Generation Maintenance Scheduling in Power Market Based on Genetic Algorithm

Abstract

This paper presents a genetic algorithm methodology for finding the optimum preventive maintenance schedule of generating units of a GENCO in a restructured power system. In this environment, management of GENCOs and grid is separated, each maximizing its own benefit. Therefore, the principle to draw up the unit maintenance scheduling will be changed significantly. The profit of a GENCO is defined as the total profit, which is the sum of the individual units' profits from the auctions over the horizon. So every GENCO hopes to put its maintenance on the weeks when the market clearing price (MCP) is lowest, so that maintenance investment loss (MIL) descends. Therefore, objective function for the GENCO is to sell electricity as much as possible, according to the market clearing price forecast. Various technical constraints such as generation capacity, duration of maintenances and maintenance continuity are being taken in to account. The objective function of ISO is to maximize the reserve capacity of the system at every time interval, subject to the energy purchase cost doesn't increase from a predetermined amount when the units of GENCOs are in outage for maintenance. Therefore two objective functions will be finding for fairly maintenance scheduling in restructured power systems. Each GENCO specifies optimum maintenance scheduling according to its objective function and hands it over to ISO. The ISO compares its reliability index with reliability index related to maintenance scheduling of GENCOs. If they are close enough in terms of reliability, it will be approved; otherwise the units, which violate the constraints, will be identified and ISO asks for time rescheduling