ORIGINAL PAPER
Enhancing power matching and extraction in hybrid renewable energy systems through HawkDeep Gradient Fuzzy Recurrent Control
1
St. Peter Institue of Higher Education and Research, India
Submission date: 2025-05-29
Final revision date: 2025-09-29
Acceptance date: 2025-10-09
Publication date: 2026-04-02
Corresponding author
Polityka Energetyczna – Energy Policy Journal 2026;29(1):171-204
KEYWORDS
TOPICS
ABSTRACT
Hybrid renewable energy systems are one of the highly suitable solutions for the growing energy demand. However, the performance of this system is significantly affected by the power imbalance, unstable DC-bus voltage, and reduced system efficiency. To overcome these issues, a novel HawkDeep Gradient Fuzzy Recurrent Control framework is proposed. This method is used to optimize the management of power characteristics and stabilize the system performance in High Renewable Energy Systems. Moroever the current control algorithms frequently rely on predefined rules, which are not flexible enough to deal with sudden and erratic variations in load demands and power generation. To resolve this, an Intelligent Hawk Fuzzy Control Algorithm is proposed, which integrates fuzzy logic with Reflective Quasi-Hawk Optimization to rapidly get the best answers, thereby guaranteeing the balanced power supply and demand even in the event of sudden inrush currents. Furthermore, the mismatch in ramp rates causes temporary power imbalances and instability in the direct current bus voltage, stressing the system and reducing efficiency. Therefore, a Deep Recurrent Policy Gradient technique is introduced, which integrates Gated Recurrent Units with Deep Deterministic Policy Gradient. The method optimizes control actions for stable power regulation in which the Gated Recurrent Units deal with temporal dynamics to rectify ramp rate discrepancies and power imbalances in multiport direct current converters. Experimental results demonstrate that the proposed model achieves an accuracy of 0.98 and a net output power of 72.1kW under variable conditions, ensuring efficient and stable operation at medium-scale power levels.
CONFLICT OF INTEREST
The Author has no conflicts of interest to declare.
METADATA IN OTHER LANGUAGES:
Polish
Poprawa dopasowania mocy i wydobycia energii w hybrydowych systemach energii odnawialnej dzięki zastosowaniu koncepcji sterowania rekurencyjnego HawkDeep Gradient Fuzzy Recurrent Control
stabilność mikrosieci, przetworniki, panele fotowoltaiczne, energia alternatywna, zdecentralizowane wytwarzanie energii
Hybrydowe systemy energii odnawialnej są jednym z najbardziej odpowiednich rozwiązań dla rosnącego zapotrzebowania na energię. Jednak na ich wydajność znaczący wpływ mają nierównowaga mocy, niestabilne napięcie szyny prądu stałego oraz zmniejszona wydajność systemu. Aby rozwiązać te problemy, zaproponowano nowatorską strukturę HawkDeep Gradient Fuzzy Recurrent Control. Metoda ta służy do optymalizacji zarządzania charakterystyką mocy i stabilizacji wydajności systemu w systemach o wysokim udziale energii odnawialnej. Ponadto obecne algorytmy sterowania często opierają się na z góry określonych regułach, które nie są wystarczająco elastyczne, aby poradzić sobie z nagłymi i nieregularnymi zmianami zapotrzebowania na energię i jej wytwarzania. Aby rozwiązać ten problem, zaproponowano inteligentny algorytm sterowania Hawk Fuzzy, który integruje logikę rozmytą z optymalizacją Reflective Quasi-Hawk w celu szybkiego uzyskania najlepszych odpowiedzi, gwarantując w ten sposób zrównoważone dostawy energii i zapotrzebowanie na nią, nawet w przypadku nagłych prądów rozruchowych. Ponadto niedopasowanie szybkości narastania powoduje tymczasową nierównowagę mocy i niestabilność napięcia szyny prądu stałego, obciążając system i zmniejszając jego wydajność. W związku z tym wprowadzono technikę głębokiego rekurencyjnego gradientu polityki, która łączy bramkowane jednostki rekurencyjne z głębokim deterministycznym gradientem polityki. Metoda optymalizuje działania sterujące w celu stabilnej regulacji mocy, w której bramkowane jednostki rekurencyjne zajmują się dynamiką czasową w celu skorygowania rozbieżności w szybkości narastania i nierównowagi mocy w wieloportowych przetwornikach prądu stałego. Wyniki eksperymentów pokazują, że proponowany model osiąga dokładność 0,98 i moc wyjściową netto 72,1 kW w zmiennych warunkach, zapewniając wydajną i stabilną pracę przy średnich poziomach mocy.
REFERENCES (32)
1.
Aljafari et al. 2023 – Aljafari, B., Devarajan, G., Subramani, S. and Vairavasundaram, S. 2023. Intelligent RBF-Fuzzy Controller Based Non-Isolated DC-DC Multi-Port Converter for Renewable Energy Applications. Sustainability 15(12), https://doi.org/10.3390/su1512....
