IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i8p8020-8051d53598.html
   My bibliography  Save this article

Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

Author

Listed:
  • Meng Xiong

    (State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China)

  • Feng Gao

    (State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China)

  • Kun Liu

    (State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China)

  • Siyun Chen

    (State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China)

  • Jiaojiao Dong

    (State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China)

Abstract

Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC) method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long- and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.

Suggested Citation

  • Meng Xiong & Feng Gao & Kun Liu & Siyun Chen & Jiaojiao Dong, 2015. "Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control," Energies, MDPI, vol. 8(8), pages 1-32, August.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:8:p:8020-8051:d:53598
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/8/8020/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/8/8020/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marzband, Mousa & Ghadimi, Majid & Sumper, Andreas & Domínguez-García, José Luis, 2014. "Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode," Applied Energy, Elsevier, vol. 128(C), pages 164-174.
    2. Haifeng Zhang & Feng Gao & Jiang Wu & Kun Liu & Xiaolin Liu, 2012. "Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market," Energies, MDPI, vol. 5(11), pages 1-20, November.
    3. Marzband, Mousa & Sumper, Andreas & Ruiz-Álvarez, Albert & Domínguez-García, José Luis & Tomoiagă, Bogdan, 2013. "Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets," Applied Energy, Elsevier, vol. 106(C), pages 365-376.
    4. Khalid, M. & Savkin, A.V., 2010. "A model predictive control approach to the problem of wind power smoothing with controlled battery storage," Renewable Energy, Elsevier, vol. 35(7), pages 1520-1526.
    5. Breton, Simon-Philippe & Moe, Geir, 2009. "Status, plans and technologies for offshore wind turbines in Europe and North America," Renewable Energy, Elsevier, vol. 34(3), pages 646-654.
    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. Jae Woong Shim & Heejin Kim & Kyeon Hur, 2019. "Incorporating State-of-Charge Balancing into the Control of Energy Storage Systems for Smoothing Renewable Intermittency," Energies, MDPI, vol. 12(7), pages 1-13, March.
    2. Izaskun Garrido & Aitor J. Garrido & Stefano Coda & Hoang B. Le & Jean Marc Moret, 2016. "Real Time Hybrid Model Predictive Control for the Current Profile of the Tokamak à Configuration Variable (TCV)," Energies, MDPI, vol. 9(8), pages 1-14, August.
    3. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.
    4. Abebe Tilahun Tadie & Zhizhong Guo, 2019. "Optimal Planning of Grid Scale PHES Through Characteristics-Based Large Scale Data Clustering and Emission Constrained Optimization," Energies, MDPI, vol. 12(11), pages 1-19, June.
    5. Xiuyun Wang & Yibing Zhou & Junyu Tian & Jian Wang & Yang Cui, 2018. "Wind Power Consumption Research Based on Green Economic Indicators," Energies, MDPI, vol. 11(10), pages 1-24, October.
    6. Yanjuan Yu & Hongkun Chen & Lei Chen, 2018. "Comparative Study of Electric Energy Storages and Thermal Energy Auxiliaries for Improving Wind Power Integration in the Cogeneration System," Energies, MDPI, vol. 11(2), pages 1-16, January.
    7. Tiezhou Wu & Xiao Shi & Li Liao & Chuanjian Zhou & Hang Zhou & Yuehong Su, 2019. "A Capacity Configuration Control Strategy to Alleviate Power Fluctuation of Hybrid Energy Storage System Based on Improved Particle Swarm Optimization," Energies, MDPI, vol. 12(4), pages 1-11, February.

