Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Optimal operation of energy storage units in distributed system using social spider optimization algorithm

1 Department of electrical engineering, Sowmesara branch, Islamic Azad University, Sowmesara, Iran
2 Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran

Optimal placement and performance of energy storage units are two important issues for power systems. Energy storage units help in energy supply and elevated reliability in the network. Energy storage system (ESS) cause many of the variables of planning, designing, and exploiting the network to alter, and thus planning and operational methods for the network will change as well. These units also assist in reducing the peak load with the aim of profit maximization. In this paper, a method is presented to optimize the performance of energy storage units in the power market. To solve the problem, a new optimization algorithm called social spider optimization (SSO) algorithm is employed. Cost reduction, losses reduction and voltage profile improvement are three main scenarios which are considered. The proposed method is applied to a 33-bus standard distribution system and results show that this novel method is efficient to use for ESSs optimal operation.
  Article Metrics

Keywords social spider optimization algorithm; energy storage units; optimal operation; cost reduction; losses reduction; voltage profile

Citation: Mehdi Tabasi, Pouyan Asgharian. Optimal operation of energy storage units in distributed system using social spider optimization algorithm. AIMS Electronics and Electrical Engineering, 2019, 3(4): 309-327. doi: 10.3934/ElectrEng.2019.4.309


  • 1. Jordehi AR (2016) Allocation of distributed generation units in electric power systems: A review. Renew Sust Energ Rev 56: 893-905.    
  • 2. El-Fergany AA and El-Hameed MA (2017) Efficient frequency controllers for autonomous two-area hybrid microgrid system using social-spider optimizer. IET Gener Transm Dist 11: 637-648.    
  • 3. Luo XH, Wang J, Dooner M, et al. (2015) Overview of current development in electrical energy storage technologies and the application potential in power system operation. Appl Energ 137: 511-536.    
  • 4. Asgharian P and Noroozian R (2019) Modeling and Efficient Control of Microturbine Generation System With Battery Energy Storage for Sensitive Loads. Iranian Journal of Electrical and Electronic Engineering 15: 76-86.
  • 5. Wong LA, Ramachandaramurthy VK, Taylor P, et al. (2019) Review on the optimal placement, sizing and control of an energy storage system in the distribution network. J Energy Storage 21: 489-504.    
  • 6. Guney MS and Tepe Y (2017) Classification and assessment of energy storage systems. Renew Sust Energ Rev 75: 1187-1197.    
  • 7. Palizban O, Kauhaniemi K and Guerrero JM (2014) Microgrids in active network management-Part I: Hierarchical control, energy storage, virtual power plants, and market participation. Renew Sust Energ Rev 36: 428-439.    
  • 8. Azizivahed A, Naderi E, Narimani H, et al. (2018) A New Bi-Objective Approach to Energy Management in Distribution Networks with Energy Storage Systems. IEEE T Sustain Energ 9: 56-64.    
  • 9. Ho WS, Macchietto S, Lim JS, et al. (2016) Optimal scheduling of energy storage for renewable energy distributed energy generation system. Renew Sust Energ Rev 58: 1100-1107.    
  • 10. Grisales LF, Grajales A, Montoya OD, et al. (2017) Optimal location, sizing and operation of energy storage in distribution systems using multi-objective approach. IEEE Lat Am T 15: 1084-1090.    
  • 11. Oudalov A, Cherkaoui R and Beguin A (2007) Sizing and Optimal Operation of Battery Energy Storage System for Peak Shaving Application. In: 2007 IEEE Lausanne Power Tech, pp. 621-625.
  • 12. Jin J, Xu Y, Khalid Y, et al. (2018) Optimal Operation of Energy Storage With Random Renewable Generation and AC/DC Loads. IEEE T Smart Grid 9: 2314-2326.
  • 13. Macedo LH, Franco JF, Rider MJ, et al. (2015) Optimal Operation of Distribution Networks Considering Energy Storage Devices. IEEE T Smart Grid 6: 2825-2836.    
  • 14. Bozchalui MC and Sharma R (2014) Optimal operation of Energy Storage in distribution systems with Renewable Energy Resources. In: 2014 Clemson University Power Systems Conference, pp. 1-6.
  • 15. Elsayed WT, Hegazy YG, Bendary FM, et al. (2016) Modified social spider algorithm for solving the economic dispatch problem. Engineering Science and Technology, an International Journal 19: 1672-1681.    
  • 16. Yu JJQ and Li VOK (2016) A social spider algorithm for solving the non-convex economic load dispatch problem. Neurocomputing 171: 955-965.    
  • 17. Sun S, Qi H, Sun J, et al. (2017) Estimation of thermophysical properties of phase change material by the hybrid SSO algorithms. Int J Therm Sci 120: 121-135.    
  • 18. Mesbahi T, Rizoug N, Bartholomeüs P, et al. (2017) Optimal Energy Management for a Li-Ion Battery/Supercapacitor Hybrid Energy Storage System Based on a Particle Swarm Optimization Incorporating Nelder-Mead Simplex Approach. IEEE Transactions on Intelligent Vehicles 2: 99-110.
  • 19. Lili Z, Guoguang Y, Libin Y, et al. (2017) Study on Optimization of Operating Parameters of Hybrid Energy Storage System. In: 2017 International Conference on Smart Grid and Electrical Automation (ICSGEA), pp. 184-188.
  • 20. Mbungu NT, Naidoo R, Bansal RC, et al. (2017) Optimisation of grid connected hybrid photovoltaic-wind-battery system using model predictive control design. IET Renew Power Gen 11: 1760-1768.    
  • 21. Alharbi H and Bhattacharya K (2018) Stochastic Optimal Planning of Battery Energy Storage Systems for Isolated Microgrids. IEEE T Sustain Energ 9: 211-227.    
  • 22. Moghaddas-Tafreshi SM and Mashhour E (2009) Distributed generation modeling for power flow studies and a three-phase unbalanced power flow solution for radial distribution systems considering distributed generation. Electr Pow Syst Res 79: 680-686.    
  • 23. Michline Rupa JA and Ganesh S (2014) Power Flow Analysis for Radial Distribution System Using Backward/Forward Sweep Method. International Journal of Electrical, Computer, Energetic, Electronic and Engineering 8: 1621-1625.
  • 24. Theo WL, Lim JS, Ho WS, et al. (2017) Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods. Renew Sust Energ Rev 67: 531-573.    
  • 25. Yu JJQ and Li VOK (2015) A social spider algorithm for global optimization. Appl Soft Comput 30: 614-627.    
  • 26. El-bages MS and Elsayed WT (2017) Social spider algorithm for solving the transmission expansion planning problem. Electr Pow Syst Res 143: 235-243.    
  • 27. Rostami M, Kavousi-Fard MA and Niknam T (2015) Expected Cost Minimization of Smart Grids With Plug-In Hybrid Electric Vehicles Using Optimal Distribution Feeder Reconfiguration. IEEE T Ind Inform 11: 388-397.    


Reader Comments

your name: *   your email: *  

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved