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Fig. 1 | Energy Informatics

Fig. 1

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

Fig. 1

Analysis of the discharging abilities of the VPP depending on its SoC: One important property of a fleet of BESSs is the relationship of aggregated SoC versus its aggregated charging and discharging abilities. For lower (higher) SoC of the VPP, more individual BESS are fully discharged (charged), and therefore cannot discharge (charge) any further and, thus, the discharging (charging) ability of the VPP reduces. This property depends not only on the BESSs themselves, but also on what is requested from the VPP (VPP schedule) and how this schedule is implemented via the BESS (disaggregation algorithm). The two figures here illustrates this by showing the calculated normalized VPP SoC and its associated maximum discharging ability. The figure on the left varies the heterogeneity of the fleet of BESSs (see also Table 1, that is different distributions of energy content to charging power ratios, while having the same aggregated VPP energy storage size and discharging power). Total number of BESS are 370, with homogenous meaning all BESS being identical, normal a set of BESS from the case study and heterogenous a set with diverse BESS properties. The heterogenous set performs worst since with a maximum discharging schedule all BESS have to contribute and the BESS with high power but small energy content quickly gets depleted. In contrast, the homogenous set can all be discharged uniformly until all BESS are depleted at the same moment. The figure on the right shows the influence for varying VPP schedules and disaggregation algorithms for a normal set of 370 BESSs and one month of operation. The BESS are tried to be operated so that the VPP follows a cyclic or random schedule. Each dot in the figure corresponds to the SoC and discharging situation for one quarter of an hour in this month. Clearly, a more random schedule is beneficial to allow the fleet of BESSs to recover and, thus, provide more discharging flexibility. Further, the first-fit decreasing algorithm achieves a much better exploitation of the abilities of the fleet than a random disaggregation

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