A novel hybrid multi operator evolutionary algorithm for dynamic distributed generation optimization and optimal...

Area of Science:

• Electrical Engineering
• Power Systems Engineering
• Optimization Techniques

Background:

• Distributed generation (DG) integration and network reconfiguration are crucial for modern distribution networks.
• Prior research has not thoroughly investigated the combined impact of these strategies.
• Technical objectives like power loss reduction, voltage deviation minimization, and voltage stability improvement are key for efficient network planning and operation.

Purpose of the Study:

• To investigate the impact of changing solar irradiation and load demand on distribution networks.
• To address the complex mixed-integer non-linear problem of large-scale DG integration and network reconfiguration.
• To develop an innovative, hybrid evolutionary algorithm for optimizing these combined challenges.

Main Methods:

• A novel hybrid evolutionary algorithm combining genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO).
• Incorporation of representative constraint handling techniques to balance exploration and exploitation.
• Testing on IEEE 33 and 69-bus systems under various scenarios, including changing solar irradiation and load demands.

Main Results:

• The proposed hybrid multi-operator EA achieved near-global optimal solutions for large-scale problems.
• Demonstrated a power loss reduction exceeding 86%.
• Achieved voltage deviation improvement of over 90% and load capacity increase of over 700% by integrating DGs with a focus on the voltage stability index.

Conclusions:

• The hybrid EA effectively tackles complex, large-scale distribution network optimization problems.
• Integrating DGs with network reconfiguration, prioritizing voltage stability, significantly enhances network performance.
• The developed method offers a robust approach for improving the planning and operation of power distribution systems.