NONE 20260722123424324 wseas/wseas content-registration-form@crossref.org content-registration-form EQUATIONS 2944-9146 2732-9976 Juan Camilo Rincón Aguilar Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, COLOMBIA Sergio Raul Rivera Rodriguez Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, COLOMBIA This paper presents a methodology for finding scalable implementation paths for distributed green hydrogen systems in urban residential communities, based on a pre-2024-2025 study. Specifically, a multi-objective optimization approach is applied using the MOPSO (Multi- Objective Particle Swarm Optimization) algorithm, which efficiently addresses the many factors that influence the adoption of these systems. Furthermore, the research is extended to two other optimization methods: AGEII (Approximation-guided Evolutionary Multi-Objective Algorithm II) and FDV (Fuzzy Decision Variables Framework). These methods are compared in terms of their performance and effectiveness in generating optimal solutions. The results show that the AGE-II method significantly outperforms the other two methods, achieving more efficient and suitable solutions for the implementation of green hydrogen systems in urban environments. In this way, this study contributes to the discussion on the development of sustainable technologies and the transition to renewable energy. 07 22 2026 07 22 2026 7 2 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232021.2026.6.2 https://wseas.com/journals/equations/2026/a04equations-002(2026).pdf Y. Wu and L. Zhong, “Decarbonizing urban residential communities with green hydrogen systems,” Nature Cities, vol. 2, pp. 81–92, 2025. [Online]. Available: https://doi.org/10.1038/s44284-024-00178-7 L. Zhong and Y. Wu, “Probabilistic deployment pathways of scaling up distributed green hydrogen systems for urban residential communities in north america,” 6 2024. [Online]. Available: https: //www.researchsquare.com/article/rs-4327177/v1 M. de Minas y Energ´ıa, “Resolucion 40509 de 2024,” 11 ´ 2024. [Online]. Available: https://normativame.minenergia.gov.co/ normatividad/7079/norma/ Start Initialize Population Evaluate Population Apply Optimization Algorithm Update Pareto Front Stopping Criterion Met? End No Yes Fig. 2. Flow Diagram of the Optimization Process T. Valencia-Zuluaga, D. Agudelo-Martinez, D. Arango-Angarita, C. Acosta-Urrego, S. Rivera, D. Rodr´ıguez-Medina, and J. Gers, “A fast decomposition method to solve a security-constrained optimal power flow (scopf) problem through constraint handling,” IEEE Access, vol. 9, pp. 52 812–52 824, 2021. J. Garcia-Guarin, D. Alvarez, A. Bretas, and S. Rivera, “Schedule optimization in a smart microgrid considering demand response constraints,” Energies, vol. 13, no. 17, 2020. [Online]. Available: https://www.mdpi.com/1996-1073/13/17/4567 GitHub, “Platemo,” 2025, accedido el 17 de Mayo de 2025. [Online]. Available: https://github.com/BIMK/PlatEMO C. Coello Coello and M. Lechuga, “Mopso: a proposal for multiple objective particle swarm optimization,” in Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600), vol. 2, 2002, pp. 1051–1056 vol.2. A. M. Sanchez, G. E. Coria, A. A. Romero, and S. R. Rivera, “An improved methodology for the hierarchical coordination of pev charging,” IEEE Access, vol. 7, pp. 141 754–141 765, 2019. D. Baimbetov, M. Salem, S. Syrlybekkyzy, N. Ermek Talantovich, Y. Muralev, and M. Alhuyi Nazari, “Optimal scheduling of a multienergy hub with renewables, hydrogen vehicles, and storage systems,” IEEE Access, vol. 13, pp. 161 709–161 728, 2025. M. Wagner and F. Neumann, “A fast approximation-guided evolutionary multi-objective algorithm,” in Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, ser. GECCO ’13. New York, NY, USA: Association for Computing Machinery, 2013, p. 687–694. [Online]. Available: https://doi.org/10.1145/2463372.2463448 X. Yang, J. Zou, S. Yang, J. Zheng, and Y. Liu, “A fuzzy decision References variables framework for large-scale multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 27, no. 3, pp. 445–459, 2023.