Power optimization in photovoltaic systems by integrating bio-inspired algorithms in a charge controller: a review
DOI:
https://doi.org/10.56294/saludcyt20251615Keywords:
photovoltaic energy, dc/dc converter, AI, genetic algorithmAbstract
Photovoltaics is not only presented as a viable response to the global energy crisis, but also as a fundamental tool to mitigate the negative impacts of climate change. However, the efficiency of photovoltaic systems is often compromised by factors such as climate variability, shading, and the intrinsic complexity of solar cells. In this context, the implementation of bio-inspired algorithms in the charge controller stands as an innovative and promising approach to optimize the power generated by solar panels, thus maximizing the overall performance of the system.
The convergence between engineering and nature represents a fascinating path towards efficiency in solar energy generation. By incorporating bio-inspired algorithms into the charge controller of a photovoltaic system, we not only advance power optimization, but also open the door to new perspectives for research in renewable energy. Therefore, this research not only contributes to current knowledge in the field of photovoltaics, but also invites us to continue exploring the infinite possibilities offered by the integration of biological principles in engineering for the benefit of a sustainable future.
The results obtained not only validated the effectiveness of these approaches in power optimization, but also revealed a significant improvement in the stability and adaptability of the system under changing conditions.
References
1. Lefore N, Closas A, Schmitter P. Solar for all: A framework to deliver inclusive and environmentally sustainable solar irrigation for smallholder agriculture. Energy Policy. 2021;154.
2. Halassa E, Mazouz L, Seghiour A, Chouder A, Silvestre S. Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions. Energies (Basel). 2023;16(9).
3. Dhal KG, Das A, Ray S, Gálvez J, Das S. Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation. Archives of Computational Methods in Engineering. 2020;27(3).
4. Lasso Cardona LA, Rincón Reyes E, Estrada Holguín GD. INTRODUCCIÓN A LA EVALUACIÓN DE CAPACIDADES: UNA REVISIÓN TEÓRICA. REVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA (RCTA). 2023;2(36).
5. Shehab M, Abu-Hashem MA, Shambour MKY, Alsalibi AI, Alomari OA, Gupta JND, et al. A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization. Vol. 30, Archives of Computational Methods in Engineering. 2023.
6. Hijjawi U, Lakshminarayana S, Xu T, Piero Malfense Fierro G, Rahman M. A review of automated solar photovoltaic defect detection systems: Approaches, challenges, and future orientations. Vol. 266, Solar Energy. 2023.
7. Mellit A, Kalogirou SA. MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives. Vol. 70, Energy. 2014.
8. Li X, Wang X, Ma Y, Deng W, Wu Y, Li J, et al. Recent progress in functional two-dimensional photovoltaic photodetectors and related emerging applications. Vol. 11, Journal of Materials Chemistry A. 2023.
9. Giri NC, Mohanty RC. Agrivoltaic system: Experimental analysis for enhancing land productivity and revenue of farmers. Energy for Sustainable Development. 2022;70.
10. Lambora A, Gupta K, Chopra K. Genetic Algorithm- A Literature Review. En: Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019. 2019.
11. Pérez García, D. ., García Reina, F. ., & Hernández Eduardo, D. . (2019). Reducing electrical energy losses by distribution using a novel measurement and control technology for decision making. COLOMBIAN JOURNAL OF ADVANCED TECHNOLOGIES, 2(34), 144–150. https://doi.org/10.24054/rcta.v2i34.75 (Original work published October 4, 2020).
12. Chudinzow D, Nagel S, Güsewell J, Eltrop L. Vertical bifacial photovoltaics – A complementary technology for the European electricity supply? Appl Energy. 2020;264.
13. Ishaque K, Salam Z. A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition. IEEE Transactions on Industrial Electronics. 2013;60(8).
14. Srinivas P, Swapna P. Maximum Power Point Tracking Algorithm for Advanced Photovoltaic Systems. International Journal on Recent and Innovation Trends in Computing and Communication. 2022;10(9).
15. Katche ML, Makokha AB, Zachary SO, Adaramola MS. A Comprehensive Review of Maximum Power Point Tracking (MPPT) Techniques Used in Solar PV Systems. Vol. 16, Energies. 2023.
16. Elbarbary ZMS, Alranini MA. Review of maximum power point tracking algorithms of PV system. Frontiers in Engineering and Built Environment. 2021;1(1).
