Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Life cycle planning of battery energy storage system in off-grid wind–solar–diesel microgrid

  • Author(s): Yuhan Zhang 1, 2 ; Jianxue Wang 1 ; Alberto Berizzi 3 ; Xiaoyu Cao 1
    • View affiliations
  • Source: Volume 12, Issue 20, 13 November 2018, p. 4451 – 4461
    DOI:  10.1049/iet-gtd.2018.5521 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Received 08/02/2018, Accepted 07/08/2018, Revised 21/07/2018, Published 05/09/2018

For off-grid microgrids in remote areas (e.g. sea islands), proper configuring the battery energy storage system (BESS) is of great significance to enhance the power-supply reliability and operational feasibility. This study presents a life cycle planning methodology for BESS in microgrids, where the dynamic factors such as demand growth, battery capacity fading and components’ contingencies are modelled under a multi-timescale decision framework. Under a yearly timescale, the optimal DER capacity allocation is carried out to meet the demand growth, while the investment decisions of BESS are made periodically to yield the optimal sizing, type selection and replacement plans of BESS during the entire lifetime of the microgrid. Then, under an hourly timescale, the long-term probabilistic sequential simulation is adopted to comprehensively evaluate the investment decisions and derive detailed operation indicators. Moreover, a decomposition–coordination algorithm is developed to address the presented planning model, which iteratively strengthens the feasible space of investment-decision model by substituting the operation indicators until an acceptable sub-optimal solution is obtained. Case studies on a wind–solar–diesel microgrid in Kythnos Island, Greece illustrate the effectiveness of the proposed method. This study provides a practical and meaningful reference for BESS planning in off-grid microgrids.

Inspec keywords: energy storage; secondary cells; power distribution planning; battery storage plants; power generation economics; distributed power generation; hybrid power systems; investment; wind power plants; solar power stations; power grids

Other keywords: off-grid wind–solar–diesel microgrid; remote areas; multitimescale decision framework; power-supply reliability; hourly timescale; optimal sizing; replacement plans; operational feasibility; optimal DER capacity allocation; battery energy storage system; investment-decision model; operation indicators; life cycle planning methodology; demand growth; BESS planning; battery capacity fading; long-term probabilistic sequential simulation

Subjects: Wind power plants; Power system planning and layout; Distributed power generation; Power system management, operation and economics; Solar power stations and photovoltaic power systems

References

    1. 1)
      • 4. Saez-De-Ibarra, A., Milo, A., Gaztanaga, H., et al: ‘Co-optimization of storage system sizing and control strategy for intelligent photovoltaic power plants market integration’, IEEE Trans. Sustain. Energy, 2016, 7, (4), pp. 1749–1761.
    2. 2)
      • 12. Ibrahim, A.I., Tamer, K., Azah, M.: ‘Impact of battery's model accuracy on size optimization process of a standalone photovoltaic system’, Sustainability, 2016, 8, (9), pp. 894–906.
    3. 3)
      • 13. Amrollahi, M., Bathaee, S.: ‘Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response’, Appl. Energy, 2017, 202, pp. 66–77.
    4. 4)
      • 14. Paliwal, P., Patidar, N.P., Nema, R.K.: ‘Determination of reliability constrained optimal resource mix for an autonomous hybrid power system using particle swarm optimization’, Renew. Energy, 2014, 63, (1), pp. 194–204.
    5. 5)
      • 31. Jayanta, D.M., Nikos, K.: ‘Overview of challenges, prospects, environmental impacts and policies for renewable energy and sustainable development in Greece’, Renew. Sustain. Energy Rev., 2013, 23, (1), pp. 431–442.
    6. 6)
      • 17. Tamer, K., Ibrahim, A.I., Azah, M.: ‘A review on sizing methodologies of photovoltaic array and storage battery in a standalone photovoltaic system’, Energy Convers. Manage., 2016, 120, pp. 430–448.
    7. 7)
      • 11. Mallol-Poyato, R., Jimnez-Fernndez, S., Daz-Villar, P., et al: ‘Joint optimization of a microgrid's structure design and its operation using a two-steps evolutionary algorithm’, Energy, 2016, 94, pp. 775–785.
    8. 8)
      • 21. Wang, X.F., McDonald, J.: ‘Modern power system planning’ (McGraw-Hill Companies, London, UK, 1994).
    9. 9)
      • 19. Shang, C., Dipti, S., Thomas, R.: ‘Generation-scheduling-coupled battery sizing of stand-alone hybrid power systems’, Energy, 2016, 114, pp. 671–682.
    10. 10)
      • 6. Lambert, T., Gilman, P., Lilienthal, P.: ‘Micropower system modeling with HOMER’ (John Wiley & Sons, Inc., New York, 2006).
    11. 11)
      • 23. Elena, M.K., John, C., Craig, B.: ‘A comparison of lead–acid and lithium-based battery behavior and capacity fade in off-grid renewable charging applications’, Energy, 2013, 60, (1), pp. 492–500.
    12. 12)
      • 9. Jiang, Q.Y., Hong, H.S.: ‘Wavelet-based capacity configuration and coordinated control of hybrid energy storage system for smoothing out wind power fluctuations’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 1363–1372.
    13. 13)
      • 16. Arabali, A., Ghofrani, M., Etezadi-Amoli, M., et al: ‘Stochastic performance assessment and sizing for a hybrid power system of solar/wind/energy storage’, IEEE Trans. Sustain. Energy, 2014, 5, (2), pp. 363–371.
    14. 14)
      • 10. Xiao, J., Bai, L.Q., Li, F.X., et al: ‘Sizing of energy storage and diesel generators in an isolated microgrid using discrete Fourier transform (DFT)’, IEEE Trans. Sustain. Energy, 2014, 5, (3), pp. 907–916.
    15. 15)
      • 30. Jardini, J.A., Tahan, C., Gouvea, M.R., et al: ‘Daily load profiles for residential, commercial and industrial low voltage consumers’, IEEE Trans. Power Deliv., 2002, 15, (1), pp. 375–380.
    16. 16)
      • 18. Wang, S., Li, Z., Wu, L., et al: ‘New metrics for assessing the reliability and economics of microgrids in distribution system’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 2852–2861.
    17. 17)
      • 29. The National Technical University (NTUA): ‘Pilot Kythnos microgrid’, 2010. Available at http://www.microgrids.eu/index.php?page=kythnos&id=2, accessed May 2015.
    18. 18)
      • 26. Zhang, N., Kang, C.Q., Duan, C.G., et al: ‘Simulation methodology of multiple wind farms operation considering wind speed correlation’, Int. J. Electr. Power Energy Syst., 2010, 30, (4), pp. 264–273.
    19. 19)
      • 25. National Aeronautics and Space Administration (NASA): ‘The atmospheric science data center (ASDC)’, 2014. Available at https://eosweb.larc.nasa.gov, accessed August 2014.
    20. 20)
      • 5. Zidar, M., Georgilakis, P.S., Hatziargyriou, N.D., et al: ‘Review of energy storage allocation in power distribution networks: applications, methods and future research’, IET Gener. Transm. Distrib., 2016, 10, (3), pp. 645–652.
    21. 21)
      • 28. Sankarakrishnan, A., Billinton, R.: ‘Sequential Monte Carlo simulation for composite power system reliability analysis with time varying loads’, IEEE Trans. Power Syst., 1995, 10, pp. 1540–1545.
    22. 22)
      • 7. Zhang, Y.M., Tang, X.S., Qi, Z.P., et al: ‘The Ragone plots guided sizing of hybrid storage system for taming the wind power’, Int. J. Electr. Power Energy Syst., 2015, 65, (1), pp. 246–253.
    23. 23)
      • 15. Xu, L., Ruan, X.B., Mao, C.X., et al: ‘An improved optimal sizing method for wind–solar–battery hybrid power system’, IEEE Trans. Sustain. Energy, 2013, 4, (3), pp. 774–785.
    24. 24)
      • 2. Razman, A., Normazlina, M.I., Chee, W.T.: ‘Components sizing of photovoltaic stand-alone system based on loss of power supply probability’, Renew. Sustain. Energy Rev., 2018, 81, (2), pp. 2731–2743.
    25. 25)
      • 27. Mora-López, L., Sidrach-De-Cardona, M.: ‘Multiplicative ARMA models to generate hourly series of global irradiation’, Sol. Energy, 1998, 63, (5), pp. 283–291.
    26. 26)
      • 22. Sperck, R., Daniel, K., Chris, M.: ‘Issues in electricity planning with computer models: illustration with Elfin and WASP’, Util. Policy, 1998, 7, (1), pp. 201–219.
    27. 27)
      • 1. Liu, Y.F., Yu, S.S., Zhu, Y., et al: ‘Modeling, planning, application and management of energy systems for isolated areas: a review’, Renew. Sustain. Energy Rev., 2018, 82, (1), pp. 460–470.
    28. 28)
      • 24. Masoud, M.T., Mohammad-Reza, H.Y., Vahid, E., et al: ‘Optimum sizing and optimum energy management of a hybrid energy storage system for lithium battery life improvement’, J. Power Sources, 2013, 244, (1), pp. 2–10.
    29. 29)
      • 20. Cao, X.Y., Wang, J.X., Zeng, B.: ‘A chance constrained information-gap decision model for multi-period microgrid planning’, IEEE Trans. Power Syst., 2018, 33, (3), pp. 2684–2695.
    30. 30)
      • 8. Wen, S.L., Lan, H., Yu, D., et al: ‘Optimal sizing of hybrid energy storage sub-systems in PV/diesel ship power system using frequency analysis’, Energy, 2017, 140, (1), pp. 198–208.
    31. 31)
      • 3. Imre, G.: ‘Grid energy storage’ (U.S. Department of Energy, USA, 2013).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2018.5521
Loading

Related content

content/journals/10.1049/iet-gtd.2018.5521
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address