IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v149y2018icp709-717.html
   My bibliography  Save this article

Power ramp-rate control algorithm with optimal State of Charge reference via Dynamic Programming

Author

Listed:
  • Duchaud, Jean-Laurent
  • Notton, Gilles
  • Darras, Christophe
  • Voyant, Cyril

Abstract

This article presents a new control algorithm for an Intermittent Renewable Energy Systems coupled with an Energy Storage System (ESS) connected to a load and to the grid. It aims to limit grid power ramp-rate, to optimize energy trading and to manage the ESS State of Charge (SoC) under several constraints. The strategy uses a ramp-rate limiter algorithm with a SoC feedback control. The controller reference is optimized with a Dynamic Programming algorithm using weather and consumption predictions. Every 2 h, the optimization is refreshed with the actual SoC and new predictions. The algorithm is applied on real data measured in Ajaccio and shows a light improvement in the plant operation cost and ensures that the ESS is not depleted at the end of the day.

Suggested Citation

  • Duchaud, Jean-Laurent & Notton, Gilles & Darras, Christophe & Voyant, Cyril, 2018. "Power ramp-rate control algorithm with optimal State of Charge reference via Dynamic Programming," Energy, Elsevier, vol. 149(C), pages 709-717.
    • Handle: RePEc:eee:energy:v:149:y:2018:i:c:p:709-717
    DOI: 10.1016/j.energy.2018.02.064
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218302925
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.02.064?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. Farouk Odeim & Jürgen Roes & Angelika Heinzel, 2015. "Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System," Energies, MDPI, vol. 8(7), pages 1-26, June.
    2. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    3. Kou, Peng & Gao, Feng & Guan, Xiaohong, 2015. "Stochastic predictive control of battery energy storage for wind farm dispatching: Using probabilistic wind power forecasts," Renewable Energy, Elsevier, vol. 80(C), pages 286-300.
    4. Steffen, Bjarne & Weber, Christoph, 2016. "Optimal operation of pumped-hydro storage plants with continuous time-varying power prices," European Journal of Operational Research, Elsevier, vol. 252(1), pages 308-321.
    5. Kusakana, Kanzumba, 2017. "Energy management of a grid-connected hydrokinetic system under Time of Use tariff," Renewable Energy, Elsevier, vol. 101(C), pages 1325-1333.
    6. Jiang, Yibo & Xu, Jian & Sun, Yuanzhang & Wei, Congying & Wang, Jing & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao & Tang, Bowen, 2017. "Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system," Applied Energy, Elsevier, vol. 190(C), pages 1126-1137.
    7. Gallego-Castillo, Cristobal & Cuerva-Tejero, Alvaro & Lopez-Garcia, Oscar, 2015. "A review on the recent history of wind power ramp forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1148-1157.
    8. Rigo-Mariani, Rémy & Sareni, Bruno & Roboam, Xavier & Turpin, Christophe, 2014. "Optimal power dispatching strategies in smart-microgrids with storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 649-658.
    9. Notton, Gilles, 2015. "Importance of islands in renewable energy production and storage: The situation of the French islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 260-269.
    10. Torreglosa, Juan P. & García, Pablo & Fernández, Luis M. & Jurado, Francisco, 2015. "Energy dispatching based on predictive controller of an off-grid wind turbine/photovoltaic/hydrogen/battery hybrid system," Renewable Energy, Elsevier, vol. 74(C), pages 326-336.
    11. Olatomiwa, Lanre & Mekhilef, Saad & Ismail, M.S. & Moghavvemi, M., 2016. "Energy management strategies in hybrid renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 821-835.
    12. Ho, Wai Shin & Macchietto, Sandro & Lim, Jeng Shiun & Hashim, Haslenda & Muis, Zarina Ab. & Liu, Wen Hui, 2016. "Optimal scheduling of energy storage for renewable energy distributed energy generation system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1100-1107.
    13. Voyant, Cyril & Notton, Gilles & Darras, Christophe & Fouilloy, Alexis & Motte, Fabrice, 2017. "Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case," Energy, Elsevier, vol. 125(C), pages 248-257.
    14. Lorenzi, Guido & Silva, Carlos Augusto Santos, 2016. "Comparing demand response and battery storage to optimize self-consumption in PV systems," Applied Energy, Elsevier, vol. 180(C), pages 524-535.
    15. Dufo-López, Rodolfo & Lujano-Rojas, Juan M. & Bernal-Agustín, José L., 2014. "Comparison of different lead–acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems," Applied Energy, Elsevier, vol. 115(C), pages 242-253.
    16. Javier Marcos & Iñigo De la Parra & Miguel García & Luis Marroyo, 2014. "Control Strategies to Smooth Short-Term Power Fluctuations in Large Photovoltaic Plants Using Battery Storage Systems," Energies, MDPI, vol. 7(10), pages 1-27, October.
    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. Duchaud, Jean-Laurent & Notton, Gilles & Fouilloy, Alexis & Voyant, Cyril, 2019. "Hybrid renewable power plant sizing – Graphical decision tool, sensitivity analysis and applications in Ajaccio and Tilos," Applied Energy, Elsevier, vol. 254(C).
    2. Huo, Yujia & Gruosso, Giambattista, 2020. "A novel ramp-rate control of grid-tied PV-Battery systems to reduce required battery capacity," Energy, Elsevier, vol. 210(C).
    3. Elkholy, M.H. & Metwally, Hamid & Farahat, M.A. & Senjyu, Tomonobu & Elsayed Lotfy, Mohammed, 2022. "Smart centralized energy management system for autonomous microgrid using FPGA," Applied Energy, Elsevier, vol. 317(C).

