智能电网中基于预测的存储管理

Balakrishnan Narayanaswamy, Vikas K. Garg, T. S. Jayram
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引用次数: 4

摘要

经济和环境方面的考虑促使人们对将更多可再生能源发电并入电网产生了兴趣。不幸的是,可再生能源的间歇性可用性为这些资源的纳入增加了障碍。分布式存储被认为是从不同资源中提取价值的一种手段。然而,巨大的存储成本要求设计能够以最小的存储大小管理间歇性资源的算法。与此同时,计量、通信和天气预报方面的进步使人们能够根据对未来的预测对能源的生产、分配和消耗进行实时管理。在本文中,我们专注于本地存储管理的在线算法,该算法使用间歇性可再生资源可用性的短期预测。与之前的工作相比,我们开发的算法即使在需求、价格和可用性是任意的(可能是非随机的),并且效用函数是非凹的情况下,也具有性能的理论界限。我们的基本贡献是证明了适当地贴现未来福利如何导致即使在最坏的情况下也表现出出色的实际性能的存储管理算法。我们用实验证实了这些理论保证,证明了我们算法的有效性和智能电网存储的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction based storage management in the smart grid
Economic and environmental concerns have fostered interest in incorporating greater amounts of electricity from renewable energy sources into the grid. Unfortunately, the intermittent availability of renewable power has raised a barrier to the inclusion of these sources. Distributed storage is perceived as a means to extract value from the different resources. However, the large cost of storage requires the design of algorithms that can manage intermittent resources with minimum storage size. At the same time, advances in metering, communication, and weather prediction allow real time management of energy generation, distribution and consumption based on predictions of the future. In this paper, we focus on online algorithms for local storage management that use short term predictions of intermittent renewable resource availability. In contrast to prior work, we develop algorithms that come with theoretical bounds on performance even when demand, prices and availability are arbitrary (possibly non-stochastic), and the utility functions non-concave. Our fundamental contribution is to prove how appropriate discounting of future welfare leads to storage management algorithms that exhibit excellent practical performance even in the worst-case scenario. We substantiate these theoretical guarantees with experiments that demonstrate the effectiveness of our algorithms and the value of storage in the smart grid.
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