Optimización Multihoraria de la Generación Eléctrica: Modelando Combustible y Redes para satisfacer demandas variables nodales

Carlos Xavier Guerrero-Berrones; Xavier Alfonso Proaño-Maldonado; Ramiro Fernando Berrones-Berrones; Bryan Fernando García-Suárez

Autores/as

Guerrero-Berrones Carlos Xavier Universidad Técnica de Cotopaxi. Latacunga, Ecuador. https://orcid.org/0009-0006-4775-117X
Proaño-Maldonado Xavier Alfonso Universidad Técnica de Cotopaxi. Latacunga, Ecuador. https://orcid.org/0000-0002-8271-8838
Berrones-Berrones Ramiro Fernando Universidad Israel. Quito, Ecuador. https://orcid.org/0009-0003-9908-4717
García-Suárez Bryan Fernando Escuela Superior Politécnica de Chimborazo. Riobamba, Ecuador. https://orcid.org/0009-0002-9433-3529

Palabras clave:

Optimización del sistema de generación; Flujo de Potencia AC; Red de transmisión; Stock de combustibles

Resumen

El despacho económico de generación representa una metodología fundamental en el funcionamiento de los sistemas eléctricos, orientada a optimizar los costos de producción eléctrica al mismo tiempo que se satisface de manera efectiva con la demanda eléctrica. Por consiguiente, el estudio presente lleva a cabo la Optimización Multihoraria de la Generación Eléctrica (OMGE), la cual modela el stock de combustible y las redes necesarias para atender las demandas nodales variables, aspectos fundamentales para garantizar una operación óptima y sostenible. Este análisis se enriquece con la inclusión de estudios de caso que evidencian la implementación práctica del modelo sugerido, mostrando cómo la integración de restricciones de transmisión y gestión de combustibles puede mejorar significativamente el despacho económico de generación.

Palabras clave: Optimización del sistema de generación; Flujo de Potencia AC; Red de transmisión; Stock de combustibles.

Abstract

The economic dispatch of generation represents a fundamental methodology in the operation of electricity systems, aimed at optimizing electricity production costs while effectively meeting the electricity demand. Therefore, the present study carries out the Multi-hour Optimization of Electricity Generation (MOEG), which models the fuel stock and the networks necessary to meet the variable nodal demands, fundamental aspects to guarantee an optimal and sustainable operation. This analysis is enriched with the inclusion of case studies that demonstrate the practical implementation of the suggested model, showing how the integration of transmission and fuel management constraints can significantly improve the economic dispatch of generation.

Keywords: Generation System Optimization; AC Power Flow; Transmission Network; Fuel Stock.

Información del manuscrito:
Fecha de recepción: 15 de julio de 2024.
Fecha de aceptación: 05 de septiembre de 2024.
Fecha de publicación: 07 de octubre de 2024.

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Citas

Abdin, I. F., and E. Zio. 2019. “Optimal Planning of Electric Power Systems.” Springer Optimization and Its Applications 152:53–65. doi: 10.1007/978-3-030-28565-4_10.

Ali, Mohammad, Mohsen Assili, and Amin Hajizadeh. 2020. “Multi-Objective Hydrothermal Generation Scheduling and Fuel Dispatch Management Considering Liquid Fuel Dispatch Network Modeling.” Electric Power Systems Research 187(April):106436. doi: 10.1016/j.epsr.2020.106436.

Anglani, N., and G. Petrecca. 2010. “Fossil Fuel and Biomass Fed Distributed Generation and Utility Plants : Analysis of Energy and Environmental Performance Indicators.” 964–69.

Chanana, Saurabh, Venkta Narsi Reddy, Vaibhav Goel, Aastha Chaudhary, and Ashwani Kumar. 2008. “Distributed Generation Location Based on Fuel Cost Minimization in Deregulated Electricity Markets.” IEEE Transactions on Power Systems 1–2.

Chen, Chun-lung, and Nanming Chen. 2001. “Direct Search Method for Solving Economic Dispatch Problem Considering Transmission Capacity Constraints.” IEEE Transactions on Power Systems 16(4):764–69.

Chen, Zhe, Chuangxin Guo, Shufeng Dong, Yi Ding, and Hangyin Mao. 2021. “Distributed Robust Dynamic Economic Dispatch of Integrated Transmission and Distribution Systems.” IEEE Transactions on Industry Applications 57(5):4500–4512.

Du, Xiao, Xingyu Lin, Zhiyun Peng, Sui Peng, Junjie Tang, and Wenyuan Li. 2021. “Chance-Constrained Optimal Power Flow Based on a Linearized Network Model.” International Journal of Electrical Power & Energy Systems 130:106890. doi: https://doi.org/10.1016/j.ijepes.2021.106890.