2.
Alqahtani et al. 2024 – Alqahtani, B., Yang, J. and Paul, M.C. 2024. Reliability and dispatchability improvement of a hybrid system consisting of PV, wind, and bio-energy connected to pumped hydropower energy storage. Energy Conversion and Management 304, https://doi.org/10.1016/j.enco....
3.
Jai Andaloussi et al. 2021 – Jai Andaloussi, Z., Raihani, A., El Magri, A., Lajouad, R. and El Fadili, A. 2021. Novel nonlinear control and optimization strategies for hybrid renewable energy conversion system. Modelling and Simulation in Engineering 2021(1), https://doi.org/10.1155/2021/3....
4.
Bashar, A. and Smys, S. 2021. Integrated renewable energy system for stand-alone operations with optimal load dispatch strategy. Journal of Electronics 3(02), pp. 89–98, https://doi.org/10.36548/jei.2....
5.
Benadli et al. 2021 – Benadli, R., Bjaoui, M., Khiari, B. and Sellami, A. 2021. Sliding mode control of hybrid renewable energy system operating in grid connected and stand-alone mode. Power Electronics and Drives 6(1), pp. 144–166, https://doi.org/10.2478/pead-2....
6.
Das et al. 2021 – Das, B.K., Tushar, M.S.H. and Hassan, R. 2021. Techno-economic optimisation of stand-alone hybrid renewable energy systems for concurrently meeting electric and heating demand. Sustainable Cities and Society 68, https://doi.org/10.1016/j.scs.....
7.
Dehghani et al. 2020 – Dehghani, M., Taghipour, M., Gharehpetian, G.B. and Abedi, M. 2020. Optimized fuzzy controller for MPPT of grid-connected PV systems in rapidly changing atmospheric conditions. Journal of Modern Power Systems and Clean Energy 9(2), pp. 376–383, https://doi.org/10.35833/MPCE.....
8.
Foti et al. 2021 – Foti, S., Testa, A., De Caro, S., Tornello, L.D., Scelba, G. and Cacciato, M. 2021. Multi-level multi-input converter for hybrid renewable energy generators. Energies 14(6), https://doi.org/10.3390/en1406....
9.
Heenkenda et al. 2023 – Heenkenda, A., Elsanabary, A., Seyedmahmoudian, M., Mekhilef, S., Stojcevski, A. and Ab Aziz, N.F. 2023. Unified power quality conditioners based different structural arrangements: A comprehensive review. IEEE Access 11, pp. 43435–43457, https://doi.org/10.1109/ACCESS....
10.
Hossain et al. 2021 – Hossain, M.A., Chakrabortty, R.K., Elsawah, S., Gray, E.M. and Ryan, M.J. 2021. Predicting wind power generation using hybrid deep learning with optimization. IEEE Transactions on Applied Superconductivity 31(8), pp. 1–5, https://doi.org/10.1109/TASC.2....
11.
Huang et al. 2023 – Huang, N., Wang, S., Wang, R., Cai, G., Liu, Y. and Dai, Q. 2023. Gated spatial-temporal graph neural network based short-term load forecasting for wide-area multiple buses. International Journal of Electrical Power & Energy Systems 145, https://doi.org/10.1016/j.ijep....
12.
Karthikeyan, N. and Jebaselvi, G.A. 2024. A bidirectional four-port DC–DC converter for grid connected and isolated loads of hybrid renewable energy system using hybrid approach. Analog Integrated Circuits and Signal Processing 118(3), pp. 467–487, https://doi.org/10.1007/s10470....
13.
Kumar, G.A. and Shivashankar, 2022. Optimal power point tracking of solar and wind energy in a hybrid wind solar energy system. International Journal of Energy and Environmental Engineering 13(1), pp. 77–103, https://doi.org/10.1007/s40095....
14.
Liu et al. 2022 – Liu, S., You, H., Liu, Y., Feng, W. and Fu, S. 2022. Research on optimal control strategy of wind–solar hybrid system based on power prediction. ISA Transactions 123, pp. 179–187, https://doi.org/10.1016/j.isat....
15.
Ma, J. and Liu, F. 2022. Bearing fault diagnosis with variable speed based on fractional hierarchical range entropy and hunter–prey optimization algorithm–optimized random forest. Machines 10(9), https://doi.org/10.3390/machin....
16.
Mahmoud et al. 2022 – Mahmoud, F.S., Diab, A.A.Z., Ali, Z.M., El-Sayed, A.H.M., Alquthami, T., Ahmed, M. and Ramadan, H.A. 2022. Optimal sizing of smart hybrid renewable energy system using different optimization algorithms. Energy Reports 8, pp. 4935–4956, https://doi.org/10.1016/j.egyr....
17.
