(40h) 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 model³and 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:

1.    
International Energy
Agency. World Energy Outlook 2016. 2016; WEO-2016.

2.    
Yang Z, Zhang J, Kintner-Meyer MCW, Lu X, Choi D, Lemmon JP, Liu J. Chem. Rev. 2011; 111:3577.

3.    
Ma RX et al. In preparation.

4.    
You D, Zhang H, Chen
J. Electrochim. Acta 2009; 54:6827.

5.    
Zhao P, Zhang HM, Zhou
HT, Yi BL. Electrochimica Acta 2005; 51:1091.

6.    
Gu S, Gong K, Yan EZ, Yan YS. Energy Environ. Sci. 2014; 7:2986.

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.