Optimal Dispatch of Diesel-Photovoltaic Hybrid Systems in Isolated Communities with Socioeconomic Prediction of Electricity Demand
| dc.creator | Páez Chica, Carlos Arturo | |
| dc.date | 2026-01-13 | |
| dc.description | The optimization of economic dispatch in hybrid diesel photovoltaic systems within Non-Interconnected Zones (NIZ) is essential to enhance energy sustainability and reduce operating costs. The variability of renewable generation and the uncertainty of electricity demand hinder efficient planning, underscoring the need for advanced optimization models. The purpose of this research was to develop an economic dispatch model for diesel generators integrated with photovoltaic generation, incorporating electricity demand forecasting. The methodology was based on formulating a quadratic programming problem and applying vector autoregressive models supported by socioeconomic variables. Simulations were carried out in Python using the IPOPT (Interior Point Optimizer) solver. The proposed model aimed to optimize operational efficiency by reducing CO₂ emissions and production costs. The analysis was applied to a modified version of the IEEE 33-bus distribution system. The results showed that the optimal dispatch reduced generation costs by 32.1%, decreasing from USD 15 853.83 in the base scenario to USD 10 769.82 with the inclusion of photovoltaic generation. Likewise, daily fuel consumption decreased by 4 227.4 gallons, while CO₂ emissions were reduced by 41 926.1 kg. In addition, solar generation contributed 4 249.2 kWh per day, equivalent to 5.09% of total demand, directly reducing technical losses from 292 kW to 243 kW. In conclusion, the results demonstrate that the integration of predictive models and optimization techniques improves operational performance and supports sustainable energy planning in isolated communities. | en-US |
| dc.description | La optimización del despacho económico en sistemas híbridos diésel-fotovoltaico en zonas no interconectadas (ZNI) es clave para potenciar la sostenibilidad energética y reducir costos operativos. La variabilidad de la generación renovable y la incertidumbre en la demanda dificultan una planificación eficiente, lo que resalta la necesidad de modelos avanzados de optimización. El propósito de esta investigación fue crear un modelo de despacho económico de generadores a diésel integrados con generación fotovoltaica, considerando el pronóstico de la demanda eléctrica. La metodología se basó en la formulación de un problema de programación cuadrática y la aplicación de vectores autorregresivos sustentados en variables socioeconómicas. Las simulaciones se realizaron en Python, y el solver IPOPT (Interior Point Optimizer). El modelo buscó optimizar la eficacia operativa, disminuyendo las emisiones de CO₂ y los costos de producción. El análisis se aplicó a una versión modificada del sistema IEEE de 33 nodos. Los resultados mostraron que el despacho óptimo reduce los costos de generación en un 32,1 %, pasando de USD 15 853,83 en el escenario base a USD 10 769,82 con la incorporación de la generación fotovoltaica. De igual forma, se logró una disminución diaria en el consumo de combustible de 4 227,4 galones y una reducción en las emisiones de CO₂ de 41 926,1 kg. Asimismo, la generación solar aportó 4 249,2 kWh por día, equivalente al 5,09 % de la demanda total, contribuyendo directamente a la disminución de las pérdidas técnicas, que pasaron de 292 kW a 243 kW. En conclusión, los resultados demuestran que la integración de modelos predictivos y técnicas de optimización mejora el desempeño operativo y favorece la planificación energética sostenible en comunidades aisladas. | es-ES |
