IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v123y2020ics1364032120300368.html
   My bibliography  Save this article

Robust planning of distributed battery energy storage systems in flexible smart distribution networks: A comprehensive study

Author

Listed:
  • Bozorgavari, Seyed Aboozar
  • Aghaei, Jamshid
  • Pirouzi, Sasan
  • Nikoobakht, Ahmad
  • Farahmand, Hossein
  • Korpås, Magnus

Abstract

This paper presents a robust planning of distributed battery energy storage systems (DBESSs) from the viewpoint of distribution system operator (DSO) to increase the network flexibility. Initially, the deterministic model of the proposed problem is expressed by minimizing the difference between the DBESS planning, degradation and operation (charging) costs and the revenue of DBESS from selling its stored energy subject to the constraints of AC power flow equations in the presence of RESs and DBESSs, and technical limits of the network indexes, variable renewable energy sources (vRESs) and DBESSs. This problem is modeled as a non-linear programming (NLP), then, an equivalent linear programming (LP) model is proposed using the first-order expansion of Taylor's series for linearization of power flow equations and a polygon for linearization of circular inequalities. Also, to model the uncertain parameters in the proposed problem including forecasted active and reactive loads, energy and charging/discharging prices and the output power of vRES, the bounded uncertainty-based robust optimization (BURO) framework is proposed in the next step. Finally, the proposed scheme is applied to 19-bus MV CIGRE benchmark grid by GAMS software to investigate the capability and efficiency of the model.

