Abstract

Maintenance of power plants is aimed at extending the life and reducing the risk of
sudden breakdown of power generating units. Traditionally, power generating units
have been scheduled for maintenance in periods to ensure that the demand of the
system is fully met and the reliability of the system is maximized. However, in a
deregulated power industry, the pressure of maintaining generating units is also
driven by the potential revenue received by participating in the electricity market.
Ideally, hydropower generating units are required to operate during periods when
electricity prices are high and to be able to be taken offline for maintenance when the
price is low. Therefore, determination of the optimum time periods for maintenance
of generating units in a power system has become an important task from both a
system reliability and an economic point of view. Due to the extremely large number
of potential maintenance schedules, a systematic approach is required to ensure that
optimal or near-optimal maintenance schedules are obtained within an acceptable
timeframe.

Metaheustics are high-level algorithmic frameworks that aim to solve combinatorial
optimisation problems with a large search space in a reasonable computational run
time. Inspired by the foraging behavior of ant colonies, Ant Colony Optimisation
(ACO) is a relatively new metaheuristic for combinatorial optimisation. The
application of ACO to a number of different applications has provided encouraging
results when applied to scheduling, including the job-shop, flow-shop, machine
tardiness and resource-constrained project scheduling problems.
In this thesis, a formulation is developed that enables ACO to be applied to the
generalized power plant maintenance scheduling optimisation (PPMSO) problem.
The formulation caters for all constraints generally encountered as part of real-world
PPMSO problems, including system demands and reliability levels, precedence rules
between maintenance tasks, public holidays and minimum outage durations in the
case of shortening of maintenance tasks. As part of the formulation, a new heuristic
and a new local search strategy have been developed. The new ACO-PPMSO
formulation has been tested extensively on two benchmark PPMSO problems from
the literature, including a 21-unit and a 22-unit problem. It was found that the ACOPPMSO
formulation resulted in significant improvements in performance for both
case studies compared with the results obtained in previous studies. In addition, the
new heuristic formulation was found to be useful in finding maintenance schedules
that result in more evenly spread reserve capacity and resource allocations. When
tested using a modified version of the 21-unit and the 22-unit problems, the new
local search strategy specifically designed for duration shortening was found to be
effective in searching locally for maintenance schedules that require minimal
shortening of outage duration. The ACO-PPMSO formulation was also successfully
able to cater for all constraints as specified in both original and the modified versions
of the two benchmark case studies.

In order to further test the ACO-PPMSO formulation developed, it was first applied
to a scaled-down version of the Hydro Tasmania hydropower system (five power
stations) and then to the full system (55 generating units). As part of the studies, the
ACO-PPMSO formulation was linked with the simulation model used by Hydro
Tasmania to assess the impact of various maintenance schedules on the total energy
in storage of the system at the end of the planning horizon, the total thermal
generation, the total number of days where the reliability level is not met, as well as
the total unserved energy throughout the planning horizon. A number of constraints
were considered, including the anticipated system demands, a 30% capacity
reliability level, the minimum and maximum durations between related maintenance
tasks, the precedence constraints and the minimum outage duration of each task in
the case of shortening of maintenance tasks. The maintenance schedule was
optimised for the maximum end-of-horizon total energy in storage, the minimum
thermal generation and the minimum total outage durations shortened and deferred,
under 77 different inflow conditions. The optimal maintenance schedule obtained
compared favourably with that obtained by Hydro Tasmania over many years based
on experience. Specifically, the ACO-PPMSO schedule results in higher end-ofhorizon
total energy in storage and satisfies both hard and soft constraints, which
overall equates to over $0.5 million dollars of savings when compared to the
schedule obtained using the practitioners’ experience and engineering judgment. The
ACO-PPMSO algorithm was also shown to be a useful decision-making tool for
scheduling maintenance under different circumstances when tested with four
scenarios commonly encountered in practical maintenance scheduling problems.
In conclusion, the ACO-PPMSO formulation developed, tested and applied as part of
this thesis research provides a powerful and flexible means of obtaining optimal or
near-optimal maintenance schedules for power plants.