09-09-2016, 02:16 PM
A Modified Shuffled Frog Leaping
Algorithm for Long-Term Generation
Maintenance Scheduling
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Abstract
This paper discuss a modified Shuffled frog leaping algorithm to Longterm
Generation Maintenance Scheduling to Enhance the Reliability of the units.
Maintenance scheduling establishes the outage time scheduling of units in a particular
time horizon. In a monopolistic power system, maintenance scheduling is
being done upon the technical requirements of power plants and preserving the
grid reliability. While in power system, technical viewpoints and system reliability
are taken into consideration in maintenance scheduling with respect to the economical
viewpoint. In this paper present a modified Shuffled frog leaping algorithm
methodology for finding the optimum preventive maintenance scheduling of
generating units in power system. The objective function is to maintain the units as
earlier as possible. Varies constrains such as spinning reserve, duration of maintenance
and maintenance crew are being taken into account. In case study, test
system consist of 24 buses with 32 thermal generating units is used.
Introduction
The efficient operation of an electric power system requires the solution of several
inter related problems. One problem that has proven to be particularly unyielding
is that of determining when the thermal generating units should be taken off line
for preventive maintenance. This is typically a long planning problem for power
companies and it is recognized to be a significant part of the overall operations
management of an electric power utility. As a result, utilities are interested in
including a unit maintenance scheduling component as a part of an Energy
management system.
The unit maintenance scheduling has been tackled by many authors using a
variety of objective functions. They are maximizing the minimum reserve, leveling
the risk of generation shortage, minimizing production cost and system
unreliability. Most of the earlier work in maintenance scheduling uses optimization
techniques have been employed to approach the problem. More specifically,
these are the Dynamic Programming method (DP), the Mixed Integer Programming
method (MIP),the Lagrangian relaxation method (LR), the Branch and
Bound method (BB), the Fuzzy Theorem (FT), the Artificial Neural Network
(ANN), the Simulated Annealing method (SA) and so on. The major limitation of
these approaches is to consider each generating unit separately in selecting its
outage interval, large computational time and complexity in programming.
Power system scheduling is to minimize the total generation cost subject to
system demand and reserve requirement and individual unit constraints. It has
been an active research over the years because of its significant economic impact.
To solve this difficult MIP problem, many optimization based methods have been
developed [1]. Among them, LR and its extensions are among the most successful
ones [2–6]. Many new requirements such as transmission network and environment
constraints have also been incorporated in the problem formation. Fuzzy
optimization techniques have been developed to solve optimal power flow with
fuzzy constrains [7–9], and to schedule manufacturing system with possible
breakdowns [10] The Generic Algorithm method mimics the principles of natural
genetics and natural selection to constitute search and optimization procedures.
Simulated annealing mimics the cooling phenomenon of molten metal’s to constitute
a search procedure. The Generic Algorithm and Simulated Annealing
approaches have been reported to solve a range of optimization problems in
electrical power systems with encouraging results [11]. Fuzzy optimization techniques
have been developed to solve optimal power flow with fuzzy constrains
[12–14], and to schedule manufacturing system with possible breakdowns [15]
The major limitation of these approaches is to consider each generating unit
separately in selecting its outage interval, large computational time and complexity
in programming.
2 Problem Formulation
The objective is to find the generation maintenance scheduling, such that minimize
total operating cost over the operational planning period, subject to unit maintenance
and variety of system constraints.