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Weather forecasts for microgrid energy management: Review, discussion and recommendations

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  • Agüera-Pérez, Agustín
  • Palomares-Salas, José Carlos
  • González de la Rosa, Juan José
  • Florencias-Oliveros, Olivia

Abstract

Meteorological conditions determine the renewable energy generation and, to a lesser extent, the load of microgrids. Weather forecasts are thus necessary to establish optimal plans according to the operational objectives and priorities of each microgid. Weather forecast errors are also responsible for deviations from these plans, thereby being an important source of uncertainty in the scheduling process. Despite this, weather information plays a secondary role in most of microgrid studies. This paper provides a general overview of the use of meteorological data in microgrids, focusing on the implementation of weather forecasts in microgrid energy management systems. Data sources, methodologies, uncertainty approaches and results from a selection of papers with complete information about the forecast context are analysed in detail. Additionally, similarities and differences regarding other energy forecast applications apart from microgrids are discussed. Finally, on the basis of the above, a list of recommendations for future implementations of weather forecasts in microgrid energy management systems is presented.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:265-278
    DOI: 10.1016/j.apenergy.2018.06.087
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    1. Patrao, Iván & Figueres, Emilio & Garcerá, Gabriel & González-Medina, Raúl, 2015. "Microgrid architectures for low voltage distributed generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 415-424.
    2. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    3. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    4. 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.
    5. Moghaddas Tafreshi, Seyed Masoud & Ranjbarzadeh, Hassan & Jafari, Mehdi & Khayyam, Hamid, 2016. "A probabilistic unit commitment model for optimal operation of plug-in electric vehicles in microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 934-947.
    6. Yan, Xingyu & Abbes, Dhaker & Francois, Bruno, 2017. "Uncertainty analysis for day ahead power reserve quantification in an urban microgrid including PV generators," Renewable Energy, Elsevier, vol. 106(C), pages 288-297.
    7. Wang, Chengshan & Liu, Yixin & Li, Xialin & Guo, Li & Qiao, Lei & Lu, Hai, 2016. "Energy management system for stand-alone diesel-wind-biomass microgrid with energy storage system," Energy, Elsevier, vol. 97(C), pages 90-104.
    8. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    9. 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.
    10. Li, Z. & Hurn, A.S. & Clements, A.E., 2017. "Forecasting quantiles of day-ahead electricity load," Energy Economics, Elsevier, vol. 67(C), pages 60-71.
    11. Manuela Sechilariu & Fabrice Locment & Baochao Wang, 2015. "Photovoltaic Electricity for Sustainable Building. Efficiency and Energy Cost Reduction for Isolated DC Microgrid," Energies, MDPI, vol. 8(8), pages 1-23, July.
    12. Baris Yuce & Monjur Mourshed & Yacine Rezgui, 2017. "A Smart Forecasting Approach to District Energy Management," Energies, MDPI, vol. 10(8), pages 1-22, July.
    13. Mohan, Vivek & Singh, Jai Govind & Ongsakul, Weerakorn, 2015. "An efficient two stage stochastic optimal energy and reserve management in a microgrid," Applied Energy, Elsevier, vol. 160(C), pages 28-38.
    14. Mazzola, Simone & Vergara, Claudio & Astolfi, Marco & Li, Vivian & Perez-Arriaga, Ignacio & Macchi, Ennio, 2017. "Assessing the value of forecast-based dispatch in the operation of off-grid rural microgrids," Renewable Energy, Elsevier, vol. 108(C), pages 116-125.
    15. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2017. "Sizing of a stand-alone microgrid considering electric power, cooling/heating, hydrogen loads and hydrogen storage degradation," Applied Energy, Elsevier, vol. 205(C), pages 1244-1259.
    16. Yan, Jie & Liu, Yongqian & Han, Shuang & Wang, Yimei & Feng, Shuanglei, 2015. "Reviews on uncertainty analysis of wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1322-1330.
    17. Basak, Prasenjit & Chowdhury, S. & Halder nee Dey, S. & Chowdhury, S.P., 2012. "A literature review on integration of distributed energy resources in the perspective of control, protection and stability of microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5545-5556.
    18. 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.
    19. Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.
    20. Gamarra, Carlos & Guerrero, Josep M., 2015. "Computational optimization techniques applied to microgrids planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 413-424.
    21. Ren, Ye & Suganthan, P.N. & Srikanth, N., 2015. "Ensemble methods for wind and solar power forecasting—A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 82-91.
    22. Gopalan, Sachit A. & Sreeram, Victor & Iu, Herbert H.C., 2014. "A review of coordination strategies and protection schemes for microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 222-228.
    23. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    24. Narayan, Apurva & Ponnambalam, Kumaraswamy, 2017. "Risk-averse stochastic programming approach for microgrid planning under uncertainty," Renewable Energy, Elsevier, vol. 101(C), pages 399-408.
    25. Mazzola, Simone & Astolfi, Marco & Macchi, Ennio, 2015. "A detailed model for the optimal management of a multigood microgrid," Applied Energy, Elsevier, vol. 154(C), pages 862-873.
    26. Eseye, Abinet Tesfaye & Zhang, Jianhua & Zheng, Dehua, 2018. "Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information," Renewable Energy, Elsevier, vol. 118(C), pages 357-367.
    27. Zhongwen Li & Chuanzhi Zang & Peng Zeng & Haibin Yu, 2016. "Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty," Energies, MDPI, vol. 9(7), pages 1-16, June.
    28. Mohamed El-Hendawi & Hossam A. Gabbar & Gaber El-Saady & El-Nobi A. Ibrahim, 2018. "Control and EMS of a Grid-Connected Microgrid with Economical Analysis," Energies, MDPI, vol. 11(1), pages 1-20, January.
    29. 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.
    30. Bruni, G. & Cordiner, S. & Mulone, V. & Sinisi, V. & Spagnolo, F., 2016. "Energy management in a domestic microgrid by means of model predictive controllers," Energy, Elsevier, vol. 108(C), pages 119-131.
    31. 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.
    32. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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