AIMS Energy, 2018, 6(1): 70-96. doi: 10.3934/energy.2018.1.70.

Research article

Export file:


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


  • Citation Only
  • Citation and Abstract

Optimal sizing and operation of energy storage systems considering long term assessment

Departament d’Enginyeria Electrica, Universitat Politecnica de Catalunya, Barcelona, Spain

This paper proposes a procedure for estimating the optimal sizing of Photovoltaic Generators and Energy Storage units when they are operated from the utility’s perspective. The goal is to explore the potential improvement on the overall operating conditions of the distribution system to which the Generators and Storage units will be connected. Optimization is conducted by means of a General Parallel Genetic Algorithm that seeks to maximize the technical benefits for the distribution system. The paper proposes an operation strategy for Energy Storage units based on the daily variation of load and generation; the operation strategy is optimized for an evaluation period of one year using hourly power curves. The construction of the yearly Storage operation curve results in a high-dimension optimization problem; as a result, different day-classification methods are applied in order to reduce the dimension of the optimization. Results show that the proposed approach is capable of producing significant improvements in system operating conditions and that the best performance is obtained when the day-classification is based on the similarity among daily power curves.
  Article Metrics

Keywords clustering technique; energy storage system; genetic algorithm; optimization; parallel computing; photovoltaic generation

Citation: Gerardo Guerra, Juan A. Martinez-Velasco. Optimal sizing and operation of energy storage systems considering long term assessment. AIMS Energy, 2018, 6(1): 70-96. doi: 10.3934/energy.2018.1.70


