Design of Efficient Energy Storage System to Integrate Renewable Energy Source to Support in Grid
DOI:
https://doi.org/10.61453/INTIj.20260335Keywords:
Artificial intelligence (AI), Energy storage system (ESS), Intelligent control, Internet of things (IoT), Renewable energy integrationAbstract
The trend toward green energy systems requires the integration of renewable energy (RE) into modern power grids; however, their intermittency and unpredictability pose significant challenges to reliability. An efficient energy storage system (ESS) provides a viable solution via storage excess energy when production is high and releasing it when output is low or demand is at its highest. This study proposes a novel ESS design that enhances storage efficiency and minimizes degradation by optimizing advanced battery systems through intelligent control algorithms and advanced power electronics. Real-time Internet of Things (IoT) - based monitoring enables dynamic responses to fluctuating grid conditions, while predictive artificial intelligence-driven analytics improve the scheduling of energy storage and distribution by forecasting demand and renewable generation patterns. The proposed system not only facilitates peak shaving and load leveling but also ensures stable voltage and frequency regulation, reduces dependence on fossil-fuel-based backup systems, and enhances grid fortitude by offering reliable backup energy during strikes. The operating principle and steady-state behavior of the system are presented and validated through MATLAB/Simulink simulations, demonstrating its effectiveness in enabling reliable, efficient, and stable embedding renewable energy into future smart grids
References
Amir, M., Deshmukh, R. G., Khalid, H. M., Said, Z., Raza, A., Muyeen, S. M., & Sopian, K. (2023). Energy storage technologies: An integrated survey of developments, global economical/environmental effects, optimal scheduling model, and sustainable adaption policies. Journal of Energy Storage, 72, 108694. https://doi.org/10.1016/j.est.2023.108694
Amirkhalili, S. A., Zahedi, A., Ghaffarinezhad, A., & Kanani, B. (2025). Design and evaluation of a hybrid wind/hydrogen/fuel cell energy system for sustainable off-grid power supply. International Journal of Hydrogen Energy, 100, 1456–1482. https://doi.org/10.1016/j.ijhydene.2024.12.259
Bangura, A. A., Errouha, M., Hihi, H., & Chalh, Z. (2023). Modelling and simulation of the hybrid system PV-wind. Statistics, Optimization & Information Computing, 11(1), 143–153. https://doi.org/10.19139/soic-2310-5070-1535
Bharathidasan, M., Indragandhi, V., Kuppusamy, R., Teekaraman, Y., Urooj, S., & Alwadi, N. (2022). Intelligent fuzzy based high gain non-isolated converter for DC micro-grids. Computers, Materials & Continua, 71(2), 4069–4084. https://doi.org/10.32604/cmc.2022.021846
Capra, M., Bussolino, B., Marchisio, A., Masera, G., Martina, M., & Shafique, M. (2020). Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead. IEEE Access, 8, 225134–225180. https://doi.org/10.1109/ACCESS.2020.3039858
Chidolue, O., & Iqbal, T. (2023). System monitoring and data logging using PLX-DAQ for solar-powered oil well pumping. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0690–0694). IEEE. https://doi.org/10.1109/CCWC57344.2023.10099099
Dodo, U. A., Salami, B. A., Bashir, F. M., Hamdoun, H. Y., Alsadun, I. S. R., Dodo, Y. A., & Abba, S. I. (2024). Investigating the influence of erratic grid on stationary battery energy storage technologies in hybrid power systems: Techno-environ-economic perspectives. Energy, 304, 132140. https://doi.org/10.1016/j.energy.2024.132140
Gür, T. M. 2018, ‘Review of electrical energy storage technologies, materials and systems: challenges and prospects for large-scale grid storage’ , Energy & environmental science, vol. 11, no.10, pp.2696-2767. https://doi.org/10.1039/c8ee01419a
Gao, X., Dong, Y., Li, S., Zhou, J., Wang, L., & Wang, B. (2020). MOFs and COFs for batteries and supercapacitors. Electrochemical Energy Reviews, 3(1), 81–126. https://doi.org/10.1007/s41918-019-00055-1
Ghenai, C., Ahmad, F. F., & Rejeb, O. (2024). Artificial neural network-based models for short term forecasting of solar PV power output and battery state of charge of solar electric vehicle charging station. Case Studies in Thermal Engineering, 61, 105152. https://doi.org/10.1016/j.csite.2024.105152
Gulzar, M. M., Iqbal, A., Sibtain, D., & Khalid, M. (2023). An innovative converter less solar PV control strategy for a grid connected hybrid PV/wind/fuel-cell system coupled with battery energy storage. IEEE Access, 11, 23245–23259. https://doi.org/10.1109/ACCESS.2023.3252891
Hatami, M., Nasab, M. A., Chen, Y., Mohammadi, J., Ardiles-Cruz, E., & Blasch, E. (2024). ELOCESS: An ESS management framework for improved smart grid stability and flexibility. IEEE Transactions on Consumer Electronics, 71(1), 1819–1830. https://doi.org/10.1109/TCE.2024.3515877
Helwig, A., & Bell, J. (2024). What energy storage technologies will Australia need as renewable energy penetration rises? Journal of Energy Storage, 95, 112701. https://doi.org/10.1016/j.est.2024.112701
Jose, T. M. (2018). A novel sensor based approach to predictive maintenance of machines by leveraging heterogeneous computing. In 2018 IEEE SENSORS (pp. 1–4). IEEE. https://doi.org/10.1109/ICSENS.2018.8589620
Khan, M. R., Haider, Z. M., Malik, F. H., Almasoudi, F. M., Alatawi, K. S. S., & Bhutta, M. S. (2024). A comprehensive review of microgrid energy management strategies considering electric vehicles, energy storage systems, and AI techniques. Processes, 12(2), 270. https://doi.org/10.3390/pr12020270
Li, X., Li, P., Wu, Z., Luo, D., Yu, H. Y., & Lu, Z. H. (2021). Review and perspective of materials for flexible solar cells. Materials Reports: Energy, 1(1), 100001. https://doi.org/10.1016/j.matre.2020.09.001
Lu, Q., Parlikad, A. K. V., Woodall, P., Ranasinghe, G. D., & Heaton, J. (2019). Developing a dynamic digital twin at a building level: Using Cambridge campus as case study. In Emerald (pp. 67–75). https://doi.org/10.17863/CAM.38523
Madani, S. S., Shabeer, Y., Allard, F., Fowler, M., Ziebert, C., Wang, Z., & Khalilpour, K. (2025). A comprehensive review on lithium-ion battery lifetime prediction and aging mechanism analysis. Batteries, 11(4), 127. https://doi.org/10.3390/batteries11040127
Mehta, H. K., Warke, H., Kukadiya, K., & Panchal, A. K. (2019). Accurate expressions for single-diode-model solar cell parameterization. IEEE Journal of Photovoltaics, 9(3), 803–810. https://doi.org/10.1109/JPHOTOV.2019.2896264
Okunade, B. A., Adediran, F. E., Maduka, C. P., & Adegoke, A. A. (2023). Community-based mental health interventions in Africa: A review and its implications for US healthcare practices. International Medical Science Research Journal, 3(3), 68–91. https://doi.org/10.51594/imsrj.v3i3.621
Oladele, I. O., Adelani, S. O., Taiwo, A. S., Akinbamiyorin, I. M., Olanrewaju, O. F., & Orisawayi, A. O. (2025). Polymer-based nanocomposites for supercapacitor applications: A review on principles, production and products. RSC Advances, 15(10), 7509–7534. https://doi.org/10.1039/D4RA08601E
Park, H. (2024). Locational role analysis of energy storage systems based on optimal capacity needs and operations under high penetration of renewable energy. Energies, 17(3), 743. https://doi.org/10.3390/en17030743
Qiu, C., Hu, Y., Chen, Y., & Zeng, B. (2019). Deep deterministic policy gradient (DDPG)-based energy harvesting wireless communications. IEEE Internet of Things Journal, 6(5), 8577–8588. https://doi.org/10.1109/JIOT.2019.2921159
Rahman, M. M., Dadon, S. H., He, M., Giesselmann, M., & Hasan, M. M. (2024). An overview of power system flexibility: High renewable energy penetration scenarios. Energies, 17(24), 6393. https://doi.org/10.3390/en17246393
Rathore, N., Panwar, N. L., Yettou, F., & Gama, A. (2021). A comprehensive review of different types of solar photovoltaic cells and their applications. International Journal of Ambient Energy, 42(10), 1200–1217. https://doi.org/10.1080/01430750.2019.1592774
Sajjad, M., Zhang, J., Zhang, S., Zhou, J., Mao, Z., & Chen, Z. (2024). Long-life lead-carbon batteries for stationary energy storage applications. The Chemical Record, 24(3), e202300315. https://doi.org/10.1002/tcr.202300315
Sebasthirani, K., Rajesh, G., Bhavani, M. D., Varshney, N., Sathish, T., & Karthigaivel, K. (2023). IoT-based second-life storage system prototype for LiFePO4 batteries. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1–7). IEEE. https://doi.org/10.1109/ACCAI58221.2023.10199869
Yao, L., Yang, B., Cui, H., Zhuang, J., Ye, J., & Xue, J. (2016). Challenges and progresses of energy storage technology and its application in power systems. Journal of Modern Power Systems and Clean Energy, 4(4), 519–528. https://doi.org/10.1007/s40565-016-0248-x
Zalosh, R., Gandhi, P., & Barowy, A. (2021). Lithium-ion energy storage battery explosion incidents. Journal of Loss Prevention in the Process Industries, 72, 104560. https://doi.org/10.1016/j.jlp.2021.104560
Zhang, X., Huang, Y., & Fu, Y. (2024). Oscillation energy transfer and integrated stability control of grid-forming wind turbines. IEEE Transactions on Sustainable Energy, 16(2), 826–839. https://doi.org/10.1109/TSTE.2024.3485763
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