DOI:
https://doi.org/10.14483/23448393.22852Published:
2025-08-31Issue:
Vol. 30 No. 2 (2025): May-AugustSection:
Electrical, Electronic and Telecommunications EngineeringApplying an Optimization Algorithm Based on the Cauchy Distribution for Active and Reactive Power Management with Batteries in Energy Distribution Systems
Aplicación de un algoritmo de optimización basado en la distribución de Cauchy para la gestión de potencia activa y reactiva con baterías en sistemas de distribución de energía
Keywords:
Energy dispatch model, Cauchy-based distribution optimizer, Battery energy storage, Distributed energy resources, Power system optimization (en).Keywords:
modelo de despacho de energía, optimizador basado en la distribución de Cauchy, almacenamiento de energía en baterías, recursos energéticos distribuidos, optimización de sistemas de potencia (es).Downloads
Abstract (en)
Context: This study developed an energy dispatch model (EDM) using the Cauchy-based distribution optimizer (CbDO) for coordinating battery energy storage units (BESUs) and photovoltaic (PV) sources in medium-voltage distribution networks, aiming to minimize energy losses and operating costs while observing to network constraints.
Method: The CbDO was implemented in MATLAB and benchmarked against the continuous genetic algorithm (CGA), the parallel particle swarm optimizer, the parallel vortex search algorithm, and a semidefinite programming (SDP) approach. The analyzed scenarios included unitary and variable power factor operation in order to test optimization performance.
Results: The CbDO outperformed traditional methods, achieving lower energy losses and CO2 emissions, closely matching the SDP method's results in variable power factor scenarios. The most significant gains were observed when all DERs operated flexibly, validating our proposal's effectiveness in complex non-convex problems.
Conclusions: The CbDO is a viable and efficient solution for EDM, providing near-SDP performance with a simpler implementation. BESU integration and flexible power factor operation can notably enhance grid efficiency.
Abstract (es)
Contexto: Este estudio desarrolló un modelo de despacho de energía (EDM) utilizando el optimizador basado en la distribución de Cauchy (CbDO) para coordinar unidades de almacenamiento de energía en baterías (BESU) y fuentes fotovoltaicas (PV) en redes de distribución de media tensión, con el fin de minimizar las pérdidas de energía y los costos de operación a la vez que se cumplen las restricciones de la red.
Método: El CbDO fue implementado en MATLAB y comparado con el algoritmo genético continuo (CGA), el optimizador de enjambre de partículas en paralelo (PPSO), el algoritmo de búsqueda de vórtice en paralelo (PVSA) y un enfoque de programación semidefinida (SDP). Los escenarios analizados incluyeron la operación con factor de potencia unitario y variable, a fin de evaluar el desempeño de la optimización.
Resultados: El CbDO superó a los métodos tradicionales, logrando menores pérdidas de energía y emisiones de CO2, siguiendo de cerca los resultados del método SDP en los escenarios con factor de potencia variable. Las mayores mejoras se observaron cuando todos los DERs operaban de manera flexible, validando la efectividad de nuestra propuesta en problemas no convexos complejos.
Conclusiones: El CbDO constituye una solución viable y eficiente para el EDM, ofreciendo un desempeño cercano al del método SDP con una implementación más sencilla. La integración de BESUs y la operación con factor de potencia flexible pueden mejorar significativamente la eficiencia de la red.
References
L. Trahey, F. R. Brushett, N. P. Balsara, G. Ceder, L. Cheng, Y.-M. Chiang, N. T. Hahn, B. J. Ingram, S.D. Minteer, J. S. Moore et al., “Energy storage emerging: A perspective from the Joint Center for Energy Storage Research,” Proc. Natl. Acad. Sci. USA, vol. 117, no. 23, pp. 12 550–12 557, 2020. https://doi.org/10.1073/pnas.182167211
Y. Yang, S. Bremner, C. Menictas, and M. Kay, “Battery energy storage system size determination in renewable energy systems: A review,” Renew. Sust. Energy Rev., vol. 91, pp. 109–125, 2018. https://doi.org/10.1016/j.rser.2018.03.047
L. F. Grisales-Noreña, B. Cortés-Caicedo, O. D. Montoya, R. I. Bolaños, and C. A. M. Moreno, “Nonlinear programming for BESS operation for the improvement of economic, technical and environmental indices by considering grid-connected and stand-alone networks: An application to the territory of Colombia,” J. Energy Stor., vol. 98, p. 112856, 2024. https://doi.org/10.1016/j.est.2024.112856
A. Valencia, R. A. Hincapie, and R. A. Gallego, “Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks,” J. Energy Stor., vol. 34, p. 102158, Feb. 2021. https://doi.org/10.1016/j.est.2020.102158
A. Eid, O. Mohammed, and H. El-Kishky, “Efficient operation of battery energy storage systems, electric-vehicle charging stations and renewable energy sources linked to distribution systems,” J. Energy Stor., vol. 55, p. 105644, 2022. https://doi.org/10.1016/j.est.2022.105644
O. D. Montoya, W. Gil-González, R. I. Bolaños, D. F. Muñoz-Torres, J. C. Hernández, and L.F. Grisales-Noreña, “Effective Power Coordination of BESUs in Distribution Grids via the Sine-Cosine Algorithm,” in 2024 IEEE Green Tech. Conf. (GreenTech). IEEE, Apr. 2024. https://doi.org/10.1109/greentech58819.2024.10520484
L. O. P. Vásquez, J. L. Redondo, J. D. Á. Hervás, V. M. Ramírez, and J. L. Torres, “Balancing CO2 emissions and economic cost in a microgrid through an energy management system using MPC and multi-objective optimization,” Appl. Energy, vol. 347, p. 120998, 2023. https://doi.org/10.1016/j.apenergy.2023.120998
J. Elio and R. J. Milcarek, “Multi-objective electricity cost and indirect CO2 emissions minimization in commercial and industrial buildings utilizing stand-alone battery energy storage systems,” J. Cleaner Prod., vol. 417, p. 137987, 2023. https://doi.org/10.1016/j.jclepro.2023.137987
A. Molina, O. D. Montoya, and W. Gil-González, “Exact minimization of the energy losses and the CO2 emissions in isolated DC distribution networks using PV sources,” DYNA, vol. 88, no. 217, pp. 178–184, May 2021. https://doi.org/10.15446/dyna.v88n217.93099
V. Suresh, P. Janik, M. Jasinski, J. M. Guerrero, and Z. Leonowicz, “Microgrid energy management using metaheuristic optimization algorithms,” Appl. Soft Comput., vol. 134, p. 109981, Feb. 2023. https://doi.org/10.1016/j.asoc.2022.109981
S. Sankarananth, M. Karthiga, S. E., S. S., and D. P. Bavirisetti, “AI-enabled metaheuristic optimization for predictive management of renewable energy production in smart grids,” Energy Rep., vol. 10, pp. 1299–1312, Nov. 2023. https://doi.org/10.1016/j.egyr.2023. 08.005
M. Barukˇci´c, T. Varga, T. Benši´c, and V. Jerkovic´ Štil, “Optimal Allocation of Renewable Energy Sources and Battery Storage Systems Considering Energy Management System Optimization Based on Fuzzy Inference,” Energies, vol. 15, no. 19, p. 6884, Sep. 2022. https://doi.org/10.3390/en15196884
T. M. Aljohani, A. F. Ebrahim, and O. Mohammed, “Hybrid Microgrid Energy Management and Control Based on Metaheuristic-Driven Vector-Decoupled Algorithm Considering Intermittent Renewable Sources and Electric Vehicles Charging Lot,” Energies, vol. 13, no. 13, p. 3423, Jul. 2020. https://doi.org/10.3390/en13133423
Y. Yang, S. Bremner, C. Menictas, and M. Kay, “Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review,” Renew. Sust. Energy Rev., vol. 167, p. 112671, Oct. 2022. https://doi.org/10.1016/j.rser.2022.112671
V. Bhattacharjee and I. Khan, “A non-linear convex cost model for economic dispatch in microgrids,” Appl. Energy, vol. 222, pp. 637–648, Jul. 2018. https://doi.org/10.1016/j.apenergy.2018.04.001
J. Montano, J. P. Guzmán, O. D. Garzón, and A. M. R. Barrera, “Techno-economic approach for energy management system: Multi-objective optimization algorithms for energy storage in standalone and grid-connected DC microgrids,” J. Energy Stor., vol. 102, p. 114069, Nov. 2024. https://doi.org/10.1016/j.est.2024.114069
