Optimal tuning of multi-stage PID controller for dynamic frequency control of microgrid system under climate change scenarios
DOI:
https://doi.org/10.20998/2074-272X.2025.1.02Keywords:
microgrid, multi-stage PID controller, frequency control, renewable energy sources, krill herd algorithm, cuckoo search algorithmAbstract
Introduction. In recent years, the use of renewable energy has become essential to preserve the climate from pollution and global warming. To utilize renewable energy more effectively, the microgrid system has emerged, which is a combination of renewable energies such as wind and solar power. However, due to sudden and random climate fluctuations, energy deviation and instability problems have arisen. To address this, storage systems and diesel engines have been incorporated. Nevertheless, this approach has led to another issue: frequency deviation in the microgrid system. Therefore, most recent studies have focused on finding ways to reduce frequency deviation. The goal of this work is to study and compare various improvement methods in terms of frequency deviation. Methodology. We first simulated the microgrid system using the PID controller based on the following algorithms: krill herd algorithm (KHA) and cuckoo search algorithm (CSA). In the second phase, we replaced the PID controller with the multi-stage PID controller and optimized its parameters using the KHA and the CSA. In the final phase, we tested the response of the microgrid system to these methods under a range of influencing factors. Results. The results initially showed the superiority of the KHA over the other algorithms in improving the parameters of the PID controller. In the second phase, the results showed a significant advantage of the multi-stage PID controller in terms of speed and stabilization time, as well as in reducing the frequency deviation compared to the PID controller. Practical value. Based on the tests conducted on the microgrid system, we can conclude that the multi-stage PID controller based on the KHA can be relied upon to solve these types of problems within the microgrid system. References 36, tables 4, figures 10.
References
Leroutier M., Quirion P. Air pollution and CO2 from daily mobility: Who emits and Why? Evidence from Paris. Energy Economics, 2022, vol. 109, art. no. 105941. doi: https://doi.org/10.1016/j.eneco.2022.105941.
Alhamrouni I., Wahab W., Salem M., Rahman N.H.A., Awalin L. Modeling of micro-grid with the consideration of total harmonic distortion analysis. Indonesian Journal of Electrical Engineering and Computer Science, 2019, vol. 15, no. 2, pp. 581-592. doi: https://doi.org/10.11591/ijeecs.v15.i2.pp581-592.
Sathish C., Chidambaram I.A., Manikandan M. Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications. Electrical Engineering & Electromechanics, 2023, no. 1, pp. 63-70. doi: https://doi.org/10.20998/2074-272X.2023.1.09.
Manikandan K., Sasikumar S., Arulraj R. A novelty approach to solve an economic dispatch problem for a renewable integrated micro-grid using optimization techniques. Electrical Engineering & Electromechanics, 2023, no. 4, pp. 83-89. doi: https://doi.org/10.20998/2074-272X.2023.4.12.
Belbachir N., Zellagui M., Settoul S., El-Bayeh C.Z., Bekkouche B. Simultaneous optimal integration of photovoltaic distributed generation and battery energy storage system in active distribution network using chaotic grey wolf optimization. Electrical Engineering & Electromechanics, 2021, no. 3, pp. 52-61. doi: https://doi.org/10.20998/2074-272X.2021.3.09.
Das D.C., Roy A.K., Sinha N. GA based frequency controller for solar thermal–diesel–wind hybrid energy generation/energy storage system. International Journal of Electrical Power & Energy Systems, 2012, vol. 43, no. 1, pp. 262-279. doi: https://doi.org/10.1016/j.ijepes.2012.05.025.
Srinivasarathnam C., Yammani C., Maheswarapu S. Load Frequency Control of Multi-microgrid System considering Renewable Energy Sources Using Grey Wolf Optimization. Smart Science, 2019, vol. 7, no. 3, pp. 198-217. doi: https://doi.org/10.1080/23080477.2019.1630057.
Shankar R., Kumar A., Raj U., Chatterjee K. Fruit fly algorithm-based automatic generation control of multiarea interconnected power system with FACTS and AC/DC links in deregulated power environment. International Transactions on Electrical Energy Systems, 2019, vol. 29, no. 1, art. no. e2690. doi: https://doi.org/10.1002/etep.2690.
Kalyan C.N.S., Goud B.S., Kumar M.K., Bajaj M., Rubanenko O., Danylchenko D. Fruit Fly Optimization Algorithm Tuned 2DOFPID Controller for Frequency Regulation of Dual Area Power System With AC-DC Lines. 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek), 2022, pp. 1-6. doi: https://doi.org/10.1109/KhPIWeek57572.2022.9916505.
Regad M., Helaimi M., Taleb R., Toubal Maamar A.E. Optimum Synthesis of the PID Controller Parameters for Frequency Control in Microgrid Based Renewable Generations. Lecture Notes in Networks and Systems, 2020, vol. 102, pp. 546-556. doi: https://doi.org/10.1007/978-3-030-37207-1_58.
Yang C., Yao W., Fang J., Ai X., Chen Z., Wen J., He H. Dynamic event-triggered robust secondary frequency control for islanded AC microgrid. Applied Energy, 2019, vol. 242, pp. 821-836. doi: https://doi.org/10.1016/j.apenergy.2019.03.139.
Regad M., Helaimi M., Taleb R., Othman A.M., Gabbar H.A. Frequency Control in Microgrid Power System with Renewable Power Generation Using PID Controller Based on Particle Swarm Optimization. Lecture Notes in Networks and Systems, 2020, vol. 102, pp. 3-13. doi: https://doi.org/10.1007/978-3-030-37207-1_1.
Annamraju A., Nandiraju S. A novel fuzzy tuned multistage PID approach for frequency dynamics control in an islanded microgrid. International Transactions on Electrical Energy Systems, 2020, vol. 30, no. 12, art. no. e12674. doi: https://doi.org/10.1002/2050-7038.12674.
Khadanga R.K., Padhy S., Panda S., Kumar A. Design and analysis of multi‐stage PID controller for frequency control in an islanded micro‐grid using a novel hybrid whale optimization‐pattern search algorithm. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2018, vol. 31, no. 5, art. no. e2349. doi: https://doi.org/10.1002/jnm.2349.
Louarem S., Kebbab F.Z., Salhi H., Nouri H. A comparative study of maximum power point tracking techniques for a photovoltaic grid-connected system. Electrical Engineering & Electromechanics, 2022, no. 4, pp. 27-33. doi: https://doi.org/10.20998/2074-272X.2022.4.04.
Sahu P.C., Prusty R.C., Panda S. Optimal design of a robust FO-Multistage controller for the frequency awareness of an islanded AC microgrid under i -SCA algorithm. International Journal of Ambient Energy, 2022, vol. 43, no. 1, pp. 2681-2693. doi: https://doi.org/10.1080/01430750.2020.1758783.
Ali Moussa M., Derrouazin A., Latroch M., Aillerie M. A hybrid renewable energy production system using a smart controller based on fuzzy logic. Electrical Engineering & Electromechanics, 2022, no. 3, pp. 46-50. doi: https://doi.org/10.20998/2074-272X.2022.3.07.
Gandomi A.H., Talatahari S., Tadbiri F., Alavi A.H. Krill herd algorithm for optimum design of truss structures. International Journal of Bio-Inspired Computation, 2013, vol. 5, no. 5, art. no. 281-288. doi: https://doi.org/10.1504/IJBIC.2013.057191.
Yaghoobi S., Mojallali H. Tuning of a PID controller using improved chaotic Krill Herd algorithm. Optik, 2016, vol. 127, no. 11, pp. 4803-4807. doi: https://doi.org/10.1016/j.ijleo.2016.01.055.
Bolaji A.L., Al-Betar M.A., Awadallah M.A., Khader A.T., Abualigah L.M. A comprehensive review: Krill Herd algorithm (KH) and its applications. Applied Soft Computing, 2016, vol. 49, pp. 437-446. doi: https://doi.org/10.1016/j.asoc.2016.08.041.
Sarda J., Pandya K., Lee K.Y. Hybrid cross entropy – cuckoo search algorithm for solving optimal power flow with renewable generators and controllable loads. Optimal Control Applications and Methods, 2023, vol. 44, no. 2, pp. 508-532. doi: https://doi.org/10.1002/oca.2759.
Barrat J.-L., Del Gado E., Egelhaaf S.U., Mao X., Dijkstra M., Pine D.J., Kumar S.K., (…), Kwon J. Soft matter roadmap. Journal of Physics: Materials, 2024, vol. 7, no. 1, art. no. 012501. doi: https://doi.org/10.1088/2515-7639/ad06cc.
