IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i6p1371-d149401.html
   My bibliography  Save this article

Optimal Scheduling and Real-Time Control Schemes of Battery Energy Storage System for Microgrids Considering Contract Demand and Forecast Uncertainty

Author

Listed:
  • Hong-Chao Gao

    (Department of Electrical Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Korea)

  • Joon-Ho Choi

    (Department of Electrical Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Korea)

  • Sang-Yun Yun

    (Department of Electrical Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Korea)

  • Hak-Ju Lee

    (Energy System Group Energy New Business Laboratory, Korea Electric Power Research Institute, Daejeon 34056, Korea)

  • Seon-Ju Ahn

    (Department of Electrical Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Korea)

Abstract

Optimal operation of the battery energy storage system (BESS) is very important to reduce the running cost of a microgrid. Rolling horizon-based scheduling, which updates the optimal decision based on the latest information, is widely applied to microgrid operation. In this paper, the optimal scheduling of a microgrid, considering the energy cost, demand charge, and the battery wear-cost, is formulated as a mixed integer linear programming (MILP) problem. This paper also deals with two practical and important issues when applying the rolling-horizon strategy to BESS scheduling. First, to mitigate the high dependency of the load forecast on the latest information, a confidence weight parameter method is proposed. Second, a new target state of charge (SOC) assignment method is proposed to avoid the depletion of BESS and to reduce the wear-cost of the battery. In addition to the optimal scheduling, a novel real-time control scheme is proposed to mitigate the effect of the forecast uncertainty. The performance of the proposed methods is tested with data measured from a campus microgrid.

Suggested Citation

  • Hong-Chao Gao & Joon-Ho Choi & Sang-Yun Yun & Hak-Ju Lee & Seon-Ju Ahn, 2018. "Optimal Scheduling and Real-Time Control Schemes of Battery Energy Storage System for Microgrids Considering Contract Demand and Forecast Uncertainty," Energies, MDPI, vol. 11(6), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1371-:d:149401
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/6/1371/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/6/1371/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mu-Gu Jeong & Seung-Il Moon & Pyeong-Ik Hwang, 2016. "Indirect Load Control for Energy Storage Systems Using Incentive Pricing under Time-of-Use Tariff," Energies, MDPI, vol. 9(7), pages 1-20, July.
    2. Yohwan Choi & Hongseok Kim, 2016. "Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost," Energies, MDPI, vol. 9(6), pages 1-19, June.
    3. Yuchong Huo & Ping Jiang & Yuan Zhu & Shuang Feng & Xi Wu, 2015. "Optimal Real-Time Scheduling of Wind Integrated Power System Presented with Storage and Wind Forecast Uncertainties," Energies, MDPI, vol. 8(2), pages 1-21, February.
    4. Alessandro Serpi & Mario Porru & Alfonso Damiano, 2017. "An Optimal Power and Energy Management by Hybrid Energy Storage Systems in Microgrids," Energies, MDPI, vol. 10(11), pages 1-21, November.
    5. Kriett, Phillip Oliver & Salani, Matteo, 2012. "Optimal control of a residential microgrid," Energy, Elsevier, vol. 42(1), pages 321-330.
    6. Li, Jianwei & Gee, Anthony M. & Zhang, Min & Yuan, Weijia, 2015. "Analysis of battery lifetime extension in a SMES-battery hybrid energy storage system using a novel battery lifetime model," Energy, Elsevier, vol. 86(C), pages 175-185.
    7. Enrico Telaretti & Mariano Ippolito & Luigi Dusonchet, 2015. "A Simple Operating Strategy of Small-Scale Battery Energy Storages for Energy Arbitrage under Dynamic Pricing Tariffs," Energies, MDPI, vol. 9(1), pages 1-20, December.
    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. Sergio Rech, 2019. "Smart Energy Systems: Guidelines for Modelling and Optimizing a Fleet of Units of Different Configurations," Energies, MDPI, vol. 12(7), pages 1-36, April.
    2. Hafiz Abdul Muqeet & Hafiz Mudassir Munir & Haseeb Javed & Muhammad Shahzad & Mohsin Jamil & Josep M. Guerrero, 2021. "An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges," Energies, MDPI, vol. 14(20), pages 1-34, October.
    3. Aslam Amir & Hussain Shareef & Falah Awwad, 2023. "Energy Management in a Standalone Microgrid: A Split-Horizon Dual-Stage Dispatch Strategy," Energies, MDPI, vol. 16(8), pages 1-25, April.
    4. Mohammed Abdullah H. Alshehri & Youguang Guo & Gang Lei, 2023. "Energy Management Strategies of Grid-Connected Microgrids under Different Reliability Conditions," Energies, MDPI, vol. 16(9), pages 1-22, May.
    5. Amad Ali & Hafiz Abdul Muqeet & Tahir Khan & Asif Hussain & Muhammad Waseem & Kamran Ali Khan Niazi, 2023. "IoT-Enabled Campus Prosumer Microgrid Energy Management, Architecture, Storage Technologies, and Simulation Tools: A Comprehensive Study," Energies, MDPI, vol. 16(4), pages 1-19, February.
    6. Hong-Chao Gao & Joon-Ho Choi & Sang-Yun Yun & Seon-Ju Ahn, 2020. "A New Power Sharing Scheme of Multiple Microgrids and an Iterative Pairing-Based Scheduling Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    7. Giuliano Rancilio & Alexandre Lucas & Evangelos Kotsakis & Gianluca Fulli & Marco Merlo & Maurizio Delfanti & Marcelo Masera, 2019. "Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis," Energies, MDPI, vol. 12(17), pages 1-26, August.

