Abstract
This paper presents a hybrid particle swarm optimization based genetic algorithm and hybrid particle swarm optimization based evolutionary programming for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system that consist 24 buses with 32 generating unit system. Key words: Generation maintenance schedule, optimization, evolutionary programming, particle swarm optimization, genetic algorithm.
Highlights
Under the rapid development around the globe, power demand has increased drastically during the past decade
After a number of experimentations we found that there are slight changes in the solution, the best solution was found with Crossover Probability (CP) = 0.8, Mutation Probability (MP) = 0.01, and Population Size (PS) =100; these are in line with the recommendations made in the Genetic algorithm (GA) literature (Mirinda et al, 1998)
The gross reserve in any period is calculated as the difference between the sum of the capacity of all units and the HYBRID ALGORITHM FOR PARTICLE SWARM OPTIMIZATION (PSO) BASED EVOLUTIONARY PROGRAMMING (EP) FOR MAINTENANCE SCHEDULING
Summary
Under the rapid development around the globe, power demand has increased drastically during the past decade. In order to demonstrate the solution methodology using the Particle Swarm Optimization Genetic Algorithm (PSO-GA) technique for solving Generation Maintenance Scheduling problems, a test system with twelve generating units which must be maintained over a 52 week planning horizon is described in detail here. The gross reserve in any period is calculated as the difference between the sum of the capacity of all units and the HYBRID ALGORITHM FOR PARTICLE SWARM OPTIMIZATION (PSO) BASED EVOLUTIONARY PROGRAMMING (EP) FOR MAINTENANCE SCHEDULING. The net reserve is calculated as the difference between the gross reserve and the power capacity in maintenance
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