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A real-time Energy Management System for the integration of economical aspects and system operator requirements: Definition and validation

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  • Bendato, Ilaria
  • Bonfiglio, Andrea
  • Brignone, Massimo
  • Delfino, Federico
  • Pampararo, Fabio
  • Procopio, Renato

Abstract

The present paper proposes a real-time EMS able to manage uncertainties in the forecasting data as well as economical aspects and system operator requirements. As far as the first aspect is concerned, the procedure is cyclically repeated at each time-step after that loads and renewable productions have been adjusted by means of real-time measurements. The economic aspects are performed by solving an optimization problem that minimizes the overall production cost and, therefore, it fixes the active power set point for all the programmable units. The Distribution System Operator (DSO) requirements in terms of active/reactive power management and voltage control, as requested by the majority of grid codes/standards, are satisfied (if possible) by tuning the reactive power of the programmable units. The proposed EMS has been applied and validated on University of Genoa Smart Polygeneration Microgrid (SPM) and it shows its efficacy in satisfying both the economic and power quality objectives.

Suggested Citation

  • Bendato, Ilaria & Bonfiglio, Andrea & Brignone, Massimo & Delfino, Federico & Pampararo, Fabio & Procopio, Renato, 2017. "A real-time Energy Management System for the integration of economical aspects and system operator requirements: Definition and validation," Renewable Energy, Elsevier, vol. 102(PB), pages 406-416.
    • Handle: RePEc:eee:renene:v:102:y:2017:i:pb:p:406-416
    DOI: 10.1016/j.renene.2016.10.061
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    References listed on IDEAS

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    1. Bracco, Stefano & Delfino, Federico & Pampararo, Fabio & Robba, Michela & Rossi, Mansueto, 2014. "A mathematical model for the optimal operation of the University of Genoa Smart Polygeneration Microgrid: Evaluation of technical, economic and environmental performance indicators," Energy, Elsevier, vol. 64(C), pages 912-922.
    2. Basu, Ashoke Kumar & Chowdhury, S.P. & Chowdhury, S. & Paul, S., 2011. "Microgrids: Energy management by strategic deployment of DERs—A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4348-4356.
    3. Samadi, Afshin & Shayesteh, Ebrahim & Eriksson, Robert & Rawn, Barry & Söder, Lennart, 2014. "Multi-objective coordinated droop-based voltage regulation in distribution grids with PV systems," Renewable Energy, Elsevier, vol. 71(C), pages 315-323.
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    2. Rosini, A. & Labella, A. & Bonfiglio, A. & Procopio, R. & Guerrero, Josep M., 2021. "A review of reactive power sharing control techniques for islanded microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    3. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2019. "Energy management in hybrid microgrid with considering multiple power market and real time demand response," Energy, Elsevier, vol. 174(C), pages 10-23.
    4. Huan Wang & Guoqiang Zeng & Yuxing Dai & Daqiang Bi & Jingliao Sun & Xiaoqing Xie, 2017. "Design of a Fractional Order Frequency PID Controller for an Islanded Microgrid: A Multi-Objective Extremal Optimization Method," Energies, MDPI, vol. 10(10), pages 1-18, October.
    5. Niloofar Ghanbari & Hossein Mokhtari & Subhashish Bhattacharya, 2018. "Optimizing Operation Indices Considering Different Types of Distributed Generation in Microgrid Applications," Energies, MDPI, vol. 11(4), pages 1-12, April.
    6. Thomas, Dimitrios & D’Hoop, Gaspard & Deblecker, Olivier & Genikomsakis, Konstantinos N. & Ioakimidis, Christos S., 2020. "An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes," Applied Energy, Elsevier, vol. 260(C).
    7. La Fata, Alice & Brignone, Massimo & Procopio, Renato & Bracco, Stefano & Delfino, Federico & Barilli, Riccardo & Ravasi, Martina & Zanellini, Fabio, 2022. "An efficient Energy Management System for long term planning and real time scheduling of flexible polygeneration systems," Renewable Energy, Elsevier, vol. 200(C), pages 1180-1201.
    8. Yim, Jaeyun & You, Sesun & Blaabjerg, Frede & Lee, Youngwoo & Gui, Yonghao & Kim, Wonhee, 2024. "Energy management systems for forecasted demand error compensation using hybrid energy storage system in nanogrid," Renewable Energy, Elsevier, vol. 221(C).
    9. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid & Josep M. Guerrero, 2018. "A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems," Energies, MDPI, vol. 11(7), pages 1-19, July.
    10. Cassettari, Lucia & Bendato, Ilaria & Mosca, Marco & Mosca, Roberto, 2017. "Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing," Applied Energy, Elsevier, vol. 190(C), pages 841-851.
    11. Bendato, Ilaria & Bonfiglio, Andrea & Brignone, Massimo & Delfino, Federico & Pampararo, Fabio & Procopio, Renato & Rossi, Mansueto, 2018. "Design criteria for the optimal sizing of integrated photovoltaic-storage systems," Energy, Elsevier, vol. 149(C), pages 505-515.

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