The coordinated ramp-frequency regulation optimization strategy considering energy storage capacity uncertainty

Haiyun An , Tianhui Zhao , Yuqing Bao , Jingbo Zhao

AIMS Energy ›› 2026, Vol. 14 ›› Issue (2) : 449 -471.

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AIMS Energy ›› 2026, Vol. 14 ›› Issue (2) :449 -471. DOI: 10.3934/energy.2026019
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The coordinated ramp-frequency regulation optimization strategy considering energy storage capacity uncertainty
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Abstract

Energy storage systems (ESSs) serve as flexible resources that significantly contribute to the integration of uncertain renewable energy sources. They can mitigate power fluctuations through fast charging and discharging in ramping services while providing frequency regulation reserves to maintain system stability. However, traditional methods often neglect the uncertainty of storage capacity and the coupling between ramping and frequency regulation constraints, leading to potential capacity violations and inefficient joint optimization. In this paper, we propose a coordinated ramping-frequency regulation optimization strategy considering ESS capacity uncertainty. A probabilistic confidence-based model was developed to characterize the stochastic nature of capacity, and unified ramping-frequency regulation constraints were formulated for ESSs and thermal units. Nonlinear terms were linearized to derive a tractable mixed-integer quadratic programming (MIQP) model. Case studies verified that the proposed approach effectively accounts for capacity uncertainty, enables shared utilization of ramping and regulation capabilities under unified constraints, and enhances the overall efficiency of system flexibility deployment.

Keywords

energy storage systems / ramp / frequency regulation / capacity uncertainty / optimization

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Haiyun An, Tianhui Zhao, Yuqing Bao, Jingbo Zhao. The coordinated ramp-frequency regulation optimization strategy considering energy storage capacity uncertainty. AIMS Energy, 2026, 14 (2) : 449-471 DOI:10.3934/energy.2026019

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References

[1]

Yu Y, Jiang H, Zhang N, et al. (2025) Revisiting capacity value of variable renewable energy generation in power systems with high renewable energy penetration. J Mod Power Syst Clean Energy 13: 1593-1603. https://doi.org/10.35833/MPCE.2024.001101

[2]

Bao YQ, Song XR, Zhou PC (2025) Stackelberg-game-based energy optimization strategy for interactive energy use of flexible manufacturing industrial parks. Appl Energy 401: 126701. https://doi.org/10.1016/j.apenergy.2025.126701

[3]

Correa-Posada CM, Morales-España G, Dueñas P, et al. (2017) Dynamic ramping model including intraperiod ramp-rate changes in unit commitment. IEEE Trans Sustainable Energy 8: 43-50. https://doi.org/10.1109/TSTE.2016.2578302

[4]

Gong Y, Jiang Q, Baldick R (2016) Ramp event forecast based wind power ramp control with energy storage system. IEEE Trans Power Syst 31: 1831-1844. https://doi.org/10.1109/TPWRS.2015.2445382

[5]

Bao YQ, Yu QQ, Chen Y, et al. (2024) Wind power smoothing control by energy storage based on area-equilibrium empirical mode decomposition. J Mod Power Syst Clean Energy 13: 1238-1247. https://doi.org/10.35833/MPCE.2024.000674

[6]

Zhang M, Xiao Y, Gao Y, et al. (2024) An novel soft-linking model: Coordinating the regulative flexibility and operational stability of pumped storage units. Appl Energy 374: 123942. https://doi.org/10.1016/j.apenergy.2024.123942

[7]

Rajamand S (2021) Load frequency control and dynamic response improvement using energy storage and modeling of uncertainty in renewable distributed generators. J Energy Storage 37: 102467. https://doi.org/10.1016/j.est.2021.102467

[8]

Pan X, Xu H, Lu C, et al. (2016) Energy storage system control strategy in frequency regulation. 2016 IEEE International Conference on Automation Science and Engineering (CASE) , 664-669. https://doi.org/10.1109/COASE.2016.7743466

[9]

Xu C, Zhao D, Feng X, et al. (2025) Multi-damping frequency-constrained unit commitment and multi-reserve resource allocation for RES-penetrated power systems. CSEE J Power Energy Syst 1-14. https://doi.org/10.17775/CSEEJPES.2024.02590

