(679c) Cost-Effectiveness of Grid Energy Storage Technologies in Current and Future U.S. Power Systems

Authors: 
Guerra, O. J., National Renewable Energy Laboratory
Eichman, J., National Renewable Energy Laboratory
Hodge, B. M. S., National Renewable Energy Laboratory
Kurtz, J., National Renewable Energy Laboratory
The integration of high and ultra-high shares of variable renewable energies (VREs)–wind and solar power– rises some technical challenges that need to be solved to maintain the reliability and cost-effectiveness of power systems. For example, VREs are weather-dependent and therefore their power generation is uncertain and exhibit variable diurnal and seasonal patterns. These properties cause more frequent and/or steeper net demand fluctuations that require the enhancement of power system flexibility [1,2]. Energy storage devices could play a pivotal role in the transition towards low-carbon and flexible power systems. For instance, these devices can provide a variety of services including energy arbitrage, transmission and distribution congestion relief and investment deferral, demand shifting and peak reduction, spinning and non-spinning reserves, and seasonal energy storage [3]. However, the assessment of the integration of VREs and energy storage technologies using model-based system analysis poses a challenge for the modeling of power system. For instance, the modeling of VREs and storage systems requires the accommodation of short-term dynamics –wind and solar generation patterns as well as evolution of storage levels– using a chronological hourly resolution in power planning models, which result in large computational needs [4,5]. This study focuses on the techno-economic assessment of grid-energy storage technologies from energy storage owner (ESO) and system operator (SO) perspectives using price-taker and production cost models, respectively. Different modeling approaches are evaluated in terms of both accuracy and computational cost. Finally, niche markets for grid energy storage in United States are identified and impacts on electricity prices and power system operations are quantified.

References

[1] IRENA. Planning for the renewable future: Long-term modelling and tools to expand variable renewable power in emerging economies. 2017.

[2] Kroposki B, Johnson B, Zhang Y, Gevorgian V, Denholm P, Hodge B, et al. Achieving a 100% Renewable Grid: Operating Electric Power Systems with Extremely High Levels of Variable Renewable Energy. IEEE Power Energy Mag 2017;15:61–73. doi:10.1109/MPE.2016.2637122.

[3] IEA. Technology Roadmap: Energy Storage. OECD Publishing; 2014. doi:10.1787/9789264211872-en.

[4] Wogrin S, Galbally D, Reneses J. Optimizing Storage Operations in Medium- and Long-Term Power System Models. IEEE Trans POWER Syst 2016;31:3129–38. doi:10.1109/TPWRS.2015.2471099.

[5] Xu B, Wang Y, Dvorkin Y, Fernandez-Blanco R, Silva-Monroy CA, Watson J-P, et al. Scalable Planning for Energy Storage in Energy and Reserve Markets. IEEE Trans Power Syst 2017;32:4515–27. doi:10.1109/TPWRS.2017.2682790.

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