17. Shukir SS. Half-Cut Cell Solar Panels to Reduce the Effect of High Temperature and Shadow on the Productivity of Solar Panels. Journal of Alternative and Renewable Energy Sources. 2022;3(7).
18. Issaadi W, Issaadi S, Khireddine A. Comparative study of photovoltaic system optimization techniques: Contribution to the improvement and development of new approaches. Vol. 82, Renewable and Sustainable Energy Reviews. 2018.
19. Bhallamudi R, Kumarasamy S, Sundarabalan CK. Effect of dust and shadow on performance of solar photovoltaic modules: Experimental analysis. Energy Engineering: Journal of the Association of Energy Engineering. 2021;118(6).
20. Balbis Morejón M, García Reina F, Cabello Eras JJ, Sousa Santos V. Energy characterization of the operation of an air conditioning equipment in a given location. RCTA [Internet]. 2019 Jul. 4 [cited 2024 Nov. 11];2(34):71-6. Available from: https://ojs.unipamplona.edu.co/index.php/rcta/article/view/65
21. Kahwash F, Barakat B, Maheri A. Coupled thermo-electrical dispatch strategy with AI forecasting for optimal sizing of grid-connected hybrid renewable energy systems. Energy Convers Manag. 2023;293.
22. Hanif MF, Naveed MS, Metwaly M, Si J, Liu X, Mi J. Advancing solar energy forecasting with modified ANN and light GBM learning algorithms. AIMS Energy. 2024;12(2).
23. Mumbere KS, Sasaki Y, Zoka Y, Yorino N, Bedawy A, Krifa C, et al. Day-Ahead Optimal Resilient Energy Management for Grid-Connected Prosumer Microgrids. En: IEEE PES Innovative Smart Grid Technologies Conference Europe. 2023.
24. Chao KH, Rizal MN. A hybrid mppt controller based on the genetic algorithm and ant colony optimization for photovoltaic systems under partially shaded conditions. Energies (Basel). 2021;14(10).
25. Pan M, Li C, Gao R, Huang Y, You H, Gu T, et al. Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization. J Clean Prod. 2020;277.
26. Medina-Barahona, C. J., Mora, G. A., Calvache-Pabón, C., Salazar-Castro, J. A., Mora-Paz, H. A., & Mayorca-Torres, D. (2022). Creation-oriented iot architecture proposal of prototypes for application on platforms educational and research. COLOMBIAN JOURNAL OF ADVANCED TECHNOLOGIES, 1(39), 118–125. https://doi.org/10.24054/rcta.v1i39.1405 (Original work published July 28, 2022).
27. Vukadinović A, Radosavljević J, Đorđević A, Protić M, Petrović N. Multi-objective optimization of energy performance for a detached residential building with a sunspace using the NSGA-II genetic algorithm. Solar Energy. 2021;224.
28. Dabra V, Paliwal KK, Sharma P, Kumar N. Optimization of photovoltaic power system: a comparative study. Protection and Control of Modern Power Systems. 2017;2(1).
29. Yan Z, Li Y, Eslami M. Maximizing micro-grid energy output with modified chaos grasshopper algorithms. Heliyon. 2024;10(1).
30. Veza I, Karaoglan AD, Ileri E, Kaulani SA, Tamaldin N, Latiff ZA, et al. Grasshopper optimization algorithm for diesel engine fuelled with ethanol-biodiesel-diesel blends. Case Studies in Thermal Engineering. 2022;31.
31. Zhang Y, Lv Y. Research on electrical load distribution using an improved bacterial foraging algorithm. Front Energy Res. 2023;11.
32. Carrillo Gómez JK, Duran Acevedo CM, Garcia Rico RO. BACTERIAL DISCRIMINATION OF DRINKING WATER THROUGH OF AN ELECTRONIC NOSE AND A VOLATILES EXTRACTION EQUIPMENT. REVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA (RCTA). 2019;1(33).
33. Fang HW, Feng YZ, Li GP. Optimization of wave energy converter arrays by an improved differential evolution algorithm. Energies (Basel). 2018;11(12).
34. Gumede SB, Saha AK. Optimizing Recloser Settings in an Active Distribution System Using the Differential Evolution Algorithm. Energies (Basel). 2022;15(22).
35. Rahmani Y, Shahvari Y, Kia F. Application of invasive weed optimization algorithm for optimizing the reloading pattern of a VVER-1000 reactor (in transient cycles). Nuclear Engineering and Design. 2021;376.