    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. Bhowmik, Chiranjib & Bhowmik, Sumit & Ray, Amitava & Pandey, Krishna Murari, 2017. "Optimal green energy planning for sustainable development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 796-813.
    2. El-Sharafy, M. Zaki & Farag, Hany E.Z., 2017. "Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids," Applied Energy, Elsevier, vol. 206(C), pages 1102-1117.
    3. Elsied, Moataz & Oukaour, Amrane & Youssef, Tarek & Gualous, Hamid & Mohammed, Osama, 2016. "An advanced real time energy management system for microgrids," Energy, Elsevier, vol. 114(C), pages 742-752.
    4. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    5. Gonzalez de Durana, Jose & Barambones, Oscar, 2018. "Technology-free microgrid modeling with application to demand side management," Applied Energy, Elsevier, vol. 219(C), pages 165-178.
    6. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    7. Ding, Tao & Lin, Yanling & Bie, Zhaohong & Chen, Chen, 2017. "A resilient microgrid formation strategy for load restoration considering master-slave distributed generators and topology reconfiguration," Applied Energy, Elsevier, vol. 199(C), pages 205-216.
    8. Velik, Rosemarie & Nicolay, Pascal, 2014. "Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer," Applied Energy, Elsevier, vol. 130(C), pages 384-395.
    9. Raya-Armenta, Jose Maurilio & Bazmohammadi, Najmeh & Avina-Cervantes, Juan Gabriel & Sáez, Doris & Vasquez, Juan C. & Guerrero, Josep M., 2021. "Energy management system optimization in islanded microgrids: An overview and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    10. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2019. "Energy management in hybrid microgrid with considering multiple power market and real time demand response," Energy, Elsevier, vol. 174(C), pages 10-23.
    11. Nian Shi & Yi Luo, 2017. "Energy Storage System Sizing Based on a Reliability Assessment of Power Systems Integrated with Wind Power," Sustainability, MDPI, vol. 9(3), pages 1-20, March.
    12. Roslan, M.F. & Hannan, M.A. & Jern Ker, Pin & Begum, R.A. & Indra Mahlia, TM & Dong, Z.Y., 2021. "Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction," Applied Energy, Elsevier, vol. 292(C).
    13. Cheng-Shan Wang & Wei Li & Yi-Feng Wang & Fu-Qiang Han & Zhun Meng & Guo-Dong Li, 2017. "An Isolated Three-Port Bidirectional DC-DC Converter with Enlarged ZVS Region for HESS Applications in DC Microgrids," Energies, MDPI, vol. 10(4), pages 1-23, April.
    14. Arcos-Aviles, Diego & Pascual, Julio & Guinjoan, Francesc & Marroyo, Luis & Sanchis, Pablo & Marietta, Martin P., 2017. "Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting," Applied Energy, Elsevier, vol. 205(C), pages 69-84.
    15. Khalid, Muhammad & Ahmadi, Abdollah & Savkin, Andrey V. & Agelidis, Vassilios G., 2016. "Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage," Renewable Energy, Elsevier, vol. 97(C), pages 646-655.
    16. Li, Zhengmao & Xu, Yan, 2018. "Optimal coordinated energy dispatch of a multi-energy microgrid in grid-connected and islanded modes," Applied Energy, Elsevier, vol. 210(C), pages 974-986.
    17. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
    18. Li, Bei & Roche, Robin & Miraoui, Abdellatif, 2017. "Microgrid sizing with combined evolutionary algorithm and MILP unit commitment," Applied Energy, Elsevier, vol. 188(C), pages 547-562.
    19. Zenginis, Ioannis & Vardakas, John S. & Echave, Cynthia & Morató, Moisés & Abadal, Jordi & Verikoukis, Christos V., 2017. "Cooperation in microgrids through power exchange: An optimal sizing and operation approach," Applied Energy, Elsevier, vol. 203(C), pages 972-981.
    20. Fengbing Li & Kaigui Xie & Jiangping Yang, 2015. "Optimization and Analysis of a Hybrid Energy Storage System in a Small-Scale Standalone Microgrid for Remote Area Power Supply (RAPS)," Energies, MDPI, vol. 8(6), pages 1-25, May.

    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:8:y:2015:i:8:p:8020-8051:d:53598. 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.