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Abstract
This thesis addresses the problem of thermal generation
scheduling. This problem is considered one of the most
important power system planning problems. Thermal units are
specially addressed since they form the major part of the
generation plants in the Egyptian Electrical Power System. In
the scheduling problem, a schedule of turning ON and OFF the
generating units is prepared and the share of every online unit is
determined over a certain scheduling horizon. The objective of
the scheduling problem is to minimize the overall operating cost
without violating any of the constraints.
The scheduling problem consists of two main parts; the
economic dispatch problem (EDP) 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 the recent years, Artificial Intelligence (AI) techniques
proved themselves as very efficient tools in tackling various
electrical power system applications. Artificial Neural Networks
(ANN) is one of the most important AI techniques in which the
human brain is mimicked.
In literature, ANN was successfully applied to many
electrical power system applications. Fault diagnosis, power
system control, machine control, and load forecasting are among
the famous examples of these applications. All these
applications can be classified as pattern recognition and
classification problems. As an optimization tool, special type of
ANN called Hopfield Neural Network (HNN) was designed to
minimize an energy function.