Using Harris Hawks Optimization to Determine Optimal Placement and Sizing of Shunt Capacitors and Distributed Generators
DOI:
https://doi.org/10.31272/jeasd.3086Keywords:
Distributed generation, Harris Hawks Optimization, IEEE 69-bus system, Reactive power compensation, Real power Loss minimizationAbstract
The power supply problem represents one of the never-ending problems that constantly remain prominent. Consequently, we continuously improve power generators, networks, and system configurations. The installation of shunt capacitors and distributed generators represents the main improvement. Because electricity systems are constantly growing, maximizing benefits and minimizing disadvantages is necessary. Therefore, one of the most important aspects of growing algorithms is optimization. In this work, Harries Hawks Optimization (HHO) is the optimization technique suggested to calculate and determine the optimal size of the shunt capacitors and distribution generation on the radial distribution system. The reconfiguration method (RM) is an approach that refers to the best and optimal location of the distribution generation and the shunt capacitor. The main goal of selecting the optimal size (using HHO) and optimal placement (using RM) is to minimize the voltage deviation. This paper presents three cases: the first case includes determining the optimal voltage deviation after installing shunt capacitors on radial distribution networks; the second case involves calculating the optimal voltage deviation after installing distribution generation on this system. The last case is conducted to calculate the optimal voltage deviation after installing SC and DG simultaneously. Results of the proposed HHO algorithm show a considerable reduction in the voltage deviation rate. That is, the voltage deviation is minimized to 38.95%, 75.13%, and 90.23 % in the three cases, respectively. The proposed approaches (HHO and RM) are applied to the IEEE 69-bus power system. Three scenarios have been used to demonstrate the effectiveness and superiority of the suggested methods.
References
Z. A. Obaid, L. Cipcigan, and M. T. Muhssin, "Design of a hybrid fuzzy/Markov chain-based hierarchal demand-side frequency control," 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA, 2017, pp. 1-5, doi: https://doi.org/10.1109/PESGM.2017.8273821
M. T. Muhssin, L. M. Cipcigan, N. Jenkins, N. M. Cheng, and Z. A. Obaid, “Modelling of a population of Heat Pumps as a Source of load in the Great Britain power system,” 2016 International Conference on Smart Systems and Technologies (SST), pp. 109–113, Oct. 2016, doi: https://doi.org/10.1109/sst.2016.7765642
D. Sh. Wais and W. S. Majeed, "Performance of high voltage transmission line based on thyristor-control series compensator," Journal of Engineering and Sustainable Development, vol. 25, no. 5, pp. 26–38, Sep. 2021, doi: http://doi.org/10.31272/jeasd.25.5.3.
I. M. Jawad and W. S. Majeed, "Congestion mitigation in distribution network by integrated distributed generations for improving voltage profiles and minimizing the losses," Journal of Engineering and Sustainable Development, vol. 25, no. 2, pp. 78–87, Mar. 2021, doi: http://doi.org/10.31272/jeasd.25.2.9.
W. S. Majeed and G. H. Abedali, "Reliability improvement in distribution system by injected distributed generation based on zone branches methodology," Journal of Engineering and Sustainable Development, vol. 24, no. 1, pp. 79–93, Jan. 2020, doi: https://doi.org/10.31272/jeasd.24.1.6.
M. T. Mouwafi, R. A. El-Sehiemy, and A. A. A. El-Ela, "A two-stage method for optimal placement of distributed generation units and capacitors in distribution systems," Applied Energy, vol. 307, p. 118188, Feb. 2022, doi: http://doi.org/10.1016/j.apenergy.2021.118188.
M. Mahdavi, A. Awaafo, M. Dini, S. Moradi, F. Jurado, and D. Vera, "A flexible loss reduction formulation for simultaneous capacitor placement and network reconfiguration in distribution grids," Electric Power Systems Research, vol. 235, p. 110708, Oct. 2024, doi: http://doi.org/10.1016/j.epsr.2024.110708.
A. E. B. Abu-Elanien, M. M. A. Salama, and K. B. Shaban, "Modern network reconfiguration techniques for service restoration in distribution systems: A step to a smarter grid," Alexandria Engineering Journal, vol. 57, no. 4, pp. 3959–3967, Dec. 2018, doi: http://doi.org/10.1016/j.aej.2018.03.011.