Abdellah A., Larbi M., Toumi D. Open circuit fault diagnosis for a five-level neutral point clamped inverter in a grid-connected photovoltaic system with hybrid energy storage system. Electrical Engineering & Electromechanics, 2023, no. 6, pp. 33-40. doi: https://doi.org/10.20998/2074-272X.2023.6.06.
Ayat Y., Badoud A.E., Mekhilef S., Gassab S. Energy management based on a fuzzy controller of a photovoltaic/fuel cell/Li-ion battery/supercapacitor for unpredictable, fluctuating, high-dynamic three-phase AC load. Electrical Engineering & Electromechanics, 2023, no. 3, pp. 66-75. doi: https://doi.org/10.20998/2074-272X.2023.3.10.
Mohamed R., Helaimi M., Taleb R., Gabbar H.A., Othman A.M. Frequency control of microgrid system based renewable generation using fractional PID controller. Indonesian Journal of Electrical Engineering and Computer Science, 2020, vol. 19, no. 2, pp. 745-755. doi: https://doi.org/10.11591/ijeecs.v19.i2.pp745-755.
Khan S.A., Mahmood T., Awan K.S. A nature based novel maximum power point tracking algorithm for partial shading conditions. Electrical Engineering & Electromechanics, 2021, no. 6, pp. 54-63. doi: https://doi.org/10.20998/2074-272X.2021.6.08.
Kumar R.S., Reddy C.S.R., Chandra B.M. Optimal performance assessment of intelligent controllers used in solar-powered electric vehicle. Electrical Engineering & Electromechanics, 2023, no. 2, pp. 20-26. doi: https://doi.org/10.20998/2074-272X.2023.2.04.
Latif A., Pramanik A., Das D.C., Hussain I., Ranjan S. Plug in hybrid vehicle-wind-diesel autonomous hybrid power system: frequency control using FA and CSA optimized controller. International Journal of System Assurance Engineering and Management, 2018, vol. 9, no. 5, pp. 1147-1158. doi: https://doi.org/10.1007/s13198-018-0721-1.
Zerzouri N., Ben Si Ali N., Benalia N. A maximum power point tracking of a photovoltaic system connected to a three-phase grid using a variable step size perturb and observe algorithm. Electrical Engineering & Electromechanics, 2023, no. 5, pp. 37-46. doi: https://doi.org/10.20998/2074-272X.2023.5.06.
Khemis A., Boutabba T., Drid S. Model reference adaptive system speed estimator based on type-1 and type-2 fuzzy logic sensorless control of electrical vehicle with electrical differential. Electrical Engineering & Electromechanics, 2023, no. 4, pp. 19-25. doi: https://doi.org/10.20998/2074-272X.2023.4.03.
Laifa A., Ayachi B. Application of whale algorithm optimizer for unified power flow controller optimization with consideration of renewable energy sources uncertainty. Electrical Engineering & Electromechanics, 2023, no. 2, pp. 69-78. doi: https://doi.org/10.20998/2074-272X.2023.2.11.
Mahdad B., Srairi K. Interactive artificial ecosystem algorithm for solving power management optimizations. Electrical Engineering & Electromechanics, 2022, no. 6, pp. 53-66. doi: https://doi.org/10.20998/2074-272X.2022.6.09.
Tebbakh N., Labed D., Labed M.A. Optimal size and location of distributed generations in distribution networks using bald eagle search algorithm. Electrical Engineering & Electromechanics, 2022, no. 6, pp. 75-80. doi: https://doi.org/10.20998/2074-272X.2022.6.11.
Mezhoud N., Ayachi B., Amarouayache M. Multi-objective optimal power flow based gray wolf optimization method. Electrical Engineering & Electromechanics, 2022, no. 4, pp. 57-62. doi: https://doi.org/10.20998/2074-272X.2022.4.08.
Vo D.N., Schegner P., Ongsakul W. Cuckoo search algorithm for non‐convex economic dispatch. IET Generation, Transmission & Distribution, 2013, vol. 7, no. 6, pp. 645-654. doi: https://doi.org/10.1049/iet-gtd.2012.0142.
Thao N.T.P., Thang N.T. Environmental Economic Load Dispatch with Quadratic Fuel Cost Function Using Cuckoo Search Algorithm. International Journal of U- and e-Service, Science and Technology, 2014, vol. 7, no. 2, pp. 199-210. doi: https://doi.org/10.14257/ijunesst.2014.7.2.19.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 B. Alouache, M. Helaimi, A. B. Djilali, H. A. Gabbar, H. Allouache, A. Yahdou
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.