Asset individual optimization of maintenance and replacement strategies in transmission systems
Assets of extra-high voltage transmission systems are commonly maintained
and replaced according to time based strategy. Due to liberalization of energy markets and
incentive regulation in Germany, transmission system operators have to operate cost
efficiently. Saving money with a lower level of maintenance and replacement activity
endangers assets availability and supply security. Optimization algorithms are able to
allocate the intensity of maintenance during lifetime and the date of replacement for each
asset individually so that the expenditures are reduced at the same level of supply security.
The optimization model uses asset individual data about the age, the condition, the hazard
rate and the importance of each asset for the availability of the grid.
This contribution presents an application of genetic algorithm to determine an optimized
lifetime maintenance and replacement strategy for assets at a 220 kV transmission system.
A discrete number of available options for the intensity of maintenance and replacement
describes each asset. All options are characterized by operational expenditures, capital
expenditures, and energy not transmitted. Performing reliability analysis determines the
energy not transmitted of the grid. This quantifies the availability of the considered part of
the transmission system and changes according to the hazard rate of the assets. The
intensity of maintenance and the age of the asset influences the hazard rate. Thus, higher
maintenance intensity reduces the energy not transmitted, but causes higher expenditures.
Selecting exactly one option per asset generates a possible maintenance and replacement
strategy at the grid. The optimization method sums up operational expenditures, capital
expenditures, and energy not transmitted for all selected options and for all assets at the
grid to describe the maintenance and replacement strategy. Genetic Algorithm performs
an optimized selection of options, which aims on improving the operational expenditures,
capital expenditures, and energy not transmitted of the strategy simultaneously.
Finally, the method optimizes the lifetime maintenance and replacement strategy for a part
of a 220 kV transmission system with different types of assets. At the analyzed example,
the optimization method recommends mainly a reduced maintenance intensity in
combination with a replacement prior to the maximum lifetime of the assets. A comparison
to the time based maintenance confirms the reduction of energy not transmitted and the
operational expenditures. Due to these advantages, the optimization method is a tool to
support the asset manager in handling asset-individual data to develop a lifetime
maintenance and replacement strategy.
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