Integración Óptima de Generadores Fotovoltaicos en Sistemas de Distribución DC a través de la Aplicación del Algoritmo de Optimización Aritmética Modificado

Palabras clave: Algoritmo de optimización aritmética, flujo de potencia, generación de energía solar, redes de distribución, reducción de costos fotovoltaicos

Resumen

En este artículo se aborda el problema de ubicación y dimensionamiento óptimo de generadores fotovoltaicos (PV) en redes de corriente continua (CC) con el objetivo de minimizar los costos de inversión y operación de la red para la empresa de distribución de energía en un horizonte de operación de 20 años. Este problema es presentado mediante un modelo matemático de programación no lineal entera mixta (PNLEM), el cual se resuelve mediante la aplicación de una metodología de optimización del tipo maestro-esclava. La etapa maestra corresponde a una versión mejorada del algoritmo de optimización aritmética que incluye una etapa de exploración y explotación del espacio de solución que involucra la generación de nuevas soluciones a partir de la aplicación de funciones de distribución gaussiana alrededor de actual  en cada iteración . En la etapa esclava se emplea el algoritmo de flujo de potencia especializado para redes de CC, el cual permite evaluar cada posible solución obtenida de la etapa maestra en relación con la ubicación (nodos) y el dimensionamiento de los generadores PV (tamaños), y verificar que todas las restricciones asociadas al modelo de PNLEM se cumplan. El resultado principal de esta investigación corresponde a una metodología mejorada basada en la combinación del algoritmo de optimización aritmética y las funciones de distribución gaussiana para mejorar las etapas de exploración y explotación del espacio de soluciones y encontrar soluciones de mejor calidad que las reportadas en la literatura especializada. En conclusión, los resultados numéricos en los sistemas de prueba IEEE 33 e IEEE 69 nodos demostraron que el algoritmo de optimización propuesto mejoró los resultados existentes en la literatura especializada para la ubicación y el dimensionamiento de fuentes PV en sistemas de distribución de CC, lo cual genera un nuevo punto de referencia para futuras investigaciones en esta temática.

Biografía del autor/a

Nixon Andrés Solera Losada, Universidad Distrital Francisco José de Caldas, Colombia

Universidad Distrital Francisco José de Caldas, Bogotá-Colombia, nasoleral@correo.udistrital.edu.co

Juan Pablo Villalba Jaramillo, Univesidad Distrital Francisco José de Caldas, Colombia

Universidad Distrital Francisco José de Caldas, Bogotá-Colombia, jpvillalbaj@correo.udistrital.edu.co

Oscar Danilo Montoya* , Universidad Distrital Francisco José de Caldas, Colombia

Universidad Distrital Francisco José de Caldas, Bogotá-Colombia, odmontoyag@udistrital.edu.co

Referencias bibliográficas

G. Nanda et al., “Implications of Carbon Tax on Generation Expansion Plan & GHG Emission: A Case Study on Indian Power Sector,” International Journal of Emerging Electric Power Systems, vol. 3, no. 1, Aug. 2005, https://doi.org/10.2202/1553-779X.1045

M. Mosbah, A. Khattara, M. Becherif, and S. Arif, “Optimal PV Location Choice Considering Static and Dynamic Constraints,” International Journal of Emerging Electric Power Systems, vol. 18, no. 1, Feb. 2017, https://doi.org/10.1515/ijeeps-2016-0141

N. Srisaen and A. Sangswang, “Effects of PV Grid-Connected System Location on a Distribution System,” in APCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems, Dec. 2006, pp. 852–855. https://doi.org/10.1109/APCCAS.2006.342175

H. A. Kefale, E. M. Getie, and K. G. Eshetie, “Optimal Design of Grid-Connected Solar Photovoltaic System Using Selective Particle Swarm Optimization,” International Journal of Photoenergy, vol. 2021, pp. 1–9, Mar. 2021, https://doi.org/10.1155/2021/6632859

T. Kaipia, P. Salonen, J. Lassila, and J. Partanen, “Possibilities of the low voltage DC distribution systems,” in Nordac, Nordic Distribution and Asset Management Conference (Nordac, 2006), pp.1 – 10, https://www.upn.se/html-files/Glava/Referenser/Ref%206%20Possibilities%20of%20low%20voltage%20DC%20distribution.pdf

O. Ivanov, B. C. Neagu, G. Grigoras, F. Scarlatache, and M. Gavrilas, “A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids,” Mathematics, vol. 9, no. 19, p. 2375, Sep. 2021, https://doi.org/10.3390/math9192375

M. H. Moradi and M. Abedini, “A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems,” International Journal of Electrical Power & Energy Systems, vol. 34, no. 1, pp. 66–74, Jan. 2012, https://doi.org/10.1016/j.ijepes.2011.08.023

R. Kollu, S. R. Rayapudi, and V. L. N. Sadhu, “A novel method for optimal placement of distributed generation in distribution systems using HSDO,” International Transactions on Electrical Energy Systems, vol. 24, no. 4, pp. 547–561, Apr. 2014, https://doi.org/10.1002/etep.1710

S. Kaur, G. Kumbhar, and J. Sharma, “A MINLP technique for optimal placement of multiple DG units in distribution systems,” International Journal of Electrical Power & Energy Systems, vol. 63, pp. 609–617, Dec. 2014, https://doi.org/10.1016/j.ijepes.2014.06.023

S. Sultana and P. K. Roy, “Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems,” International Journal of Electrical Power & Energy Systems, vol. 63, pp. 534–545, Dec. 2014, https://doi.org/10.1016/j.ijepes.2014.06.031

S. Gupta, A. Saxena, and B. P. Soni, “Optimal Placement Strategy of Distributed Generators based on Radial Basis Function Neural Network in Distribution Networks,” Procedia Comput Sci, vol. 57, pp. 249–257, Oct. 2015, https://doi.org/10.1016/j.procs.2015.07.478

B. Mohanty and S. Tripathy, “A teaching learning based optimization technique for optimal location and size of DG in distribution network,” Journal of Electrical Systems and Information Technology, vol. 3, no. 1, pp. 33–44, May 2016, https://doi.org/10.1016/j.jesit.2015.11.007

S. Sultana and P. K. Roy, “Krill herd algorithm for optimal location of distributed generator in radial distribution system,” Appl Soft Comput, vol. 40, pp. 391–404, Mar. 2016, https://doi.org/10.1016/j.asoc.2015.11.036

T. P. Nguyen, V. N. Dieu, and P. Vasant, “Symbiotic Organism Search Algorithm for Optimal Size and Siting of Distributed Generators in Distribution Systems,” International Journal of Energy Optimization and Engineering, vol. 6, no. 3, pp. 1–28, Jul. 2017, https://doi.org/10.4018/IJEOE.2017070101

A. M. El-Zonkoly, “Optimal placement of multi-distributed generation units including different load models using particle swarm optimization,” Swarm Evol Comput, vol. 1, no. 1, pp. 50–59, Mar. 2011, https://doi.org/10.1016/j.swevo.2011.02.003

R. Deshmukh and A. Kalage, “Optimal Placement and Sizing of Distributed Generator in Distribution System Using Artificial Bee Colony Algorithm,” in 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN), Nov. 2018, pp. 178–181. https://doi.org/10.1109/GCWCN.2018.8668633

A. Bayat and A. Bagheri, “Optimal active and reactive power allocation in distribution networks using a novel heuristic approach,” Appl Energy, vol. 233–234, pp. 71–85, Jan. 2019, https://doi.org/10.1016/j.apenergy.2018.10.030

R. Sellami, F. Sher, and R. Neji, “An improved MOPSO algorithm for optimal sizing & placement of distributed generation: A case study of the Tunisian offshore distribution network (ASHTART),” Energy Reports, vol. 8, pp. 6960–6975, Nov. 2022, https://doi.org/10.1016/j.egyr.2022.05.049

