In an ever-increasingly competitive/deregulated environment, power utilities need efficient and effective tools to ensure that electrical energy of the desired quality can be provided at the lowest cost. These usually form highly constrained optimization problems. This work presents major modern optimization methods applied to power systems, including: simulated annealing, tabu search, genetic algorithms, neural networks, fuzzy programming, Lagrangian relaxation, interior point methods, ant colony search and hybrid techniques. Various applications and case studies are presented to demonstrate the potential and procedures of applying such techniques in solving complex power system optimization problems.