36. Abdulaziz S, Attlam G, Zaki G, Nabil E. Cuckoo search algorithm and particle swarm optimization based maximum power point tracking techniques. Indonesian Journal of Electrical Engineering and Computer Science. 2022;26(2).
37. Huang X, Xie Z, Huang X. Fault Location of Distribution Network Base on Improved Cuckoo Search Algorithm. IEEE Access. 2020;8.
38. Anshory I, Jamaaluddin J, Wisaksono A, Sulistiyowati I, Hindarto, Rintyarna BS, et al. Optimization DC-DC boost converter of BLDC motor drive by solar panel using PID and firefly algorithm. Results in Engineering. 2024;21.
39. Khizer M, Liaquat S, Zia MF, Kanukollu S, Al-Durra A, Muyeen SM. Selective Harmonic Elimination in a Multilevel Inverter Using Multi-Criteria Search Enhanced Firefly Algorithm. IEEE Access. 2023;11.
40. Abdollahi A, Ghadimi AA, Miveh MR, Mohammadi F, Jurado F. Optimal power flow incorporating facts devices and stochastic wind power generation using krill herd algorithm. Electronics (Switzerland). 2020;9(6).
41. Niu P, Chen K, Ma Y, Li X, Liu A, Li G. Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm. Knowl Based Syst. 2017;118.
42. Das TK, Venayagamoorthy GK, Aliyu UO. Bio-inspired algorithms for the design of multiple optimal power system stabilizers: SPPSO and BFA. IEEE Trans Ind Appl. 2008;44(5).
43. Sudhakar AVV, Karri C. Bio Inspired Algorithms in Power System Operation: A Review. En: Proceedings - 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies, ICRTEECT 2017. 2017.
44. Sultana N, Hossain SMZ, Almuhaini S, Düştegör D. Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand. Energies (Basel). 2022;15(9).
45. Karim FK, Khafaga DS, Eid MM, Towfek SK, Alkahtani HK. A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting. Biomimetics. 2023;8(3).
46. Hilali A, Mardoude Y, Essahlaoui A, Rahali A, Ouanjli N El. Migration to solar water pump system: Environmental and economic benefits and their optimization using genetic algorithm Based MPPT. Energy Reports. 2022;8.
47. Guenounou O, Belkaid A, Colak I, Dahhou B, Chabour F. Optimization of fuzzy logic controller based maximum power point tracking using hierarchical genetic algorithms. En: 9th International Conference on Smart Grid, icSmartGrid 2021. 2021.
48. Ghavifekr AA. Application of heuristic techniques and evolutionary algorithms in microgrids optimization problems. En: Power Systems. 2021.
49. Yadav K, Kumar B, Guerrero JM, Lashab A. A hybrid genetic algorithm and grey wolf optimizer technique for faster global peak detection in PV system under partial shading. Sustainable Computing: Informatics and Systems. 2022;35.
50. Rahman I, Mohamad-Saleh J. Plug-in electric vehicle charging optimization using bio-inspired computational intelligence methods. En: Studies in Systems, Decision and Control. 2018.
51. Rahman I, Mohamad-Saleh J. Plug-in Electric Vehicle Charging Optimization Using Bio-Inspired Computational Intelligence Methods. En: Amini MH, Boroojeni KG, Iyengar SS, Pardalos PM, Blaabjerg F, Madni AM, editores. Sustainable Interdependent Networks: From Theory to Application [Internet]. Cham: Springer International Publishing; 2018. p. 135-47. Disponible en: https://doi.org/10.1007/978-3-319-74412-4_9
52. Boonraksa T, Boonraksa P, Marungsri B. Optimal Capacitor Location and Sizing for Reducing the Power Loss on the Power Distribution Systems due to the Dynamic Load of the Electric Buses Charging System using the Artificial Bee Colony Algorithm. Journal of Electrical Engineering and Technology. 2021;16(4).
53. Dhawale D, Kamboj VK, Anand P. An optimal solution to unit commitment problem of realistic integrated power system involving wind and electric vehicles using chaotic slime mould optimizer. Journal of Electrical Systems and Information Technology. 2023;10(1).
54. Roni MHK, Rana MS, Pota HR, Hasan MM, Hussain MS. Recent trends in bio-inspired meta-heuristic optimization techniques in control applications for electrical systems: a review. Vol. 10, International Journal of Dynamics and Control. 2022.
