Integrating multiple slack bus operations and metaheuristic techniques for power flow optimization

Area of Science:

• Electrical Engineering
• Power Systems Analysis

Background:

• Modern energy grids are increasingly complex, necessitating realistic power flow studies.
• Optimal Power Flow (OPF) analysis is crucial for power system stability and efficiency.

Purpose of the Study:

• To implement and evaluate a Multiple Slack Bus Operation (MSO) framework for enhanced OPF realism and efficiency.
• To comparatively assess metaheuristic algorithms (PSO, CSA, GWO) within the MSO framework for power system optimization.

Main Methods:

• Implementation of the Multiple Slack Bus Operation (MSO) framework.
• Comparative evaluation of Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Grey Wolf Optimization (GWO).
• Assessment on Reduced Nordic 44 and IEEE benchmark systems under various load conditions.

Main Results:

• The MSO framework alone reduced system losses by 5%.
• Integration of the Grey Wolf Optimization (GWO) algorithm with MSO achieved the most significant reduction in total system losses, an additional 8.3% decrease.
• GWO demonstrated superior performance in minimizing system loss, optimizing line loading, voltage angle, and generation distribution.

Conclusions:

• The Grey Wolf Optimization (GWO) algorithm, within the Multiple Slack Bus Operation (MSO) framework, is highly effective for optimizing power flow and reducing system losses.
• The MSO framework combined with metaheuristic algorithms offers significant potential for improving power network efficiency.
• This approach provides a foundation for advanced power flow optimization strategies in future power systems.