Suggested Citation

  • Bozorgavari, Seyed Aboozar & Aghaei, Jamshid & Pirouzi, Sasan & Nikoobakht, Ahmad & Farahmand, Hossein & Korpås, Magnus, 2020. "Robust planning of distributed battery energy storage systems in flexible smart distribution networks: A comprehensive study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    • Handle: RePEc:eee:rensus:v:123:y:2020:i:c:s1364032120300368
    DOI: 10.1016/j.rser.2020.109739
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032120300368
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2020.109739?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    2. Hemmati, Reza & Saboori, Hedayat, 2016. "Emergence of hybrid energy storage systems in renewable energy and transport applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 11-23.
    3. Jangkyum Kim & Yohwan Choi & Seunghyoung Ryu & Hongseok Kim, 2017. "Robust Operation of Energy Storage System with Uncertain Load Profiles," Energies, MDPI, vol. 10(4), pages 1-15, March.
    4. Jannesar, Mohammad Rasol & Sedighi, Alireza & Savaghebi, Mehdi & Guerrero, Josep M., 2018. "Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration," Applied Energy, Elsevier, vol. 226(C), pages 957-966.
    5. Bucciarelli, Martina & Paoletti, Simone & Vicino, Antonio, 2018. "Optimal sizing of energy storage systems under uncertain demand and generation," Applied Energy, Elsevier, vol. 225(C), pages 611-621.
    6. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Villafáfila-Robles, Roberto, 2012. "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2154-2171.
    7. Cervantes, Jairo & Choobineh, Fred, 2018. "Optimal sizing of a nonutility-scale solar power system and its battery storage," Applied Energy, Elsevier, vol. 216(C), pages 105-115.
    8. Pirouzi, Sasan & Aghaei, Jamshid & Niknam, Taher & Shafie-khah, Miadreza & Vahidinasab, Vahid & Catalão, João P.S., 2017. "Two alternative robust optimization models for flexible power management of electric vehicles in distribution networks," Energy, Elsevier, vol. 141(C), pages 635-651.
    9. Qiu, Jing & Zhao, Junhua & Yang, Hongming & Wang, Dongxiao & Dong, Zhao Yang, 2018. "Planning of solar photovoltaics, battery energy storage system and gas micro turbine for coupled micro energy grids," Applied Energy, Elsevier, vol. 219(C), pages 361-369.
    10. Grover-Silva, Etta & Girard, Robin & Kariniotakis, George, 2018. "Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm," Applied Energy, Elsevier, vol. 219(C), pages 385-393.
    11. Pirouzi, Sasan & Aghaei, Jamshid & Niknam, Taher & Farahmand, Hossein & Korpås, Magnus, 2018. "Exploring prospective benefits of electric vehicles for optimal energy conditioning in distribution networks," Energy, Elsevier, vol. 157(C), pages 679-689.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gerber, Daniel L. & Nordman, Bruce & Brown, Richard & Poon, Jason, 2023. "Cost analysis of distributed storage in AC and DC microgrids," Applied Energy, Elsevier, vol. 344(C).
    2. Li, Bei & Li, Jiangchen, 2021. "Probabilistic sizing of a low-carbon emission power system considering HVDC transmission and microgrid clusters," Applied Energy, Elsevier, vol. 304(C).
    3. Norouzi, Mohammadali & Aghaei, Jamshid & Pirouzi, Sasan & Niknam, Taher & Fotuhi-Firuzabad, Mahmud, 2022. "Flexibility pricing of integrated unit of electric spring and EVs parking in microgrids," Energy, Elsevier, vol. 239(PB).
    4. Hamidpour, Hamidreza & Aghaei, Jamshid & Pirouzi, Sasan & Niknam, Taher & Nikoobakht, Ahmad & Lehtonen, Matti & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Coordinated expansion planning problem considering wind farms, energy storage systems and demand response," Energy, Elsevier, vol. 239(PD).
    5. AkbaiZadeh, MohammadReza & Niknam, Taher & Kavousi-Fard, Abdollah, 2021. "Adaptive robust optimization for the energy management of the grid-connected energy hubs based on hybrid meta-heuristic algorithm," Energy, Elsevier, vol. 235(C).
    6. Dini, Anoosh & Hassankashi, Alireza & Pirouzi, Sasan & Lehtonen, Matti & Arandian, Behdad & Baziar, Ali Asghar, 2022. "A flexible-reliable operation optimization model of the networked energy hubs with distributed generations, energy storage systems and demand response," Energy, Elsevier, vol. 239(PA).
    7. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    8. Gu, Chenjia & Wang, Jianxue & Zhang, Yao & Li, Qingtao & Chen, Yang, 2022. "Optimal energy storage planning for stacked benefits in power distribution network," Renewable Energy, Elsevier, vol. 195(C), pages 366-380.
    9. Feng, Li & Liu, Jiajun & Lu, Haitao & Liu, Bingzhi & Chen, Yuning & Wu, Shenyu, 2022. "Robust operation of distribution network based on photovoltaic/wind energy resources in condition of COVID-19 pandemic considering deterministic and probabilistic approaches," Energy, Elsevier, vol. 261(PB).
    10. Krzysztof Zagrajek, 2021. "A Survey Data Approach for Determining the Probability Values of Vehicle-to-Grid Service Provision," Energies, MDPI, vol. 14(21), pages 1-38, November.
    11. Li, Yang & Feng, Bo & Wang, Bin & Sun, Shuchao, 2022. "Joint planning of distributed generations and energy storage in active distribution networks: A Bi-Level programming approach," Energy, Elsevier, vol. 245(C).
