Thermal generation scheduling is considered as one of the most
important power system operational planning problems. The
scheduling problem consists of two main parts; the economic
dispatch problem and the unit commitment problem (UCP). The former
belongs to nonlinear programming problems while the later is a
combinatorial optimization problem. Dynamic economic dispatch
problem (DEDP) is a special type of the generation scheduling
problem in which all the units are considered online during the
entire scheduling horizon. In this book, Hopfield Neural Network
(HNN) is applied to solve the generation scheduling and the DEDP. A
detailed study of the effect of the HNN parameters on the solution
quality of EDP including transmission losses is made. The most
appropriate values of the parameters are tuned. A new algorithm is
proposed to eliminate the gap between the solution obtained by HNN
and the actual solution. Three software packages are developed
under the MATLAB(r) platform to solve EDP, ramp rate constrained
DEDP and UCP. To demonstrate the effectiveness of the proposed
algorithms and enhancements, several test systems reported in
literature, with different complexities, are solved.
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