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Renewable source penetration and microgrids: Effects of MILP – Based control strategies

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  • Bartolucci, Lorenzo
  • Cordiner, Stefano
  • Mulone, Vincenzo
  • Rocco, Vittorio
  • Rossi, Joao Luis

Abstract

The implementation of the Distributed Generation (DG) concept requires to face with technical issues regarding integration and control of energy fluxes at the grid nodes. A predictive control strategy integrating renewable and non-renewable sources, as well as energy storage systems, is a potential solution to face with the aforementioned problems. The behavior of a smart building consisting of 30 apartments has been considered in this work. The Hybrid Renewable System (HRS) has been controlled by a Model Predictive Control (MPC) strategy. The HRS includes both sub-systems for the conversion of renewable energy sources and non-renewable ones, connected to the main grid. Several scenarios have been tested under different weather conditions and renewable sources penetration quotas. Results obtained with the MPC control strategy have been compared with a Rule Based Control (RBC) one, turning out that the use of MPC improves the integration of the residential microgrid with the renewable sources available at the grid thanks to the predictive system smoothing out the energy demand profile and absorbing the peak of production from the photovoltaic and wind farms, even in cases of high RES penetration.

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  • Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Rocco, Vittorio & Rossi, Joao Luis, 2018. "Renewable source penetration and microgrids: Effects of MILP – Based control strategies," Energy, Elsevier, vol. 152(C), pages 416-426.
    • Handle: RePEc:eee:energy:v:152:y:2018:i:c:p:416-426
    DOI: 10.1016/j.energy.2018.03.145
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    Cited by:

    1. Tabar, Vahid Sohrabi & Abbasi, Vahid, 2019. "Energy management in microgrid with considering high penetration of renewable resources and surplus power generation problem," Energy, Elsevier, vol. 189(C).
    2. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Rocco, Vittorio & Rossi, Joao Luis, 2018. "Hybrid renewable energy systems for renewable integration in microgrids: Influence of sizing on performance," Energy, Elsevier, vol. 152(C), pages 744-758.
    3. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    4. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Santarelli, Marina, 2019. "Hybrid renewable energy systems: Influence of short term forecasting on model predictive control performance," Energy, Elsevier, vol. 172(C), pages 997-1004.

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