Tracking control of wind-photovoltaic-storage hybrid system under uncertainty in renewable power generation

Yujing Shi; Mifeng Ren; Lan Cheng; Jianhua Zhang; Junghui Chen; Yujing Shi; Mifeng Ren; Lan Cheng; Jianhua Zhang; Junghui Chen

doi:10.3934/electreng.2026015

AIMS Electronics and Electrical Engineering

2026, Volume 10, Issue 2: 368-394. doi: 10.3934/electreng.2026015

Previous Article Next Article

Research article

Tracking control of wind-photovoltaic-storage hybrid system under uncertainty in renewable power generation

1.
College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2.
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China
3.
Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, Taoyuan, Taiwan 32032, Republic of China

Received: 01 November 2025 Revised: 19 March 2026 Accepted: 30 March 2026 Published: 12 May 2026

Accurately tracking planned output is a core requirement for ensuring the stable grid connection of hybrid wind-photovoltaic-storage systems. However, the strong randomness of wind and solar power output not only affects the tracking accuracy but also leads to frequent charge and discharge cycles of the energy storage system, thereby shortening its service life. To address this, we propose a control method for the energy storage system that accounts for wind and solar uncertainty, with the dual objectives of improving the planned output tracking performance and extending the energy storage system lifespan. By optimally controlling the charge and discharge of the energy storage system, this method enables the output of the hybrid wind-photovoltaic-storage system to track the planned output. The approach models wind and solar power joint distribution using Copula functions and applies K-means clustering to extract representative scenarios, reducing computational complexity. An interval control mechanism defines acceptable deviation ranges around planned output, minimizing unnecessary energy storage system activity during minor fluctuations. The core contribution is a generalized mutual entropy–proximal policy optimization control strategy, which quantifies non-Gaussian tracking errors using generalized mutual entropy while leveraging proximal policy optimization for adaptive learning and improved robustness. Real-world data validation demonstrated significant improvements: 31.6% reduction in average tracking error, 12.6% increase in output compliance within acceptable ranges, 33.4% reduction in maximum tracking error, and approximately 35% fewer ESS charge/discharge cycles. These results confirmed the method's effectiveness in addressing renewable generation uncertainty while improving tracking performance and extending energy storage system service life.
- generalized correntropy,
- reinforcement learning,
- tracking control,
- uncertainty and non-Gaussianity,
- wind-photovoltaic-storage hybrid system
Citation: Yujing Shi, Mifeng Ren, Lan Cheng, Jianhua Zhang, Junghui Chen. Tracking control of wind-photovoltaic-storage hybrid system under uncertainty in renewable power generation[J]. AIMS Electronics and Electrical Engineering, 2026, 10(2): 368-394. doi: 10.3934/electreng.2026015

Related Papers:

Abstract

Accurately tracking planned output is a core requirement for ensuring the stable grid connection of hybrid wind-photovoltaic-storage systems. However, the strong randomness of wind and solar power output not only affects the tracking accuracy but also leads to frequent charge and discharge cycles of the energy storage system, thereby shortening its service life. To address this, we propose a control method for the energy storage system that accounts for wind and solar uncertainty, with the dual objectives of improving the planned output tracking performance and extending the energy storage system lifespan. By optimally controlling the charge and discharge of the energy storage system, this method enables the output of the hybrid wind-photovoltaic-storage system to track the planned output. The approach models wind and solar power joint distribution using Copula functions and applies K-means clustering to extract representative scenarios, reducing computational complexity. An interval control mechanism defines acceptable deviation ranges around planned output, minimizing unnecessary energy storage system activity during minor fluctuations. The core contribution is a generalized mutual entropy–proximal policy optimization control strategy, which quantifies non-Gaussian tracking errors using generalized mutual entropy while leveraging proximal policy optimization for adaptive learning and improved robustness. Real-world data validation demonstrated significant improvements: 31.6% reduction in average tracking error, 12.6% increase in output compliance within acceptable ranges, 33.4% reduction in maximum tracking error, and approximately 35% fewer ESS charge/discharge cycles. These results confirmed the method's effectiveness in addressing renewable generation uncertainty while improving tracking performance and extending energy storage system service life.

References

[1]	Liu B, Lund JR, Liao S, Jin X, Liu L, Cheng C (2020) Optimal power peak shaving using hydropower to complement wind and solar power uncertainty. Energ Convers Manage 209: 112628. https://doi.org/10.1016/j.enconman.2020.112628 doi: 10.1016/j.enconman.2020.112628
[2]	Yu Q, Gao S, Sun G, Qin R (2023) Optimization of wind and solar energy storage system capacity configuration based on the Parzen window estimation method. J Renew Sustain Energ 15. https://doi.org/10.1063/5.0172720 doi: 10.1063/5.0172720
[3]	Rekioua D (2023) Energy storage systems for photovoltaic and wind systems: A review. Energies 16: 3893. https://doi.org/10.3390/en16093893 doi: 10.3390/en16093893
[4]	Choopani K, Effatnejad R, Hedayati M (2020) Coordination of energy storage and wind power plant considering energy and reserve market for a resilience smart grid. J Energy Storage 30: 101542. https://doi.org/10.1016/j.est.2020.101542 doi: 10.1016/j.est.2020.101542
[5]	Deepa R, Shakila Devi GT, Balamurugan KS (2025) Hybrid photovoltaic wind renewable energy sources for microgrid using osprey optimization algorithm and augmented physics informed neural network. J Renew Sustain Energ 17. https://doi.org/10.1063/5.0257110 doi: 10.1063/5.0257110
[6]	Yin S, Chen S, Pan N, Bu Y, Lin C, Yang J (2025) Optimization of multi-energy collaborative economic operation in plateau zero-carbon parks considering the consumption of wind and solar energy. J Renew Sustain Energ 17. https://doi.org/10.1063/5.0289642 doi: 10.1063/5.0289642
[7]	Li H, Wang S, Gao J (2019) Research on Energy Storage Control Strategy of Wind Solar Storage Combined Generation System Based on Tracking Plan. Electrical & Energy Management Technology, 71‒78. https://doi.org/10.16628/j.cnki.2095-8188.2019.04.013 doi: 10.16628/j.cnki.2095-8188.2019.04.013
[8]	Niu R, Guo J, Li X, Shu J, Ma X, Liu L (2020) Energy storage control strategy of wind-photovoltaic-storage hybrid system. Therm. Power Gener 49: 150‒155. https://doi.org/10.19666/j.rlfd.202005142 doi: 10.19666/j.rlfd.202005142
[9]	Qi X, Li X, He S (2018) FMPC-VMD Based Control Strategy for Tracking Generation Plans of Multi-type Energy Storage System. In 2018 China International Conference on Electricity Distribution (CICED), 2253‒2259. https://doi.org/10.1109/ciced.2018.8592371
[10]	Zhang S, Ma C, Yang Z, Wang Y, Wu H, Ren Z (2023) Deep Deterministic Policy Gradient Algorithm Based Wind-Photovoltaic-Storage Hybrid System Joint Dispatch. Electric Power 56: 68‒76.
[11]	Yin Q, Wang Z, Sun T, Li W, Chong F (2019) Environmental and economic dispatch with photovoltaic and wind power. In 2019 IEEE Sustainable Power and Energy Conference (iSPEC), 1108‒1112. https://doi.org/10.1109/ispec48194.2019.8975242
[12]	Wang Z, Zhu H, Zhang D, Goh H, Dong Y, Wu T (2023) Modelling of wind and photovoltaic power output considering dynamic spatio-temporal correlation. Applied Energy 352: 121948. https://doi.org/10.1016/j.apenergy.2023.121948 doi: 10.1016/j.apenergy.2023.121948
[13]	Lin S, Liu C, Shen Y, Li F, Li D, Fu Y (2021) Stochastic planning of integrated energy system via Frank-Copula function and scenario reduction. IEEE T Smart Grid 13: 202‒212. https://doi.org/10.1109/tsg.2021.3119939 doi: 10.1109/tsg.2021.3119939
