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Cited by 115Optimal Power Flow DistributionElectric Power System OptimizationSmart Grid Energy Management

Optimal dispatch for battery energy storage station in distribution network considering voltage distribution improvement and peak load shifting

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Li, Xiangjun, Ma, Rui, Gan, Wei, Yan, Shijie·2022·Journal of Modern Power Systems and Clean Energy
DOI 10.35833/mpce.2020.000183
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Abstract

Distribution networks are commonly used to demonstrate low-voltage problems. A new method to improve voltage quality is using battery energy storage stations (BESSs), which has a four-quadrant regulating capacity. In this paper, an optimal dispatching model of a distributed BESS considering peak load shifting is proposed to improve the voltage distribution in a distribution network. The objective function is to minimize the power exchange cost between the distribution network and the transmission network and the penalty cost of the voltage deviation. In the process, various constraints are considered, including the node power balance, single/two-way power flow, peak load shifting, line capacity, voltage deviation, photo-voltaic station operation, main transformer capacity, and power factor of the distribution network. The big$M$method is used to linearize the nonlinear variables in the objective function and constraints, and the model is transformed into a mixed-integer linear programming problem, which significantly improves the model accuracy. Simulations are performed using the modified IEEE 33-node system. A typical time period is selected to analyze the node voltage variation, and the results show that the maximum voltage deviation can be reduced from 14.06% to 4.54%. The maximum peak-valley difference of the system can be reduced from 8.83 to 4.23 MW, and the voltage qualification rate can be significantly improved. Moreover, the validity of the proposed model is verified through simulations.

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  • A BESS is a combination of a voltage source converter and a battery pack, such that active and reactive power can be controlled independently.

    “The BESS is a combination of a voltage source converter and a battery pack, such that the active and reactive power can be controlled independently [4][5][6][7][8][9].”

    From Dynamic Optimal Power Dispatch in Unbalanced Distribution Networks with Single-Phase Solar PV Units and BESS (2023) · cites this paper as [8]

    The “Operation Constraints of BESS” section models active and reactive power as separately controlled outputs within a shared capacity limit, and identifies a power converter. The “Simulation Background” section identifies the battery type. Our copy does not specify a voltage-source converter or describe the BESS as precisely a converter-and-battery-pack combination; control of the two outputs is also bounded by the shared capacity limit. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 36

