Warianty tytułu
Optymalizacja heurystyczna penetracji energii fotowoltaicznej w celu zapewnienia odporności na wahania częstotliwości systemu
Języki publikacji
Abstrakty
Renewable energy can make the utility grid unstable by causing some problems, such as frequency fluctuations, voltage surges, and power instability because of the inconsistency of renewable energy resources. This paper focused on studying the effect of intermittent renewable energy represented by a PV-integrated grid on the frequency system response and grid voltage surge. Heuristic Optimization methods, Teaching learning-based optimization (TLBO), and particle swarm optimization (PSO) have been utilized to optimize the penetration of PV energy enhancing the supply frequency response. Both optimization methods have been implemented with different values of irradiance. Although they have similar performances, the simulation result showed that the TLBO method has a slightly better low-frequency oscillation than the PSO method. It is found that the TLBO algorithm presents a good power quality response of the grid-connected system. This is due to the fact of TLBO is faster than the PSO algorithm because it does not need specific parameters. The system is applied to a feeder in a distribution network in Baghdad power sector. The results are obtained by using the MATLAB package.
Energia odnawialna może spowodować niestabilność sieci elektroenergetycznej, powodując pewne problemy, takie jak wahania częstotliwości, skoki napięcia i niestabilność mocy z powodu niespójności zasobów energii odnawialnej. W artykule skupiono się na badaniu wpływu przerywanej energii odnawialnej reprezentowanej przez zintegrowaną sieć fotowoltaiczną na odpowiedź systemu częstotliwości i udary napięcia sieciowego. Aby zoptymalizować przenikanie energii fotowoltaicznej, zwiększając charakterystykę częstotliwościową zasilania, zastosowano metody optymalizacji heurystycznej, optymalizacji opartej na uczeniu się (TLBO) i optymalizacji roju cząstek (PSO). Obie metody optymalizacji zostały zaimplementowane przy różnych wartościach natężenia napromieniowania. Chociaż mają one podobne właściwości, wynik symulacji pokazał, że metoda TLBO charakteryzuje się nieco lepszymi oscylacjami w zakresie niskich częstotliwości niż metoda PSO. Stwierdzono, że algorytm TLBO zapewnia dobrą odpowiedź dotyczącą jakości energii w systemie podłączonym do sieci. Wynika to z faktu, że TLBO jest szybszy od algorytmu PSO, ponieważ nie wymaga określonych parametrów. System stosowany jest w polu zasilającym w sieci dystrybucyjnej w sektorze energetycznym Bagdadu. Wyniki uzyskuje się za pomocą pakietu MATLAB.
Czasopismo
Rocznik
Tom
Strony
40--44
Opis fizyczny
Bibliogr. 23 poz., rys.
Twórcy
autor
- University of Technology-Iraq, Baghdad
autor
- University of Baghdad, Dept. Of Electrical Engineering, Baghdad, Iraq
Bibliografia
- [1] H. L. W. a. W. G.Scott, "Distributed Power Generation, planning and evaluation”, (2000), Publisher:New York : Marcel Dekker.," 2000.
- [2] R. Takahashi, Umemura, A., Tamura, J., Kimura, M., & Hino, N, "Simulation analyses of stabilization control of power system frequency fluctuations resulting from wind farm output by gas turbine thermal power plant. https://doi.org/10.1002/eej.23321," Electrical Engineering in Japan, vol. 214, no. 2, 2021.
- [3] J. Hossain, & Mahmud, A, " Renewable Energy Integration. https://doi.org/10.1007/978-981-4585-27-9," Springer Singapore., 2014.
- [4] B. Al Kindhi, Lasminto, U., Triana, M. I., Damarnegara, S., & Anavatti, S. G. , "[4] (2023). Sensor and internet of things based integrated inundation mitigation for smart city. International Journal of Electrical and Computer Engineering (IJECE), 13(3), 2695. https://doi.org/10.11591/ijece.v13i3.pp2695-2703," 2023.
- [5] Ahmed Abbas Abdul-Hamza and Hanan M. Habbi, "[6] (2016). Fault detection and diagnosis based on artificial neural network. International Journal of Scientific & Engineering Research, VOL. 7, NO. 5, 2016, PP- 1690-1697. https://www.ijser.org/research-paper-publishing-may-2016_page8.aspx.," 2016.
- [6] T. S. Tagare, & Narendra, R, "Lifetime enhanced energy efficient wireless sensor networks using renewable energy. https://doi.org/10.11591/ijece.v13i3.pp3088-3098," International Journal of Electrical and Computer Engineering (IJECE),, vol. 13, no. 3, p. 3088, 2023.
- [7] M. G. Dozein, Chaspierre, G., Mancarella, P., Panciatici, P., & van Cutsem, T. , " Frequency Response From Solar PV: A Dynamic Equivalence Closed-Loop System Identification Approach,https://doi.org/10.1109/JSYST.2021.3051938," IEEE Systems Journal,, vol. 16, no. 1, pp. 713–722, 2022, doi: https://doi.org/10.1109/JSYST.2021.3051938.
- [8] L. Zhou, Chen, Y., Luo, A., Guerrero, J. M., Zhou, X., Chen, Z., & Wu, W., "Robust two degrees-of-freedom single-current control strategy for LCL-type grid-connected DG system under grid-frequency fluctuation and grid-impedance variation. https://doi.org/10.1049/iet-pel.2016.0120," IET Power Electronics,, vol. 9, no. 14, pp. 2682–2691, 2016.
