Pappala, Venkata Swaroop:

Application of PSO for optimization of power systems under uncertainty

(2009), VII, 149 Seiten
Dissertation / Fach: Elektrotechnik
Fakultät für Ingenieurwissenschaften » Elektrotechnik und Informationstechnik » Elektrische Anlagen und Netze
Erlich, István (Doktorvater, Betreuerin)
Venayagamoorthy, Ganesh Kumar (GutachterIn)
Duisburg, Essen, Univ., Diss., 2009
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. An adaptive particle swarm
optimization algorithm (PSO) is presented in this thesis. The algorithm has special
features like adaptive swarm size, parameter free update strategies, progressive
neighbourhood topologies, self learning parameter free penalty approach etc.
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. From the investigations
on a benchmark problem, it was found that the new modelling results in high
quality solutions along with improved convergence.
The final focus of this thesis is to investigate the impact of unpredictable nature
of demand and renewable generation on the power system operation. These quantities
should be treated as a stochastic processes evolving over time. A new PSO
based uncertainty modelling technique is used to abolish the restrictions imposed
by the conventional modelling algorithms. The stochastic models are able to incorporate
the information regarding the uncertainties and generate day ahead UC
schedule that are optimal to not just the forecasted scenario for the demand and
renewable generation in feed but also to all possible set of scenarios. These models
will assist the operator to plan the operation of the power system considering
the stochastic nature of the uncertainties. The power system can therefore optimally
handle huge penetration of renewable generation to provide economic operation
maintaining the same reliability as it was before the introduction of uncertainty.