O. D. Melgar Dominguez, M. Pourakbari Kasmaei, M. Lavorato, and J. R. S. Mantovani, “Optimal siting and sizing of renewable energy sources, storage devices, and reactive support devices to obtain a sustainable electrical distribution systems,” Energy Systems, vol. 9, no. 3, pp. 529–550, Aug. 2018, https://doi.org/10.1007/s12667-017-0254-8

M. Shahzad, W. Akram, M. Arif, U. Khan, and B. Ullah, “Optimal Siting and Sizing of Distributed Generators by Strawberry Plant Propagation Algorithm,” Energies, vol. 14, no. 6, p. 1744, Mar. 2021, https://doi.org/10.3390/en14061744

P. D. P. Reddy, V. C. V. Reddy, and T. G. Manohar, “Application of flower pollination algorithm for optimal placement and sizing of distributed generation in Distribution systems,” Journal of Electrical Systems and Information Technology, vol. 3, no. 1, pp. 14–22, May 2016, https://doi.org/10.1016/j.jesit.2015.10.00

F. F. Amigue, S. N. Essiane, S. P. Ngoffe, and A. T. Nelem, “Optimal Placement and Sizing of Distributed Energy Generation in an Electrical Network Using the Hybrid Algorithm of Bee Colonies and Newton Raphson,” Journal of Power and Energy Engineering, vol. 08, no. 06, pp. 9–21, Oct. 2020, https://doi.org/10.1016/j.jesit.2015.10.002

L. Abualigah, A. Diabat, S. Mirjalili, M. Abd Elaziz, and A. H. Gandomi, “The Arithmetic Optimization Algorithm,” Comput Methods Appl Mech Eng, vol. 376, p. 113609, Apr. 2021, https://doi.org/10.1016/j.cma.2020.113609

B. Cortés-Caicedo, F. Molina-Martin, L. F. Grisales-Noreña, O. D. Montoya, and J. C. Hernández, “Optimal Design of PV Systems in Electrical Distribution Networks by Minimizing the Annual Equivalent Operative Costs through the Discrete-Continuous Vortex Search Algorithm,” Sensors, vol. 22, no. 3, p. 851, Jan. 2022, https://doi.org/10.3390/s22030851

A. H. Khoso, M. M. Shaikh, and A. A. Hashmani, “A New and Efficient Nonlinear Solver for Load Flow Problems,” Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5851–5856, Jun. 2020, https://doi.org/10.48084/etasr.3604

X. Zhou, Q. Ai, and M. Yousif, “Two kinds of decentralized robust economic dispatch framework combined distribution network and multi-microgrids,” Appl Energy, vol. 253, p. 113588, Nov. 2019, https://doi.org/10.1016/j.apenergy.2019.113588

X. Chen, Z. Li, W. Wan, L. Zhu, and Z. Shao, “A master–slave solving method with adaptive model reformulation technique for water network synthesis using MINLP,” Sep Purif Technol, vol. 98, pp. 516–530, Sep. 2012, https://doi.org/10.1016/j.seppur.2012.06.039

B. Doğan and T. Ölmez, “A new metaheuristic for numerical function optimization: Vortex Search algorithm,” Inf Sci (N Y), vol. 293, pp. 125–145, Feb. 2015, https://doi.org/10.1016/j.ins.2014.08.053

A. Garces, “Uniqueness of the power flow solutions in low voltage direct current grids,” Electric Power Systems Research, vol. 151, pp. 149–153, Oct. 2017, https://doi.org/10.1016/j.epsr.2017.05.031

O. D. Montoya and W. Gil-González, “On the numerical analysis based on successive approximations for power flow problems in AC distribution systems,” Electric Power Systems Research, vol. 187, p. 106454, Oct. 2020, https://doi.org/10.1016/j.epsr.2020.106454

