Optimal hybrid photovoltaic distributed generation and distribution static synchronous compensators planning to minimize active power losses using adaptive acceleration coefficients particle swarm optimization algorithms
DOI:
https://doi.org/10.20998/2074-272X.2023.6.15Keywords:
photovoltaic distributed generation, distribution static synchronous compensators, power losses, voltage profile, acceleration coefficients , particle swarm optimization algorithmsAbstract
The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.
References
Kyryk V.V., Bohomolova O.S. Justification of optimal location of connection of the distributed generation source and value of its power. Electrical Engineering & Electromechanics, 2019, no. 2, pp. 55-60. doi: https://doi.org/10.20998/2074-272X.2019.2.08.
Gopal Reddy S., Ganapathy S., Manikandan M. Power quality improvement in distribution system based on dynamic voltage restorer using PI tuned fuzzy logic controller. Electrical Engineering & Electromechanics, 2022, no. 1, pp. 44-50. doi: https://doi.org/10.20998/2074-272X.2022.1.06.
Gad A.G. Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering, 2022, vol. 29, no. 5, pp. 2531-2561. doi: https://doi.org/10.1007/s11831-021-09694-4.
Devabalaji K.R., Ravi K. Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using Bacterial Foraging Optimization Algorithm. Ain Shams Engineering Journal, 2016, vol. 7, no. 3, pp. 959-971. doi: https://doi.org/10.1016/j.asej.2015.07.002.
Hassan H.A., Zellagui M. MVO Algorithm for Optimal Simultaneous Integration of DG and DSTATCOM in Standard Radial Distribution Systems Based on Technical-Economic Indices. 2019 21st International Middle East Power Systems Conference (MEPCON), 2019, pp. 277-282. doi: https://doi.org/10.1109/MEPCON47431.2019.9007995.
Ibrahim K., Sirjani R., Shareef H. Performance Assesement of Pareto and Non-Pareto Approaches for the Optimal Allocation of DG and DSTATCOM in the Distribution System. Technical Gazette, 2020, vol. 27, no. 5, pp. 1654-1661. doi: https://doi.org/10.17559/TV-20181214103448.
Kouadri R., Slimani L., Bouktir T. Slime mould algorithm for practical optimal power flow solutions incorporating stochastic wind power and static VAR compensator device. Electrical Engineering & Electromechanics, 2020, no. 6, pp. 45-54. doi: https://doi.org/10.20998/2074-272X.2020.6.07.
Frahat M., Hatata A.Y., Saadawi M.M., Kaddah S.S. Grasshopper Optimization-based Optimal Sizing of DG/DSTATCOM in Distribution Networks. Mansoura Engineering Journal, 2022, vol. 47, no. 2, pp. 6-16. doi: https://doi.org/10.21608/bfemu.2022.238659.
Ansari A., Byalihal S.C. Application of hybrid TLBO-PSO algorithm for allocation of distributed generation and STATCOM. Indonesian Journal of Electrical Engineering and Computer Science, 2022, vol. 29, no. 1, pp. 38-48. doi: https://doi.org/10.11591/ijeecs.v29.i1.pp38-48.
Djabali C., Bouktir T. Simultaneous allocation of multiple distributed generation and capacitors in radial network using genetic-salp swarm algorithm. Electrical Engineering & Electromechanics, 2020, no. 4, pp. 59-66. doi: https://doi.org/10.20998/2074-272X.2020.4.08.
Manohara M., Veera Reddy V.C., Vijaya Kumar M. Northern Goshawk Optimization for Optimal Allocation of Multiple Types of Active and Reactive Power Distribution Generation in Radial Distribution Systems for Techno-Environmental Benefits. International Journal of Intelligent Engineering and Systems, 2023, vol. 16, no. 1, pp. 91-99. doi: https://doi.org/10.22266/ijies2023.0228.08.
Ferminus Raj A., Gnana Saravanan A. An optimization approach for optimal location & size of DSTATCOM and DG. Applied Energy, 2023, vol. 336, art. no. 120797. doi: https://doi.org/10.1016/j.apenergy.2023.120797.
Isha G., Jagatheeswari P., Jasmine Gnana Malar A. Elitist Harris Hawks Optimized Voltage Stability Enhancement in Radial Distribution System. Journal of Electrical Engineering & Technology, 2023, vol. 18, no. 4, pp. 2683-2693. doi: https://doi.org/10.1007/s42835-023-01375-5.
Pratap A., Tiwari P., Maurya R., Singh B. Minimisation of electric vehicle charging stations impact on radial distribution networks by optimal allocation of DSTATCOM and DG using African vulture optimisation algorithm. International Journal of Ambient Energy, 2022, vol. 43, no. 1, pp. 8653-8672. doi: https://doi.org/10.1080/01430750.2022.2103731.
Bharatbhai N.M., Gupta A.R. Active–Reactive Power Support with Optimal Allocation of DG and DSTATCOM in Distribution System Using Flower Pollination and Artificial Bee Colony Algorithm with Load Growth. Lecture Notes in Electrical Engineering, 2022, vol. 852, pp. 169-190. doi: https://doi.org/10.1007/978-981-16-9239-0_14.
