Self-scheduling of generation companies via stochastic optimization considering uncertainty of units

This paper provides a novel self-scheduling model for price-taker generation companies (GENCOs) participating in a day-ahead energy market. Also, this paper models the effect of uncertainty of generating units' forced outage considered by stochastic optimization approach in the self-scheduling. This approach allows the producer to maximize its profit while controlling the risk of profit variability. A scenario generation technique is considered to produce the scenarios for modeling the uncertainty source. Moreover, a well-known scenario reduction tool is applied to reduce the computational burden of the problem. A proposed methodology solves a set of stochastic mixed-integer linear programming (MILP) problems. The framework is effectively applied to a test system and the effect of GENCOs' unavailability and risk are obtained and discussed.