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AI-Driven Optimization of Hybrid Renewable Energy Systems for Smart and Sustainable Cities

Students & Supervisors

Student Authors
Khadiza Tul Nur Aiman
Bachelor of Science in Computer Science & Engineering, FST
Mahmudur Rahman Mitul
Bachelor of Science in Computer Science & Engineering, FST
Supervisors
Md. Mortuza Ahmmed
Associate Professor, Faculty, FST

Abstract

Global emphasis on sustainability and increasing urban energy needs have consolidated the need for high-performance renewable energy systems. Hybrid Renewable Energy Systems (HRES) consisting of solar photovoltaic (PV), wind, and energy storage are a perfect choice for supply of intermittent urban loads with negligible environmental impact. The randomness of renewable sources, however, leads to operation problems that need to be resolved through efficient smart optimization. This study visualizes an ANN-GA powered AI optimization system for energy cost, reliability, and efficiency-based intelligent city HRES. MATLAB/Simulink simulation shows 18% reduced operating cost, 22% improved energy reliability, and 21% CO₂ saving compared to conventional rule-based control. Various plots demonstrate system architecture, dispatch, battery SOC, and optimization flow. This study suggests AI as the future of self-sustaining, autonomous urban energy grids.

Keywords

Hybrid Renewable Energy Systems Optimization Smart Cities Sustainability Artificial Intelligence

Publication Details

  • Type of Publication:
  • Conference Name: 8Th International Conference on Mechanical Engineering and Renewable Energy
  • Date of Conference: 10/12/2025 - 10/12/2025
  • Venue: CUET (Chattogram University of Engineering and Technology) "
  • Organizer: Department of Mechanical Engineering, CUET
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