  1. X. Li, D. Hui, and X. Lai, "Battery energy storage station (BESS) - based smoothing control of photovoltaic (PV) and wind power genera- tion fluctuations," IEEE Transactions on Sustainable Energy, vol. 4, no. 2, pp. 464-473, Apr. 2013.
  2. Y. Li, H. Zhang, X. Liang et al., "Event-triggered-based distributed co- operative energy management for multi-energy systems," IEEE Trans- actions on Industrial Informatics, vol. 15, no. 4, pp. 2008-2022, Apr. 2019.
  3. R. Li, W. Wang, and M. Xia, "Cooperative planning of active distribu- tion system with renewable energy sources and energy storage sys- tems," IEEE Access, vol. 6, pp. 5916-5926, Dec. 2017.
  4. D. Zarrilli, A. Giannitrapani, S. Paoletti et al., "Energy storage opera- tion for voltage control in distribution networks: a receding horizon ap- proach," IEEE Transactions on Control Systems Technology, vol. 26, no. 3, pp. 599-609, Mar. 2018.
  5. Z. Wang, H. Lin, and Y. Ma, "A control strategy of modular multilev- el converter with integrated battery energy storage system based on battery side capacitor voltage control," Energies, vol. 12, no. 11, pp. 2151-2170, Jun. 2019.
  6. J. Zhao and B. Shen, "The strategies for improving energy efficiency of power system with increasing share of wind power in China," Ener- gies, vol. 12, no. pp. 2376-2397, Jun. 2019.
  7. D. Zhu, W. Liu, Y. Hu et al., "A practical load-source coordinative method for further reducing curtailed wind power in China with ener- gy-intensive loads," Energies, vol. 11, no. 11, pp. 2925-2938, Oct. 2018.
  8. M. Ndawula, S. Djokic, and I. Hernando-Gil, "Reliability enhance- ment in power networks under uncertainty from distributed energy re- sources," Energies, vol. 12, no. 3, pp. 531-554, Feb. 2019.
  9. S. Lakshminarayana, Y. Xu, H. Poor et al., "Cooperation of storage operation in a power network with renewable generation," IEEE Trans- actions on Smart Grid, vol. 7, no. 4, pp. 2108-2122, Jul. 2016.
  10. A. Azizivahed, M. Barani, S. Razavi et al., "Energy storage manage- ment strategy in distribution networks utilised by photovoltaic resourc- es," IET Generation, Transmission & Distribution, vol. 12, no. 21, pp. 5627-5638, Nov. 2018.
  11. A. Davda, B. Azzopardi, B. Parekh et al., "Dispersed generation en- able loss reduction and voltage profile improvement in distribution net- work -case study, Gujarat, India," IEEE Transactions on Power Sys- tems, vol. 29, no. 3, pp. 1242-1249, May 2014.
  12. C. Zhang, H. Chen, Z. Liang et al., "Interval voltage control method for transmission systems considering interval uncertainties of renew- able power generation and load demand," IET Generation, Transmis- sion & Distribution, vol. 12, no. 17, pp. 4016-4025, Sept. 2018.
  13. A. Bokhari, A. Raza, M. Diaz-Aguiló et al., "Combined effect of CVR and DG penetration in the voltage profile of low-voltage second- ary distribution networks," IEEE Transactions on Power Delivery, vol. 31, no. 1, pp. 286-293, Feb. 2016.
  14. N. Meena, S. Parashar, A. Swarnkar et al., "Improved elephant herd- ing optimization for multiobjective DER accommodation in distribu- tion systems," IEEE Transactions on Industrial Informatics, vol. 14, no. 3, pp. 1029-1039, Mar. 2018.
  15. X. Li, Y. Huang, and S. Yang. "Demand response measures and its quantitative effects in promoting renewable energy accommodation in high proportional renewable energy scenario," The Journal of Engi- neering, vol. 13, pp. 1367-1372, Oct. 2017.
  16. B. Zeng, J. Zhang, X. Yang et al., "Integrated planning for transition to low-carbon distribution system with renewable energy generation and demand response," IEEE Transactions on Power Systems, vol. 29, no. 3, pp. 1153-1165, May 2014.
  17. H. Zhang, Y. Li, D. W. Gao et al., "Distributed optimal energy man- agement for energy internet," IEEE Transactions on Industrial Infor- matics, vol. 13, no. 6, pp. 3081-3097, Dec. 2017.
  18. M. Asensio, P. M. de Quevedo, G. Muñoz-Delgado et al., "Joint distri- bution network and renewable energy expansion planning considering demand response and energy storage -Part I: stochastic programming model," IEEE Transactions on Smart Grid, vol. 9, no. 2, pp. 655-666, Mar. 2018.
  19. M. Asensio, P. M. de Quevedo, G. Muñoz-Delgado et al., "Joint distri- bution network and renewable energy expansion planning considering demand response and energy storage-Part II: numerical results," IEEE Transactions on Smart Grid, vol. 9, no. 2, pp. 667-675, Mar. 2018.
  20. P. M. de Quevedo, G. Muñoz-Delgado, and J. Contreras, "Impact of electric vehicles on the expansion planning of distribution systems con- sidering renewable energy, storage, and charging stations," IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 794-804, Jan. 2019.