V. Monteiro et al., “The Role of Front-End AC/DC Converters in Hybrid AC/DC Smart Homes: Analysis and Experimental Validation,” Electronics, vol. 10, no. 21, p. 2601, Oct. 2021, https://doi.org/10.3390/electronics10212601

T. Shen, Y. Li, and J. Xiang, “A Graph-Based Power Flow Method for Balanced Distribution Systems,” Energies, vol. 11, no. 3, p. 511, Feb. 2018, https://doi.org/10.3390/en11030511

W. Gil-González, O. D. Montoya, L. F. Grisales-Noreña, A.-J. Perea-Moreno, and Q. Hernandez-Escobedo, “Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves,” Sustainability, vol. 12, no. 7, p. 2983, Apr. 2020, https://doi.org/10.3390/su12072983

O. Sahin and B. Akay, “Comparisons of metaheuristic algorithms and fitness functions on software test data generation,” Appl Soft Comput, vol. 49, pp. 1202–1214, Dec. 2016, https://doi.org/10.1016/j.asoc.2016.09.045

R. R. Sahoo and M. Ray, “PSO based test case generation for critical path using improved combined fitness function,” Journal of King Saud University - Computer and Information Sciences, vol. 32, no. 4, pp. 479–490, May 2020, https://doi.org/10.1016/j.jksuci.2019.09.010

M. D. Hraiz, J. A. M. García, R. Jiménez Castaneda, and H. Muhsen, “Optimal PV Size and Location to Reduce Active Power Losses While Achieving Very High Penetration Level With Improvement in Voltage Profile Using Modified Jaya Algorithm,” IEEE J Photovolt, vol. 10, no. 4, pp. 1166–1174, Jul. 2020, https://doi.org/10.1109/JPHOTOV.2020.2995580

R. Zheng, H. Jia, L. Abualigah, Q. Liu, and S. Wang, “An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems,” Mathematical Biosciences and Engineering, vol. 19, no. 1, pp. 473–512, 2022, https://doi.org/10.3934/mbe.2022023

F. S. Gharehchopogh, I. Maleki, and Z. A. Dizaji, “Chaotic vortex search algorithm: metaheuristic algorithm for feature selection,” Evol Intell, vol. 15, no. 3, pp. 1777–1808, Sep. 2022, https://doi.org/10.1007/s12065-021-00590-1

N. C. Sahoo and K. Prasad, “A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems,” Energy Convers Manag, vol. 47, no. 18–19, pp. 3288–3306, Nov. 2006, https://doi.org/10.1016/j.enconman.2006.01.004

L. F. Grisales-Noreña, O. D. Montoya, and C. A. Ramos-Paja, “An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm,” J Energy Storage, vol. 29, p. 101488, Jun. 2020, https://doi.org/10.1016/j.est.2020.101488

P. Wang, W. Wang, and D. Xu, “Optimal Sizing of Distributed Generations in DC Microgrids With Comprehensive Consideration of System Operation Modes and Operation Targets,” IEEE Access, vol. 6, pp. 31129–31140, May. 2018, https://doi.org/10.1109/ACCESS.2018.2842119

C. M. Castiblanco-Pérez, D. E. Toro-Rodríguez, O. D. Montoya, and D. A. Giral-Ramírez, “Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm,” Electronics , vol. 10, no. 12, p. 1452, Jun. 2021, https://doi.org/10.3390/electronics10121452

Cómo citar
[1]
N. A. Solera Losada, J. P. Villalba Jaramillo, y O. D. Montoya Giraldo, «Integración Óptima de Generadores Fotovoltaicos en Sistemas de Distribución DC a través de la Aplicación del Algoritmo de Optimización Aritmética Modificado», TecnoL., vol. 25, n.º 55, p. e2418, nov. 2022.

Descargas

Los datos de descargas todavía no están disponibles.
Publicado
2022-11-11
Sección
Artículos de investigación

Métricas

Crossref Cited-by logo

Algunos artículos similares: