Multiobjective Optimization Applied to Maintenance Policy for Electrical Networks

A major goal for managers of electric power networks is maximum asset performance. Minimal life cycle cost and maintenance optimization becomes crucial in reaching this goal, while meeting demands from customers and regulators. This necessitates the determination of the optimal balance between preventive and corrective maintenance in order to obtain the lowest total cost. The approach of this paper is to study the problem of balance between preventive and corrective maintenance as a multiobjective optimization problem, with customer interruptions on one hand and the maintenance budget of the network operator on the other. The problem is solved with meta-heuristics developed for the specific problem, in conjunction with an evolutionary particle swarm optimization algorithm. The maintenance optimization is applied in a case study to an urban distribution system in Stockholm, Sweden. Despite a general decreased level of maintenance (lower total maintenance cost), better network performance can be offered to the customers. This is achieved by focusing the preventive maintenance on components with a high potential for improvements. Besides this, this paper displays the value of introducing more maintenance alternatives for every component and choosing the right level of maintenance for the components with respect to network performance.