[10]

Shi Y, Xu B, Wang D, et al. (2018) Using battery storage for peak shaving and frequency regulation: Joint optimization for superlinear gains. IEEE Trans Power Syst 33: 2882-2894. https://doi.org/10.1109/TPWRS.2017.2749512

[11]

Jo J, Park J (2020) Demand-Side management with shared energy storage system in smart grid. IEEE Trans Smart Grid 11: 4466-4476. https://doi.org/10.1109/TSG.2020.2980318

[12]

Nazemi M, Dehghanian P, Lu X, et al. (2021) Uncertainty-Aware deployment of mobile energy storage systems for distribution grid resilience. IEEE Trans Smart Grid 12: 3900-3912. https://doi.org/10.1109/TSG.2021.3064312

[13]

Li J, Zhang M, Shuai Z, et al. (2025) Two-Stage hybrid optimization of aggregated distributed generalized energy storages for complete uncertainty elimination. IEEE Trans Smart Grid 16: 2075-2086. https://doi.org/10.1109/TSG.2024.3525070

[14]

Cheng J, Chu F, Zhou M (2018) An improved model for parallel machine scheduling under time-of-use electricity price. IEEE Trans Automat Sci Eng 15: 896-899. https://doi.org/10.1109/TASE.2016.2631491

[15]

Liu Y, Zeng W, Chen M, et al. (2024) Chance-constrained scheduling considering frequency support from electric vehicles under multiple uncertainties. IET Renewable Power Gener 18: 4348-4359. https://doi.org/10.1049/rpg2.13171

[16]

Qiu Z, Zhang W, Lu S, et al. (2022) Charging-rate-based battery energy storage system in wind farm and battery storage cooperation bidding problem. CSEE J Power Energy Syst 8: 659-668. https://doi.org/10.17775/CSEEJPES.2021.00230

[17]

Kim HJ, Sioshansi R, Lannoye E, et al. (2025) Assessing the capacity value of energy storage that provides frequency regulation. IEEE Trans Power Syst 40: 2661-2673. https://doi.org/10.1109/TPWRS.2024.3501095

[18]

Zhang S, Mishra Y, Ledwich G, et al. (2013) The operating schedule for battery energy storage companies in electricity market. J Mod Power Syst Clean Energy 1: 271-280. https://doi.org/10.1007/s40565-013-0039-6

[19]

Goh HH, Ou Y, Dai W, et al. (2025) Frequency-constrained unit commitment considering coordinated frequency control strategy of wind turbines and energy storage system. J Mod Power Syst Clean Energy 13: 1933-1944. https://doi.org/10.35833/MPCE.2024.000976

[20]

Chen Y, Quan H, Xiao J, et al. (2025) Frequency stability-constrained optimal scheduling with energy storage support for microgrid under uncertainty. 2025 10th Asia Conference on Power and Electrical Engineering (ACPEE), 2411-2416. https://doi.org/10.1109/ACPEE64358.2025.11041225

[21]

He G, Chen Q, Kang C, et al. (2017) Cooperation of wind power and battery storage to provide frequency regulation in power markets. IEEE Trans Power Syst 32: 3559-3568. https://doi.org/10.1109/TPWRS.2016.2644642

[22]

Wen Y, Li W, Huang G, et al. (2016) Frequency dynamics constrained unit commitment with battery energy storage. IEEE Trans Power Syst 31: 5115-5125. https://doi.org/10.1109/TPWRS.2016.2521882

[23]

Martin S, Onori S, Rajagopal R (2022) Effect of state of charge uncertainty on battery energy storage systems. IFAC-PapersOnLine 55: 740-745. https://doi.org/10.1016/j.ifacol.2022.11.270

[24]

Rosewater D, Ferreira S, Schoenwald D, et al. (2019) Battery energy storage state-of-charge forecasting: Models, optimization, and accuracy. IEEE Trans Smart Grid 10: 2453-2462. https://doi.org/10.1109/TSG.2018.2798165

[25]

Peng S, Chen C, Shi H, et al. (2017) State of charge estimation of battery energy storage systems based on adaptive unscented kalman filter with a noise statistics estimator. IEEE Access 5: 13202-13212. https://doi.org/10.1109/ACCESS.2017.2725301

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