Application of Genetic Algorithm for Solving Optimum Power Flow Problems

Literature Review

The optimal unit commitment allocation of the electric power system has caught the attention of researchers in recent years. The EDP is commonly formulated as an optimization problem, which aim to minimize the total generation cost of the power system while satisfying specified constraints. For this purpose, generators are commonly modeled using smooth quadratic functions, which relate power output to production cost.

Many methods were proposed to solve the EDP, which resulted in optimal power system units generation scheduling. Chen and Chen (2001) used the conventional LaGrange relaxation approach, where the first order gradient method and multi-pass dynamic programming were combined together. Moreover, the authors stated that the proposed method has no restrictions on generator cost function, and it performs a direct search of the feasible solution at each step. Various mathematical programming methods such as dynamic programming, linear programming, homogenous linear programming, and nonlinear programming techniques (Chowdhary & Rahman, 1990; Lee & Breipohl, 1993), have been applied to ED problem. Most of these techniques are not capable of solving efficiently optimization problems with a non-convex, non-continuous, and highly nonlinear solution space. Recently, as an alternative to the conventional mathematical approaches, the meta-heuristic optimization techniques such as genetic algorithms, Tabu search, simulated annealing, and particle swarm optimization are considered as realistic and powerful solution schemes to obtain the global or near global optimums in power system optimization problems (Selvakumar & Thanushkodi, 2007).