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Virtual Energy Storage in RES-Powered Smart Grids with Nonlinear Model Predictive Control

Author

Listed:
  • Dimitrios Trigkas

    (Centre for Research and Technology Hellas (CERTH), Chemical Process & Energy Resources Institute (CPERI), P.O. Box 60361, 57001 Thessaloniki, Greece)

  • Chrysovalantou Ziogou

    (Centre for Research and Technology Hellas (CERTH), Chemical Process & Energy Resources Institute (CPERI), P.O. Box 60361, 57001 Thessaloniki, Greece)

  • Spyros Voutetakis

    (Centre for Research and Technology Hellas (CERTH), Chemical Process & Energy Resources Institute (CPERI), P.O. Box 60361, 57001 Thessaloniki, Greece)

  • Simira Papadopoulou

    (Centre for Research and Technology Hellas (CERTH), Chemical Process & Energy Resources Institute (CPERI), P.O. Box 60361, 57001 Thessaloniki, Greece
    Department of Industrial Engineering and Management, International Hellenic University (IHU), P.O. Box 141, 57001 Thermi, Greece)

Abstract

The integration of a variety of heterogeneous energy sources and different energy storage systems has led to complex infrastructures and made apparent the urgent need for efficient energy control and management. This work presents a non-linear model predictive controller (NMPC) that aims to coordinate the operation of interconnected multi-node microgrids with energy storage capabilities. This control strategy creates a superstructure of a smart-grid consisting of distributed interconnected microgrids, and has the ability to distribute energy among a pool of energy storage means in an optimal way, formulating a virtual central energy storage platform. The goal of this work is the optimal exploitation of energy produced and stored in multi-node microgrids, and the reduction of auxiliary energy sources. A small-scale multi-node microgrid was used as a basis for the mathematical modelling and real data were used for the model validation. A number of operation scenarios under different weather conditions and load requests, demonstrates the ability of the NMPC to supervise the multi-node microgrid resulting to optimal energy management and reduction of the auxiliary power devices operation.

Suggested Citation

  • Dimitrios Trigkas & Chrysovalantou Ziogou & Spyros Voutetakis & Simira Papadopoulou, 2021. "Virtual Energy Storage in RES-Powered Smart Grids with Nonlinear Model Predictive Control," Energies, MDPI, vol. 14(4), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1082-:d:501694
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    References listed on IDEAS

    as
    1. 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.
    2. Bizon, Nicu & Radut, Marin & Oproescu, Mihai, 2015. "Energy control strategies for the Fuel Cell Hybrid Power Source under unknown load profile," Energy, Elsevier, vol. 86(C), pages 31-41.
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    Cited by:

    1. Luigi Fortuna & Arturo Buscarino, 2022. "Nonlinear Technologies in Advanced Power Systems: Analysis and Control," Energies, MDPI, vol. 15(14), pages 1-4, July.
    2. Panyawoot Boonluk & Sirote Khunkitti & Pradit Fuangfoo & Apirat Siritaratiwat, 2021. "Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand," Energies, MDPI, vol. 14(5), pages 1-20, March.

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