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Impact of solar and wind forecast uncertainties on demand response of isolated microgrids

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  • Neves, Diana
  • Brito, Miguel C.
  • Silva, Carlos A.

Abstract

A flexible load management may improve significantly the economic dispatch, especially for isolated energy systems with a significant share of renewables. For that purpose, renewable resources and load forecasts ought to be taken in account for optimal demand response programs.

Suggested Citation

  • Neves, Diana & Brito, Miguel C. & Silva, Carlos A., 2016. "Impact of solar and wind forecast uncertainties on demand response of isolated microgrids," Renewable Energy, Elsevier, vol. 87(P2), pages 1003-1015.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p2:p:1003-1015
    DOI: 10.1016/j.renene.2015.08.075
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    Cited by:

    1. Mayank Singh & Rakesh Chandra Jha, 2019. "Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization," Energies, MDPI, vol. 12(3), pages 1-25, January.
    2. Huang, Pei & Sun, Yongjun, 2019. "A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty," Renewable Energy, Elsevier, vol. 134(C), pages 215-227.
    3. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    4. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Nwulu, Nnamdi I. & Xia, Xiaohua, 2017. "Optimal dispatch for a microgrid incorporating renewables and demand response," Renewable Energy, Elsevier, vol. 101(C), pages 16-28.
    6. Hu, Maomao & Xiao, Fu, 2020. "Quantifying uncertainty in the aggregate energy flexibility of high-rise residential building clusters considering stochastic occupancy and occupant behavior," Energy, Elsevier, vol. 194(C).
    7. 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.
    8. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    9. Bracco, Stefano & Delfino, Federico & Ferro, Giulio & Pagnini, Luisa & Robba, Michela & Rossi, Mansueto, 2018. "Energy planning of sustainable districts: Towards the exploitation of small size intermittent renewables in urban areas," Applied Energy, Elsevier, vol. 228(C), pages 2288-2297.
    10. Pedro, Hugo T.C. & Lim, Edwin & Coimbra, Carlos F.M., 2018. "A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 513-525.
    11. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    12. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.
    13. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Træholt, Chresten, 2018. "Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage," Renewable Energy, Elsevier, vol. 123(C), pages 204-217.
    14. Neves, Diana & Pina, André & Silva, Carlos A., 2018. "Assessment of the potential use of demand response in DHW systems on isolated microgrids," Renewable Energy, Elsevier, vol. 115(C), pages 989-998.
    15. Weiwei Cui & Lin Li & Zhiqiang Lu, 2019. "Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(2), pages 154-173, March.
    16. Robert Basmadjian & Amirhossein Shaafieyoun & Sahib Julka, 2021. "Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods," Energies, MDPI, vol. 14(21), pages 1-23, November.
    17. Tianyao Zhang & Weibin Huang & Shijun Chen & Yanmei Zhu & Fuxing Kang & Yerong Zhou & Guangwen Ma, 2023. "The Scheduling Research of a Wind-Solar-Hydro Hybrid System Based on a Sand-Table Deduction Model at Ultra-Short-Term Scales," Energies, MDPI, vol. 16(7), pages 1-18, April.
    18. Rasheed, Muhammad Babar & R-Moreno, María D., 2022. "Minimizing pricing policies based on user load profiles and residential demand responses in smart grids," Applied Energy, Elsevier, vol. 310(C).
    19. Gandhi, Oktoviano & Zhang, Wenjie & Kumar, Dhivya Sampath & Rodríguez-Gallegos, Carlos D. & Yagli, Gokhan Mert & Yang, Dazhi & Reindl, Thomas & Srinivasan, Dipti, 2024. "The value of solar forecasts and the cost of their errors: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    20. Obara, Shin'ya & Sato, Katsuaki & Utsugi, Yuta, 2018. "Study on the operation optimization of an isolated island microgrid with renewable energy layout planning," Energy, Elsevier, vol. 161(C), pages 1211-1225.
    21. Pondini, Maddalena & Colla, Valentina & Signorini, Annamaria, 2017. "Models of control valve and actuation system for dynamics analysis of steam turbines," Applied Energy, Elsevier, vol. 207(C), pages 208-217.
    22. Young-Sik Jang & Mun-Kyeom Kim, 2017. "A Dynamic Economic Dispatch Model for Uncertain Power Demands in an Interconnected Microgrid," Energies, MDPI, vol. 10(3), pages 1-16, March.

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