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Photovoltaic self-consumption optimization for Home Microgrid: A Deep Reinforcement Learning approach

Author

Listed:
  • Mohamed Saâd EL HARRAB

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Michel Nakhla

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

Abstract

Increasing penetration of renewable energy sources (PV, Wind) due to environmental constraints, impose several technical challenges to power system operation. The fluctuating and intermittent nature of wind and solar energy requires constant supply-demand balance for electric grid stability purposes. Self-consumption is a regulatory framework intended to promote local consumption over export. Thus, self-consumption will raise the profit of PV electricity from grid-connected residential systems and lower the stress on the electricity distribution grid. This work presents a novel Deep Reinforcement Learning (DRL) Based Energy Management System (EMS) to control a Home Microgrid system powered by renewable energy sources (PV arrays) and equipped with an energy storage system. An optimal energy scheduling is carried out to maximize the benefits of available renewable resources through self-consumption. A DRL approach is used to make optimal decisions and generate the optimal management strategies.

Suggested Citation

  • Mohamed Saâd EL HARRAB & Michel Nakhla, 2022. "Photovoltaic self-consumption optimization for Home Microgrid: A Deep Reinforcement Learning approach," Post-Print hal-03746179, HAL.
    • Handle: RePEc:hal:journl:hal-03746179
    as

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