| dc.format | application/pdf | |
| dc.format | text/xml | |
| dc.format | application/zip | |
| dc.identifier | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3435 | |
| dc.identifier | 10.22430/22565337.3435 | |
| dc.language | eng | |
| dc.publisher | Instituto Tecnológico Metropolitano (ITM) | en-US |
| dc.relation | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3435/3889 | |
| dc.relation | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3435/4102 | |
| dc.relation | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3435/4119 | |
| dc.relation | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3435/4120 | |
| dc.relation | /*ref*/F. Ahmed, R. Al-Abri, H. Yousef, and A. M. Massoud, "An Optimal Energy Dispatch Management System for Hybrid Power Plants: PV-Grid-Battery-Diesel Generator-Pumped Hydro Storage," IEEE Access, vol. 12, pp. 143307-143326, 2024. https://doi.org/10.1109/ACCESS.2024.3470652 | |
| dc.relation | /*ref*/O. Ayan, and B. E. Turkay, "Techno-Economic Comparative Analysis of Grid-Connected and Islanded Hybrid Renewable Energy Systems in 7 Climate Regions, Turkey," IEEE Access, vol. 11, pp. 48797-48825, 2023. https://doi.org/10.1109/ACCESS.2023.3276776 | |
| dc.relation | /*ref*/A. Akbari-Dibavar, B. Mohammadi-Ivatloo, K. Zare, T. Khalili, and A. Bidram, "Economic-Emission Dispatch Problem in Power Systems With Carbon Capture Power Plants," IEEE Trans. Ind. Appl., vol. 57, no. 4, pp. 3341-3351, Jul.-Aug. 2021. https://doi.org/10.1109/TIA.2021.3079329 | |
| dc.relation | /*ref*/X. Zhu, G. Ruan, H. Geng, H. Liu, M. Bai, and C. Peng, "Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions," IEEE Trans. Ind. Appl., vol. 60, no. 4, pp. 5565-5576, July-Aug. 2024. https://doi.org/10.1109/TIA.2024.3395570 | |
| dc.relation | /*ref*/J. J. Daniel Raj, R. Mohan Das, S. Vinod Kumar, M. Jayanthi, A. Sujin Jose, and V. Tejas, "Electricity Demand Forecasting Using ML," in 2023 3rd Int. Conf, Pervasive Comput. Soc. Netw. (ICPCSN), Salem, India, 2023, pp. 547-551. https://doi.org/10.1109/ICPCSN58827.2023.00095 | |
| dc.relation | /*ref*/H. Iftikhar, S. Mancha Gonzales, J. Zywiołek, and J. L. López-Gonzales, "Electricity Demand Forecasting Using a Novel Time Series Ensemble Technique," IEEE Access, vol. 12, pp. 88963-88975, 2024. https://doi.org/10.1109/ACCESS.2024.3419551 | |
| dc.relation | /*ref*/Y. Yao, R. Ding, H. Xu, X. Zhang, Y. Geng, and R. Liu, "Day-Ahead Economic Dispatch Based on CVaR Under Extreme Weather Conditions," in 2024 3rd Asian Conf. Front. Power Ener. (ACFPE), Chengdu, China, 2024, pp. 610-614. https://doi.org/10.1109/ACFPE63443.2024.10800843 | |
| dc.relation | /*ref*/M. Sumorek, and A. Idzkowski, "Time Series Forecasting for Energy Production in Stand-Alone and Tracking Photovoltaic Systems Based on Historical Measurement Data," Energies, vol. 16, no. 17, p. 6367, Sep. 2023. https://doi.org/10.3390/en16176367 | |
| dc.relation | /*ref*/X. Zhang, T. Ding, C. Mu, O. Han, Y. Huang, and M. Shahidehpour, "Dual Stochastic Dual Dynamic Programming for Multi-Stage Economic Dispatch With Renewable Energy and Thermal Energy Storage," IEEE Trans. Power Syst., vol. 39, no. 2, pp. 3725-3737, Mar. 2024. https://doi.org/10.1109/TPWRS.2023.3288859 | |
