A New Optimization Algorithm for Optimal Wind Turbine Location Problem in Constantine City Electric Distribution Network Based Active Power Loss Reduction

Document Type: Original Manuscript


1 Department of Electrotechnic, Mentouri University of Constantine, Constantine, Algeria

2 Department of Electrical Engineering, University of Batna, Fesdis, Batna, Algeria

3 Department of Electrical Engineering, École de Technologie Supérieure, University of Québec, Montréal, Canada



The wind turbine has grown out to be one of the most common Renewable Energy Sources (RES) around the world in recent years. This study was intended to position the Wind Turbine (WT) on a wind farm to achieve the highest performance possible in Electric Distribution Network (EDN). In this paper a new optimization algorithm namely Salp Swarm Algorithm (SSA) is applied to solve the problem of optimal integration of Distributed Generation (DG) based WT (location and sizing) in EDN. The proposed algorithm is applied on practical Algerian EDN in Constantine city 73-bus in presence single and multiple WT-DGs for reducing the total active power loss. The validity of the proposed algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization algorithms. A numerical simulation including comparative studies was presented to demonstrate the performance and applicability of the proposed algorithm.

Graphical Abstract

A New Optimization Algorithm for Optimal Wind Turbine Location Problem in Constantine City Electric Distribution Network Based Active Power Loss Reduction


  • Proposed Salp Swarm Algorithm (SSA) for optimal wind turbine location problem
  •  Applied on practical Algerian Electric Distribution System (EDS)
  • Optimal integration and planning of single and multiple WT sources in the EDS
  • The proposed algorithm is better than other optimization algorithms.
  • This model could be used to perform the renewable energy integration analysis on EDS


El-Khattam, W., & Salama, M. M. (2004). Distributed generation technologies, definitions and benefits. Electric power systems research, 71(2), 119-128.

Malen, J., & Marcus, A. A. (2017). Promoting clean energy technology entrepreneurship: The role of external context. Energy Policy, 102, 7-15.

Wang, H., & Huang, J. (2016). Cooperative planning of renewable generations for interconnected microgrids. IEEE Transactions on Smart Grid, 7(5), 2486-2496.

Omer, A. M. (2008). Energy, environment and sustainable development. Renewable and sustainable energy reviews, 12(9), 2265-2300.

Sultana, U., Khairuddin, A. B., Aman, M. M., Mokhtar, A. S., & Zareen, N. (2016). A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system. Renewable and Sustainable Energy Reviews, 63, 363-378.

Mehigan, L., Deane, J. P., Gallachóir, B. Ó., & Bertsch, V. (2018). A review of the role of distributed generation (DG) in future electricity systems. Energy, 163, 822-836.

Atwa, Y. M., & El-Saadany, E. F. (2011). Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems. IET Renewable Power Generation, 5(1), 79-88.

Marmidis, G., Lazarou, S., & Pyrgioti, E. (2008). Optimal placement of wind turbines in a wind park using Monte Carlo simulation. Renewable energy, 33(7), 1455-1460.

Grady, S. A., Hussaini, M. Y., & Abdullah, M. M. (2005). Placement of wind turbines using genetic algorithms. Renewable energy, 30(2), 259-270.

Hou, P., Hu, W., Soltani, M., & Chen, Z. (2015). Optimized placement of wind turbines in large-scale offshore wind farm using particle swarm optimization algorithm. IEEE Transactions on Sustainable Energy, 6(4), 1272-1282.

Song, M., Chen, K., & Wang, J. (2018). Three-dimensional wind turbine positioning using Gaussian particle swarm optimization with differential evolution. Journal of Wind Engineering and Industrial Aerodynamics, 172, 317-324.


González, J. S., Rodriguez, A. G. G., Mora, J. C., Santos, J. R., & Payan, M. B. (2010). Optimization of wind farm turbines layout using an evolutive algorithm. Renewable energy, 35(8), 1671-1681.

