Hybrid Renewable Energy Sources Offer Cost-Effective Energy Solutions

Strategic Insights -

Investigators at the Department of Electrical and Communication Engineering, Karpaga Vinayaga College of Engineering and Technology, in Tamil Nadu, India, have proposed a new hybrid technique for efficient incorporation and management of hybrid photovoltaic (PV) and wind turbine (WT) renewable energy sources (RESs) in microgrids (MGs). The suggested method, called the 'Osprey Optimization Algorithm-Augmented Physics-Informed Neural Network' (OOA-APINN), aims to increase the economic performance of hybrid PV-wind RES by lowering the cost of energy (COE). The OOA is employed to optimize the operational parameters of the PV-wind system and the integrated energy storage, ensuring efficient energy management (EM) and cost-effective operation.

Key Takeaways:

  • The integration of hybrid PV and WT RESs into MGs offers a considerable opportunity for clean and cost-effective energy solutions, but optimization of the trade-off between generation and usage of energy is a major challenge.
  • The OOA-APINN technique uses the Osprey optimization algorithm and augmented physics-informed neural network to optimize energy management and cost-effectiveness in MGs.
  • The suggested method achieves the lowest COE at $0.16/kWh, demonstrating a substantial enhancement in cost-effectiveness compared to other optimization methods.
  • The OOA-APINN technique is executed on the MATLAB platform and evaluated with various existing approaches, including coati optimization algorithm, fuzzy decision maker based multi-objective optimization algorithm, multi-objective particle swarm optimization, robust optimization, and gray wolf cuckoo search algorithm.
  • The proposed method ensures improved energy stability, reliability, and cost-effectiveness in MGs.
  • The study concluded that the OOA-APINN technique is more cost-effective than existing approaches, with a 30% reduction in COE compared to the next best method.