Skip to main content
Log in

Robust Optimization Dispatch Method for Distribution Network Considering Four-Quadrant Power Output of Energy Storage Devices

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

This paper describes a technique for improving distribution network dispatch by using the four-quadrant power output of distributed energy storage systems to address voltage deviation and grid loss problems resulting from the large integration of distributed generation into the distribution network. The approach creates an optimization dispatch model for an active distribution network. The objective function aims to minimize power purchase costs, network loss costs, and voltage deviation penalties. In addition, the method employs an interval robust optimization technique to handle uncertainties related to solar turbine output and load demand. To solve the optimal power flow problem for AC in the distribution network, this paper implements the second-order cone relaxation technique to convert it into a solvable second-order cone programming problem. Moreover, the Big-M method is used to handle the nonlinear terms in the objective function. Finally, simulation experiments are conducted on the IEEE33 node system to verify the effectiveness and superiority of the proposed method. The simulation results indicate that the system's operating cost can be significantly reduced. Additionally, it has a positive impact on reducing voltage deviation and system loss, ultimately improving the operation of the distribution network system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ding T, Yang Q, Yang Y, Li C, Bie Z, Blaabjerg F (2018) A data-driven stochastic reactive power optimization considering uncertainties in active distribution networks and decomposition method. IEEE Trans Smart Grid 9(5):4994–5004. https://doi.org/10.1109/TSG.2017.2677481

    Article  Google Scholar 

  2. Xie Y, Zhai S, Li W, Jiang R, Wu Q, Zhang J (2022) Active-reactive power coordinated optimization of distribution network with photovoltaic based on PSO-CSA algorithm. In: 2022 7th international conference on power and renewable energy (ICPRE). p 313–318. https://doi.org/10.1109/ICPRE55555.2022.9960468.

  3. Zhang X, Ding T, Qu M (2023) An algorithmic approach for inner max-min model under norm-2 type uncertainty set in data-driven distributionally robust optimization. IEEE Trans Power Syst 38(2):1755–1758. https://doi.org/10.1109/TPWRS.2022.3216163

    Article  ADS  Google Scholar 

  4. de Souza PA et al (2022) Analysis of reactive power control using battery energy storage systems for a real distribution feeder. J Control Autom Electr Syst 33(4):1198–1216. https://doi.org/10.1007/s40313-021-00877-9

    Article  Google Scholar 

  5. Dong L, Li J, Pu T, Chen N (2019) Distributionally robust optimization model of active distribution network considering uncertainties of source and load. J Mod Power Syst Clean Energy 7(6):1585–1595. https://doi.org/10.1007/s40565-019-0558-x

    Article  Google Scholar 

  6. Liu L, Dong S, Lu K, Ge M, Nan B, Zhao H (2022) Double-layer control strategy for power distribution of energy storage system based on AOE and simulation analysis in methods and applications for modeling and simulation of complex systems. In: Fan W, Zhang L, Li N, Song X (eds) Communications in computer and information science. Springer Nature, Singapore, pp 235–245. https://doi.org/10.1007/978-981-19-9195-0_20

    Chapter  Google Scholar 

  7. Qiang L, Feijie Z, Fuyin G et al (2021) Optimized energy storage system configuration for voltage regulation of distribution network with PV access[J]. Front Energy Res 9

  8. Bollen MHJ, Yang Y, Hassan F (2008) Integration of distributed generation in the power system - a power quality approach. In: 2008 13th International Conference on Harmonics and Quality of Power, Wollongong, NSW, Australia, pp 1–8. https://doi.org/10.1109/ICHQP.2008.4668746

  9. Heidari H, Tohidi S, Zare K (2022) Mitigation of voltage fluctuations in grid-connected microgrids. In: 2020 28th Iranian conference on electrical engineering (ICEE). p 1–5. https://doi.org/10.1109/ICEE50131.2020.9260759

  10. Zou Y, Hu Y, Cao S (2019) Model predictive control of electric spring for voltage regulation and harmonics suppression. Math Probl Eng 2019:e7973591. https://doi.org/10.1155/2019/7973591

    Article  Google Scholar 

  11. Zhuohuan L, Tao Y, Yixuan C et al (2020) Multi-objective optimization dispatching strategy for wind-thermal-storage generation system incorporating temporal and spatial distribution control of air pollutant dispersion[J]. IEEE Access 844263-44275

  12. Cox J, Hamilton WT, Newman AM, Martinek J (2023) Optimal sizing and dispatch of solar power with storage. Optim Eng 24(4):2579–2617. https://doi.org/10.1007/s11081-022-09786-5

    Article  Google Scholar 

  13. Liu J, Le Q, Chen B, Xu X, Yang X, Zhao E (2022) Research on optimal configuration and operation strategy of distributed energy storage system. In: 2022 IEEE international conference on advances in electrical engineering and computer applications (AEECA). p 307–312. https://doi.org/10.1109/AEECA55500.2022.9919053.

  14. Gao H, Liu J, Wang L (2018) Robust coordinated optimization of active and reactive power in active distribution systems. IEEE Transactions on Smart Grid 9(5):4436–4447. https://doi.org/10.1109/TSG.2017.2657782

    Article  Google Scholar 

  15. Saeedi S, Hassan Hosseini SM (2022) Stochastic coordination of the wind and solar energy using energy storage system based on real-time pricing. Soft Comput 26(18):9607–9620. https://doi.org/10.1007/s00500-022-06789-3

    Article  Google Scholar 

  16. Lefebvre H, Schmidt M, Thürauf J (2023) Using column generation in column-and-constraint generation for adjustable robust optimization. Optimization Online. Accessed 16 Dec 2023. [Online]

  17. Cho YH, Chae JG, Lee D (2022) Similarity-based optimization framework for curtailment service providers through collaborative filtering and generalized dynamic factor model. IEEE Trans Smart Grid 14(2):1056–1069

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Key Science and Technology Project of China Southern Power Grid Corporation No.GZKJXM20220052

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yue Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Xiao, XB., He, XM. et al. Robust Optimization Dispatch Method for Distribution Network Considering Four-Quadrant Power Output of Energy Storage Devices. J. Electr. Eng. Technol. 19, 919–930 (2024). https://doi.org/10.1007/s42835-024-01813-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-024-01813-y

Keywords

Navigation