Unit maintenance scheduling by means of fuzzy-game theory, considering uncertainty in rival-Genco's data

Abstract

Gencos, in a restructured power system, try to schedule their generators' maintenance in order to maximize their profit. Besides, Unit Maintenance Scheduling (UMS) as a mid-term plan has a significant effect on the Genco's profit in a power market. In a regulated power system, UMS is usually determined by a central system, such as System Operator (SO). On the other hand, in a de-regulated power system, UMS is determined through multiple interactions between the market players, mainly Gencos and SO. Considering these, it would be a desire to solve both short term and mid-term problems, in a single framework. In this case, Gencos can offer a price curve with an outlook to mid-term goals in a game-theoretic approach. The approach would be based on some predictions of parameters such as production cost factors. This article, addresses the uncertainty in the cost factors of a Rival-Genco in a fuzzy game theoretic scheme and experimental results show the effectiveness of the proposed approach.