Abstract
Introduction: The growing complexity of contemporary electrical systems, associated with the progressive incorporation of renewable energy sources and the sustained increase in demand, has highlighted the limitations of traditional planning approaches. These methods present difficulties in managing large volumes of geospatial information and integrating dynamic technical, environmental, and operational constraints, which reduces their effectiveness in current scenarios. Development: In response to these limitations, an integrated system combining Geographic Information Systems (GIS) and Artificial Intelligence (AI) has been designed and validated with the aim of optimizing electrical network planning while minimizing energy losses and infrastructure costs. The methodological approach implemented combines geospatial analysis using geopandas, multi-objective optimization based on genetic algorithms using DEAP, technical validation through power flow simulations with pandapower, and interactive visualization of results using Folium. The model allows for the explicit incorporation of spatial constraints, including the automatic exclusion of environmentally sensitive areas, as well as the management of non-linearities inherent to the electrical system. Conclusions: The results obtained show an 18% reduction in energy losses and a 12% reduction in infrastructure costs, surpassing the performance of conventional methods in terms of flexibility, capacity to integrate constraints, and technical feasibility in multiple scenarios. GIS-IA integration is consolidating itself as a relevant methodological advance for the planning of modern electrical networks, offering technically and economically efficient solutions. Future research will focus on improving the computational scalability of the system and incorporating resilience models in the face of climate change scenarios.
References
1. Abdel Rahman MAE. An overview of land degradation, desertification and sustainable land management using GIS and remote sensing applications. Rendiconti Lincei 2023;34:767–808. https://doi.org/10.1007/s12210-023-01155-3.
2. Abdollahi A, Pradhan B. Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model. Science of the Total Environment 2023;879. https://doi.org/10.1016/j.scitotenv.2023.163004.
3. Abdrabo KI, KANTOUSH SA, Esmaiel A, Saber M, Sumi T, Almamari M, et al. An integrated indicator-based approach for constructing an urban flood vulnerability index as an urban decision-making tool using the PCA and AHP techniques: A case study of Alexandria, Egypt. Urban Climate 2023;48. https://doi.org/10.1016/j.uclim.2023.101426.
4. Aghaloo K, Ali T, Aghaloo Y-R, Sharifi A. Optimal site selection for the solar-wind hybrid renewable energy systems in Bangladesh using an integrated GIS-based BWM-fuzzy logic method. Energy Conversion and Management 2023;283. https://doi.org/10.1016/j.enconman.2023.116899.
5. Berg CD, Schiller JH, Boffetta P, Cai J, Connolly C, Kerpel-Fronius A, et al. Air Pollution and Lung Cancer: A Review by International Association for the Study of Lung Cancer Early Detection and Screening Committee. Journal of Thoracic Oncology 2023;18:1277–89. https://doi.org/10.1016/j.jtho.2023.05.024.
6. Chen H, Wang L. GIS-based spatial analysis for power grid planning: A review and case study. Sustainable Cities and Society. 2022;85:104030.
7. Chen S, Ogawa Y, Zhao C, Sekimoto Y. Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach. ISPRS Journal of Photogrammetry and Remote Sensing 2023;195:129–52. https://doi.org/10.1016/j.isprsjprs.2022.11.006.
8. Davis NN, Badger J, Hahmann AN, Hansen BO, Mortensen NG, Kelly M, et al. The Global Wind Atlas: A High-Resolution Dataset of Climatologies and Associated Web-Based Application. Bulletin of the American Meteorological Society 2023;104:E1507–25. https://doi.org/10.1175/BAMS-D-21-0075.1.
9. Elsayed SK, Hegazy YG, Bendary FM. Multi-objective optimization for integrated renewable energy systems planning using GIS and metaheuristics. Energy Reports. 2023;9:1154-1172.
10. Gaagai A, Aouissi HA, Bencedira S, Hinge G, Athamena A, Heddam S, et al. Application of Water Quality Indices, Machine Learning Approaches, and GIS to Identify Groundwater Quality for Irrigation Purposes: A Case Study of Sahara Aquifer, Doucen Plain, Algeria. Water (Switzerland) 2023;15. https://doi.org/10.3390/w15020289.
11. García MP, Fernández JA, Santos JR. Geospatial data management for smart grid applications: Challenges and opportunities. ISPRS Int J Geo-Information. 2023;12(2):45.
12. Gómez R, López JC, Rider MJ. An integrated GIS and optimization framework for the planning of sustainable power distribution networks with high penetration of renewables. Electric Power Systems Research. 2023;214:108925.
