IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v146y2020icp2060-2069.html
   My bibliography  Save this article

Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage

Author

Listed:
  • Alvarado-Barrios, Lázaro
  • Rodríguez del Nozal, Álvaro
  • Boza Valerino, Juan
  • García Vera, Ignacio
  • Martínez-Ramos, Jose L.

Abstract

A Stochastic Model for the Unit Commitment (SUC) problem of a hybrid microgrid for a short period of 24 h is presented. The microgrid considered in the problem is composed of a wind turbine (WT), a photovoltaic plant (PV), a diesel generator (DE), a microturbine (MT) and a Battery Energy Storage System (BESS). The problem is addressed in three stages. First, based on the historical data of the demanded power in the microgrid, an ARMA model is used to obtain the demand prediction. Second, the 24-h-ahead SUC problem is solved, based on generators’ constraints, renewable generation and demand forecast and the statistical distribution of the error in the demand estimation. In this problem, a spinning reserve of the dispatchable units is considered, able to cover the uncertainties in the demand estimation. In the third stage, once the SUC problem has been solved, a case study is established in real time, in which the demand estimation error in every moment is known. Therefore, the objective of this stage is to select the spinning reserve of the units in an optimal way to minimize the cost in the microgrid operation.

Suggested Citation

  • Alvarado-Barrios, Lázaro & Rodríguez del Nozal, Álvaro & Boza Valerino, Juan & García Vera, Ignacio & Martínez-Ramos, Jose L., 2020. "Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage," Renewable Energy, Elsevier, vol. 146(C), pages 2060-2069.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:2060-2069
    DOI: 10.1016/j.renene.2019.08.032
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119312194
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.08.032?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. Soshinskaya, Mariya & Crijns-Graus, Wina H.J. & Guerrero, Josep M. & Vasquez, Juan C., 2014. "Microgrids: Experiences, barriers and success factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 659-672.
    2. Ibrahim Alsaidan & Abdulaziz Alanazi & Wenzhong Gao & Hongyu Wu & Amin Khodaei, 2017. "State-Of-The-Art in Microgrid-Integrated Distributed Energy Storage Sizing," Energies, MDPI, vol. 10(9), pages 1-14, September.
    3. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    4. Mohamed Abd el Motaleb, Ahmad & Kazim Bekdache, Sarah & Barrios, Lázaro Alvarado, 2016. "Optimal sizing for a hybrid power system with wind/energy storage based in stochastic environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1149-1158.
    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. Tostado-Véliz, Marcos & León-Japa, Rogelio S. & Jurado, Francisco, 2021. "Optimal electrification of off-grid smart homes considering flexible demand and vehicle-to-home capabilities," Applied Energy, Elsevier, vol. 298(C).
    2. Vishnu Suresh & Przemyslaw Janik & Jacek Rezmer & Zbigniew Leonowicz, 2020. "Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm," Energies, MDPI, vol. 13(3), pages 1-15, February.
    3. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.
    4. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Rezaee Jordehi, Ahmad & Jurado, Francisco, 2022. "A Stochastic-IGDT model for energy management in isolated microgrids considering failures and demand response," Applied Energy, Elsevier, vol. 317(C).
    5. Gomes, I.L.R. & Melicio, R. & Mendes, V.M.F., 2021. "A novel microgrid support management system based on stochastic mixed-integer linear programming," Energy, Elsevier, vol. 223(C).
    6. Harsh, Pratik & Das, Debapriya, 2022. "Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Sandelic, Monika & Peyghami, Saeed & Sangwongwanich, Ariya & Blaabjerg, Frede, 2022. "Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    8. Qing, Ke & Du, Yuefang & Huang, Qi & Duan, Chao & Hu, Weihao, 2024. "Energy scheduling for microgrids with renewable energy sources considering an adjustable convex hull based uncertainty set," Renewable Energy, Elsevier, vol. 220(C).
    9. Wei Wu & Shih-Chieh Chou & Karthickeyan Viswanathan, 2023. "Optimal Dispatching of Smart Hybrid Energy Systems for Addressing a Low-Carbon Community," Energies, MDPI, vol. 16(9), pages 1-19, April.
    10. Jing Liu & Xin-Lei Zhou & Lu-Qi Zhang & Yue-Ping Xu, 2023. "Forecasting Short-term Water Demands with an Ensemble Deep Learning Model for a Water Supply System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2991-3012, June.
    11. Tadeusz A. Grzeszczyk & Michal K. Grzeszczyk, 2022. "Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models," Energies, MDPI, vol. 15(5), pages 1-20, March.
    12. Jiang, Sufan & Wu, Chuanshen & Gao, Shan & Pan, Guangsheng & Liu, Yu & Zhao, Xin & Wang, Sicheng, 2022. "Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty," Renewable Energy, Elsevier, vol. 195(C), pages 395-406.
    13. Yong-Rae Lee & Hyung-Joon Kim & Mun-Kyeom Kim, 2021. "Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids," Energies, MDPI, vol. 14(2), pages 1-21, January.
    14. Manzano, J.M. & Salvador, J.R. & Romaine, J.B. & Alvarado-Barrios, L., 2022. "Economic predictive control for isolated microgrids based on real world demand/renewable energy data and forecast errors," Renewable Energy, Elsevier, vol. 194(C), pages 647-658.