- [9] I. Isknan, Asbayou, A., Hamid Adaliou, A., Ihlal, A., & Bouhouch, L., "Comparative study and simulation of advanced MPPT control algorithms for a photovoltaic system. https://doi.org/10.11591/ijeecs.v30.i1.pp46-56.," Indonesian Journal of Electrical Engineering and Computer Science,, vol. 30, no. 1, p. 46, 2023.
- [10] A. M. Abdul Hussain, & Habbi, H. M. D. , "[11] (2018). Maximum Power Point Tracking Photovoltaic Fed Pumping System Based on PI Controller. 2018 Third Scientific Conference of Electrical Engineering (SCEE), 78–83. https://doi.org/10.1109/SCEE.2018.8684120," 2018.
- [11] R. K. Varma, & Akbari, M. , "Simultaneous Fast Frequency Control and Power Oscillation Damping by Utilizing PV Solar System as PV-STATCOM. https://doi.org/10.1109/TSTE.2019.2892943," IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 415– 425, 2020.
- [12] D. Rimorov, Joos, G., & Kamwa, I. , "Design and implementation of combined frequency/oscillation damping controller for type 4 wind turbines., https://doi.org/10.1109/PSCC.2016.7540909," 2016 Power Systems Computation Conference (PSCC), pp. 1–7., 2016.
- [13] M. Yang, Cui, Y., & Wang, J., "Multi-Objective optimal scheduling of island microgrids considering the uncertainty of renewable energy output. https://doi.org/10.1016/j.ijepes.2022.108619," International Journal of Electrical Power & Energy Systems, vol. 144, 2023.
- [14] M. M. A. Awan, Javed, M. Y., Asghar, A. B., & Ejsmont, K., " Performance Optimization of a Ten Check MPPT Algorithm for an Off-Grid Solar Photovoltaic System ,https://doi.org/10.3390/en15062104," Energies, vol. 15, no. 6, p. 2104, 2022, doi: https://doi.org/10.3390/en15062104.
- [15] P. P. Kulkarni*, & Deshmukh, S. P. , "Different Converter Topologies for Solar Photovoltaic System with methods for Maximum Power Point Tracking Algorithms. , . https://doi.org/10.35940/ijitee.J1202.0981119," International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 11, pp. 1112–1118, 2019.
- [16] J. E. Mendoza, López, M. E., Fingerhuth, S. C., Peña, H. E., & Salinas, C. A., "[15] (2013). Low voltage distribution planning considering micro distributed generation.. https://doi.org/10.1016/j.epsr.2013.05.020," Electric Power Systems Research,, vol. 103, pp. 233–240, 2013.
- [17] A. Q. Al-Shetwi, Issa, W. K., Aqeil, R. F., Ustun, T. S., Al-Masri, H. M. K., Alzaareer, K., Abdolrasol, M. G. M., & Abdullah, M. A. , " Active Power Control to Mitigate Frequency Deviations in Large-Scale Grid-Connected PV System Using Grid-Forming Single-Stage Inverters https://doi.org/10.3390/en15062035," Energies, vol. 15, no. 6, p. 2035, 2022, doi: https://doi.org/10.3390/en15062035.
- [18] K. Liao, Xu, Y., Yin, M., & Chen, Z., " A Virtual Filter Approach for Wind Energy Conversion Systems for Mitigating Power System Frequency Fluctuations . https://doi.org/10.1109/TSTE.2019.2922302," IEEE Transactions on Sustainable Energy, vol. 11, no. 3, pp. 1268– 1277, 2020.
- [19] R. Rajan, Fernandez, F. M., & Yang, Y., " Primary frequency control techniques for large-scale PV-integrated power systems: A review.https://doi.org/10.1016/j.rser.2021.110998," Renewable and Sustainable Energy Reviews,, vol. 144, 2021.
- [20] Q. Zhang, Xie, J., Pan, X., Zhang, L., & Fu, D. , "A Short-Term Optimal Scheduling Model for Wind-Solar-Hydro-Thermal Complementary Generation System Considering Dynamic Frequency Response. https://doi.org/10.1109/ACCESS.2021.3119924," IEEE Access, vol. 9, pp. 142768–142781., 2021.
- [21] M. M. Mohamed, el Zoghby, H. M., Sharaf, S. M., & Mosa, M. A., "Optimal virtual synchronous generator control of battery/supercapacitor hybrid energy storage system for frequency response enhancement of photovoltaic/diesel microgrid.https://doi.org/10.1016/j.est.2022.104317," Journal of Energy Storage, , vol. 51, 2022.
- [22] V. D. Paduani, Yu, H., Xu, B., & Lu, N., "A Unified Power-Setpoint Tracking Algorithm for Utility-Scale PV Systems With Power Reserves and Fast Frequency Response Capabilities, https://doi.org/10.1109/TSTE.2021.3117688," IEEE Transactions on Sustainable Energy, vol. 13, no. 1, pp. 479– 490., 2022.
- [23] K. Saadaoui, Rhazi, K. S., Mejdoub, Y., & Aboudou, A. , "Modelling and simulation for energy management of a hybrid microgrid with droop controller, https://doi.org/10.11591/ijece.v13i3.pp2440-2448," International Journal of Electrical and Computer Engineering (IJECE), , vol. 13, no. 3, p. 2440, 2023.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-33c196f4-f40a-4af1-9b98-5d4e16115627