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An intertemporal decision framework for electrochemical energy storage management


Dispatchable energy storage is necessary to enable renewable-based power systems that have zero or very low carbon emissions. The inherent degradation behaviour of electrochemical energy storage (EES) is a major concern for both EES operational decisions and EES economic assessments. Here, we propose a decision framework that addresses the intertemporal trade-offs in terms of EES degradation by deriving, implementing and optimizing two metrics: the marginal benefit of usage and the average benefit of usage. These metrics are independent of the capital cost of the EES system, and, as such, separate the value of EES use from the initial cost, which provides a different perspective on storage valuation and operation. Our framework is proved to produce the optimal solution for EES life-cycle profit maximization. We show that the proposed framework offers effective ways to assess the economic values of EES, to make investment decisions for various applications and to inform related subsidy policies.

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Fig. 1: Schematic of the intertemporal decision framework for EES degradation management.
Fig. 2: Results of a lithium-ion EES for energy arbitrage in CAISO using MBU and LCOD methods.
Fig. 3: Annual revenues and degradations of a lithium-ion EES for energy arbitrage in CAISO.
Fig. 4: Optimal daily revenues and usages for energy arbitrage based on CAISO 2016 price scenarios.
Fig. 5


  1. Chu, S. & Majumdar, A. Opportunities and challenges for a sustainable energy future. Nature 488, 294–303 (2012).

    Article  Google Scholar 

  2. Braff, W. A., Mueller, J. M. & Trancik, J. E. Value of storage technologies for wind and solar energy. Nat. Clim. Change 6, 964–969 (2016).

    Article  Google Scholar 

  3. Stephan, A., Battke, B., Beuse, M. D., Clausdeinken, J. H. & Schmidt, T. S. Limiting the public cost of stationary battery deployment by combining applications. Nat. Energy 1, 16079 (2016).

    Article  Google Scholar 

  4. Fares, R. L. & Webber, M. E. The impacts of storing solar energy in the home to reduce reliance on the utility. Nat. Energy 2, 17001 (2017).

    Article  Google Scholar 

  5. Shearing, P. R. Batteries: imaging degradation. Nat. Energy 1, 16173 (2016).

    Article  Google Scholar 

  6. Perez, A., Moreno, R., Moreira, R., Orchard, M. & Strbac, G. Effect of battery degradation on multi-service portfolios of energy storage. IEEE Trans. Sustain. Energy 7, 1718–1729 (2016).

    Article  Google Scholar 

  7. Hoke, A., Brissette, A., Smith, K., Pratt, A. & Maksimovic, D. Accounting for lithium-ion battery degradation in electric vehicle charging optimization. IEEE J. Emerg. Sel. Top. Power Electron. 2, 691–700 (2014).

    Article  Google Scholar 

  8. Farzin, H., Fotuhi-Firuzabad, M. & Moeini-Aghtaie, M. A practical scheme to involve degradation cost of lithium-ion batteries in vehicle-to-grid applications. IEEE Trans. Sustain. Energy 7, 1730–1738 (2016).

    Article  Google Scholar 

  9. Zhang, Z., Wang, J. X. & Wang, X. L. An improved charging/discharging strategy of lithium batteries considering depreciation cost in day-ahead microgrid scheduling. Energy Convers. Manag. 105, 675–684 (2015).

    Article  Google Scholar 

  10. Bordin, C. et al. A linear programming approach for battery degradation analysis and optimization in offgrid power systems with solar energy integration. Renew. Energy 101, 417–430 (2017).

    Article  Google Scholar 

  11. Shi, Y., Xu, B., Wang, D. & Zhang, B. Using battery storage for peak shaving and frequency regulation: joint optimization for superlinear gains. IEEE Trans. Power Syst. (in the press);

  12. Xu, B., Zhao, J., Zheng, T., Litvinov, E. & Kirschen, D. S. Factoring the cycle aging cost of batteries participating in electricity markets. IEEE Trans. Power Syst. 33, 2248–2259 (2018).

    Article  Google Scholar 

  13. Tant, J., Geth, F., Six, D., Tant, P. & Driesen, J. Multiobjective battery storage to improve PV integration in residential distribution grids. IEEE Trans. Sustain. Energy 4, 182–191 (2013).

    Article  Google Scholar 

  14. He, G., Chen, Q., Kang, C., Pinson, P. & Xia, Q. Optimal bidding strategy of battery storage in power markets considering performance-based regulation and battery cycle life. IEEE Trans. Smart Grid 7, 2359–2367 (2016).

    Article  Google Scholar 

  15. Kazemi, M. & Zareipour, H. Long-term scheduling of battery storage systems in energy and regulation markets considering battery’s lifespan. IEEE Trans. Smart Grid (in the press);

  16. Swierczynski, M., Stroe, D. I., Stan, A.-I., Teodorescu, R. & Sauer, D. U. Selection and performance-degradation modeling of LiMO2/Li4Ti5O12 and LiFePO4/C battery cells as suitable energy storage systems for grid integration with wind power plants: an example for the primary frequency regulation service. IEEE Trans. Sustain. Energy 5, 90–101 (2014).

    Article  Google Scholar 

  17. Duggal, I. & Venkatesh, B. Short-term scheduling of thermal generators and battery storage with depth of discharge-based cost model. IEEE Trans. Power Syst. 30, 2110–2118 (2015).

    Article  Google Scholar 

  18. Cau, G., Cocco, D., Petrollese, M., Kaer, S. K. & Milan, C. Energy management strategy based on short-term generation scheduling for a renewable microgrid using a hydrogen storage system. Energy Convers. Manag. 87, 820–831 (2014).

