IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i7p884-d103203.html
   My bibliography  Save this article

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Author

Listed:
  • César Hernández-Hernández

    (Department of Informatics, Agrifood Campus of International Excellence ceiA3, CIESOL Research Center on Solar Energy, University of Almería, 04120 Almería, Spain)

  • Francisco Rodríguez

    (Department of Informatics, Agrifood Campus of International Excellence ceiA3, CIESOL Research Center on Solar Energy, University of Almería, 04120 Almería, Spain)

  • José Carlos Moreno

    (Department of Informatics, Agrifood Campus of International Excellence ceiA3, CIESOL Research Center on Solar Energy, University of Almería, 04120 Almería, Spain)

  • Paulo Renato Da Costa Mendes

    (Department of Automation and Systems (DAS), Federal University of Santa Catarina, Federal University of Santa Catarina, Florianópolis-SC CEP 88040-970, Brazil)

  • Julio Elias Normey-Rico

    (Department of Automation and Systems (DAS), Federal University of Santa Catarina, Federal University of Santa Catarina, Florianópolis-SC CEP 88040-970, Brazil)

  • José Luis Guzmán

    (Department of Informatics, Agrifood Campus of International Excellence ceiA3, CIESOL Research Center on Solar Energy, University of Almería, 04120 Almería, Spain)

Abstract

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.