    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. 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.
    2. Hojin Kim & Jaewoo So & Hongseok Kim, 2022. "Carbon-Neutral Cellular Network Operation Based on Deep Reinforcement Learning," Energies, MDPI, vol. 15(12), pages 1-13, June.
    3. Kim, Minsoo & Park, Taeseop & Jeong, Jaeik & Kim, Hongseok, 2023. "Stochastic optimization of home energy management system using clustered quantile scenario reduction," Applied Energy, Elsevier, vol. 349(C).
    4. Jungsub Sim & Minsoo Kim & Dongjoo Kim & Hongseok Kim, 2021. "Cloud Energy Storage System Operation with Capacity P2P Transaction," Energies, MDPI, vol. 14(2), pages 1-13, January.
    5. Barelli, L. & Bidini, G. & Bonucci, F. & Castellini, L. & Fratini, A. & Gallorini, F. & Zuccari, A., 2019. "Flywheel hybridization to improve battery life in energy storage systems coupled to RES plants," Energy, Elsevier, vol. 173(C), pages 937-950.
    6. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    7. Asad, R. & Kazemi, A., 2014. "A novel distributed optimal power sharing method for radial dc microgrids with different distributed energy sources," Energy, Elsevier, vol. 72(C), pages 291-299.
    8. Bertolini, Marina & D'Alpaos, Chiara & Moretto, Michele, 2018. "Do Smart Grids boost investments in domestic PV plants? Evidence from the Italian electricity market," Energy, Elsevier, vol. 149(C), pages 890-902.
    9. Xu, Ying & Ren, Li & Zhang, Zhongping & Tang, Yuejin & Shi, Jing & Xu, Chen & Li, Jingdong & Pu, Dongsheng & Wang, Zhuang & Liu, Huajun & Chen, Lei, 2018. "Analysis of the loss and thermal characteristics of a SMES (Superconducting Magnetic Energy Storage) magnet with three practical operating conditions," Energy, Elsevier, vol. 143(C), pages 372-384.
    10. Cezar Antônio Rigo & Edemar Morsch Filho & Laio Oriel Seman & Luís Loures & Valderi Reis Quietinho Leithardt, 2023. "Instance and Data Generation for the Offline Nanosatellite Task Scheduling Problem," Data, MDPI, vol. 8(3), pages 1-14, March.
    11. Bizon, Nicu, 2019. "Hybrid power sources (HPSs) for space applications: Analysis of PEMFC/Battery/SMES HPS under unknown load containing pulses," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 14-37.
    12. Menon, Ramanunni P. & Paolone, Mario & Maréchal, François, 2013. "Study of optimal design of polygeneration systems in optimal control strategies," Energy, Elsevier, vol. 55(C), pages 134-141.
    13. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Ekşioğlu, Sandra D. & Castillo-Villar, Krystel K., 2021. "Designing a reliable electric vehicle charging station expansion under uncertainty," International Journal of Production Economics, Elsevier, vol. 236(C).
    14. Dusonchet, L. & Favuzza, S. & Massaro, F. & Telaretti, E. & Zizzo, G., 2019. "Technological and legislative status point of stationary energy storages in the EU," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 158-167.
    15. Omaji Samuel & Nadeem Javaid & Mahmood Ashraf & Farruh Ishmanov & Muhammad Khalil Afzal & Zahoor Ali Khan, 2018. "Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid," Energies, MDPI, vol. 11(6), pages 1-29, June.
    16. Rigo, Cezar Antônio & Seman, Laio Oriel & Camponogara, Eduardo & Morsch Filho, Edemar & Bezerra, Eduardo Augusto & Munari, Pedro, 2022. "A branch-and-price algorithm for nanosatellite task scheduling to improve mission quality-of-service," European Journal of Operational Research, Elsevier, vol. 303(1), pages 168-183.
    17. Poolla, Chaitanya & Ishihara, Abraham K. & Milito, Rodolfo, 2019. "Designing near-optimal policies for energy management in a stochastic environment," Applied Energy, Elsevier, vol. 242(C), pages 1725-1737.
    18. Wang, Ge & Zhang, Qi & Li, Hailong & McLellan, Benjamin C. & Chen, Siyuan & Li, Yan & Tian, Yulu, 2017. "Study on the promotion impact of demand response on distributed PV penetration by using non-cooperative game theoretical analysis," Applied Energy, Elsevier, vol. 185(P2), pages 1869-1878.
    19. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    20. Boram Kim & Sunghwan Bae & Hongseok Kim, 2017. "Optimal Energy Scheduling and Transaction Mechanism for Multiple Microgrids," Energies, MDPI, vol. 10(4), pages 1-17, April.

    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:gam:jeners:v:11:y:2018:i:6:p:1371-:d:149401. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.