| dc.relation | /*ref*/C. Hu, G. Wen, S. Wang, J. Fu, and W. Yu, "Distributed Multiagent Reinforcement Learning With Action Networks for Dynamic Economic Dispatch," IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 7, pp. 9553-9564, Jul. 2024. https://doi.org/10.1109/TNNLS.2023.3234049 | |
| dc.relation | /*ref*/J. Namaganda-Kiyimba, and J. Mutale, "Gender Considerations in Load Estimation for Rural Electrification," in 2020 IEEE Conf. Technol. Sustain. (SusTech), Santa Ana, CA, USA, 2020, pp. 1-8. https://doi.org/10.1109/SusTech47890.2020.9150501 | |
| dc.relation | /*ref*/R. Guanoluisa-Pineda, A. Ibarra, D. Arcos-Aviles, W. Martinez, E. Motoasca, and F. Guinjoan, "Short-Term forecasting of photovoltaic power in an isolated area of Ecuador using deep learning techniques," in 2022 11th Int. Conf. Renew. Energy Res. Appli. (ICRERA), Istanbul, Turkey, 2022, pp. 408-413. https://doi.org/10.1109/ICRERA55966.2022.9922772 | |
| dc.relation | /*ref*/J. Rodrigues Dos Reis et al., "Medium and Long Term Energy Forecasting Methods: A Literature Review," IEEE Access, vol. 13, pp. 29305-29326, 2025. https://doi.org/10.1109/ACCESS.2025.3540999 | |
| dc.relation | /*ref*/D. O. Garzón Medina, R. Caneloi dos Santos, T. Sousa, and J. C. Lopes, "Comparative Analysis of Artificial Neural Networks and Statistical Models Applied to Demand Forecasting," in 2019 IEEE PES Innov. Smart Grid Technol. Conf. – Lat. Am. (ISGT Latin America), Gramado, Brazil, 2019, pp. 1-6. https://doi.org/10.1109/ISGT-LA.2019.8895277 | |
| dc.relation | /*ref*/Ş. Özdemır, Y. Demır, and Ö. Yildirim, "The Effect of Input Length on Prediction Accuracy in Short-Term Multi-Step Electricity Load Forecasting: A CNN-LSTM Approach," IEEE Access, vol. 13, pp. 28419-28432, 2025. https://doi.org/10.1109/ACCESS.2025.3540636 | |
| dc.relation | /*ref*/L. N. F. Da Silva et al., "Innovative Strategy for the Socioeconomic Variables Impact Evaluation on Non-Technical Losses," in 2024 IEEE PES Innov. Smart Grid Technol. Eur. (ISGT EUROPE), Dubrovnik, Croatia, 2024, pp. 1-5. https://doi.org/10.1109/ISGTEUROPE62998.2024.10863265 | |
| dc.relation | /*ref*/L. M. Pastore, and L. de Santoli, “Socio-economic implications of implementing a carbon-neutral energy system: A Green New Deal for Italy,” Energy, vol. 322, p. 135682, May. 2025. https://doi.org/10.1016/j.energy.2025.135682 | |
| dc.relation | /*ref*/N. Ammar, M. Sulaiman, and A. F. Mohamad Nor, “Analysis Load Forecasting of Power System Using of Fuzzy Logic and Artificial Neural Network.” J. Telecom. Electr. Comp. Engin., vol. 9, no. 3, pp.181-92, Sep. 2017. https://jtec.utem.edu.my/jtec/article/view/1560 | |
| dc.relation | /*ref*/S. Bigerna, C. A. Bollino, and S. Micheli, "Overview of socio-economic issues for smart grids development," in 2015 Int. Conf. Smart Cities Green ICT Syst. (SMARTGREENS), Lisbon, Portugal, 2015, pp. 1-6. https://ieeexplore.ieee.org/document/7297987 | |
| dc.relation | /*ref*/L. Török, "Effects of Energy Economic Variables on the Economic Growth of the European Union (2010–2019)," Energies, vol. 16, no. 16, p. 6094, Aug. 2023. https://doi.org/10.3390/en16166094 | |