Bhadoriya J.S., Gupta A.R. Techno-economic analysis of the DNO operated distribution system for active and reactive power support using modified particle swarm optimisation. International Journal of Ambient Energy, 2022, vol. 43, no. 1, pp. 7061-7076. doi: https://doi.org/10.1080/01430750.2022.2059779.
Zellagui M., Lasmari A., Settoul S., El‐Sehiemy R.A., El‐Bayeh C.Z., Chenni R. (2021). Simultaneous allocation of photovoltaic DG and DSTATCOM for techno‐economic and environmental benefits in electrical distribution systems at different loading conditions using novel hybrid optimization algorithms. International Transactions on Electrical Energy Systems, 2021, vol. 31, no. 8, art. no. e12992. doi: https://doi.org/10.1002/2050-7038.12992.
Tebbakh N., Labed D., Labed M.A. Optimal size and location of distributed generations in distribution networks using bald eagle search algorithm. Electrical Engineering & Electromechanics, 2022, no. 6, pp. 75-80. doi: https://doi.org/10.20998/2074-272X.2022.6.11.
Ghatak S.R., Sannigrahi S., Acharjee P. Optimal Placement of DSTATCOM and DG using Modified SFLA based Technique for Techno-Economic and Environmental Benefits. Recent Advances in Electrical & Electronic Engineering, 2018, vol. 11, no. 3, pp. 334-347. doi: https://doi.org/10.2174/2352096511666180312155907.
Lasmari A., Zellagui M., Chenni R., Semaoui S., El-Bayeh C.Z., Hassan H.A. Optimal energy management system for distribution systems using simultaneous integration of PV-based DG and DSTATCOM units. Energetika, 2020, vol. 66, no. 1, pp. 1-14. doi: https://doi.org/10.6001/energetika.v66i1.4294.
Settoul S., Chenni R., Hasan H.A., Zellagui M., Kraimia M.N. MFO Algorithm for Optimal Location and Sizing of Multiple Photovoltaic Distributed Generations Units for Loss Reduction in Distribution Systems. 2019 7th International Renewable and Sustainable Energy Conference (IRSEC), 2019, pp. 1-6. doi: https://doi.org/10.1109/IRSEC48032.2019.9078241.
Settoul S., Zellagui M., Abdelaziz A.Y., Chenni R. Optimal Integration of Renewable Distributed Generation in Practical Distribution Grids based on Moth-Flame optimization Algorithm. 2019 International Conference on Advanced Electrical Engineering (ICAEE), 2019, pp. 1-5. doi: https://doi.org/10.1109/ICAEE47123.2019.9014662.
Abd Shukor S.F., Musirin I., Abd Hamid Z., Mohamad Zamani M.K., Zellagui M., Suyono H. Intelligent based technique for under voltage load shedding in power transmission systems. Indonesian Journal of Electrical Engineering and Computer Science, 2020, vol. 17, no. 1, pp. 110-117. doi: https://doi.org/10.11591/ijeecs.v17.i1.pp110-117.
Kellogg W.D., Nehrir M.H., Venkataramanan G., Gerez V. Generation unit sizing and cost analysis for stand-alone wind, photovoltaic, and hybrid wind/PV systems. IEEE Transactions on Energy Conversion, 1998, vol. 13, no. 1, pp. 70-75. doi: https://doi.org/10.1109/60.658206.
Chaturvedi K.T., Pandit M., Srivastava L. Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch. International Journal of Electrical Power & Energy Systems, 2009, vol. 31, no. 6, pp. 249-257. doi: https://doi.org/10.1016/j.ijepes.2009.01.010.
Ziyu T., Dingxue Z. A Modified Particle Swarm Optimization with an Adaptive Acceleration Coefficients. 2009 Asia-Pacific Conference on Information Processing, 2009, pp. 330-332. doi: https://doi.org/10.1109/APCIP.2009.217.
Mirjalili S., Lewis A., Sadiq A.S. Autonomous Particles Groups for Particle Swarm Optimization. Arabian Journal for Science and Engineering, 2014, vol. 39, no. 6, pp. 4683-4697. doi: https://doi.org/10.1007/s13369-014-1156-x.
Chen K., Zhou F., Wang Y., Yin L. An ameliorated particle swarm optimizer for solving numerical optimization problems. Applied Soft Computing, 2018, vol. 73, pp. 482-496. doi: https://doi.org/10.1016/j.asoc.2018.09.007.
Chen K., Zhou F., Yin L., Wang S., Wang Y., Wan F. A hybrid particle swarm optimizer with sine cosine acceleration coefficients. Information Sciences, 2018, vol. 422, pp. 218-241. doi: https://doi.org/10.1016/j.ins.2017.09.015.
Ratnaweera A., Halgamuge S.K., Watson H.C. Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation, 2004, vol. 8, no. 3, pp. 240-255. doi: https://doi.org/10.1109/TEVC.2004.826071.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 M. A. Labed, M. Zellagui, M. Benidir, H. Sekhane, N. Tebbakh
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
