Electrical optimization: the power of bio-Inspired algorithms
DOI:
https://doi.org/10.22201/ceide.16076079e.2025.26.2.8Keywords:
Nature, Bio-inspiration, Algorithm, Bio-Inspired Algorithm, Optimization, Power Systems.Abstract
Every day, we rely on electrical systems that keep us connected, but behind the electricity that powers our homes, there is a complex challenge: how to make these systems work more efficiently and cost-effectively. This is where bio-inspired algorithms come in. These algorithms, based on behaviors observed in nature, offer an innovative way to solve problems that seem difficult to tackle. While their use has a direct economic impact, they can also improve the sustainability of electrical systems by reducing their environmental footprint. From optimizing energy consumption to enhancing operational safety, these algorithms find smart solutions through patterns that are not always evident to the human eye. In a world where electrical systems are becoming increasingly complex, these algorithms emerge as powerful, flexible allies capable of offering us a cleaner and more efficient energy future.
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References
Arora, S., y Singh, S. (2019). Butterfly optimization algorithm: A novel approach for global optimization. Soft Computing, 23(3), 715–734. https://doi.org/10.1007/s00500-018-3102-4.
Chopra, N., y Mohsin Ansari, M. (2022). Golden jackal optimization: A novel nature-inspired optimizer for engineering applications. Expert Systems with Applications, 198, 116924. https://doi.org/10.1016/j.eswa.2022.116924.
Dora, B. K., Rajan, A., Mallick, S., y Halder, S. (2023). Optimal Reactive Power Dispatch problem using exchange market based Butterfly Optimization Algorithm. Applied Soft Computing, 147. https://doi.org/10.1016/j.asoc.2023.110833.
Jakšić, Z., Devi, S., Jakšić, O., y Guha, K. (2023). A comprehensive review of bio-inspired optimization algorithms including applications in microelectronics and nanophotonics. Biomimetics, 8(3). https://doi.org/10.3390/biomimetics8030278.
Kennedy, J., y Eberhart, R. (1995). Particle swarm optimization. IEEE International Conference on Neural Networks – Conference Proceedings, 4. https://doi.org/10.4018/ijmfmp.2015010104.
Ma, R., Li, X., Gao, W., Lu, P., y Wang, T. (2020). Random-fuzzy chance-constrained programming optimal power flow of wind integrated power considering voltage stability. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.3040382.
Márquez Vera, M. A. (2023). Inteligencia de enjambre: De los sistemas naturales a los artificiales. Revista Digital Universitaria, 24(1). https://doi.org/10.22201/cuaieed.16076079e.2023.24.1.11.
n.d. (2023, junio 30). Plataforma Nacional de Energía, Ambiente y Sociedad. https://energia.conacyt.mx/planeas/electricidad/sistema-electrico-nacional.
Peraza-Vázquez, H., Peña-Delgado, A., Ranjan, P., Barde, C., Choubey, A., y Morales-Cepeda, A. B. (2021). A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade. Mathematics, 10(1), 102. https://doi.org/10.3390/math10010102.
Peraza-Vázquez, H., Peña-Delgado, A., Merino-Treviño, M., Morales-Cepeda, A. B., y Sinha, N. (2024). A novel metaheuristic inspired by horned lizard defense tactics. Artificial Intelligence Review, 57(3). https://doi.org/10.1007/s10462-023-10653-7.
Sastry, K., Goldberg, D., y Kendall, G. (s/f). Genetic algorithms. En Search Methodologies (pp. 97–125). Springer US. https://doi.org/10.1007/0-387-28356-0_4.
Shaheen, M. A. M., Hasanien, H. M., Mekhamer, S. F., y Talaat, H. E. A. (2019). Optimal power flow of power systems including distributed generation units using sunflower optimization algorithm. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2933489.
Ullah, K., Ali, S., Khan, T. A., Khan, I., Jan, S., Shah, I. A., y Hafeez, G. (2020). An optimal energy optimization strategy for smart grid integrated with renewable energy sources and demand response programs. Energies, 13(21). https://doi.org/10.3390/en13215718.
Wang, R., Li, Q., Zhang, B., y Wang, L. (2019). Distributed consensus-based algorithm for economic dispatch in a microgrid. IEEE Transactions on Smart Grid, 10(4). https://doi.org/10.1109/TSG.2018.2833108.
Wang, Z., Younesi, A., Liu, M. V., Guo, G. C., y Anderson, C. L. (2023). AC optimal power flow in power systems with renewable energy integration: A review of formulations and case studies. IEEE Access, 11. https://doi.org/10.1109/ACCESS.2023.3314330.
Zadehbagheri, M., Ildarabadi, R., y Javadian, A. M. (2023). Optimal power flow in the presence of HVDC lines along with optimal placement of FACTS in order to power system stability improvement in different conditions: Technical and economic approach. IEEE Access, 11. https://doi.org/10.1109/ACCESS.2023.3283573.
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