O. D. Montoya, L. F. Grisales-Noreña, and W. Gil-González, “Multi-Objective Battery Coordination in Distribution Networks to Simultaneously Minimize CO2 Emissions and Energy Losses,” Sustainability, vol. 16, no. 5, p. 2019, Feb. 2024. https://doi.org/10.3390/su16052019
G. Gonzales-Calienes, M. Kannangara, and F. Bensebaa, “Economic and Environmental Viability of Lithium-Ion Battery Recycling—Case Study in Two Canadian Regions with Different Energy Mixes,” Batteries, vol. 9, no. 7, p. 375, Jul. 2023. https://doi.org/10.3390/batteries9070375
L. Chen and A. P. Wemhoff, “Predicting embodied carbon emissions from purchased electricity for United States counties,” Appl. Energy, vol. 292, p. 116898, Jun. 2021. https://doi.org/10.1016/j.apenergy.2021.116898
A. González-Dumar, S. Arango-Aramburo, and C. M. Correa-Posada, “Quantifying power system flexibility for the energy transition in Colombia,” Int. J. Electr. Power Energy Syst., vol. 155, p. 109614, Jan. 2024. https://doi.org/10.1016/j.ijepes.2023.109614
C. Yang, Y. Sun, Y. Zou, F. Zheng, S. Liu, B. Zhao, M. Wu, and H. Cui, “Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods,” Energies, vol. 16, no. 16, p. 5974, Aug. 2023. https://doi.org/10.3390/en16165974
A. Casavola, G. Franzè, D. Menniti, and N. Sorrentino, “Voltage regulation in distribution networks in the presence of distributed generation: A voltage set-point reconfiguration approach,” Electr. Power Syst. Res., vol. 81, no. 1, pp. 25–34, Jan. 2011. https://doi.org/10.1016/j.epsr.2010.07.009
J. Gomes and O. Saavedra, “A Cauchy-based evolution strategy for solving the reactive power dispatch problem,” Int. J. Electr. Power Energy Syst., vol. 24, no. 4, pp. 277–283, May 2002. https://doi.org/10.1016/s0142-0615(01)00039-4
O. D. Montoya, L. F. Grisales-Noreña, and W. Gil-González, “Simultaneous siting and sizing of PVs and D-STATCOMs in medium-voltage grids using the Cauchy-based distribution optimizer,” Results Eng., vol. 25, p. 104407, 2025. https://doi.org/10.1016/j.rineng.2025.04407
A. A. El-Fergany, “Reviews on Load Flow Methods in Electric Distribution Networks,” Arch. Comput. Methods Eng., Oct. 2024. https://doi.org/10.1007/s11831-024-10191-7
N. D. Lagaros, M. Kournoutos, N. A. Kallioras, and A. N. Nordas, “Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants,” Optim. Eng., vol. 24, no. 4, pp. 2251–2298, Jan. 2023. https://doi.org/10.1007/s11081-022-09782-9
S. K. Dinkar and K. Deep, “Opposition-based antlion optimizer using Cauchy distribution and its application to data clustering problem,” Neural Comput. Appl., vol. 32, no. 11, pp. 6967–6995, Apr. 2019. https://doi.org/10.1007/s00521-019-04174-0
M. Wang, J.-S. Wang, X.-D. Li, M. Zhang, and W.-K. Hao, “Harris Hawk Optimization Algorithm Based on Cauchy Distribution Inverse Cumulative Function and Tangent Flight Operator,” Appl. Intell., vol. 52, no. 10, pp. 10 999–11 026, Jan. 2022. https://doi.org/10.1007/s10489-021-03080-0
L. Grisales-Noreña, B. Cortés-Caicedo, O. D. Montoya, J. Hernandéz, and G. Alcalá, “A battery energy management system to improve the financial, technical, and environmental indicators of Colombian urban and rural networks,” J. Energy Stor., vol. 65, p. 107199, Aug. 2023. https://doi.org/10.1016/j.est.2023.107199
W. Gil-González, O. D. Montoya, E. Holguín, A. Garces, and L. F. Grisales-Noreña, “Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model,” J. Energy Stor., vol. 21, pp. 1–8, Feb. 2019. https://doi.org/10.1016/j.est.2018.10.025
V. M. Garrido-Arévalo, O. D. Montoya, W. Gil-González, L. F. Grisales-Noreña, and J. C. Hernández, “An SDP relaxation in the complex domain for the efficient coordination of BESS and DGs in single-phase distribution grids while considering reactive power capabilities,” J. Energy Stor., vol. 90, p. 111913, Jun. 2024. https://doi.org/10.1016/j.est.2024.111913
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
License
Copyright (c) 2025 Maria Camila Vega Peña, Oscar Danilo Montoya Giraldo, Walter Gil-González
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
From the edition of the V23N3 of year 2018 forward, the Creative Commons License "Attribution-Non-Commercial - No Derivative Works " is changed to the following:
Attribution - Non-Commercial - Share the same: this license allows others to distribute, remix, retouch, and create from your work in a non-commercial way, as long as they give you credit and license their new creations under the same conditions.
2.jpg)
