New classification-based global optimization approach for sustainable active power distribution networks.

Area of Science:

• Electrical Engineering
• Optimization Theory
• Power Systems Analysis

Background:

• Active power distribution networks face challenges with voltage drops and power losses due to radial topology and unidirectional power flow.
• Integration of distributed energy resources (DERs) and advanced optimization is crucial for grid flexibility and efficiency.
• Conventional metaheuristic methods often lack interpretability and structured optimization frameworks.

Purpose of the Study:

• To propose a novel Classification-based Global Optimization (CGO) approach for optimal placement and sizing of distributed generation (DG) and capacitor banks (CBs) in active distribution networks.
• To enhance voltage stability and reduce active power losses in radial distribution systems.
• To provide a computationally efficient and interpretable optimization framework.

Main Methods:

• The Classification-based Global Optimization (CGO) approach classifies distribution buses based on voltage sensitivity and power flow characteristics.
• A deterministic global optimization function is applied for optimal DG and CB placement and sizing.
• The methodology is validated on IEEE 33-bus and IEEE 69-bus test systems.

Main Results:

• Achieved a 94.75% reduction in active power losses for the IEEE 33-bus system and 98.061% for the IEEE 69-bus system with simultaneous DG and CB integration.
• Significantly improved voltage stability, with the voltage stability index (VSI) reaching 0.9740 (33-bus) and 0.9773 (69-bus).
• Demonstrated superior computational efficiency with average simulation times of 18.62 s (33-bus) and 21.45 s (69-bus).

Conclusions:

• The proposed CGO approach offers a scalable and interpretable solution for optimizing active distribution networks.
• The method effectively reduces energy losses and enhances voltage stability, supporting renewable energy integration.
• CGO provides a structured alternative to conventional metaheuristic methods, improving computational efficiency and network performance.