    Article  Google Scholar 

  19. Wang, J. et al. Degradation of lithium ion batteries employing graphite negatives and nickel–cobalt–manganese oxide plus spinel manganese oxide positives: Part 1, aging mechanisms and life estimation. J. Power Sources 269, 937–948 (2014).

    Article  Google Scholar 

  20. Xu, B., Oudalov, A., Ulbig, A., Andersson, G. & Kirschen, D. Modeling of lithium-ion battery degradation for cell life assessment. IEEE Trans. Smart Grid 9, 1131–1140 (2018).

    Article  Google Scholar 

  21. Boyd, S. & Vandenberghe, L. Convex Optimization (Cambridge Univ. Press, Cambridge, 2004).

    Book  Google Scholar 

  22. Yang, P. & Nehorai, A. Joint optimization of hybrid energy storage and generation capacity with renewable energy. IEEE Trans. Smart Grid 5, 1566–1574 (2014).

    Article  Google Scholar 

  23. Lujano-Rojas, J. M., Dufo-Lopez, R., Bernal-Agustin, J. L. & Catalao, J. P. S. Optimizing daily operation of battery energy storage systems under real-time pricing schemes. IEEE Trans. Smart Grid 8, 316–330 (2017).

    Article  Google Scholar 

  24. Luo, F. et al. Coordinated operational planning for wind farm with battery energy storage system. IEEE Trans. Sustain. Energy 6, 253–262 (2015).

    Article  Google Scholar 

  25. Varian, H. R Microeconomic Analysis (W. W. Norton: New York, 1978).

    Google Scholar 

  26. Aggarwal, S. K., Saini, L. M. & Kumar, A. Electricity price forecasting in deregulated markets: A review and evaluation. Int. J. Electr. Power Energy Syst. 31, 13–22 (2009).

    Article  Google Scholar 

  27. Conejo, A. J., Plazas, M. A., Espinola, R. & Molina, A. B. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans. Power Syst. 20, 1035–1042 (2005).

    Article  Google Scholar 

  28. DOE Global Energy Storage Database (Sandia National Laboratories);

  29. Su, Y. S., Fu, Y., Cochell, T. & Manthiram, A. A strategic approach to recharging lithium-sulphur batteries for long cycle life. Nat. Commun. 4, 2985 (2013).

    Article  Google Scholar 

  30. Zheng, J. M. et al. Electrolyte additive enabled fast charging and stable cycling lithium metal batteries. Nat. Energy 2, 17012 (2017).

    Article  Google Scholar 

  31. Peterson, S. B., Apt, J. & Whitacre, J. F. Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization. J. Power Sources 195, 2385–2392 (2010).

    Article  Google Scholar 

  32. Ciez, R. E. & Whitacre, J. F. Comparative techno-economic analysis of hybrid micro-grid systems utilizing different battery types. Energy Convers. Manag. 112, 435–444 (2016).

    Article  Google Scholar 

  33. Schmidt, O., Hawkes, A., Gambhir, A. & Staffell, I. The future cost of electrical energy storage based on experience rates. Nat. Energy 2, 17110 (2017).

    Article  Google Scholar 

  34. Darling, R. M., Gallagher, K. G., Kowalski, J. A., Ha, S. & Brushett, F. R. Pathways to low-cost electrochemical energy storage: a comparison of aqueous and nonaqueous flow batteries. Energy Environ. Sci. 7, 3459–3477 (2014).

    Article  Google Scholar 

  35. Kittner, N., Lill, F. & Kammen, D. M. Energy storage deployment and innovation for the clean energy transition. Nat. Energy 2, 17125 (2017).

    Article  Google Scholar 

  36. Ecker, M. et al. Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries. J. Power Sources 248, 839–851 (2014).

    Article  Google Scholar 

  37. Grolleau, S. et al. Calendar aging of commercial graphite/LiFePO4 cell—predicting capacity fade under time dependent storage conditions. J. Power Sources 255, 450–458 (2014).

    Article  Google Scholar 

  38. Keil, P. et al. Calendar aging of lithium-ion batteries I. Impact of the graphite anode on capacity fade. J. Electrochem. Soc. 163, A1872–A1880 (2016).

    Article  Google Scholar 

  39. Akhil, A. A. et al. DOE/EPRI 2013 Electricity Storage Handbook in Collaboration with NRECA (Sandia National Laboratories, 2013).

  40. Guidelines and Discount Rates for Benefit–Cost Analysis of Federal Programs Circular a-94 (US Office of Management and Budget, 2016).

  41. He, G., Chen, Q., Kang, C., Xia, Q. & Poolla, K. Cooperation of wind power and battery storage to provide frequency regulation in power markets. IEEE Trans. Power Syst. 32, 3559–3568 (2017).

    Article  Google Scholar 

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This work was partially supported by the US Department of Energy under Grant DEEE0007165.

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Authors and Affiliations



G.H., J.F.W. and Q.C. conceived and designed the research. G.H. developed the decision framework. G.H. and J.F.W. carried out the simulations and analyses. All authors contributed to writing the article.

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Correspondence to Jay F. Whitacre.

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The authors declare no competing interests.

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Supplementary Information

Supplementary Notes 1–3, Supplementary Figures 1–4 and Supplementary References.

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He, G., Chen, Q., Moutis, P. et al. An intertemporal decision framework for electrochemical energy storage management. Nat Energy 3, 404–412 (2018).

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