An Analytical Model to Optimize the Cost-Effectiveness of Flow Batteries for the Electrical Grid

The world continues to modernize its electrical grid with more renewable sources, among which wind and solar photovoltaics are fastest growing¹. While these sectors are becoming more competitively priced², their grid penetration is still limited by the variability of their supply. Secondary energy storage would then go far to enable renewable power, although such energy storage must likewise be cost-effective.

Redox flow batteries (RFBs) reversibly store energy in solubilized redox couples, which are stored external to the electrodes. This allows independent design of power and energy capacity and avails RFBs to a range of applications. The design space is further widened by an influx of chemical, geometric and operational innovations. Other advantages of RFBs include fast response, good efficiency and long cycle life.

While RFBs are getting closer to market, their grid-scale costs are confounded by the abundance of optimizable design conditions; moreover, experimental scale-up studies can exhaust a great deal of time and capital. A unified model, on the other hand, could quickly predict performance and cost characteristics of candidate RFBs, provided it be generalizable and contain an optimization framework to distill design variables into the lowest cost basis. We detail such a model³ and validate its robustness for several state-of-the-art RFBs (Figure 1). Our model could direct future efforts toward the most technically and economically promising designs (Figure 2), ushering in a new pace for RFB development.

Figure 1. Model validation with charge discharge data for the (a) all-vanadium⁴,
(b) polysulfide-bromide⁵, (c) polysulfide-iron⁶, and (d) zinc-iron⁷ batteries.

Figure 2. Modeled (a) battery capital costs, (b) installed capital costs,
(c) cell efficiencies, and (d) stack efficiencies for zinc-ferricyanide and various state-of-the-art RFBs^3-7.

References:

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3.     Ma RX et al. In preparation.

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7.     Gong K, Ma RX, Conforti KM, Kuttler KJ, Grunewald JB, Yeager KL, Bazant MZ, Gu S, Yan YS. Energy Environ. Sci. 2014; 7:2986.