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Application of PSO for Optimization of Power Systems under Uncertainty (Paperback)
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Application of PSO for Optimization of Power Systems under Uncertainty (Paperback)
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Doctoral Thesis / Dissertation from the year 2009 in the subject
Electrotechnology, grade: 1.0, University of Duisburg-Essen
(Institute of Electrical Power Systems), course: Electrical
Engineering, language: English, comment: The primary objective of
this dissertation was to develop a black box optimization tool. The
algorithm should be able to solve complex nonlinear, multimodal,
discontinuous and mixed-integer power system optimization problems
without any model reduction. A duty cycle based unit commitment
modelling proposed in this thesis results in 80% reduction in the
problem dimension. The final focus of this thesis was to
investigate the impact of unpredictable nature of demand and
renewable generation on the power system operation., abstract: The
primary objective of this dissertation is to develop a black box
optimization tool. The algorithm should be able to solve complex
nonlinear, multimodal, discontinuous and mixed-integer power system
optimization problems without any model reduction. Although there
are many computational intelligence (CI) based algorithms which can
handle these problems, they require intense human intervention in
the form of parameter tuning, selection of a suitable algorithm for
a given problem etc. The idea here is to develop an algorithm that
works relatively well on a variety of problems with minimum human
effort. The most significant optimization task in the power system
operation is the scheduling of various generation resources (Unit
Commitment, UC). The current practice used in UC modelling is the
binary approach. This modelling results in a high dimension
problem. This in turn leads to increased computational effort and
decreased efficiency of the algorithm. A duty cycle based modelling
proposed in this thesis results in 80 percent reduction in the
problem dimension. The stern uptime and downtime requirements are
also included in the modelling. Therefore, the search process
mostly starts in a feasible solution space. Fro
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