Exploring the effectiveness of adaptive randomized sine cosine algorithm in wind integrated scenario based power...

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Power Systems

Background:

• Modern power systems face challenges with increasing demand and limited infrastructure.
• Efficient transmission and responsiveness are crucial for grid stability and economic operation.

Purpose of the Study:

• To introduce the Adaptive Randomized Sine Cosine Algorithm (ARSCA) for optimal placement and setting of FACTS devices.
• To enhance power system efficiency, reduce operational costs, and improve voltage stability.

Main Methods:

• Implementation of the ARSCA for optimizing Thyristor-Controlled Series Capacitors (TCSC), Thyristor-Controlled Phase Shifters (TCPS), and Static VAR Compensators (SVC).
• Testing the ARSCA on the IEEE 30-bus system under dynamic load scenarios.
• Comparison with benchmark algorithms like Sine Cosine Algorithm (SCA), Improved Grey Wolf Optimization (IGWO), and Whale Optimization Algorithm (WOA).

Main Results:

• ARSCA minimized active power losses to 1.7655 MW.
• Achieved a minimum generation cost of 807.17 $/h and reduced gross system cost to 883.53 $/h.
• Demonstrated faster convergence, consistent solution accuracy, improved voltage stability, and better reactive power management compared to other algorithms.

Conclusions:

• ARSCA provides an efficient, scalable, cost-effective, and stable solution for power system optimization.
• The algorithm's robust exploration and exploitation mechanisms are key to its performance.
• Future research should explore ARSCA's scalability in larger networks and adaptability under uncertainty.