Optimal Power Dispatch for Large Scale Power System Using Stochastic Search Algorithms

Dynamic strategy based fast decomposed GA coordinated with FACTS devices to enhance the optimal power flow

“…Instead of point-to-point search, GA searches from population to population. The advantages of GA over traditional techniques are [19]:…”

“…This continuous GA also has the advantage of requiring less storage than the binary GA because a single floating-point number represents the variable instead of bits N integers. The continuous GA is inherently faster than the binary GA, because the chromosomes do not have to be decoded prior to the evaluation of the cost function [19]. Fig.…”

“…The test examples have been run on a 2.6-Ghz Pentium-IV PC. The generators data and cost coefficients are taken from [19].…”

“…In [19], they presented a standard GA, in [3] the authors presented an enhanced GA, and then in [20], they proposed an Improved evolutionary programming (IEP).…”

“…The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. In [10] the authors proposed a simple combined genetic algorithm and evolutionary programming applied to the OPF problem in large-scale power systems, to accelerate the processes of the optimization, the controllable variables are decomposed to active and passive constraints. The active constraints are taken to minimize the fuel cost function; the passive constraints are taken and integrated to an efficient power flow problem to make corrections to the active power of the slack bus.…”