Suggested Citation

  • César Hernández-Hernández & Francisco Rodríguez & José Carlos Moreno & Paulo Renato Da Costa Mendes & Julio Elias Normey-Rico & José Luis Guzmán, 2017. "The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management," Energies, MDPI, vol. 10(7), pages 1-24, June.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:884-:d:103203
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/7/884/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/7/884/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
    2. Carmeli, Maria Stefania & Castelli-Dezza, Francesco & Mauri, Marco & Marchegiani, Gabriele & Rosati, Daniele, 2012. "Control strategies and configurations of hybrid distributed generation systems," Renewable Energy, Elsevier, vol. 41(C), pages 294-305.
    3. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
    4. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico, 2014. "An integrated framework of agent-based modelling and robust optimization for microgrid energy management," Applied Energy, Elsevier, vol. 129(C), pages 70-88.
    5. Gutiérrez-Alcaraz, G. & Galván, E. & González-Cabrera, N. & Javadi, M.S., 2015. "Renewable energy resources short-term scheduling and dynamic network reconfiguration for sustainable energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 256-264.
    6. Eghtedarpour, N. & Farjah, E., 2012. "Control strategy for distributed integration of photovoltaic and energy storage systems in DC micro-grids," Renewable Energy, Elsevier, vol. 45(C), pages 96-110.
    7. Kurohane, Kyohei & Uehara, Akie & Senjyu, Tomonobu & Yona, Atsushi & Urasaki, Naomitsu & Funabashi, Toshihisa & Kim, Chul-Hwan, 2011. "Control strategy for a distributed DC power system with renewable energy," Renewable Energy, Elsevier, vol. 36(1), pages 42-49.
    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. Andrzej Pawłowski & José Luis Guzmán & Manuel Berenguel & Francisco G. Acíen & Sebastián Dormido, 2018. "Application of Predictive Feedforward Compensator to Microalgae Production in a Raceway Reactor: A Simulation Study," Energies, MDPI, vol. 11(1), pages 1-17, January.
    2. Ramos-Teodoro, Jerónimo & Rodríguez, Francisco & Berenguel, Manuel & Torres, José Luis, 2018. "Heterogeneous resource management in energy hubs with self-consumption: Contributions and application example," Applied Energy, Elsevier, vol. 229(C), pages 537-550.
    3. Sébastien Bissey & Sébastien Jacques & Jean-Charles Le Bunetel, 2017. "The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing," Energies, MDPI, vol. 10(11), pages 1-24, October.
    4. Houben, Nikolaus & Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael & Auer, Hans & Ajanovic, Amela & Haas, Reinhard, 2023. "Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria," Applied Energy, Elsevier, vol. 337(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. Karabiber, Abdulkerim & Keles, Cemal & Kaygusuz, Asim & Alagoz, B. Baykant, 2013. "An approach for the integration of renewable distributed generation in hybrid DC/AC microgrids," Renewable Energy, Elsevier, vol. 52(C), pages 251-259.
    2. Valdés, R. & Lucio, J.H. & Rodríguez, L.R., 2013. "Operational simulation of wind power plants for electrolytic hydrogen production connected to a distributed electricity generation grid," Renewable Energy, Elsevier, vol. 53(C), pages 249-257.
    3. Bo, Yaolong & Xia, Yanghong & Wei, Wei & Li, Zichen & Zhao, Bo & Lv, Zeyan, 2023. "Hyperfine optimal dispatch for integrated energy microgrid considering uncertainty," Applied Energy, Elsevier, vol. 334(C).
    4. Howell, Shaun & Rezgui, Yacine & Hippolyte, Jean-Laurent & Jayan, Bejay & Li, Haijiang, 2017. "Towards the next generation of smart grids: Semantic and holonic multi-agent management of distributed energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 193-214.
    5. Torreglosa, Juan P. & García-Triviño, Pablo & Fernández-Ramirez, Luis M. & Jurado, Francisco, 2016. "Control strategies for DC networks: A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 319-330.
    6. Craparo, Emily & Karatas, Mumtaz & Singham, Dashi I., 2017. "A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts," Applied Energy, Elsevier, vol. 201(C), pages 135-147.
    7. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    8. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2016. "Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties," Energies, MDPI, vol. 9(4), pages 1-21, April.
    9. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    10. Abbaspour, M. & Satkin, M. & Mohammadi-Ivatloo, B. & Hoseinzadeh Lotfi, F. & Noorollahi, Y., 2013. "Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES)," Renewable Energy, Elsevier, vol. 51(C), pages 53-59.
    11. Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
    12. Sousa, Tiago & Morais, Hugo & Soares, João & Vale, Zita, 2012. "Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints," Applied Energy, Elsevier, vol. 96(C), pages 183-193.
    13. Anvari-Moghaddam, Amjad & Rahimi-Kian, Ashkan & Mirian, Maryam S. & Guerrero, Josep M., 2017. "A multi-agent based energy management solution for integrated buildings and microgrid system," Applied Energy, Elsevier, vol. 203(C), pages 41-56.
    14. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    15. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    16. Thiaux, Yaël & Dang, Thu Thuy & Schmerber, Louis & Multon, Bernard & Ben Ahmed, Hamid & Bacha, Seddik & Tran, Quoc Tuan, 2019. "Demand-side management strategy in stand-alone hybrid photovoltaic systems with real-time simulation of stochastic electricity consumption behavior," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    17. Saher Javaid & Mineo Kaneko & Yasuo Tan, 2021. "Safe Operation Conditions of Electrical Power System Considering Power Balanceability among Power Generators, Loads, and Storage Devices," Energies, MDPI, vol. 14(15), pages 1-27, July.
    18. Zhang, Yue & Farnoosh, Arash, 2019. "Analyzing the dynamic impact of electricity futures on revenue and risk of renewable energy in China," Energy Policy, Elsevier, vol. 132(C), pages 678-690.
    19. Llaria, Alvaro & Curea, Octavian & Jiménez, Jaime & Camblong, Haritza, 2011. "Survey on microgrids: Unplanned islanding and related inverter control techniques," Renewable Energy, Elsevier, vol. 36(8), pages 2052-2061.
    20. Tang, Ruoli & Li, Xin & Lai, Jingang, 2018. "A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization," Applied Energy, Elsevier, vol. 228(C), pages 254-264.

    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:10:y:2017:i:7:p:884-:d:103203. 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.