55. Ni J, Wu L, Fan X, Yang SX. Bioinspired intelligent algorithm and its applications for mobile robot control: A survey. Vol. 2016, Computational Intelligence and Neuroscience. 2016.
56. Medina MSG, Aguilar J, Rodriguez-Moreno MD. A Bioinspired Emergent Control for Smart Grids. IEEE Access. 2023;11.
57. Ahmad M, Numan A, Mahmood D. A Comparative Study of Perturb and Observe (P&O) and Incremental Conductance (INC) PV MPPT Techniques at Different Radiation and Temperature Conditions. Engineering and Technology Journal. 2022;40(2).
58. Saber H, Bendaouad AE, Rahmani L, Radjeai H. A comparative study of the FLC, INC and P&O methods of the MPPT algorithm for a PV system. En: 2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022. 2022.
59. MPPT Algorithms (PSO, FA, and MFA) for PV System Under Partial Shading Condition, Case Study: BTS in Algazalia, Baghdad. International Journal of Smart grid. 2020;
60. Watanabe RB, Ando Junior OH, Leandro PGM, Salvadori F, Beck MF, Pereira K, et al. Implementation of the Bio-Inspired Metaheuristic Firefly Algorithm (FA) Applied to Maximum Power Point Tracking of Photovoltaic Systems. Energies (Basel). 2022;15(15).
61. Sun L, Wang J, Tang L. A Powerful Bio-Inspired Optimization Algorithm Based PV Cells Diode Models Parameter Estimation. Front Energy Res. 2021;9.
62. Sun L, Wang J, Tang L. A Powerful Bio-Inspired Optimization Algorithm Based PV Cells Diode Models Parameter Estimation. Front Energy Res. 28 de abril de 2021;9.
63. Haddou A, Tariba NE, Ikken N, Bouknadel A, Omari HEL, Omari HEL. Comparative study of new MPPT control approaches for a photovoltaic system. International Journal of Power Electronics and Drive Systems. 2020;11(1).
64. UU Republik Indonesia, Munawir M, Nasional BS, Susanta G, Jatimnet.com, MajaMojokerto.net, et al. PENENTUAN ALTERNATIF LOKASI TEMPAT PEMBUANGAN AKHIR (TPA) SAMPAH DI KABUPATEN SIDOARJO. Energies (Basel). 2022;15(1).
65. Chellakhi A, El Beid S, Abouelmahjoub Y. An improved adaptable step-size P&O MPPT approach for standalone photovoltaic systems with battery station. Simul Model Pract Theory. 2022;121.
66. Shafi MA, Khan M, Bibi S, Shafi MY, Rabbani N, Ullah H, et al. EFFECT OF PARASITIC PARAMETERS AND ENVIRONMENTAL CONDITIONS ON I-V AND P-V CHARACTERISTICS OF 1D5P MODEL SOLAR PV CELL USING LTSPICE-IV. East European Journal of Physics. 2022;2022(2).
67. Provensi LL, de Souza RM, Grala GH, Bergamasco R, Krummenauer R, Andrade CMG. Modeling and Simulation of Photovoltaic Modules Using Bio-Inspired Algorithms. Inventions. 2023;8(5).
68. Ma Y, Yang H, Zhang D, Ni Q. Operation flexibility evaluation and its application to optimal planning of bundled wind-thermal-storage generation system. Electronics (Switzerland). 2019;8(1).
69. Grala GH, Provensi LL, Krummenauer R, da Motta Lima OC, de Alcantara GP, Andrade CMG. Investigation of the Use of Evolutionary Algorithms for Modeling and Simulation of Bifacial Photovoltaic Modules. Inventions. 2023;8(6).
70. System P, Adaptive U, Dawan P, Sriprapha K, Kittisontirak S, Boonraksa T. Comparison of Power Output Forecasting on the Inference Systems and Particle Swarm. energies MDPI. 2020;
71. Dawan P, Sriprapha K, Kittisontirak S, Boonraksa T, Junhuathon N, Titiroongruang W, et al. Comparison of power output forecasting on the photovoltaic system using adaptive neuro-fuzzy inference systems and particle swarm optimization-artificial neural network model. Energies (Basel). 2020;13(2).
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Msc. Luis E. Neira Ropero, PhD. Aldo Pardo García, PhD. Francisco Lopez Monsalvo, Msc. Jorge Luis Diaz Rodriguez (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
