Allocation and Sizing of Photovoltaic Systems to Reduce Power Losses and Economic Aspects using a new PSO approach

Authors

Keywords:

Optimal size and location, Dynamic Momentum, Adapted MPSO, Loss minimization, Cost minimization.

Abstract

In this paper is proposed a new method for optimal allocation and sizing of Photovoltaics (PVs) systems in power distribution network to reduce losses in feeders and minimize the economic aspects of the PVs implementation. The proposed technique, called Modified Particle Swarm Optimization with Dynamic Momentum (MPSO-DM) is proposed so that can be applied in distribution system, that requires a high number of decision variables in the optimization process. This becomes necessary because the classical Particle Swarm Optimization (PSO), in general, presents a performance drop in the exploration of the search space when applied to these situations. The proposed method was tested in a real distribution network of the Federal University of Paraíba (79 buses), which results show a improvement in performance, yielding better solutions when compared to the traditional Modified Particle Swarm Optimization (MPSO) method and MPSO with linear decrement inertia weight.

Downloads

Download data is not yet available.

Author Biographies

Alex Moreira, Universidade Federal de Pernambuco

Possui o título de engenheiro eletricista (2016) pelo Instituto Federal da Paraíba, João Pessoa, Brasil e mestrado em 2018 pela Universidade Federal da Paraíba, João Pessoa, Brasil. Atualmente é estudante de doutorado do programa de pós graduação da Universidade Federal de Pernambuco. Seus interesses estão na operação de sistemas elétricos com inserção de fontes renováveis de energia não convencionais.

Yuri Molina, Universidade Federal da Paraíba

Possui graduação em engenharia elétrica pela Universidade Nacional de Engenharia, Lima, Peru e Doutorado em engenharia elétrica (2009) pela Universidade Católica do Rio de Janeiro, Brasil. Atualmente é professor adjunto do departamento de engenharia elétrica da Universidade Federal da Paraíba. Seus interesses incluem operação de sistemas elétricos em ambientes competitivos.

Ronaldo Aquino, Universidade Federal de Pernambuco

Possui graduação em engenharia elétrica pela Universidade Federal de Pernambuco (1983), Recife, Brasil e doutorado em engenharia elétrica pela Universidade Federal a Paraíba, Campina Grande, Brasil (2001). Atualmente é professor titular do departamento de engenharia elétrica da UFPE. Seus interesses de pesquisa incluem a aplicação de inteligência artificial em problemas de geração, transmissão e distribuição de energia elétrica.

Zocimo Ñaupari, Universidad Nacional de Ingenieria, Lima, Perú.

Possui graduação em engenharia elétrica em 1998 pela Universidade Nacional de Engenharia , Lima, Peru e mestrado em 2006 pela Universidade Federal do Maranhão, São Luiz, Brasil. Atualmente trabalha como professor da Universidade Nacional de Engenharia, Lima, Peru. Seus interesses de pesquisa incluem a otimização de sistemas elétricos de potência, com ênfase em métodos heurísticos, bem como o desenvolvimento de novos mercados de energia.

References

I. E. Agency, World Energy Outlook 2017, vol. 4 of 10. 3 ed., 7 2017.

F. A. C. M. dos Santos and F. M. S. M. dos Santos, “Geração distribuída versus centralizada,” Millenium-Journal of Education, Technologies, and Health, vol. 35, no. 13, 2016.

L. d. M. Guedes, Alocação de unidades de geração distribuída considerando perdas e aspectos econômicos. PhD thesis, Universidade de Brasília, Distrito Federal, 2013.

M. Albadi, H. Soliman, M. A. Thani, A. Al-Alawi, S. Al-Ismaili, A. AlNabhani, and H. Baalawi, “Optimal allocation of pv systems to minimize losses in distribution networks using ga and pso: Masirah island case study.,” Journal of Electrical Systems, vol. 13, no. 4, 2017.

A. Khare and S. Rangnekar, “A review of particle swarm optimization and its applications in solar photovoltaic system,” Applied Soft Computing, vol. 13, no. 5, pp. 2997–3006, 2013.

M. Pesaran H.A, P. D. Huy, and V. K. Ramachandaramurthy, “A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms,” Renewable and Sustainable Energy Reviews, vol. 75, pp. 293–312, 2017.

A. A. Hassan, F. H. Fahmy, A. E.-S. A. Nafeh, and M. A. Abuelmagd, “Genetic single objective optimisation for sizing and allocation of renewable dg systems,” International Journal of Sustainable Energy, vol. 36, no. 6, pp. 545–562, 2017.

S. Dahal and H. Salehfar, “Optimal location and sizing of distributed generators in distribution networks,” in 2013 North American Power Symposium (NAPS), pp. 1–6, 2013.

P. Prakash and D. K. Khatod, “Optimal sizing and siting techniques for distributed generation in distribution systems: A review,” Renewable and Sustainable Energy Reviews, vol. 57, pp. 111 – 130, 2016.

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942–1948 vol.4, 1995.

M. H. Albadi, H. M. Soliman, E. F. El-Saadany, M. A. Thani, A. AlAlawi, S. Al-Ismaili, A. Al-Nabhani, and H. Baalawi, “Optimal allocation of pv systems in distribution networks using pso,” in 2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), pp. 1–5, 2017.

S. M. Arif, A. Hussain, and D. R. Shin, “A survey on particle swarm optimization for use in distributed generation placement and sizing,” in MATEC Web of Conferences, vol. 70, p. 10013, 2016.

A. Jayavarma and T. Joseph, “Optimal placement of solar pv in distribution system using particle swarm optimization,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, pp. 329–337, 2013.

J. S. de Souza, Y. P. Molina, C. S. de Araujo, W. P. de Farias, and I. S. de Araujo, “Modified particle swarm optimization algorithm for sizing photovoltaic system,” IEEE Latin America Transactions, vol. 15, no. 2, pp. 283–289, 2017.

K. Bhumkittipich and W. Phuangpornpitak, “Optimal placement and sizing of distributed generation for power loss reduction using particle swarm optimization,” Energy Procedia, vol. 34, pp. 307 – 317, 2013. 10th Eco-Energy and Materials Science and Engineering Symposium.

M. Mohammadi and M. A. Nasab, “Pso based multiobjective approach for optimal sizing and placement of distributed generation,” Research Journal of Applied Sciences, Engineering and Technology, vol. 2, no. 8, pp. 832–837, 2011.

Y. A. Beromi, M. Sedighizadeh, and M. Sadighi, “A particle swarm optimization for sitting and sizing of distributed generation in distribution network to improve voltage profile and reduce thd and losses,” in 2008 43rd International Universities Power Engineering Conference, pp. 1–5, Sep. 2008.

E. T. Oldewage et al., The perils of particle swarm optimization in high dimensional problem spaces. PhD thesis, University of Pretoria, 2017.

T. Hendtlass, “Particle swarm optimisation and high dimensional problem spaces,” in 2009 IEEE Congress on Evolutionary Computation, pp. 1988–1994, May 2009.

Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp. 69–73, May 1998.

R. C. Eberhart and Y. Shi, “Tracking and optimizing dynamic systems with particle swarms,” in Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), vol. 1, pp. 94–100 vol. 1, May 2001.

G. Chen, X. Huang, J. Jia, and Z. Min, “Natural exponential inertia weight strategy in particle swarm optimization,” in 2006 6th World Congress on Intelligent Control and Automation, vol. 1, pp. 3672–3675, June 2006.

J. Xin, G. Chen, and Y. Hai, “A particle swarm optimizer with multi-stage linearly-decreasing inertia weight,” in 2009 International Joint Conference on Computational Sciences and Optimization, vol. 1, pp. 505–508, April 2009.

A. F. Moreira, “Alocação e dimensionamento de painéis fotovoltaicos utilizando otimização por enxame de partículas para reduzir as perdas visando os aspectos econômicos,” Master’s thesis, Universidade Federal da Paraíba, João Pessoal, 2018.

I. PVPS et al., “Trends in photovoltaic applications,” Survey report of selected IEA countries between 1992 and 2015, 2016.

Downloads

Published

2022-03-08

How to Cite

Moreira, A., Molina, Y., Aquino, R. ., & Ñaupari, Z. (2022). Allocation and Sizing of Photovoltaic Systems to Reduce Power Losses and Economic Aspects using a new PSO approach. IEEE Latin America Transactions, 20(6), 977–985. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6168

Issue

Vol. 20 No. 6 (2022): Ordinary Issue

Section

Articles