  21. M. Zidar, P. S. Georgilakis, N. D. Hatziargyriou et al., "Review of en- ergy storage allocation in power distribution networks: applications, methods and future research," IET Generation, Transmission & Distri- bution, vol. 10, no. 3, pp. 645-652, Feb. 2016.
  22. X. Li and S. Wang. "A review on energy management, operation con- trol and application methods for grid battery energy storage systems," CSEE Journal of Power and Energy Systems, vol. 7, no. 5, pp. 1026- 1040, Sept. 2021.
  23. X. Shen, M. Shahidehpour, Y. Han et al., "Expansion planning of ac- tive distribution networks with centralized and distributed energy stor- age systems," IEEE Transactions on Sustainable Energy, vol. 8, no. 1, pp. 126-134, Jan. 2017.
  24. A. W. Bizuayehu, A. A. S. de la Nieta, J. Contreras et al., "Impacts of stochastic wind power and storage participation on economic dispatch in distribution systems," IEEE Transactions on Sustainable Energy, vol. 7, no. 3, pp. 1336-1345, Jul. 2016.
  25. Y. Zhang, S. Ren, Z. Dong et al., "Optimal placement of battery ener- gy storage in distribution networks considering conservation voltage re- duction and stochastic load composition," IET Generation, Transmis- sion & Distribution, vol. 11, no. 15, pp. 3862-3870, Oct. 2017.
  26. W. Gan, X. Ai, J. Fang et al., "Security constrained co-planning of transmission expansion and energy storage," Applied Energy, vol. 239, pp. 383-394, Apr. 2019.
  27. Y. Wang, K. Tan, X. Peng et al., "Coordinated control of distributed energy-storage systems for voltage regulation in distribution net- works," IEEE Transactions on Power Delivery, vol. 31, no. 3, pp. 1132-1141, Jun. 2016.
  28. A. Giannitrapani, S. Paoletti, A. Vicino et al., "Optimal allocation of energy storage systems for voltage control in LV distribution net- works," IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2859- 2870, Nov. 2017.
  29. D. Zarrilli, A. Giannitrapani, S. Paoletti et al., "Energy storage opera- tion for voltage control in distribution networks: a receding horizon ap- proach," IEEE Transactions on Control Systems Technology, vol. 26, no. 2, pp. 599-609, Mar. 2018.
  30. H. Sugihara, K. Yokoyama, O. Saeki et al., "Economic and efficient voltage management using customer-owned energy storage systems in a distribution network with high penetration of photovoltaic systems," IEEE Transactions on Power Systems, vol. 28, no. 1, pp. 102-111, Feb. 2013.
  31. L. Wang, D. Liang, A. Crossland et al., "Coordination of multiple en- ergy storage units in a low-voltage distribution network," IEEE Trans- actions on Smart Grid, vol. 6, no. 6, pp. 2906-2918, Nov. 2015.
  32. S. Sami, M. Cheng, J. Wu et al., "A virtual energy storage system for voltage control of distribution networks," CSEE Journal of Power and Energy Systems, vol. 4, no. 2, pp. 146-154, Jun. 2018.
  33. G. Chang, S. Chu, and H. Wang, "An improved backward/forward sweep load flow algorithm for radial distribution systems," IEEE Transactions on Power Systems, vol. 22, no. 2, pp. 882-884, May 2007.
  34. J. Teng and C. Chang. "Backward/forward sweep-based harmonic anal- ysis method for distribution systems," IEEE Transactions on Power Delivery, vol. 22, no. 3, pp. 1665-1672, Jul. 2007.
  35. T. Nguyen, M. Crow, and A. Elmore. "Optimal sizing of a vanadium redox battery system for microgrid systems," IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 729-737, Jul. 2015.
  36. J. Lei, Q. Gong, J. Liu et al., "Optimal allocation of a VRB energy storage system for wind power applications considering the dynamic efficiency and life of VRB in active distribution networks," IET Re- newable Power Generation, vol. 13, no. 4, pp. 563-571, Mar. 2019.

Cited by

Sorted by citations

Authors
Li, X., Ma, R., Gan, W., & Yan, S.
Affiliations
  • China Electric Power Research Institute (CN)
  • Northeastern University (CN)
ORCID
Venue
Journal of Modern Power Systems and Clean Energy
Publisher
Journal of Modern Power Systems and Clean Energy
Published
2022
Volume
10
Issue
1
Pages
131-139
ISSN
  • 2196-5420 (ISSN-L)
  • 2196-5625
DOI
10.35833/mpce.2020.000183
OpenAlex
W3140772225
Cited by
115
References
0
Citations by year
2026: 11 · 2025: 19 · 2024: 32 · 2023: 33 · 2022: 14 · 2021: 6
FWCI
6.71
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Modern Power Systems and Clean Energy (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
Keywords
optimal dispatch, distribution networks, peak load shifting, voltage deviation, big-M method, mixed-integer linear programming, power factor, IEEE 33-bus system

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