Scalable, approximate dynamic programming algorithms for high-dimensional storage portfolios

适用于高维存储组合的可扩展近似动态规划算法

• 批准号: 1127975
• 负责人: Warren Powell
• 金额: $ 36万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1127975&HistoricalAwards=false
• 关键词: Scalable; approximate; dynamic; programming; algorithms

AbstractThe objective of this research is to develop methods to design and control heterogeneous portfolios of energy storage devices for the power grid. The approach of this research is to use approximate dynamic programming to develop optimal control policies, that will then be used to understand the economic value of different technologies in the context of a complete power grid. Intellectual meritThe intellectual merit of the project is the development of scalable algorithmic technologies for solving high-dimensional stochastic optimization problems arising in energy storage. We propose to use the framework of approximate dynamic programming coupled with tools from machine learning and convex optimization. We exploit convexity which makes it possible to construct effective approximations that scale to handle large numbers of storage devices. Broader impactsThe broader impacts of the research will be: 1) The research will guide the design of storage devices so that they meet the specific needs of the power grid in the presence of large supplies of intermittent energy such as wind and solar. 2) Renewable energy, coupled with appropriately designed storage, should dramatically reduce the need for coal. 3) The research, including the approximate dynamic programming models and algorithms, will be made available using a special website with datasets, software, published research and working papers, and downloadable presentations. The results will be integrated in courses at Princeton University, and presented at conferences and workshops to a broad community spanning energy systems and economics, as well as the algorithmic communities.

摘要本研究的目的是开发设计和控制电网中不同种类的储能设备组合的方法。这项研究的方法是使用近似动态规划来制定最优控制策略，然后将其用于在完整电网的背景下了解不同技术的经济价值。智力价值该项目的智力优势是开发了可伸缩的算法技术，用于解决能源储存中出现的高维随机优化问题。我们建议使用近似动态规划的框架，并结合机器学习和凸优化的工具。我们利用凸性来构造可扩展以处理大量存储设备的有效近似。更广泛的影响研究的更广泛的影响将是：1)研究将指导存储设备的设计，以便在存在大量间歇性能源(如风能和太阳能)的情况下满足电网的特定需求。2)可再生能源，加上适当设计的存储，应该会极大地减少对煤炭的需求。3)这项研究，包括近似动态规划模型和算法，将通过一个特别网站提供，其中包括数据集、软件、发表的研究和工作论文以及可下载的演示文稿。结果将被整合到普林斯顿大学的课程中，并在会议和研讨会上向能源系统和经济学以及算法社区的广泛社区展示。