13. Haghighizadeh A, Rajabi O, Nezarat A, Hajyani Z, Haghmohammadi M, Hedayatikhah S, et al. Comprehensive analysis of heavy metal soil contamination in mining Environments: Impacts, monitoring Techniques, and remediation strategies. Arabian Journal of Chemistry 2024;17. https://doi.org/10.1016/j.arabjc.2024.105777.
14. Huang F, Jiang S, Zhan W, Bechtel B, Liu Z, Demuzere M, et al. Mapping local climate zones for cities: A large review. Remote Sensing of Environment 2023;292. https://doi.org/10.1016/j.rse.2023.113573.
15. Ibrahim H, Yaseen ZM, Scholz M, Ali M, Gad M, Elsayed S, et al. Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study. Water (Switzerland) 2023;15. https://doi.org/10.3390/w15040694.
16. International Energy Agency. Electricity Grids and Secure Energy Transitions. Paris: IEA; 2023.
17. International Renewable Energy Agency. Planning for the renewable future: Long-term modelling and tools to expand variable renewable power in emerging economies. Abu Dhabi: IRENA; 2023.
18. Kumar A, Mishra S. A comprehensive review on application of evolutionary and swarm intelligence algorithms in power system optimization problems. Arch Comput Methods Eng. 2021;28(6):4279-4301.
19. Li R, Wang W, Wu X. Challenges and opportunities for big data in power systems. CSEE J Power Energy Syst. 2021;7(2):225-235.
20. Majumder S, Roy S, Bose A, Roy Chowdhury IR. Multiscale GIS based-model to assess urban social vulnerability and associated risk: Evidence from 146 urban centers of Eastern India. Sustainable Cities and Society 2023;96. https://doi.org/10.1016/j.scs.2023.104692.
21. Muzakkir Quamar MM, Al-Ramadan B, Khan K, Shafiullah M, El Ferik S. Advancements and Applications of Drone-Integrated Geographic Information System Technology—A Review. Remote Sensing 2023;15. https://doi.org/10.3390/rs15205039.
22. Pereira HM, Martins IS, Rosa IMD, Kim H, Leadley P, Popp A, et al. Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050. Science 2024;384:458–65. https://doi.org/10.1126/science.adn3441.
23. Rekik S, El Alimi S. Optimal wind-solar site selection using a GIS-AHP based approach: A case of Tunisia. Energy Conversion and Management: X 2023;18. https://doi.org/10.1016/j.ecmx.2023.100355.
24. Rezvani SMHS, Falcão Silva MJ, Komljenovic D, Almeida NM. A Systematic Literature Review on Urban Resilience Enabled with Asset and Disaster Risk Management Approaches and GIS-Based Decision Support Tools. Applied Sciences (Switzerland) 2023;13. https://doi.org/10.3390/app13042223.
25. Sanchez TW, Shumway H, Gordner T, Lim T. The prospects of artificial intelligence in urban planning. International Journal of Urban Sciences 2023;27:179–94. https://doi.org/10.1080/12265934.2022.2102538.
26. Thurner L, Scheidler A, Schäfer F, Menke JH, Dollichon J, Meier F, Braun M. Pandapower—An open-source Python tool for convenient modeling, analysis, and optimization of electric power systems. IEEE Trans Power Syst. 2023;38(1):860-863.
27. Wang Y, Zhang N, Li Y, Kang C, Huang J. Multi-objective transmission expansion planning considering renewable energy integration and N-1 security criterion: A decomposition-based evolutionary approach. Int J Electr Power Energy Syst. 2023;144:108532.
28. Yang T, Wang J, Huang J, Kelly FJ, Li G. Long-term Exposure to Multiple Ambient Air Pollutants and Association with Incident Depression and Anxiety. JAMA Psychiatry 2023;80:305–13. https://doi.org/10.1001/jamapsychiatry.2022.4812.
29. Zhang J, Ma X, Zhang J, Sun D, Zhou X, Mi C, et al. Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model. Journal of Environmental Management 2023;332. https://doi.org/10.1016/j.jenvman.2023.117357.
30. Zhang X, Wang Q, Wang J. Resilience-oriented planning of integrated electricity and natural gas systems: A review of models and methods. Renew Sustain Energy Rev. 2022;158:112098.
31. Zheng Y, Lin Y, Zhao L, Wu T, Jin D, Li Y. Spatial planning of urban communities via deep reinforcement learning. Nature Computational Science 2023;3:748–62. https://doi.org/10.1038/s43588-023-00503-5.
32. Zhou Y, Chen S, Zhang Y. Integrating GIS and AI for smart grid: A review of applications in planning, operation, and maintenance. Energy AI. 2022;10:100190.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Dariel Adonis Gutiérrez Sánchez, Raciel David Lago Solano (Author)