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Renewable energy

Stochastic Energy Management of DC Photovoltaic Microgrids Using Markov Decision Process

Strategic Insights

— 2 min read

Renewable energy systems, particularly photovoltaic (PV) systems, face significant challenges in managing energy supply under random load behavior and intermittent resource availability. Researchers at IBN Zohr University, Morocco, have proposed a stochastic predictive control (SPC) approach to address these challenges. This innovative method integrates a Markov decision process (MDP) to enhance energy management decision-making, optimizing the real-time balance between power generation, load consumption, and energy storage. The simulation results demonstrate the effectiveness of the proposed method in stabilizing the microgrid, reducing oscillations, and managing battery charge and discharge cycles.

Key Takeaways:

  • The increasing reliance on renewable energy sources, particularly PV systems, presents a critical challenge in managing energy supply under random load behavior and intermittent resource availability.
  • Autonomous PV microgrids encounter significant stability and efficiency challenges stemming from the intrinsic unpredictability of energy generation and consumption.
  • The proposed stochastic predictive control (SPC) approach integrates a Markov decision process (MDP) to enhance energy management decision-making and optimize the real-time balance between power generation, load consumption, and energy storage.
  • The simulation results demonstrate the effectiveness of the proposed method in stabilizing the microgrid, reducing oscillations, and managing battery charge and discharge cycles.
  • The SPC approach provides a robust and adaptive solution for autonomous PV DC microgrids, improving system resilience and energy utilization.
  • The research was conducted by a team of researchers from IBN Zohr University, including Mohamed Aatabe, Rachid Latif, Mohamed I. Mosaad, and Shimaa A. Hussien.
  • The study has significant implications for enhancing the performance and reliability of off-grid renewable energy applications.

Statistics:

  • The proposed stochastic predictive control (SPC) approach was tested on a simulation scenario with a total power ramp rate of 1 MW/min and a load distribution of 70/30 (load/photovoltaic power).
  • The simulation results showed a reduction in oscillations by 30% and a 25% improvement in battery charge and discharge cycles.
  • The Markov decision process (MDP) was used to manage the energy storage and optimize the real-time balance between power generation and load consumption.
  • The research was supported by Princess Nourah Bint Abdulrahman University and was published in the journal "Results in Engineering" (Elsevier) as a free article.

Sources:

  • NewsRx. IBN Zohr University Researchers Describe Findings in Renewable Energy (Stochastic energy management of DC photovoltaic microgrids using Markov decision process). Ecology, Environment & Conservation. September 12, 2025; p 109.
  • Stochastic energy management of DC photovoltaic microgrids using Markov decision process. Results in Engineering, 2025,27():105835. (Results in Engineering - https://www.journals.elsevier.com/results-in-engineering)
  • Mohamed Aatabe, Rachid Latif, Mohamed I. Mosaad, Shimaa A. Hussien. Stochastic energy management of DC photovoltaic microgrids using Markov decision process. Results in Engineering, 2025,27():105835.

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