    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. Iolanda Saviuc & Herbert Peremans & Steven Van Passel & Kevin Milis, 2019. "Economic Performance of Using Batteries in European Residential Microgrids under the Net-Metering Scheme," Energies, MDPI, vol. 12(1), pages 1-28, January.
    2. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    3. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    4. Zhao, Bo & Chen, Jian & Zhang, Leiqi & Zhang, Xuesong & Qin, Ruwen & Lin, Xiangning, 2018. "Three representative island microgrids in the East China Sea: Key technologies and experiences," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 262-274.
    5. Avilés A., Camilo & Oliva H., Sebastian & Watts, David, 2019. "Single-dwelling and community renewable microgrids: Optimal sizing and energy management for new business models," Applied Energy, Elsevier, vol. 254(C).
    6. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    7. Bui, Duong Minh & Chen, Shi-Lin & Lien, Keng-Yu & Chang, Yung-Ruei & Lee, Yih-Der & Jiang, Jheng-Lun, 2017. "Investigation on transient behaviours of a uni-grounded low-voltage AC microgrid and evaluation on its available fault protection methods: Review and proposals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1417-1452.
    8. Kirchhoff, Hannes & Strunz, Kai, 2019. "Key drivers for successful development of peer-to-peer microgrids for swarm electrification," Applied Energy, Elsevier, vol. 244(C), pages 46-62.
    9. Meng, Ming & Niu, Dongxiao, 2011. "Modeling CO2 emissions from fossil fuel combustion using the logistic equation," Energy, Elsevier, vol. 36(5), pages 3355-3359.
    10. Dutta, Rohan & Ghosh, Parthasarathi & Chowdhury, Kanchan, 2011. "Customization and validation of a commercial process simulator for dynamic simulation of Helium liquefier," Energy, Elsevier, vol. 36(5), pages 3204-3214.
    11. Quynh T.T Tran & Eleonora Riva Sanseverino & Gaetano Zizzo & Maria Luisa Di Silvestre & Tung Lam Nguyen & Quoc-Tuan Tran, 2020. "Real-Time Minimization Power Losses by Driven Primary Regulation in Islanded Microgrids," Energies, MDPI, vol. 13(2), pages 1-17, January.
    12. Ruhang, Xu, 2020. "Efficient clustering for aggregate loads: An unsupervised pretraining based method," Energy, Elsevier, vol. 210(C).
    13. Hirsch, Adam & Parag, Yael & Guerrero, Josep, 2018. "Microgrids: A review of technologies, key drivers, and outstanding issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 402-411.
    14. Thomas Sachs & Anna Gründler & Milos Rusic & Gilbert Fridgen, 2019. "Framing Microgrid Design from a Business and Information Systems Engineering Perspective," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(6), pages 729-744, December.
    15. Miguel Carpintero-Rentería & David Santos-Martín & Josep M. Guerrero, 2019. "Microgrids Literature Review through a Layers Structure," Energies, MDPI, vol. 12(22), pages 1-22, November.
    16. Barman, Mayur & Dev Choudhury, N.B. & Sutradhar, Suman, 2018. "A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India," Energy, Elsevier, vol. 145(C), pages 710-720.
    17. Hong, Bowen & Zhang, Weitong & Zhou, Yue & Chen, Jian & Xiang, Yue & Mu, Yunfei, 2018. "Energy-Internet-oriented microgrid energy management system architecture and its application in China," Applied Energy, Elsevier, vol. 228(C), pages 2153-2164.
    18. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    19. O’Brien, Thomas & Reeb, Tyler, 2016. "Freight Efficiency Strategies: Operational Modernization at Distribution Nodes," Institute of Transportation Studies, Working Paper Series qt4fg9f0gv, Institute of Transportation Studies, UC Davis.
    20. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(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:renene:v:146:y:2020:i:c:p:2060-2069. 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/renewable-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.