Abstract:
Power Economic Dispatch (ED) is necessary and vital step in power system
operational planning. It is nonconvex constrained optimization problem defined as the
process of calculating the generation of the generating units for the minimum total
production cost in such a way that both equality and inequality constraints are satisfied. In
system operation studies generators are represented by input-output curves. These
characteristics curves are inherently nonlinear and non-smooth due to valve point effect,
multiple fuels and operational constraints such as prohibited operating zones. The
accurate economic dispatch depends mainly upon the accurate representation of these
curves and their handling in the optimization process.
Generally, economic dispatch is formulated as convex problem and has been
solved using mathematical programming techniques by approximating generator
input/output characteristic curves of monotonically increasing nature thus resulting in an
inaccurate dispatch. However, the nonconvex ED problem cannot be handled effectively
by such approaches. The Genetic algorithm is the potential solution methodology due to
its inherent ability to address the convex and nonconvex problems equally. This
dissertation presents the application of genetic algorithm (GA) for the solution of
economic dispatch problem independently as well as in hybrid form in conjunction with
the other techniques.
The problem is addressed first by developing a extensible and flexible
computational framework called “PED_Frame” as common environment which becomes
a platform for the computer implementation of different algorithms under consideration.
This framework has been used for implementation of economic dispatch algorithms for (i)
GA based models, (ii) Hybrid models.
Economic dispatch problem has been formulated in binary coded genetic
algorithm environment based on real power search and λ search methodologies. Two
biological mechanism “inversion” and “deletion-regeneration” has also been mapped as
an operator with crossover probability. Various GA based evolution models have been
constructed by adopting different initial population generation schemes, selectionvi
methods, and crossover operators. Convex ED studies have been conducted using
standard test systems and results have been compared with λ iteration approach.
GA based hybrid approach for convex ED dispatch is proposed. This approach
initially run GA based ED with λ-search and passes the control to conventional λ iteration
technique. This approach gives another systematic method for selection of initial value of
λ. The results of the proposed approach on standard test system show that costs of
generation by this approach is almost the same as the λ iteration alone, however, it takes
less number of iterations.
The performance of GA based economic dispatch problem has been evaluated
with reference to different evolution models on the basis of empirical data available by
actually running the program for the nonconvex ED due to valve point effect.
National utility system has been reviewed with reference to its operation
problems. Four test systems close to original network have been developed and tested by
load flow analysis using Newton’s Raphson algorithm. Finally 12-Machine 32 bus test
circuit, 15, 25 and 34 Machines systems for economic dispatch studies have been
developed. ED studies have been conducted using test circuit
The Genetic algorithm has the inherent ability to bring the solution to the global
minimum region of search space in a short time and then takes longer time to converge to
the solution. This research work proposed hybrid approaches to fine tune the near optimal
results produced by GA. In this context, three hybrid approaches have been used for the
solution of nonconvex economic dispatch problem with valve point effect. These include
(i) A Synergy of GA and ED using Newton’s Second Order Approach, (ii) Neuro-Genetic
Hybrid Approach, and (iii) Hybrid of GA and Sequential Quadratic Programming. These
models have been tested on standard test systems and the results obtained from all the
three hybrid approaches offer significant improvement in the generation cost showing the
promise of the proposed approaches.