| dc.relation | /*ref*/Y. -R. Lee, H. -J. Kang, and M. -K. Kim, "Optimal Operation Approach With Combined BESS Sizing and PV Generation in Microgrid," IEEE Access, vol. 10, pp. 27453-27466, 2022. https://doi.org/10.1109/ACCESS.2022.3157294 | |
| dc.relation | /*ref*/N. Roy et al., "Load Forecast using ANN & VAR techniques for North Eastern Regional (NER) Grid of India," in 2021 9th IEEE Int. Conf. Power Syst. (ICPS), Kharagpur, India, 2021, pp. 1-5. https://doi.org/10.1109/ICPS52420.2021.9670298 | |
| dc.relation | /*ref*/R. Nur Hasanah, R. P. Ravie O.M.P., and H. Suyono, "Comparison Analysis of Electricity Load Demand Prediction using Recurrent Neural Network (RNN) and Vector Autoregressive Model (VAR)," in 2020 12th Int. Conf. Electr. Engin. (ICEENG), Cairo, Egypt, 2020, pp. 23-29. https://doi.org/10.1109/ICEENG45378.2020.9171778 | |
| dc.relation | /*ref*/S. Gorjian, H. Sharon, H. Ebadi, K. Kant, F. Bontempo Scavo, and G. M. Tina, “Recent technical advancements, economics and environmental impacts of floating photovoltaic solar energy conversion systems,” J. Clean. Prod., vol. 278, p. 124285, Jan. 2021. https://doi.org/10.1016/j.jclepro.2020.124285 | |
| dc.relation | /*ref*/G. Zhang, W. Wang, J. Du, and H. Sheng, "Multiobjective Economic Optimal Dispatch for the Island Isolated Microgrid under Uncertainty Based on Interval Optimization," Math. Probl. Engin., vol. 2021, p. 9983104, Oct. 2021. https://doi.org/10.1155/2021/9983104 | |
| dc.relation | /*ref*/I. Sulaeman, G. R. Chandra Mouli, A. Shekhar, and P. Bauer, ‘‘Comparison of AC and DC nanogrid for office buildings with EV charging, PV and battery storage,’’ Energies, vol. 14, no. 18, p. 5800, Sep. 2021. https://doi.org/10.3390/en14185800 | |
| dc.relation | /*ref*/C. Anzures, J. Posada, K. Osorio, J. R. Vidal Medina, V. M. Sanchez, and Y. U. Lopez, "Operación de Sistemas de Generación y Suministro de Energía Eléctrica en Zonas no Interconectadas de Colombia," in 2018 IEEE ANDESCON, Santiago de Cali, Colombia, 2018, pp. 1-6. https://doi.org/10.1109/ANDESCON.2018.8564585 | |
| dc.relation | /*ref*/M. F. Ishraque et al., "Techno-Economic and Power System Optimization of a Renewable Rich Islanded Microgrid Considering Different Dispatch Strategies," IEEE Access, vol. 9, pp. 77325-77340, 2021. https://doi.org/10.1109/ACCESS.2021.3082538 | |
| dc.relation | /*ref*/T. P. Van Hong, K. Dang Tuan, and D. Vo Ngoc, "Applied Stochastic Fractal Search Algorithm to solve Economic Emission Dispatch Problems," in 2022 Int. Conf. Green Ener., Comput. Sustain. Technol. (GECOST), Miri Sarawak, Malaysia, 2022, pp. 1-5. https://doi.org/10.1109/GECOST55694.2022.10010664 | |
| dc.relation | /*ref*/J. -t. Yu, C. -H. Kim, A. Wadood, T. Khurshaid, and S. -B. Rhee, "Jaya Algorithm With Self-Adaptive Multi-Population and Lévy Flights for Solving Economic Load Dispatch Problems," IEEE Access, vol. 7, pp. 21372-21384, 2019. https://doi.org/10.1109/ACCESS.2019.2899043 | |
| dc.relation | /*ref*/N. Singh et al., “Novel Heuristic Optimization Technique to Solve Economic Load Dispatch and Economic Emission Load Dispatch Problems,” Electronics, vol. 12, no. 13, p. 2921, Jul. 2023. https://doi.org/10.3390/electronics12132921 | |
| dc.relation | /*ref*/A. A. K. Ismaeel, E. H. Houssein, D. Sami Khafaga, E. Abdullah Aldakheel, A. S. AbdElrazek, and M. Said, "Performance of Osprey Optimization Algorithm for Solving Economic Load Dispatch Problem," Mathematics, vol. 11, no. 19, p. 4107, Sep. 2023. https://doi.org/10.3390/math11194107 | |
| dc.relation | /*ref*/Z. Pan et al., "Multi-Agent Learning-Based Nearly Non-Iterative Stochastic Dynamic Transactive Energy Control of Networked Microgrids," IEEE Trans. Smart Grid, vol. 13, no. 1, pp. 688-701, Jan. 2022. https://doi.org/10.1109/TSG.2021.3116598 | |
| dc.relation | /*ref*/H. Lotfi, "A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns," Sustainability, vol. 15, no. 1, p. 442, Dec. 2023. https://doi.org/10.3390/su15010442 | |
| dc.relation | /*ref*/Z. Wu et al., "The Electricity Load Prediction Model for Residential Buildings: A Critical Review of Output Types, Prediction Methods and Driving Factors," Buildings, vol. 15, no. 6, p. 925, Mar. 2025. https://doi.org/10.3390/buildings15060925 | |
| dc.relation | /*ref*/H. Sun, and B. Han, "Regional Power Grid Load-forecast considering Socio-economic factors," in 2023 2nd Int. Conf. Adv. Electron., Electric. Green Ener. (AEEGE), Singapore, Singapore, 2023, pp. 70-74. https://doi.org/10.1109/AEEGE58828.2023.00021 | |
| dc.relation | /*ref*/J. Ahlrichs, S. Wenninger, C. Wiethe, and B. Häckel, “Impact of socio-economic factors on local energetic retrofitting needs - A data analytics approach,” Energy Policy, vol. 160, p. 112646, Jan. 2022. https://doi.org/10.1016/j.enpol.2021.112646 | |
| dc.relation | /*ref*/A. R. Singh, D. Koteswara Raju, L. Phani Raghav, and R. Seshu Kumar, “State-of-the-art review on energy management and control of networked microgrids,” Sustain. Energy Technol. Assess., vol. 57, p. 103248, Jun. 2023. https://doi.org/10.1016/j.seta.2023.103248 | |
| dc.relation | /*ref*/R. Maqbool, and S. Arome Akubo, “Solar energy for sustainability in Africa: The challenges of socio-economic factors and technical complexities,” Int. J. Energy Res., vol. 46, no. 12, pp. 16336-16354, Jul. 2022. https://doi.org/10.1002/er.8425 | |
| dc.relation | /*ref*/N. S. Kelepouris, A. I. Nousdilis, A. S. Bouhouras, and G. C. Christoforidis, "Cost-Effective Hybrid PV-Battery Systems in Buildings Under Demand Side Management Application," IEEE Trans. Ind. Appl., vol. 58, no. 5, pp. 6519-6528, Sept.-Oct. 2022. https://doi.org/10.1109/TIA.2022.3186295 | |
| dc.relation | /*ref*/ | |
| dc.rights | Copyright (c) 2026 TecnoLógicas | en-US |
| dc.rights | https://creativecommons.org/licenses/by-nc-sa/4.0 | en-US |
| dc.source | TecnoLógicas; Vol. 29 No. 65 (2026); e3435 | en-US |
| dc.source | TecnoLógicas; Vol. 29 Núm. 65 (2026); e3435 | es-ES |
| dc.source | 2256-5337 | |
| dc.source | 0123-7799 | |
| dc.subject | vectores autorregresivos | es-ES |
| dc.subject | previsión energética | es-ES |
| dc.subject | modelos de optimización | es-ES |
| dc.subject | sistemas fotovoltaicos | es-ES |
| dc.subject | autoregressive vectors | en-US |
| dc.subject | energy forecasting | en-US |
| dc.subject | optimization models | en-US |
| dc.subject | photovoltaic systems | en-US |
| dc.title | Optimal Dispatch of Diesel-Photovoltaic Hybrid Systems in Isolated Communities with Socioeconomic Prediction of Electricity Demand | en-US |
| dc.title | Despacho óptimo de sistemas híbridos diésel-fotovoltaico en comunidades aisladas con predicción socioeconómica de la demanda eléctrica | es-ES |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | Research Papers | en-US |
| dc.type | Artículos de investigación | es-ES |
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