    12. Nikoobakht, Ahmad & Aghaei, Jamshid & Mokarram, Mohammad Jafar & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Adaptive robust co-optimization of wind energy generation, electric vehicle batteries and flexible AC transmission system devices," Energy, Elsevier, vol. 230(C).
    13. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    14. Huang, Pei & Sun, Yongjun & Lovati, Marco & Zhang, Xingxing, 2021. "Solar-photovoltaic-power-sharing-based design optimization of distributed energy storage systems for performance improvements," Energy, Elsevier, vol. 222(C).
    15. Dan Craciunescu & Laurentiu Fara, 2023. "Investigation of the Partial Shading Effect of Photovoltaic Panels and Optimization of Their Performance Based on High-Efficiency FLC Algorithm," Energies, MDPI, vol. 16(3), pages 1-28, January.
    16. Arul Rajagopalan & Dhivya Swaminathan & Meshal Alharbi & Sudhakar Sengan & Oscar Danilo Montoya & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly, 2022. "Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods," Energies, MDPI, vol. 15(23), pages 1-18, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    2. Mortaz, Ebrahim & Vinel, Alexander & Dvorkin, Yury, 2019. "An optimization model for siting and sizing of vehicle-to-grid facilities in a microgrid," Applied Energy, Elsevier, vol. 242(C), pages 1649-1660.
    3. Saboori, Hedayat & Hemmati, Reza, 2017. "Maximizing DISCO profit in active distribution networks by optimal planning of energy storage systems and distributed generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 365-372.
    4. Kebede, Abraham Alem & Kalogiannis, Theodoros & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Shi, Jing & Xu, Ying & Liao, Meng & Guo, Shuqiang & Li, Yuanyuan & Ren, Li & Su, Rongyu & Li, Shujian & Zhou, Xiao & Tang, Yuejin, 2019. "Integrated design method for superconducting magnetic energy storage considering the high frequency pulse width modulation pulse voltage on magnet," Applied Energy, Elsevier, vol. 248(C), pages 1-17.
    6. Panda, Deepak Kumar & Das, Saptarshi, 2021. "Economic operational analytics for energy storage placement at different grid locations and contingency scenarios with stochastic wind profiles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    7. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.
    8. Muhammad Umair Mutarraf & Yacine Terriche & Kamran Ali Khan Niazi & Fawad Khan & Juan C. Vasquez & Josep M. Guerrero, 2019. "Control of Hybrid Diesel/PV/Battery/Ultra-Capacitor Systems for Future Shipboard Microgrids," Energies, MDPI, vol. 12(18), pages 1-23, September.
    9. Gaurav Chaudhary & Jacob J. Lamb & Odne S. Burheim & Bjørn Austbø, 2021. "Review of Energy Storage and Energy Management System Control Strategies in Microgrids," Energies, MDPI, vol. 14(16), pages 1-26, August.
    10. Qin, Chao (Chris) & Loth, Eric, 2021. "Isothermal compressed wind energy storage using abandoned oil/gas wells or coal mines," Applied Energy, Elsevier, vol. 292(C).
    11. Shaohua Hu & Xinlong Zhou & Yi Luo & Guang Zhang, 2019. "Numerical Simulation Three-Dimensional Nonlinear Seepage in a Pumped-Storage Power Station: Case Study," Energies, MDPI, vol. 12(1), pages 1-15, January.
    12. Jannesar, Mohammad Rasol & Sedighi, Alireza & Savaghebi, Mehdi & Guerrero, Josep M., 2018. "Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration," Applied Energy, Elsevier, vol. 226(C), pages 957-966.
    13. Li, Yang & Vilathgamuwa, Mahinda & Choi, San Shing & Farrell, Troy W. & Tran, Ngoc Tham & Teague, Joseph, 2019. "Development of a degradation-conscious physics-based lithium-ion battery model for use in power system planning studies," Applied Energy, Elsevier, vol. 248(C), pages 512-525.
    14. Katsanevakis, Markos & Stewart, Rodney A. & Lu, Junwei, 2017. "Aggregated applications and benefits of energy storage systems with application-specific control methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 719-741.
    15. Yuan, Qiheng & Zhou, Keliang & Yao, Jing, 2020. "A new measure of wind power variability with implications for the optimal sizing of standalone wind power systems," Renewable Energy, Elsevier, vol. 150(C), pages 538-549.
    16. Frate, G.F. & Cherubini, P. & Tacconelli, C. & Micangeli, A. & Ferrari, L. & Desideri, U., 2019. "Ramp rate abatement for wind power plants: A techno-economic analysis," Applied Energy, Elsevier, vol. 254(C).
    17. Simpson, J.G. & Hanrahan, G. & Loth, E. & Koenig, G.M. & Sadoway, D.R., 2021. "Liquid metal battery storage in an offshore wind turbine: Concept and economic analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    18. Saboori, Hedayat & Hemmati, Reza & Ghiasi, Seyyed Mohammad Sadegh & Dehghan, Shahab, 2017. "Energy storage planning in electric power distribution networks – A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1108-1121.
    19. Zhongfu Tan & Qingkun Tan & Shenbo Yang & Liwei Ju & Gejirifu De, 2018. "A Robust Scheduling Optimization Model for an Integrated Energy System with P2G Based on Improved CVaR," Energies, MDPI, vol. 11(12), pages 1-15, December.
    20. Zhao, Chunyang & Andersen, Peter Bach & Træholt, Chresten & Hashemi, Seyedmostafa, 2023. "Grid-connected battery energy storage system: a review on application and integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:123:y:2020:i:c:s1364032120300368. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.