Biswas, P. P., Suganthan, P. N., & Amaratunga, G. A. (2017, June). Optimal placement of wind turbines in a windfarm using L-SHADE algorithm. In IEEE Congress on Evolutionary Computation (CEC) (pp. 83-88). IEEE.

Ramli, M. A., Bouchekara, H. R. E. H., & Alghamdi, A. S. (2018). Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm. Renewable energy, 121, 400-411.

Eroğlu, Y., & Seçkiner, S. U. (2012). Design of wind farm layout using ant colony algorithm. Renewable Energy, 44, 53-62.

Chen, K., Song, M. X., He, Z. Y., & Zhang, X. (2013). Wind turbine positioning optimization of wind farm using greedy algorithm. Journal of Renewable and Sustainable Energy, 5(2), 023128.

Kayalvizhi, S., & Vinod Kumar, D. M. (2018). Optimal planning of active distribution networks with hybrid distributed energy resources using grid-based multi-objective harmony search algorithm. Applied Soft Computing, 67, 387-398.

Beşkirli, M., Koç, İ., Haklı, H., & Kodaz, H. (2018). A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm. Renewable energy, 121, 301-308.

Nadjemi, O., Nacer, T., Hamidat, A., & Salhi, H. (2017). Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms. Renewable and Sustainable Energy Reviews, 70, 1352-1365.

Zare, M., Azizipanah-Abarghooee, R., Hooshmand, R. A., & Malekpour, M. (2017). Optimal reconfigurattion of distribution systems by considering switch and wind turbine placements to enhance reliability and efficiency. IET Generation, Transmission & Distribution, 12(6), 1271-1284.

Hendrawati, D., Soeprijanto, A., & Ashari, M., (2019). Turbine wind placement with staggered layout as a strategy to maximize annual energy production in onshore wind farms.International Journal of Energy Economics and Policy. 9(2), 334-340.

Shin, J. S., & Kim, J. O. (2016). Optimal design for offshore wind farm considering inner grid layout and offshore substation location. IEEE Transactions on Power Systems, 32(3), 2041-2048.

Rezk, H., Fathy, A., Diab, A. A. Z., & Al-Dhaifallah, M. (2019). The application of water cycle optimization algorithm for optimal placement of wind turbines in wind farms. Energies, 12(22), 4335.

Settoul, S., Chenni, R., Zellagui, M., Nouri, H. (2019, December). Optimal integration of renewable distributed generation using the whale optimization algorithm for techno-economic analysis. 4th International Conference on Electrical Engineering and Control Applications (ICEECA), Constantine, Algeria 513-532

Settoul, S., Zellagui, M., Abdelaziz, A.Y., & Chenni, R. (2019, November). optimal integration of renewable distributed generation in practical distribution grids based on moth-flame optimization algorithm. International Conference on Advanced Electrical Engineering (ICAEE), Algiers, Algeria 1-6.

Hassan, H. A., & Zellagui, M. (2019, December). MVO Algorithm for Optimal Simultaneous Integration of DG and DSTATCOM in Standard Radial Distribution Systems Based on Technical-Economic Indices. In 2019 21st International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, 277-282.

Settoul, S., Chenni, R., Hasan, H. A., Zellagui, M., & Kraimia, M. N. (2019, November). MFO Algorithm for Optimal Location and Sizing of Multiple Photovoltaic Distributed Generations Units for Loss Reduction in Distribution Systems. In 2019 7th International Renewable and Sustainable Energy Conference (IRSEC), Agadir, Morocco, 1-6

Lasmari, A., Zellagui, M., Chenni, R., Semaoui, S., El-Bayeh, C. Z., & Hassan, H. A. (2020). Optimal energy management system for distribution systems using simultaneous integration of PV-based DG and DSTATCOM units. Energetika, 66(1), 1-14.

Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.

Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M., & Heidari, A. A. (2020). Salp swarm algorithm: theory, literature review, and application in extreme learning machines. In Nature-Inspired Optimizers (pp. 185-199). Springer, Cham.