M. Al-Kaabi, V. Dumbrava, and M. Eremia, "Single and multi-objective optimal power flow based on hunger games search with Pareto concept optimization," Energies, vol. 15, no. 22, 2022, doi: http://doi.org/10.3390/en15228328.
D. Yousri, E. F. El-Saadany, Y. Shaker, T. S. Babu, A. F. Zobaa, and D. Allam, "Mitigating mismatch power loss of series–parallel and total-cross-tied array configurations using novel enhanced heterogeneous hunger games search optimizer," Energy Reports, vol. 8, pp. 9805–9827, Nov. 2022, doi: http://doi.org/10.1016/j.egyr.2022.07.153.
V. Bathina, R. Devarapalli, and F. P. García Márquez, "Hybrid approach with combining cuckoo-search and grey-wolf optimizer for solving optimal power flow problems," Journal of Electrical Engineering & Technology, vol. 18, no. 3 pp. 1637–1653, May 2023, doi: http://doi.org/10.1007/s42835-022-01301-1.
M. Al-Kaabi, V. Dumbrava, and M. Eremia, "Grey wolf optimizer for solving single objective functions optimal power flow," in 2023 13th International Symposium on Advanced Topics in Electrical Engineering (ATEE), Mar. 2023, pp. 1–5, doi: http://doi.org/10.1109/ATEE58038.2023.10108149.
O. Akdag, A. Ates, and C. Yeroglu, "Modification of Harris hawks optimization algorithm with random distribution functions for optimum power flow problem," Neural Computing and Applications, vol. 33, no. 6, pp. 1959–1985, Mar. 2021, doi: http://doi.org/10.1007/s00521-020-05073-5.
A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, "Harris hawks optimization: Algorithm and applications," Future Generation Computer Systems, vol. 97, pp. 849–872, Aug. 2019, doi: http://doi.org/10.1016/j.future.2019.02.028.
M. Al-Kaabi, V. Dumbrava, and M. Eremia, "A slime mould algorithm programming for solving single and multi-objective optimal power flow problems with Pareto front approach: A case study of the Iraqi super grid high voltage," Energies, vol. 15, no. 20, 2022, doi: http://doi.org/10.3390/en15207473.
W. Bai, I. Eke, and K. Y. Lee, "An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem," Control Engineering Practice, vol. 61, pp. 163–172, Apr. 2017, doi: http://doi.org/10.1016/j.conengprac.2017.02.010.
A. Ali et al., "A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques," Scientific Reports, vol. 14, no. 1, p. 6187, Mar. 2024, doi: http://doi.org/10.1038/s41598-024-56590-5.
M. Al-Kaabi, L. Al-Bahrani, V. Dumbrava, and M. Eremia, "Optimal power flow with four objective functions using improved differential evolution algorithm: Case study IEEE 57-bus power system," in 2021 10th International Conference on Energy and Environment (CIEM), Oct. 2021, pp. 1–5, doi: http://doi.org/10.1109/CIEM52821.2021.9614925.
K. M. Sallam, M. A. Hossain, S. Elsayed, R. K. Chakrabortty, M. J. Ryan, and M. A. Abido, "Optimal power flow considering intermittent solar and wind generation using multi-operator differential evolution algorithm," Electric Power Systems Research, vol. 232, p. 110377, Jul. 2024, doi: http://doi.org/10.1016/j.epsr.2024.110377.
M. Martínez, C. Mateo, T. Gómez, B. Alonso, and P. Frías, "A hybrid particle swarm optimization approach for explicit flexibility procurement in distribution network planning, International Journal of Electrical Power & Energy Systems, vol. 161, 2024, https://doi.org/10.1016/j.ijepes.2024.110215.
M. Madhusudhan, N. Kumar, and H. Pradeepa, "Optimal location and capacity of DG systems in distribution network using genetic algorithm," International Journal of Information Technology, vol. 13, no. 1, pp. 155–162, Feb. 2021, doi: http://doi.org/10.1007/s41870-020-00545-2.
A. Ghorai, B. Mandal, P. K. Roy, and C. Paul, "Oppositional based artificial rabbits optimization applied for optimal allocation of nonlinear DG in distribution networks considering total harmonic distortion limit," Electric Power Systems Research, vol. 231, p. 110334, Jun. 2024, doi: http://doi.org/10.1016/j.epsr.2024.110334.
