The world’s electrical grid is increasingly powered by renewable sources, with wind and solar at the helm; indeed, these two renewables are expected to see the foremost and second-most capacity additions into the next decade^1,2. Further deployment, however, is still checked by the intermittency of their supply. There is then urgent need for complementary storage technologies that are sufficiently economical, so as not to impinge upon the competitiveness of variable renewables and historically low cost of electricity².

As RFBs approach the market, quantitative methods are needed to assess their efficacy and economy for grid-scale applications. To this end, we prescribe an objective for global optimization within a generalized electrochemical and economic model³. Performance characteristics, robustly scalable, can be validated even at laboratory scales, then combined with bulk pricing for required components into capital cost. To determine pareto optimal design and operating conditions at the desired application scale, we implement a genetic algorithm. This optimization not only simulates costs and efficiencies associated with scale-up of novel batteries, but also enables fair comparison between batteries that may have significantly different performance characteristics and cost structures (Figure 1).

The results for state-of-the-art RFBs indicate that model and experimental performance are in agreement, and that zinc-ferricyanide should outshine other RFBs in utility-scale applications. This expeditious integration of experimental and theoretical analyses can thereby help the RFB community to inform the truly cost-effective path forward.

References:

1. International Energy Agency. World Energy Outlook 2016 (WEO-2016). 2016.
2. U.S. Energy Administration. Annual Energy Outlook 2016 (AEO-2016). 2016.
3. Ma RX et al. In preparation.
4. Holland JH. Adaptation in Natural and Artificial Systems. 1975.
5. You D, Zhang H, Chen J. Electrochim. Acta 2009; 54:6827.
6. Zhao P, Zhang HM, Zhou HT, Yi BL. Electrochimica Acta 2005; 51:1091.
7. Gu S, Gong K, Yan EZ, Yan YS. Energy Environ. Sci. 2014; 7:2986.
8. Gong K, Ma RX, Conforti KM, Kuttler KJ, Grunewald JB, Yeager KL, Bazant MZ, Gu S, Yan YS. Energy Environ. Sci. 2014; 7:2986.