Multi-Objectives Techniques for Distributed Generation Installation under Multi-Load Model

Azlina Abdullah, Ismail Musirin, Muhammad Murtadha Othman, Siti Rafidah Abdul Rahim, Nor Azwan Mohamed Kamari

Abstract


Integration of renewable energy resources into distribution system is indeed crucial for several compelling reasons, encompassing technological advancement, economic advantages and environmental sustainability.  In this study, a single and multi-objective Integrated Immune Moth Evolutionary Programming (IIMFEP and MO-IIMFEP) was proposed to optimally integrate Distributed Generation (DG) based on Photovoltaic (PV) solar panels on a Radial Distribution System (RDS). The IIMFEP and MO-IIMFEP are utilized to identify the optimal size and location of a DG within a multi-load model to minimize total active power losses, total voltage deviation and total operating cost. In this paper, a comparison is made between various voltage-dependent load models and the significance of considering multiple load models is highlighted. Consequently, there are four categories of voltage-dependent loads are used: constant power (CP), commercial, residential and industrial. In MO-IIMFEP, fuzzy logic determines the optimal compromise solution among non-dominated Pareto solutions. The IEEE 69-Bus Radial Distribution Systems (RDS) is used to validate the efficacy of IIMFEP and MO-IIMFEP and the outcomes are contrasted with EP, MFO, MO-EP and MO-MFO, respectively. The findings indicate that the proposed algorithms exhibited superior performance compared to the other two regarding the best solutions for single-objective and multi-objective problems. 


Keywords


Total active power loss; multi-objective optimization; total voltage deviation; DG; distribution system.

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References


M. Kumar, A. M. Soomro, W. Uddin, and L. Kumar, “Optimal multi-objective placement and sizing of distributed generation in distribution system: a comprehensive review,” Energies (Basel), vol. 15, no. 21, p. 7850, Oct. 2022.

A. Oymak and M. R. Tur, “A short review on the optimization methods using for distributed generation planning ” International Journal of Smart Grid, vol. 6, no 3, pp. 54-64, Sept. 2022.

G. Tahiliani and A. R. Gupta, “Voltage profile improvement by optimal DG allocation using atom search optimisation in radial and mesh distribution system,” International Journal of Engineering, Science and Technology, vol. 14, no. 3, pp. 112–121, Aug. 2022.

V. J. F. Kakeu, A. T. Boum, and C. F. Mbey, “Optimal reliability of a smart grid”, International Journal of Smart Grid, Vol.5, No.2, June, 2021

A. A. Mohamed, S. Kamel, A. Selim, T. Khurshaid, and S. B. Rhee, “Developing a hybrid approach based on analytical and metaheuristic optimization algorithms for the optimization of renewable DG allocation considering various types of loads,” Sustainability (Switzerland), vol. 13, no. 8, Apr. 2021.

F. Zishan,“Investigating the reliability and optimal capacity of microgrid electricity storage systems with the aim of reducing costs” International Journal of Smart Grid, Vol.8, No.3, September, 2024.

H. Babanezhad and I. Colak, "Modified genetic algorithm for cost minimization in future multi-microgrid network," 2023 12th International Conference on Renewable Energy Research and Applications (ICRERA), Oshawa, ON, Canada, pp. 451-460, 2023.

A. S. Machava, K. K. Kaberere, and G. A. Vilanculo, “A method for optimal distributed generation allocation considering load demand uncertainties,” International Journal of Electrical and Electronic Engineering and Telecommunications, vol. 11, no. 3, pp. 210–217, May 2022.

Z. Abdul Hamid, I. Musirin, A. Yasier Azman, and M. Murtadha Othman, “Continuous domain ant colony optimization for distributed generation placement and losses minimization,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 9, no. 2, p. 261, Jun. 2020.

M. Ali Shaik, P. L. Mareddy, and N. Visali, “Enhancement of voltage profile in the distribution system by reconfiguring with DG placement using equilibrium optimizer,” Alexandria Engineering Journal, vol. 61, no. 5, pp. 4081–4093, May 2022.

Y. G. Jahed, S. Y. M. Mousavi, and S. Golestan, “Optimal sizing and siting of distributed generation systems incorporating reactive power tariffs via water flow optimization,” Electrical Power System Research,vol. 231, 2024.

U. Sultana, A. B. Khairuddin, A. S. Mokhtar, N. Zareen, and B. Sultana, “Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system,” Energy, vol. 111, pp. 525–536, Sep. 2016.

A. Ramadan, M. Ebeed, S. Kamel, E. M. Ahmed, and M. Tostado-Véliz, “Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions,” Ain Shams Engineering Journal, vol. 14, no. 2, Mar. 2023.

W. Sultana, S. D. Jebaseelan, “Optimal allocation of solar PV and wind energy power for radial distribution system using spider monkey optimization,” Sustainable Computing: Informatics and Systems, vol. 42, 2024.

H. P. C, K. Subbaramaiah, and P. Sujatha, “Optimal DG unit placement in distribution networks by multi-objective whale optimization algorithm & its techno-economic analysis,” Electric Power Systems Research, vol. 214, Jan. 2023.

D. Yang, J. Jia, W. Wu, W. Cai, D. An, K. Luo, and B. Yang, “Optimal placement and sizing of distributed generators based on multiobjective particle swarm optimization,” Front Energy Res, vol. 9, Nov. 2021.

Z. Belboul, B. Toual, A. Kouzou, and A. Bensalem, “Optimal sizing of Hybrid PV/Wind/Battery/Diesel microgrid system using a multi-objective grasshopper optimization algorithm: A case study in Djelfa City Algeria,” in 3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2022.

M. G. Hemeida, A. A. Ibrahim, A. A. A. Mohamed, S. Alkhalaf, and A. M. B. El-Dine, “Optimal allocation of distributed generators DG based manta ray foraging optimization algorithm (MRFO),” Ain Shams Engineering Journal, vol. 12, no. 1, pp. 609–619, Mar. 2021.

E. A. Mostafa and N. K. Bahgaat, “A comparison between using a firefly algorithm and a modified PSO technique for stability analysis of a PV system connected to grid”, International Journal of Smart Grid, Vol. 1, No. 1, 2017

T. L. Duong, P. T. Nguyen, N. D. Vo, and M. P. Le, “A newly effective method to maximize power loss reduction in distribution networks with highly penetrated distributed generations,” Ain Shams Engineering Journal, vol. 12, no. 2, pp. 1787–1808, Jun. 2021.

I. Salman, K. M. Saffer, H. H. Safi, S. A. Mostafa, and B. A. Khalaf, “Salp swarm and gray wolf optimizer for improving the efficiency of power supply network in radial distribution systems,” Journal of Intelligent Systems, vol. 32, no. 1, Jan. 2023.

A. Selim, S. Kamel, A. S. Alghamdi, and F. Jurado, “Optimal placement of DGs in distribution system using an improved harris hawks optimizer based on single- and multi-objective approaches,” IEEE Access, vol. 8, pp. 52815–52829, 2020.

S. K. Dash, S. Mishra, A. Y. Abdelaziz, M. L. Alghaythi, and A. Allehyani, “Optimal allocation of distributed generators in active distribution networks using a new oppositional hybrid sine cosine muted differential evolution algorithm,” Energies (Basel), vol. 15, no. 6, Mar. 2022.

R. Fathi, B. Tousi, and S. Galvani, “Allocation of renewable resources with radial distribution network reconfiguration using improved salp swarm algorithm,” Appl Soft Comput, vol. 132, Jan. 2023.

S. Sannigrahi and P. Acharjee, “Maximization of system benefits with the optimal placement of DG and DSTATCOM considering load variations,” in Procedia Computer Science, Elsevier B.V., 2018.

T. Yuvaraj, K. Ravi, and K. R. Devabalaji, “Optimal allocation of DG and DSTATCOM in radial distribution system using cuckoo search optimization algorithm,” Modelling and Simulation in Engineering, vol. 2017, 2017.

N. R. Godha Dagade, V. N. Bapat, and I. Korachagaon, “Improved ACO for planning and performance analysis of multiple distributed generations in distribution system for various load models,” in 2020 2nd International Sustainability and Resilience Conference: Technology and Innovation in Building Designs, Institute of Electrical and Electronics Engineers Inc., Nov. 2020.

D. Rama Prabha and T. Jayabarathi, “Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm,” Ain Shams Engineering Journal, vol. 7, no. 2, pp. 683–694, Jun. 2016.

S. Verma, M. Pant, and V. Snasel, “A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems,” IEEE Access, vol. 9, pp. 57757–57791, 2021.

T. Hoang, B. Huy, and D. Kim, “Multi-objective optimal power flow using multi-objective search group algorithm,” IEEE Access. Vol. 10, pp. 77837-77856, 2022.

S. A. Syed Mustaffa, I. Musirin, M. K. Mohamad Zamani, and M. M. Othman, “Pareto optimal approach in multi-objective chaotic mutation immune evolutionary programming (MOCMIEP) for optimal distributed generation photovoltaic (DGPV) integration in power system,” Ain Shams Engineering Journal, vol. 10, no. 4, pp. 745–754, Dec. 2019.

M. Ebeed, D. Ahmed, S. Kamel, F. Jurado, M.F. Shaaban, A. Ali, and A .Refai , “Optimal energy planning of multi-microgrids at stochastic nature of load demand and renewable energy resources using a modified Capuchin Search Algorithm,” Neural Comput Appl, vol. 35, no. 24, pp. 17645–17670, Aug. 2023.

A. Eid, S. Kamel, M. H. Hassan, and B. Khan, “A comparison study of multi-objective bonobo optimizers for optimal integration of distributed generation in distribution systems,” Front Energy Res, vol. 10, Feb. 2022.

P. A. Vikhar, “Evolutionary algorithms: A critical review and its future prospects,” in Proceedings - International Conference on Global Trends in Signal Processing, Information Computing and Communication, ICGTSPICC 2016, Institute of Electrical and Electronics Engineers Inc., Jun. 2017.

S. Mirjalili, “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowl Based Syst, vol. 89, pp. 228–249, Nov. 2015.

J. E. Hunt and D. E. Cooke, “Learning using an artificial immune system,” Journal of Network and Computer Applications ,vol. 19, pp. 189–212, 1996.

D. Jia, G. Zheng, and M. Khurram Khan, “An effective memetic differential evolution algorithm based on chaotic local search,” Inf Sci (N Y), vol. 181, no. 15, pp. 3175–3187, Aug. 2011.

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.




DOI (PDF): https://doi.org/10.20508/ijrer.v15i3.14938.g9088

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