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retention of the battery drops below the nominal rating, and even then, it can only drop by an additional 5% of that rating until rebalancing must occur. Figure II-6a shows the LCOS as a function of lower capacity limit at three different electrolyte cost fractions. A base case Ccap of 400 $ kWh-1 was selected, and this is divided into the electrolyte cost fraction and the power cost fraction. The fraction which is due to electrolyte costs (either 15, 30, or 45% of 400 $ kWh-1) is then scaled by the OF. The dashed horizontal line shows the LCOS for the base case where a 95% caplim is used (and thus there is no oversizing). Figure II-6b shows the corresponding OF at each lower caplim value, as well as the resulting Ccap due to scaling from the OF in each electrolyte cost fraction case. We see the LCOS curves for the oversized cases in Figure II-6a resemble those shown in Figure II-4. This is reasonable, as the same variable (caplim) is changing on the x-axis, with the only difference being an addition to the Ccap that increases as the OF increases. As we begin to oversize and allow for more fade, we see a decrease in the resulting LCOS, but the returns of the greater capacity range are diminished by the escalating cost of oversizing and the LCOS eventually increases above the baseline case at very low caplim values (< 50%). Thus, an optimum for oversizing exists where LCOS is minimized. For the three electrolyte cost fractions considered, this optimum ranges from 70-80%, corresponding to moderate oversizing between 18-34%, though less aggressive oversizing still leads to LCOS savings. As the electrolyte cost fraction increases, the LCOS increases because more capital expenditure must be scaled by the OF. 34PDF Image | Bringing Redox Flow Batteries to the Grid
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