Farrag, Mahmoud Ali, Kareem Mohamed Ali, and Shaimaa Omran. 2019. “AC Load Flow Based Model for Transmission Expansion Planning.” Electric Power Systems Research 171:26–35. doi: https://doi.org/10.1016/j.epsr.2019.02.006.

Al Farsi, F. N., M. H. Albadi, N. Hosseinzadeh, and A. H. Al Badi. 2015. “Economic Dispatch in Power Systems.” 2015 IEEE 8th GCC Conference and Exhibition, GCCCE 2015. doi: 10.1109/IEEEGCC.2015.7060068.

Hlalele, Thabo G., Raj M. Naidoo, Jiangfeng Zhang, and Ramesh C. Bansal. 2020. “Dynamic Economic Dispatch with Maximal Renewable Penetration under Renewable Obligation.” Ieee Access 8:38794–808.

Huppmann, Daniel, and Ruud Egging. 2014. “Market Power, Fuel Substitution and Infrastructure–A Large-Scale Equilibrium Model of Global Energy Markets.” Energy 75:483–500.

Jordehi, A. Rezaee. 2015. “Particle Swarm Optimisation (PSO) for Allocation of FACTS Devices in Electric Transmission Systems: A Review.” Renewable and Sustainable Energy Reviews 52:1260–67.

Kheshti, Mostafa, Lei Ding, Shicong Ma, and Bing Zhao. 2018. “Double Weighted Particle Swarm Optimization to Non-Convex Wind Penetrated Emission/Economic Dispatch and Multiple Fuel Option Systems.” Renewable Energy 125:1021–37.

Kishore, T. S., and S. K. Singal. 2014. “Optimal Economic Planning of Power Transmission Lines: A Review.” Renewable and Sustainable Energy Reviews 39:949–74. doi: https://doi.org/10.1016/j.rser.2014.07.125.

Lasemi, Mohammad Ali, Mohsen Assili, and Mohammadreza Baghayipour. 2014. “Modification of Multi‐area Economic Dispatch with Multiple Fuel Options, Considering the Fuelling Limitations.” IET Generation, Transmission & Distribution 8(6):1098–1106.

Lasemi, Mohammad Ali, Mohsen Assili, and Amin Hajizadeh. 2020. “Multi-Objective Hydrothermal Generation Scheduling and Fuel Dispatch Management Considering Liquid Fuel Dispatch Network Modeling.” Electric Power Systems Research 187:106436.

Liu, Xiaoping, Ming Ding, Jianghong Han, Pingping Han, and Yali Peng. 2010. “Dynamic Economic Dispatch for Microgrids Including Battery Energy Storage.” 2nd International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2010 (2):914–17.

Onmez, Yusuf S. ¨. 2013. “Estimation of Fuel Cost Curve Parameters for Thermal Power Plants Using the ABC Algorithm.” TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 21(Sup.1):1827–41. doi: 10.3906/elk-1203-10.

Ordoudis, Christos, Pierre Pinson, Juan M. Morales, and Marco Zugno. 2016. “An Updated Version of the IEEE RTS 24-Bus System for Electricity Market and Power System Operation Studies.” Technical University of Denmark 13.

Romero, Alexander Emanuel Torres, David Humberto Cardenas Villacres, and Raquel de los Angeles Salas Ibarra. 2024. “Análisis de Flujo AC Aplicados a Un Sistema Eléctrico de Potencia.” INGENIO 7(1):47–56.

Ross, Dale W., and Sungkook Kim. 1980. “DYNAMIC ECONOMIC DISPATCH OF GENERATION.” IEEE Transactions on Power Apparatus and Systems (6):2060–68.

Sakib, Nazmus, Md Emdadul Hoque, Fazlur Rashid, Muhammad Aziz, Md Shehab Uddin, and Md. Tariqul Islam. 2024. “Effects of Dispatch Algorithms and Fuel Variation on Techno-Economic Performance of Hybrid Energy System in Remote Island.” Journal of Energy Storage 78:109919.

Smil, Vaclav. 2000. “Energy in the Twentieth Century: Resources, Conversions, Costs, Uses, and Consequences.” Annual Review of Energy and the Environment 25(1):21–51.

Wang, Yan, and Sarah M. Ryan. 2010. “Effects of Uncertain Fuel Costs on Fossil Fuel and Electric Energy Flows in the US.” Energy Systems 1:209–43.

Yang, Y., W. Wu, and B. Wang. 2020. “Adjustable Robust Economic Dispatch: Case Study on Its Application and Evaluation in Power System.” Pp. 1439–43 in 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2).