Various optimization algorithms for efficient placement and sizing of photovoltaic distributed generations in...

Area of Science:

• Electrical Engineering
• Power Systems
• Renewable Energy Integration

Background:

• Renewable distributed generations (RDGs) like photovoltaic (PV) and wind turbines (WTs) are crucial for modern distribution systems.
• Optimal integration of RDGs improves voltage profiles and minimizes power losses, addressing key challenges in distributed generation (DG) allocation.
• Existing optimization techniques require comprehensive evaluation for effective RDG placement and sizing.

Purpose of the Study:

• To determine the optimal location and capacity of RDGs in radial distributed systems (RDS).
• To compare the performance of various optimization algorithms for RDG integration.
• To validate the effectiveness of selected algorithms on standard and real-world power systems.

Main Methods:

• Two-phase approach: Loss Sensitivity Factor (LSF) for node selection, followed by optimization algorithms for RDG placement and sizing.
• Algorithms evaluated: Salp Swarm Algorithm (SSA), Marine Predictor Algorithm (MPA), Grey Wolf Optimizer (GWO), Improved Grey Wolf Optimizer (IGWO), and Seagull Optimization Algorithm (SOA).
• Validation on IEEE 33, 69, and 118-bus RDS, and a 15-bus real-world system from Egypt.

Main Results:

• The Marine Predictor Algorithm (MPA) and Salp Swarm Algorithm (SSA) demonstrated superior performance.
• Significant improvements in voltage profiles were observed across all tested systems.
• Substantial reduction in power losses was achieved with the optimal integration of RDGs.

Conclusions:

• MPA and SSA are highly effective optimization techniques for RDG allocation in RDS.
• The proposed methodology successfully enhances power quality and reduces losses in distribution systems.
• The findings provide valuable insights for utilities seeking to integrate renewable energy sources efficiently.