  • 1. New York Independent System Operator (2014) A Review of Distributed Energy Resources.
  • 2. Farret FA, Godoy SM (2006) Integration of Alternative Sources of Energy. John Wiley Press, 301–332.
  • 3. Ackerman T, Andersson G, Söder L (2001) Distributed generation: a definition. Electr Power Syst Res 57: 195–204.    
  • 4. Willis HL, Scott WG (2000) Distributed power generation: planning and evaluation. Crc Press.
  • 5. Sandia National Laboratories and NRECA (2015) DOE/EPRI Electricity Storage Handbook.
  • 6. Luo F, Meng K, Dong ZY, et al. (2015) Coordinated operational planning for wind farm with battery energy storage system. IEEE T Sustain Energ 6: 253–262.    
  • 7. International Electrotechnical Commission (2011) Electrical Energy Storage.
  • 8. Farzin H, Fotuhi-Firuzabad M, Moeini-Aghtaie M (2017) A stochastic multi-objective framework for optimal scheduling of energy storage systems in microgrids. IEEE T Smart Grid 8: 117–127.    
  • 9. Lazaroiu GC, Dumbrava V, Balaban G, et al. (2016) Stochastic optimization of microgrids with renewable and storage energy systems. International Conference on Environment and Electrical Engineering. IEEE.
  • 10. Silvestre MLD, Graditi G, Ippolito MG, et al. (2011) Robust multi-objective optimal dispatch of distributed energy resources in micro-grids. PowerTech, 2011 IEEE Trondheim. IEEE, 1–5.
  • 11. Agamah SU, Ekonomou L (2016) Peak demand shaving and load-levelling using a combination of bin packing and subset sum algorithms for electrical energy storage system scheduling. Iet Sci Meas Technol 10: 477–484.    
  • 12. Levron Y, Shmilovitz D (2012) Power systems' optimal peak-shaving applying secondary storage. Electr Pow Syst Res 89: 80–84.    
  • 13. Jayasekara N, Wolfs P, Masoum MAS (2014) An optimal management strategy for distributed storages in distribution networks with high penetrations of PV. Electr Pow Syst Res 116: 147–157.    
  • 14. Ippolito MG, Silvestre MLD, Sanseverino ER, et al. (2014) Multi-objective optimized management of electrical energy storage systems in an islanded network with renewable energy sources under different design scenarios. Energy 64: 648–662.    
  • 15. Rahmani-Andebili M (2017) Stochastic, adaptive, and dynamic control of energy storage systems integrated with renewable energy sources for power loss minimization. Renew Energ 113: 1462–1471.    
  • 16. Meirinhos JL, Rua DE, Carvalho LM, et al. (2017) Multi-temporal Optimal Power Flow for voltage control in MV networks using Distributed Energy Resources. Electr Pow Syst Res 146: 25–32.    
  • 17. Hejazi H, Mohsenian-Rad H (2016) Energy storage planning in active distribution grids: a chance-constrained optimization with non-parametric probability functions. IEEE T Smart Grid, 1–13.
  • 18. Rahmani-Andebili M, Shen H (2017) Cooperative distributed energy scheduling for smart homes applying stochastic model predictive control. IEEE International Conference on Communications. IEEE, 1–6.
  • 19. Kargarian A, Hug G (2016) Optimal sizing of energy storage systems: a combination of hourly and intra-hour time perspectives. Iet Gener Transm Dis 10: 594–600.    
  • 20. Kerdphol T, Qudaih Y, Mitani Y (2016) Optimum battery energy storage system using PSO considering dynamic demand response for microgrids. Int J Elec Power 83: 58–66.    
  • 21. Carpinelli G, Mottola F, Proto D (2016) Probabilistic sizing of battery energy storage when time-of-use pricing is applied. Electr Pow Syst Res 141: 73–83.    
  • 22. Brown PD, Peas Lopes JA, Matos MA (2008) Optimization of pumped storage capacity in an isolated power system with large renewable penetration. IEEE T Power Syst 3: 523–531.
  • 23. Wen S, Lan H, Fu Q, et al. (2015) Economic allocation for energy storage system considering wind power distribution. IEEE T Power Syst 30: 644–652.
  • 24. Korpaas M, Holen AT, Hildrum R (2003) Operation and sizing of energy storage for wind power plants in a market system. Int J Elec Power 25: 599–606.    
  • 25. Correia PF, Jesus JMFD, Lemos JM (2014) Sizing of a pumped storage power plant in S. Miguel, Azores, using stochastic optimization. Electr Pow Syst Res 112: 20–26.
  • 26. Arabali A, Ghofrani M, Etezadi-Amoli M, et al. (2013) Genetic-algorithm-based optimization approach for energy management. IEEE T Power Deliver 28: 162–170.    
  • 27. Nick M, Cherkaoui R, Paolone M (2014) Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support. IEEE T Power Syst 29: 2300–2310.    
  • 28. Silvestre MLD, Graditi G, Sanseverino ER (2014) A generalized framework for optimal sizing of distributed energy resources in micro-grids using an indicator-based swarm approach. IEEE T Ind Inform 10: 152–162.    
  • 29. Yang P, Nehorai A (2014) Joint optimization of hybrid energy storage and generation capacity with renewable energy. IEEE T Smart Grid 5: 1566–1574.    
  • 30. Abedi S, Alimardani A, Gharehpetian GB, et al. (2012) A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems. Renew Sust Energ Rev 16: 1577–1587.    
  • 31. Meng N, Wang P, Wu H, et al. (2015) Optimal sizing of distributed generations in a connected DC micro-grid with hybrid energy storage system. Energy Conversion Congress and Exposition. IEEE, 3179–3183.
  • 32. Erdinc O, Paterakis NG, Pappi IN, et al. (2015) A new perspective for sizing of distributed generation and energy storage for smart households under demand response. Appl Energ 143: 26–37.    
  • 33. Papaefthymiou SV, Papathanassiou SA (2014) Optimum sizing of wind-pumped-storage hybrid power stations in island systems. Renew Energ 64: 187–196.    
  • 34. Dugan RC (2016) Reference Guide. The Open Distribution System Simulator (OpenDSS). EPRI.
  • 35. Dugan RC, McDermott TE (2011) An open source platform for collaborating on smart grid research. Power and Energy Society General Meeting. IEEE, 1–7.
  • 36. Martinez-Velasco JA, Guerra G (2015) Analysis of large distribution networks with distributed energy resources. Ingeniare 23: 594–608.
  • 37. Dugan RC, Taylor JA, Montenegro D (2017) Energy storage modeling for distribution planning. IEEE T Ind Appl 53: 954–962.    
  • 38. Eisen MB, Spellman PT, Brown PO, et al. (1998) Cluster analysis and display of genome-wide expression patterns. P Natl Acad Sci USA 95: 14863–14868.    
  • 39. Tsekouras GJ, Hatziargyriou ND, Dialynas EN (2007) Two-stage pattern recognition of load curves for classification of electricity customers. IEEE T Power Syst 22: 1120–1128.    
  • 40. Iglesias F, Kastner W (2013) Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies 6: 579–597.    
  • 41. Chicco G, Napoli R, Postolache P, et al. (2003) Customer characterization for improving the tariff offer. IEEE T Power Syst 18: 381–387.
  • 42. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE T Acoustics Speech Signal Process 26: 43–49.    
  • 43. Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat-Theor M 3: 1–27.    
  • 44. Passino KM (2006) Biomimicry for optimization, control, and automation. IEEE T Automat Contr 51: 1406.    
  • 45. Michalewicz Z (1996) Genetic Algorithms + Data Structures = Evolution Programs. Springer, 347–348.
  • 46. Yeh EC, Venkata SS, Sumic Z (1995) Improved distribution system planning using computational evolution. IEEE T Power Syst 11: 668–674.
  • 47. Mendoza F, Bernal-Agustin JL, Domínguez-Navarro JA (2006) NSGA and SPEA applied to multiobjective design of power distribution systems. IEEE T Power Syst 21: 1938–1945.    
  • 48. Abido MA (2006) Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE T Evolut Comput 10: 315–329.    
  • 49. Buehren M (2007) MATLAB Library for Parallel Processing on Multiple Cores. Available from:
  • 50. Martinez JA, Guerra G (2014) Parallel Monte Carlo approach for distribution reliability assessment. Iet Gener Transm Dis 8: 1810–1819.    
  • 51. Guerra G, Martinez JA (2016) Optimum allocation of distributed generation in multi-feeder systems using long term evaluation and assuming voltage-dependent loads. Sust Energ Grid Network 5: 13–26.    


This article has been cited by

  • 1. Gerardo Guerra, Juan A. Martinez-Velasco, A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing, AIMS Energy, 2018, 6, 5, 764, 10.3934/energy.2018.5.764

Reader Comments

your name: *   your email: *  

© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved