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Multiobjective optimization framework for renewable energy integration in smart grids with enhanced stability and resilience using reinforcement learning and distributed control systems

Author

Listed:
  • Mohammad Rashed M Altimania
  • Khaled Saleem S Alatawi
  • Sanjarbek Madaminov
  • Alisher Abduvokhidov
  • Abdusalom Umarov
  • Bharosh Kumar Yadav

Abstract

This research develops a multiobjective framework that couples a modified Proximal Policy Optimization agent—augmented with hierarchical experience replay and a parameterized action space—with a trust-aware consensus mechanism in a distributed control layer. Together with a temporal convolutional network–transformer forecaster and an adaptive weighted-sum multiobjective optimizer, this hybrid computational approach was validated on a university microgrid with 45% renewable penetration. Results demonstrated a 37.8% reduction in frequency fluctuations while accommodating 23.6% higher renewable energy penetration, with 42.3% improved resilience during extreme weather events. This framework establishes an effective pathway for accelerating renewable energy adoption.

Suggested Citation

  • Mohammad Rashed M Altimania & Khaled Saleem S Alatawi & Sanjarbek Madaminov & Alisher Abduvokhidov & Abdusalom Umarov & Bharosh Kumar Yadav, 2025. "Multiobjective optimization framework for renewable energy integration in smart grids with enhanced stability and resilience using reinforcement learning and distributed control systems," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1749-1776.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1749-1776.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf114
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