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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

Mohammad R. AltimaniaDepartment of Electrical Engineering, University of Tabuk , Tabuk ,Khaled Saleem S. AlatawiDepartment of Electrical Engineering, University of Tabuk , Tabuk ,Sanjarbek MadaminovUrgench State University , Kh. Alimdjan Str. 14, Urgench 220100 ,Alisher AbduvokhidovAndijan State University , Universitet Str. 129, Andijan 170100 ,A. V. UmarovBharosh Kumar YadavDepartment of Mechanical Engineering, Tribhuvan University (TU), Institute of Engineering (IOE) , Purwanchal Campus, Dharan-08 ,

International Journal of Low-Carbon Technologiesjournal2025en

ABI

Annotatsiya

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.

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Mavzular

Smart Grid Energy Management Microgrid Control and Optimization Smart Grid Security and Resilience

Identifikatorlar

DOI: 10.1093/ijlct/ctaf114

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