Abstract
The complexity of power systems is increasing as new generating units are added to power systems in order to supply power to the growing economies. This has resulted in further research into the generator maintenance scheduling (GMS) problem which seeks to ensure optimal preventive maintenance scheduling that is effective and reliable. This research is focused on developing a generator maintenance schedule using a tri-objective model. The GMS tri-objective model is solved using two solution methodologies. The first is an exact solution method using mathematical modelling software, Advanced Interactive Multidimensional Modelling System (AIMMS). The second solution method is a recently developed metaheuristic algorithm called Exchange Market Algorithm (EMA). Results show that the tri-objective model finds a trade-off solution of the individual solution methods. The metaheuristic algorithm gives a better solution for larger optimization problems.
Highlights
The generator maintenance scheduling (GMS) problem has load, maintenance window, maintenance duration, crew and non-interruption of maintenance constraints. It explored the application of two solution methods, Advanced Interactive Multidimensional Modelling System (AIMMS) and Exchange Market Algorithm (EMA) to solve the GMS problem
The EMA and AIMMS 21-unit test system models are compared with the solutions obtained in literature for minimizing the sum of squared reserves
The objective function values obtained by EMA and AIMMS are 13 287 043 and 13 286 403 respectively compared to literature values of 13 685 127 and 13 675 000
Summary
St safety margin at time period t xi,t binary variable that is 1 when generator is on maintenance yi,t binary variable that is 1 if maintenance of generator i starts at time t cmi maintenance cost of generator i fi fuel cost function ai, bi, ci fuel cost coefficients for generator i λi failure rate of generator i si cost of starting up generator i. RoFmax maximum allowed probability that a generating unit will fail before maintenance iiiiiiiimmmmmm k number of iterations iteration number nnpppppp population size ttpppppp number of the t-th member of the population g1min ,g1max minimum and maximum risk co-efficients in non-oscillating mode g2min ,g2max minimum and maximum risk co-efficients in oscillating mode
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