Optimal operation of Distributed Battery Energy Storage System
R. Chaer(1,2), G. Flieller(2) and G. Casaravilla(1) 1.Institute of Electrical Engineering (IIE), Faculty of Engineering - Universidad de la República Oriental del Uruguay, Montevideo (Uruguay) 2.Administración Del Mercado Eléctrico (ADME) 2026-02-15

Abstract

This paper presents an algorithm for the optimal-operable dispatch of distributed battery banks in systems with high integration of variable renewable energies. As a test case, the application of the algorithm is presented in a possible expansion of the Colombian system 2024-2039, subdivided into five regions each with its DBESS. The novelty lies in how to integrate a set of technologies such as stochastic dynamic programming, with reinforcement learning and optimization of the marginal benefit of an agent to obtain an operable operation policy close to the achievable optimum.

Key words: Operation, distributed battery banks, operation policy, energy dispatch.

References

[1] J. Rushkoff and T. Bazzi, ‘Current and Future Energy Storage Market Revenues Sensitivity Study’, in 2024 IEEE Electrical Energy Storage Application and Technologies Conference (EESAT), San Diego, CA, USA, 2024, pp. 1–5[Online]. Availablehttps://ieeexplore.ieee.org/document/10471205/[Accessed: 15February2025].

[2] R. Bellman, Dynamic programing. Princeton University Press, 1957.

[3] M.V.F Pereira and L. M. V. G. Pinto, ‘Multi-stage stochastic optimization applied to energy planning’, Mathematical Programming, vol. 52, pp. 359–375, 1991 [Online]. Available: https://doi.org/10.1007/BF01582895.

[4] L. Glomb et al., ‘A rolling-horizon approach for multi-period optimization’, European Journal of Operational Research, vol. 300, no. 1, pp. 189–206, Jul. 2022 [Online]. Available: 10.1016/j.ejor.2021.07.043.

[5] R. Chaer et al., ‘Machine Learning Applied to the Operation of Fully Renewable Energy Systems’, in 2023 IEEE PES GTD International Conference and Exposition (GTD), Istanbul, Turkiye, 2023, pp. 139–143[Online]. Availablehttps://ieeexplore.ieee.org/document/10261764/[Accessed: 1October2023].

[6] W. B. Powell, Approximate dynamic programming. Wiley, 2011.

[7] D. P. Bertsekas and J. N. Tsitsiklis, Neuro-Dynamic Programming. Athena Scientific, 1996.

[8] N. Jayasekara et al., ‘Optimal Operation of Distributed Energy Storage Systems to Improve Distribution Network Load and Generation Hosting Capability’, IEEE Trans. Sustain. Energy, vol. 7, no. 1, pp. 250–261, Jan. 2016 [Online]. Available: 10.1109/TSTE.2015.2487360.

[9] G. Casaravilla et al., ‘Competition between NCRE, Batteries and Flexible Thermal Units in the Planning of Generation Expansion in Uruguay’, in 2024 IEEE URUCON, Montevideo, Uruguay, 2024, pp. 1–5[Online]. Availablehttps://ieeexplore.ieee.org/document/10850235/[Accessed: 15February2025].

[10] ‘SimSEE’. IIE-FING-UdeLaR, 2022[Online]. Availablehttps://simsee.org.

[11] E. Garcia-Garrido et al., ‘Net demand short-term forecasting in a distribution substation with PV power generation’, E3S Web Conf., vol. 152, p. 01001, 2020 [Online]. Available: 10.1051/e3sconf/202015201001.

[12] IEA, ‘Batteries and Secure Energy Transitions’. IEA, 2024[Online]. Availablehttps://iea.blob.core.windows.net/assets/ cb39c1bf-d2b3-446d-8c35-aae6b1f3a4a0/BatteriesandSecureEnergyTransitions.pdf.

[13] D. R. Jiang et al., ‘A comparison of approximate dynamic programming techniques on benchmark energy storage problems: Does anything work?’, in 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), Orlando, FL, USA, 2014, pp. 1–8[Online]. Availablehttp://ieeexplore.ieee.org/document/7010626/ [Accessed: 19August2022].

[14] K. Liu et al., ‘Optimal operation strategy for distributed battery aggregator providing energy and ancillary services’, J. Mod. Power Syst. Clean Energy, vol. 6, no. 4, pp. 722–732, Jul. 2018 [Online]. Available: 10.1007/s40565-017-0325-9.