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Scaling: managing a large number of distributed battery energy storage systems

机译：扩展：管理大量分布式电池储能系统

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This paper analyzes the management of a large number of distributed battery energy storage systems (BESSs) by a energy utility in order to provide some market services. A heuristic algorithm based on two parts is proposed for this task. The first part, the aggregation, combines the abilities and behavior of the fleet of BESS into a virtual power plant (VPP) by a concise but flexible model. This VPP can be used by the utility as they are used to with traditional power plants. The second part, the disaggregation, distributes VPP control schedules back to the individual BESS by a greedy first-fit decreasing heuristic. The management of a fleet of BESS can also be modeled as a mathematical linear optimization program. The proposed heuristic is compared to and evaluated against this global optimization regarding computational performance and quality of results. It is shown, that the heuristic provides a remarkable speedup when applied to larger number of units. With it, it is possible to handle a group of at least 100,000 individual BESS. Further, the quality of the results are shown. First, the solution of the heuristic is compared to the optimal one of the mathematical program. Second, the methods are both applied and compared in a realistic case study.
机译：本文分析了一家能源公司对大量分布式电池储能系统（BESS）的管理，以提供一些市场服务。为此，提出了一种基于两部分的启发式算法。第一部分是聚合，它通过简洁而灵活的模型将BESS舰队的能力和行为组合到虚拟电厂（VPP）中。该公用事业公司可以使用该VPP，就像它们在传统发电厂中一样。第二部分是分解，它通过贪婪的首次拟合递减启发式方法将VPP控制计划分发回各个BESS。 BESS车队的管理也可以建模为数学线性优化程序。在计算性能和结果质量方面，将这种启发式方法与该全局优化进行比较和评估。结果表明，该启发式方法在应用于大量单位时可显着提高速度。使用它，可以处理一组至少100,000个单独的BESS。此外，显示了结果的质量。首先，将启发式的解与数学程序的最优解进行比较。其次，这些方法在实际案例研究中都得到了应用和比较。

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