    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. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    2. Jean-Laurent Duchaud & Cyril Voyant & Alexis Fouilloy & Gilles Notton & Marie-Laure Nivet, 2020. "Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control," Energies, MDPI, vol. 13(14), pages 1-16, July.
    3. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Cohen, Miri Weiss & Reis, Agnaldo J.R. & Silva, Sidelmo M. & Souza, Marcone J.F. & Fleming, Peter J. & Guimarães, Frederico G., 2016. "Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid," Renewable Energy, Elsevier, vol. 89(C), pages 730-742.
    4. Azuatalam, Donald & Paridari, Kaveh & Ma, Yiju & Förstl, Markus & Chapman, Archie C. & Verbič, Gregor, 2019. "Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 555-570.
    5. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
    6. Cruz, Marco R.M. & Fitiwi, Desta Z. & Santos, Sérgio F. & Catalão, João P.S., 2018. "A comprehensive survey of flexibility options for supporting the low-carbon energy future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 338-353.
    7. Rullo, P. & Braccia, L. & Luppi, P. & Zumoffen, D. & Feroldi, D., 2019. "Integration of sizing and energy management based on economic predictive control for standalone hybrid renewable energy systems," Renewable Energy, Elsevier, vol. 140(C), pages 436-451.
    8. Keles, Dogan & Dehler-Holland, Joris, 2022. "Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming," Energy Economics, Elsevier, vol. 106(C).
    9. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    10. Voyant, Cyril & Motte, Fabrice & Notton, Gilles & Fouilloy, Alexis & Nivet, Marie-Laure & Duchaud, Jean-Laurent, 2018. "Prediction intervals for global solar irradiation forecasting using regression trees methods," Renewable Energy, Elsevier, vol. 126(C), pages 332-340.
    11. Gonzalez-Moreno, A. & Marcos, J. & de la Parra, I. & Marroyo, L., 2022. "A PV ramp-rate control strategy to extend battery lifespan using forecasting," Applied Energy, Elsevier, vol. 323(C).
    12. Mahesh, Aeidapu & Sandhu, Kanwarjit Singh, 2015. "Hybrid wind/photovoltaic energy system developments: Critical review and findings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1135-1147.
    13. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    14. Udeh, Godfrey T. & Michailos, Stavros & Ingham, Derek & Hughes, Kevin J. & Ma, Lin & Pourkashanian, Mohamed, 2022. "A modified rule-based energy management scheme for optimal operation of a hybrid PV-wind-Stirling engine integrated multi-carrier energy system," Applied Energy, Elsevier, vol. 312(C).
    15. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    16. Stefan Arens & Karen Derendorf & Frank Schuldt & Karsten Von Maydell & Carsten Agert, 2018. "Effect of EV Movement Schedule and Machine Learning-Based Load Forecasting on Electricity Cost of a Single Household," Energies, MDPI, vol. 11(11), pages 1-19, October.
    17. de Oliveira e Silva, Guilherme & Hendrick, Patrick, 2017. "Photovoltaic self-sufficiency of Belgian households using lithium-ion batteries, and its impact on the grid," Applied Energy, Elsevier, vol. 195(C), pages 786-799.
    18. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    19. Federica Cucchiella & Idiano D’Adamo & Paolo Rosa, 2015. "Industrial Photovoltaic Systems: An Economic Analysis in Non-Subsidized Electricity Markets," Energies, MDPI, vol. 8(11), pages 1-16, November.
    20. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.

    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:energy:v:149:y:2018:i:c:p:709-717. 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.journals.elsevier.com/energy .

    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.