Q. Cao, H. Wang, Z. Hui, and L. Chen, "Optimal location and sizing of multi-resource distributed generator based on multi-objective artificial bee colony algorithm," Energy Engineering, vol. 121, no. 2, pp. 499–521, Jan. 2024, doi: http://doi.org/10.32604/ee.2023.042702.
M. Gandomkar, M. Vakilian, and M. Ehsan, "A genetic-based tabu search algorithm for optimal DG allocation in distribution networks," Electric Power Components and Systems, vol. 33, no. 12, pp. 1351–1362, Dec. 2005, doi: http://doi.org/10.1080/15325000590964254.
K. Balu and V. Mukherjee, "Siting and sizing of distributed generation and shunt capacitor banks in radial distribution system using constriction factor particle swarm optimization," Electric Power Components and Systems, vol. 48, no. 6–7, pp. 697–710, Aug. 2020, doi: http://doi.org/10.1080/15325008.2020.1797935.
A. S. Hassan, Y. Sun, and Z. Wang, "Multi-objective for optimal placement and sizing DG units in reducing loss of power and enhancing voltage profile using BPSO-SLFA," Energy Reports, vol. 6, pp. 1581–1589, Nov. 2020, doi: http://doi.org/10.1016/j.egyr.2020.06.013.
M. Dehghani, Z. Montazeri, and O. P. Malik, "Optimal sizing and placement of capacitor banks and distributed generation in distribution systems using spring search algorithm," International Journal of Emerging Electric Power Systems, vol. 21, no. 1, 2020, doi: http://doi.org/10.1515/ijeeps-2019-0217.
C. Venkatesan, R. Kannadasan, M. H. Alsharif, M.-K. Kim, and J. Nebhen, "A novel multiobjective hybrid technique for siting and sizing of distributed generation and capacitor banks in radial distribution systems," Sustainability, vol. 13, no. 6, 2021, doi: http://doi.org/10.3390/su13063308.
V. Janamala and K. Radha Rani, "Optimal allocation of solar photovoltaic distributed generation in electrical distribution networks using Archimedes optimization algorithm," Clean Energy, vol. 6, no. 2, pp. 271–287, 2022, doi: http://doi.org/10.1093/ce/zkac010.
J. S. Bhadoriya and A. R. Gupta, "A novel transient search optimization for optimal allocation of multiple distributed generator in the radial electrical distribution network," International Journal of Emerging Electric Power Systems, vol. 23, no. 1, pp. 23–45, 2022, doi: http://doi.org/10.1515/ijeeps-2021-0001.
H. A. Taha, M. H. Alham, and H. K. M. Youssef, "Multi-objective optimization for optimal allocation and coordination of wind and solar DGs, BESSs and capacitors in presence of demand response," IEEE Access, vol. 10, pp. 16225–16241, 2022, doi: http://doi.org/10.1109/ACCESS.2022.3149135.
S. Gopiya Naik, D. K. Khatod, and M. P. Sharma, "Optimal allocation of combined DG and capacitor for real power loss minimization in distribution networks," International Journal of Electrical Power & Energy Systems, vol. 53, pp. 967–973, Dec. 2013, doi: http://doi.org/10.1016/j.ijepes.2013.06.008.
G. Pareja, L. Lezama, G. Carmona. "A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation," Energies, vol. 15, no. 9, pp. 3063, 2022, https://doi.org/10.3390/en15093063.
I. Quadri, S. Bhowmick. "A hybrid technique for simultaneous network reconfiguration and optimal placement of distributed generation resources," Soft Comput, vol. 24, no. 15, pp. 11315–11336, 2020, https://doi.org/10.1007/s00500-019-04597-w.
A. Imran, M. Kowsalya, D.P. Kothari. "A novel integration technique for optimal network reconfiguration and distributed generation placement in power distribution networks," International Journal of Electrical Power & Energy Systems. vol. 63, pp. 461–472, 2014, https://doi.org/10.1016/j.ijepes.2014.06.011.
S. Essallah, and A. Khedher, "Optimization of distribution system operation by network reconfiguration and DG integration using MPSO algorithm", Renewable Energy Focus, vol. 34, pp. 37-46, 2020, https://doi.org/10.1016/j.ref.2020.04.002.
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Copyright (c) 2026 Ali D. Abdulazeez, Mazin T. Muhssin, Petr P. Oschepkov , Bahaa Hussein Al IGEB (Author)
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