The integration of Photovoltaic (PV) with Doubly Fed Induction Generator (DFIG) wind energy sources into smart grid systems presents significant challenges in efficient energy management and control. This study proposes a novel energy management strategy and advanced control framework specifically designed for a PV-DFIG based smart grid system. A high gain hybrid Boost (HG-HB) converter is designed to enhance the PV system integration, thereby generating improved voltage conversion efficiency with reduced power losses. Additionally, a novel Chimp optimization-Radial Basis Function Neural Network (CO-RBFNN) based Maximum Power Point Tracking (MPPT) technique is introduced, ensuring highly accurate tracking of maximum power under varying environmental conditions. The battery state of charge (SOC) is monitored using a Recurrent Neural Network (RNN) for enhanced battery performance. A superior system performance is achieved by the continuous monitoring and optimization of PV, DC link, battery, and grid parameters through an Internet of Things (IoT) enabled platform. The proposed approach is validated through Matlab simulations and experimental analysis, demonstrating a voltage conversion efficiency of 95.1%, tracking efficiency of 97.56%, and reduced SOC error. The renewable energy integration is significantly advanced by this study within smart grid environments, thereby addressing reliability challenges and critical performance.

Conflicts of Interest

The authors declare no conflicts of interest.

Open Research

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

1K. Sureshkumar and V. Ponnusamy, “Hybrid Renewable Energy Systems for Power Flow Management in Smart Grid Using an Efficient Hybrid Technique,” Transactions of the Institute of Measurement and Control 42, no. 11 (2020): 2068–2087, https://doi.org/10.1177/0142331220904818.
10.1177/0142331220904818
Web of Science® Google Scholar
2M. Abou Houran, S. M. Bukhari, M. H. Zafar, M. Mansoor, and W. Chen, “COA-CNN-LSTM: Coati Optimization Algorithm-Based Hybrid Deep Learning Model for PV/Wind Power Forecasting in Smart Grid Applications,” Applied Energy 349 (2023): 121638, https://doi.org/10.1016/j.apenergy.2023.121638.
10.1016/j.apenergy.2023.121638
Web of Science® Google Scholar
3K. A. Mahafzah, M. A. Obeidat, A. Mansour, E. R. Sanseverino, and G. Zizzo, “A New Smart Grid Hybrid DC–DC Converter With Improved Voltage Gain and Synchronized Multiple Outputs,” Applied Sciences 14, no. 6 (2024): 2274, https://doi.org/10.3390/app14062274.
10.3390/app14062274
CAS Google Scholar
4M. Samiullah, A. Iqbal, I. Ashraf, and S. Rahme, “Split Duty Super Boost Converter for High Voltage Applications in a Dc Microgrid,” IEEE Access 9 (2021): 101078–101088, https://doi.org/10.1109/ACCESS.2021.3097887.
10.1109/ACCESS.2021.3097887
Web of Science® Google Scholar
5V. F. Pires, A. Cordeiro, D. Foito, and J. F. Silva, “Dual Output and High Voltage Gain DC-DC Converter for PV and Fuel Cell Generators Connected to DC Bipolar Microgrids,” IEEE Access 9 (2021): 157124–157133, https://doi.org/10.1109/ACCESS.2021.3122877.
10.1109/ACCESS.2021.3122877
Web of Science® Google Scholar
6T. Porselvi, P. Kannan, S. A. Barathwaj, C. S. Ganesh, and R. Kothai, “ Hybrid Boost Converter for AC and DC Loads Using PV System,” in Recent Advances in Energy Technologies: Select Proceedings of ICEMT (Springer, 2022), 153–164.
Google Scholar
7B. Chandrasekar, C. Nallaperumal, S. Padmanaban, et al., “Non-isolated High-Gain Triple Port DC–DC Buck-Boost Converter With Positive Output Voltage for Photovoltaic Applications,” IEEE Access 8 (2020): 113649–113666, https://doi.org/10.1109/ACCESS.2020.3003192.
10.1109/ACCESS.2020.3003192
Web of Science® Google Scholar
8M. Forouzesh, Y. Shen, K. Yari, Y. P. Siwakoti, and F. Blaabjerg, “High-Efficiency High Step-Up DC–DC Converter With Dual Coupled Inductors for Grid-Connected Photovoltaic Systems,” IEEE Transactions on Power Electronics 33, no. 7 (2017): 5967–5982, https://doi.org/10.1109/TPEL.2017.2746750.
10.1109/TPEL.2017.2746750
Web of Science® Google Scholar
9M. S. Bhaskar, M. Meraj, A. Iqbal, S. Padmanaban, P. K. Maroti, and R. Alammari, “High Gain Transformer-Less Double-Duty-Triple-Mode DC/DC Converter for DC Microgrid,” IEEE Access 7 (2019): 36353–36370, https://doi.org/10.1109/ACCESS.2019.2902440.
10.1109/ACCESS.2019.2902440
Web of Science® Google Scholar
10P. K. Maroti, S. Padmanaban, M. S. Bhaskar, M. Meraj, A. Iqbal, and R. Al-Ammari, “High Gain Three-State Switching Hybrid Boost Converter for DC Microgrid Applications,” IET Power Electronics 12, no. 14 (2019): 3656–3667, https://doi.org/10.1049/iet-pel.2018.6403.
10.1049/iet-pel.2018.6403
Web of Science® Google Scholar
11C. Restrepo, C. Gonzalez-Castano, J. Munoz, A. Chub, E. Vidal-Idiarte, and R. Giral, “An MPPT Algorithm for PV Systems Based on a Simplified Photo-Diode Model,” IEEE Access 9 (2021): 33189–33202, https://doi.org/10.1109/ACCESS.2021.3061340.
10.1109/ACCESS.2021.3061340
Web of Science® Google Scholar
12M. F. Mahmoud, A. T. Mohamed, R. A. Swief, L. A. Said, and A. G. Radwan, “Arithmetic Optimization Approach for Parameters Identification of Different PV Diode Models With FOPI-MPPT,” Ain Shams Engineering Journal 13, no. 3 (2022): 101612, https://doi.org/10.1016/j.asej.2021.10.007.
10.1016/j.asej.2021.10.007
Web of Science® Google Scholar
13M. Etezadinejad, B. Asaei, S. Farhangi, and A. Anvari-Moghaddam, “An Improved and Fast MPPT Algorithm for PV Systems Under Partially Shaded Conditions,” IEEE Transactions on Sustainable Energy 13, no. 2 (2021): 732–742, https://doi.org/10.1109/TSTE.2021.3130827.
10.1109/TSTE.2021.3130827
Google Scholar
14J. Sahoo, S. Samanta, and S. Bhattacharyya, “Adaptive PID Controller With P&O MPPT Algorithm for Photovoltaic System,” IETE Journal of Research 66, no. 4 (2020): 442–453, https://doi.org/10.1080/03772063.2018.1497552.
10.1080/03772063.2018.1497552
Web of Science® Google Scholar
15B. Sabir, S. D. Lu, H. D. Liu, C. H. Lin, A. Sarwar, and L. Y. Huang, “A Novel Isolated Intelligent Adjustable Buck-Boost Converter With Hill Climbing MPPT Algorithm for Solar Power Systems,” PRO 11, no. 4 (2023): 1010, https://doi.org/10.3390/pr11041010.
10.3390/pr11041010
Google Scholar
16S. Bhattacharyya, S. Samanta, and S. Mishra, “Steady Output and Fast Tracking MPPT (SOFT-MPPT) for P&O and InC Algorithms,” IEEE Transactions on Sustainable Energy 12, no. 1 (2020): 293–302, https://doi.org/10.1109/TSTE.2020.2991768.
10.1109/TSTE.2020.2991768
Web of Science® Google Scholar
17I. Haseeb, A. Armghan, W. Khan, et al., “Solar Power System Assessments Using Ann and Hybrid Boost Converter Based Mppt Algorithm,” Applied Sciences 11, no. 23 (2021): 11332, https://doi.org/10.3390/app112311332.
10.3390/app112311332
CAS Google Scholar
18W. S. Abdellatif, M. S. Mohamed, S. Barakat, and A. Brisha, “A Fuzzy Logic Controller Based MPPT Technique for Photovoltaic Generation System,” International Journal on Electrical Engineering & Informatics 13, no. 2 (2021): 394–417, https://doi.org/10.15676/ijeei.2020.13.2.9.
10.15676/ijeei.2021.13.2.9
Google Scholar
19S. A. Ibrahim, A. Nasr, and M. A. Enany, “Maximum Power Point Tracking Using ANFIS for a Reconfigurable PV-Based Battery Charger Under Non-uniform Operating Conditions,” IEEE Access 9 (2021): 114457–114467, https://doi.org/10.1109/ACCESS.2021.3103039.
10.1109/ACCESS.2021.3103039
Web of Science® Google Scholar
20A. Hassan, O. Bass, and M. A. Masoum, “An Improved Genetic Algorithm Based Fractional Open Circuit Voltage MPPT for Solar PV Systems,” Energy Reports 9 (2023): 1535–1548, https://doi.org/10.1109/ACCESS.2021.3103039.
10.1016/j.egyr.2022.12.088
Web of Science® Google Scholar
21S. Obukhov, A. Ibrahim, A. A. Diab, A. S. Al-Sumaiti, and R. Aboelsaud, “Optimal Performance of Dynamic Particle Swarm Optimization Based Maximum Power Trackers for Stand-Alone PV System Under Partial Shading Conditions,” IEEE Access 8 (2020): 20770–20785, https://doi.org/10.1109/ACCESS.2020.2966430.
10.1109/ACCESS.2020.2966430
Web of Science® Google Scholar
22A. G. Abo-Khalil, W. Alharbi, A. R. Al-Qawasmi, M. Alobaid, and I. M. Alarifi, “Maximum Power Point Tracking of PV Systems Under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm,” Sustainability 13, no. 5 (2021): 2656, https://doi.org/10.3390/su13052656.
10.3390/su13052656
CAS Web of Science® Google Scholar
23R. K. Phanden, L. Sharma, J. Chhabra, and H. İ. Demir, “A Novel Modified Ant Colony Optimization Based Maximum Power Point Tracking Controller for Photovoltaic Systems,” Materials Today Proceedings 38 (2021): 89–93, https://doi.org/10.1016/j.matpr.2020.06.020.
10.1016/j.matpr.2020.06.020
Google Scholar
24C. González-Castaño, C. Restrepo, S. Kouro, and J. Rodriguez, “MPPT Algorithm Based on Artificial Bee Colony for PV System,” IEEE Access 9 (2021): 43121–43133, https://doi.org/10.1109/ACCESS.2021.3066281.
10.1109/ACCESS.2021.3066281
Web of Science® Google Scholar
25K. A. Mahafzah, A. Q. Al-Shetwi, M. A. Hannan, T. S. Babu, and N. Nwulu, “A New Cuk-Based DC-DC Converter With Improved Efficiency and Lower Rated Voltage of Coupling Capacitor,” Sustainability 15, no. 11 (2023): 8515, https://doi.org/10.3390/su15118515.
10.3390/su15118515
Web of Science® Google Scholar
26K. O. Vijay and P. Sriramalakshmi, “Comparison Between Zeta Converter and Boost Converter Using Sliding Mode Controller,” International Journal of Engineering Research & Technology (IJERT) 5, no. 7 (2016): 2278.
Google Scholar
27M. R. Banaei and H. A. Bonab, “A High Efficiency Nonisolated Buck–Boost Converter Based on ZETA Converter,” IEEE Transactions on Industrial Electronics 67, no. 3 (2019): 1991–1998, https://doi.org/10.1109/TIE.2019.2902785.
10.1109/TIE.2019.2902785
Web of Science® Google Scholar
28K. A. Mahafzah, M. A. Obeidat, A. Q. Al-Shetwi, and T. S. Ustun, “A Novel Synchronized Multiple Output DC-DC Converter Based on Hybrid Flyback-Cuk Topologies,” Batteries 8, no. 8 (2022): 93, https://doi.org/10.3390/batteries8080093.
10.3390/batteries8080093
Google Scholar
29K. A. Mahafzah and H. A. Rababah, “A Novel Step-Up/Step-Down DC-DC Converter Based on Flyback and SEPIC Topologies With Improved Voltage Gain,” International Journal of Power Electronics and Drive Systems 14, no. 2 (2023): 898–908, https://doi.org/10.11591/ijpeds.v14.i2.pp898-908.
10.11591/ijpeds.v14.i2.pp898-908
Google Scholar
30C. B. Fapi, P. Wira, M. Kamta, A. Badji, and H. Tchakounte, “Real-Time Experimental Assessment of Hill Climbing MPPT Algorithm Enhanced by Estimating a Duty Cycle for PV System,” International Journal of Renewable Energy Research 9 (2019): 1180–1189.
Web of Science® Google Scholar
31A. Chellakhi, S. El Beid, and Y. Abouelmahjoub, “An Improved Maximum Power Point Approach for Temperature Variation in PV System Applications,” International Journal of Photoenergy 1 (2021): 9973204.
Google Scholar

Volume46, Issue4

July/August 2025

Pages 1834-1854

Innovative Strategies for Renewable Energy Integration and Smart Grid Power Management

ABSTRACT

Conflicts of Interest

Open Research

Data Availability Statement

References

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

Innovative Strategies for Renewable Energy Integration and Smart Grid Power Management

ABSTRACT

Conflicts of Interest

Open Research

Data Availability Statement

References

References

Related

Information