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Abstract
The optimal power flow (OPF) problem plays a very important role in
power system planning and operational control. The solution of the OPF
problem aims to optimize a selected objective function via optimal adjustments
of the power system control variables while satisfying various equality and
inequality constraints. Generally, the OPF problem is a large-scale, highly
constrained, non-linear and non-convex multi-objective optimization problem.
In this thesis, many case studies are considered. Starting from, the
simplest form of power dispatch problem, where the fuel cost of generators is
represented by a quadratic function of real power generation and the system
constrains include only reliability constraint and generation limits constraint.
Passing through,
· The non-conventional fuel cost functions, where more accurate models
are used to provide better results in the solution of the economic power
dispatch problem,
· The environmental-economic power dispatch, where the environmental
issue is considered,
· The security constrained power dispatch, where the security constraints
are incorporated into the power dispatch problem,
· Active OPF problem, where several objective functions are considered
such as minimizing real power losses and improving voltage profile, and
finally
· Reactive OPF problem.
Recently, with the progress in evolutionary optimization techniques, it is
possible to deal with the real life multi-objective optimization problems. This
has been reflected on the OPF with an aim to formulate it as a true multiobjective
optimization problem.
In this thesis, a new evolutionary computation technique, called
differential evolution algorithm (DE), has been proposed and introduced. The
algorithm is inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multi-modal surfaces. The DE
algorithm is used as an optimization technique to find the optimal values of the
objective functions in the different case studies. Also, a multi-objective
differential evolution based approach is proposed, developed and successfully
implemented to solve the multi-objective environmental-economic power
dispatch, multi-objective OPF problem, and multi-objective reactive power
flow problem. A hierarchical clustering algorithm is also imposed to provide
the decision maker with a representative and manageable Pareto-optimal set.
Moreover, a technique based on fuzzy set theory is employed to extract the best
compromise non-dominated solution.
The different case studies are tested using three test systems, which are
wale and hale 6-bus test system, IEEE 30-bus test system, and real power
system for west delta region (52-bus test system). A comparison between
results of single and multi-objective optimization problem is discussed. Also,
the results are compared with the available literatures. The results obtained
show the effectiveness of the proposed DE algorithm in solving single and
multi-objective optimization problems.