[14]	Meng J, Dong Z, Zhu S (2025) A copula-based wind-solar complementarity coefficient: Case study of two clean energy bases, China. Energy 318: 134904. https://doi.org/10.1016/j.energy.2025.134904 doi: 10.1016/j.energy.2025.134904
[15]	Liang Y, Ma H, Liang Z, Wang H, Li J (2024) A method for configuring hybrid electrolyzers based on joint wind and photovoltaic power generation modeling using copula functions. Sustain Energy Grids 40: 101539. https://doi.org/10.1016/j.segan.2024.101539 doi: 10.1016/j.segan.2024.101539
[16]	Lanjing X, Zhiwei W, Yanfeng L (2017) The spatial and temporal variation features of wind-sun complementarity in China. Energy Convers Manage 154: 138‒148. https://doi.org/10.1016/j.enconman.2017.10.031 doi: 10.1016/j.enconman.2017.10.031
[17]	Ru Y, Wang Y, Mao W, Zheng D, Fang W (2024) Dynamic Environmental Economic Dispatch Considering the Uncertainty and Correlation of Photovoltaic–Wind Joint Power. Energies 17: 6247. https://doi.org/10.3390/en17246247 doi: 10.3390/en17246247
[18]	Gao F, Bao D, Zhao M, Wang T, Xu J (2025) Smoothing Characteristic of Wind-Solar Coupled Output Fluctuations by Hybrid Energy Storage under Multi-Scenario Planning. Transactions of China Electrotechnical Society 40: 2827‒2839. https://doi.org/10.19595/j.cnki.1000-6753.tces.241679 doi: 10.19595/j.cnki.1000-6753.tces.241679
[19]	Ren M, Cheng T, Chen J, Xu X, Cheng L (2016) Single neuron stochastic predictive PID control algorithm for nonlinear and non-Gaussian systems using the survival information potential criterion. Entropy 18: 218. https://doi.org/10.3390/e18060218 doi: 10.3390/e18060218
[20]	Zhang J, Zhou S, Ren M, Yue H (2016) Adaptive neural network cascade control system with entropy-based design. IET Control Theory Appl 10: 1151‒1160. https://doi.org/10.1049/iet-cta.2015.0992 doi: 10.1049/iet-cta.2015.0992
[21]	SUN L, DANG S, LIU G, WANG S, HU P (2025) A frequency-modulation power optimization method for energy storage power stations considering the transition state of charge-discharge and power constraints. Energy Storage Science and Technology 14: 1286‒1298. https://doi.org/10.19799/j.cnki.2095-4239.2024.1191 doi: 10.19799/j.cnki.2095-4239.2024.1191
[22]	Chen L, Qu H, Zhao J (2018) Generalized Correntropy based deep learning in presence of non-Gaussian noises. Neurocomputing 278: 41‒50. https://doi.org/10.1016/j.neucom.2017.06.080 doi: 10.1016/j.neucom.2017.06.080
[23]	Heer, J (2021) Fast & accurate gaussian kernel density estimation. In 2021 IEEE Visualization Conference (VIS), 11‒15. https://doi.org/10.1109/vis49827.2021.9623323
[24]	Ismail I, Abd Mutalip F NN, Jacob K (2023) A comprehensive review on the development of copulas in financial field. J Intell Fuzzy Syst 45: 604‒-6062. https://doi.org/10.3233/jifs-223481 doi: 10.3233/jifs-223481
[25]	Sinaga KP, Yang MS (2020) Unsupervised K-means clustering algorithm. IEEE access 8: 80716‒80727. https://doi.org/10.1109/access.2020.2988796 doi: 10.1109/access.2020.2988796
[26]	Elumalai VK (2025) A proximal policy optimization based deep reinforcement learning framework for tracking control of a flexible robotic manipulator. Results in Engineering 25: 104178. https://doi.org/10.1016/j.rineng.2025.104178 doi: 10.1016/j.rineng.2025.104178

Reader Comments

Your name:*

Email:*
© 2026 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)