职业：用于电网规模储能控制、竞价和集成分析的计算高效算法

基本信息

批准号：
2239046
负责人：
Bolun Xu
金额：
$ 50.06万
依托单位：
Columbia University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-15 至 2027-12-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239046&HistoricalAwards=false
关键词：
CAREER Computation efficient Algorithms Grid

项目摘要

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.

储能是未来低碳电力系统的基石，可减少碳排放并提高电力系统应对极端事件的可靠性。该 NSF 职业项目旨在开发新的计算工具，帮助能源存储的电网整合，其总体目标是在可持续电力系统中提供负担得起且可靠的电力供应。该项目将带来革命性的变化，使电力系统运营商和存储所有者能够更准确地投标和调度各种现有和新兴的存储技术。这将通过开发一种将基于模型的优化与机器学习相结合的新型计算框架来实现，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存储能源模型开发新颖的控制算法，以及研究将现有和新兴存储技术集成到电力市场的方法。该项目的更广泛影响包括制定大学课程和 K-12 外展计划，将数据科学纳入能源脱碳和气候变化教育，以及制定外展计划以促进社区太阳能+存储部署，重点关注纽约市的弱势社区该项目同时解决了储能并网中的多项技术挑战，包括多级不确定性、非线性和非凸存储模型以及大量网络存储资源的计算可扩展性。该项目将 i) 开发一种完全开源的分析算法，无需专为储能量身定制的专有商业求解器，以极高的计算速度解决非线性随机动态规划问题； ii) 受动态规划机会价值函数的启发，开发新的市场模型和定价方案，以经济地管理电网调度中的存储充电状态； iii) 将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所有者开发储能资源能源管理系统软件，更准确地反映储能运行特征和未来的不确定性，并有助于与储能部署相关的教育和推广活动，以实现能源的可持续性和弹性该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。