Mahmoudi et al. 2021 – Mahmoudi, S.M., Maleki, A. and Ochbelagh, D.R. 2021. Optimization of a hybrid energy system with/without considering back-up system by a new technique based on fuzzy logic controller. Energy Conversion and Management 229, https://doi.org/10.1016/j.enco....
18.
Moghaddam et al. 2023 – Moghaddam, M.J.H., Bayat, M., Mirzaei, A., Nowdeh, S.A. and Kalam, A. 2023. Multiobjective and Simultaneous Two‐Problem Allocation of a Hybrid Solar‐Wind Energy System Joint with Battery Storage Incorporating Losses and Power Quality Indices. International Journal of Energy Research 2023(1), https://doi.org/10.1155/2023/6....
19.
Nuvvula et al. 2021 – Nuvvula, R.S., Devaraj, E. and Teegala, S.K. 2021. A hybrid multiobjective optimization technique for optimal sizing of BESS‐WtE supported multi‐MW HRES to overcome ramp rate limitations on thermal stations. International Transactions on Electrical Energy Systems 31(12), https://doi.org/10.1002/2050-7....
20.
Pangedaiah et al. 2021 – Pangedaiah, B., Obulesu, Y.P. and Kota, V.R. 2021. A new architecture topology for back to back grid-connected hybrid wind and PV system. Journal of Electrical Engineering & Technology 16(3), pp. 1457–1467, https://doi.org/10.1007/s42835....
21.
Parvaneh, M.H. and Khorasani, P.G. 2020. A new hybrid method based on fuzzy logic for maximum power point tracking of photovoltaic systems. Energy Reports 6, pp. 1619–1632, https://doi.org/10.1016/j.egyr....
22.
Singh et al. 2024 – Singh, S., Subburaj, V., Sivakumar, K., Anil Kumar, R., Muthuramam, M.S., Rastogi, R., Ratansing Patil, V. and Rajaram, A. 2024. Optimum power forecasting technique for hybrid renewable energy systems using deep learning. Electric Power Components and Systems, pp. 1–18, https://doi.org/10.1080/153250....
23.
Paulsamy, K. and Karuvelam, S. 2025. Modeling and Design of a Grid-Tied Renewable Energy System Exploiting Re-Lift Luo Converter and RNN Based Energy Management. Sustainability 17(1), https://doi.org/10.3390/su1701....
24.
Rajasekaran, R. and Rani, P.U. 2021. Combined HCS–RBFNN for energy management of multiple interconnected microgrids via bidirectional DC–DC converters. Applied Soft Computing 99, https://doi.org/10.1016/j.asoc....
25.
Rosales-Asensio et al. 2021 – Rosales-Asensio, E., Rosales, A.E. and Colmenar-Santos, A. 2021. Surrogate optimization of coupled energy sources in a desalination microgrid based on solar PV and wind energy. Desalination 500, https://doi.org/10.1016/j.desa....
26.
Rizwan et al. 2021 – Rizwan, M., Hong, L., Muhammad, W., Azeem, S.W. and Li, Y. 2021. Hybrid Harris Hawks optimizer for integration of renewable energy sources considering stochastic behavior of energy sources. International Transactions on Electrical Energy Systems 31(2), https://doi.org/10.1002/2050-7....
27.
Samy et al. 2021 – Samy, M.M., Elkhouly, H.I. and Barakat, S. 2021. Multi‐objective optimization of hybrid renewable energy system based on biomass and fuel cells. International Journal of Energy Research 45(6), pp. 8214–8230, https://doi.org/10.1002/er.581....
28.
Saber et al. 2021 – Saber, H., Mazaheri, H., Ranjbar, H., Moeini-Aghtaie, M. and Lehtonen, M. 2021. Utilization of in-pipe hydropower renewable energy technology and energy storage systems in mountainous distribution networks. Renewable Energy 172, pp. 789–801, https://doi.org/10.1016/j.rene....
29.
Sahu et al. 2020 – Sahu, P.C., Prusty, R.C. and Sahoo, B.K. 2020. Modified sine cosine algorithm-based fuzzy-aided PID controller for automatic generation control of multiarea power systems. Soft Computing 24(17), pp.12919-12936, https://doi.org/10.1007/s00500....
30.
Xia, S. and Wang, C. 2024. Energy Management for Hybrid Energy Storage System in Electric Based on Deep Deterministic Policy Gradient. International Journal of Computer Science and Information Technology 2(1), pp. 198–207, https://doi.org/10.62051/ijcsi....
31.
Vadivel, S. and Ragupathy, U.S. 2021. Modeling and design of high performance converters for optimal utilization of interconnected renewable energy resources to micro grid with GOLRS controller. International Journal of Control, Automation and Systems 19, pp. 63–75, https://doi.org/10.1007/s12555....
32.
Xie et al. 2021 – Xie, J., Tuffner, F.K., Elizondo, M.A. and Schneider, K.P. 2021. Modeling and simulation of inrush currents in harmonic domain. 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5, https://doi.org/10.1109/ISGT49....
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