OPTIMIZATION TECHNIQUES IN ELECTRICAL POWER SYSTEM.

 

Techniques for power system optimisation are crucial for improving electrical grid stability, dependability, and efficiency. The complexity of power systems has been addressed by a variety of approaches brought about by recent developments, such as machine learning, soft computing, and traditional optimisation techniques. The main optimisation techniques used in power systems are described in the sections that follow:

Optimization Techniques in Electrical Power System

Machine Learning Techniques

·     End-to-end optimisation: This method streamlines procedures by combining machine learning and conventional optimisation.

·   ML-Enhanced Optimisation: Makes use of past data to enhance operational effectiveness and decision-making [1].

·      Joint-Driven Optimisation: Provides reliable solutions by combining machine learning and physical system properties [1].

Soft Computing Techniques

·       Evolutionary Algorithms: To optimise system parameters, methods such as Genetic Algorithms(GA) and Particle Swarm Optimisation (PSO) efficiently traverse vast solution spaces [2].

·     Neural networks and fuzzy logic: these techniques improve flexibility and judgement in changing circumstances [2].

Generic Algorithm

Classical and Hybrid Techniques

·   Linear and Nonlinear Programming: Strategies like gradient-based optimisation and the Simplex approach are essential for resolving a range of power system issues [3].

·    FACTS Device Optimisation: Uses classical, metaheuristic, and hybrid approaches to get the best configuration while including mixed integer and nonlinear constraints [4].

Even though power system performance is much enhanced by these optimisation techniques, uncertainties and the growing complexity of contemporary power networks still present difficulties. Subsequent studies could concentrate on creating more resilient techniques that can adjust to these changing difficulties.

[1]      Zhan, Shi., Yan, Dong., Tian, Jie., Yudong, Lu., Xinying, Wang. (2024). “Research on Power System Optimization Algorithm Frameworks Empowered by Machine Learning.”, doi: 10.1109/yac63405.2024.10598728

[2]       Dr., Aayushi, Arya., Puneet, Garg., Sameera, Vellanki., Dr., M., Latha., Dr., Mohammad, Ahmar, Khan. (2024). “Optimisation Methods Based on Soft Computing for Improving Power System Stability.”, Journal of Electrical Systems, doi: 10.52783/jes.2837

[3] Renchang, Dai., Guangyi, Liu. (2023). ”Optimization Problems.” doi: 10.1002/9781119903895.ch6

[4]  I., Marouani., Tawfik, Guesmi., Badr, M., Alshammari., Khalid, Alqunun., Ahmed, Alshammari., S., Albadran., Hsan, Hadj, Abdallah., Salem, Rahmani. (2023). “Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods.” Sustainability, doi: 10.3390/su15129348

 

Prepared by:

Siti Nor Baizura Zawawi

Rohaizah Mohd Ghazali