Unit Maintenance Scheduling: A robust model, based on fuzzy cost factors and peak loads. (July 2016)

• Record Type: Journal Article
• Authors: Bozorgi, Ali; Pedram, Mir Mohsen; Yousefi, G. Reza
• Is Part Of: International journal of electrical power & energy systems. Volume 79(2016:Jul.)
• Page Start: 142
• Page End: 149
• Publication Date: 2016-07
• Subjects: Competitive electricity market -- Dynamic non-cooperative fuzzy game -- Maintenance strategy -- Nash equilibrium -- Unit Maintenance Scheduling; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2015.11.062
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: The main scope of this paper is Mid-term Unit Maintenance Scheduling (UMS). The impact of Gencos' Bidding strategy on Gencos' profit and UMS is studied. The short-term and mid-term UMS have been studied, simultaneously. The uncertainties in load and production cost are modeled based on a fuzzy framework. The UMS problem is modeled and solved as a dynamic non-cooperative fuzzy game. Abstract: As a mid-term planning scheduling, Unit Maintenance Scheduling (UMS) has a significant effect on Generation companies (Gencos) profit. System Operator (SO) is a central entity that provides the Gencos with related maintenance schedule. This schedule would be finalized through repetitive iterations between the SO and different Gencos in the market, to come up with a generally accepted schedule. Bidding strategy, on the other side, does affect the Gencos profit. Being considered as a short-term planning, bidding process is done on the hourly basis to determine the allocated demand to each Genco for each hour of the day. These short-term and mid-term schedules planning received enough attention in the literature, but to consider both simultaneously has not been well studied. In addition, one may not have all the information for the future periods. For production cost factors and load, an estimation can be obtained from the historical data, but that would not be the exact value and there remains an uncertainty on the value of these parameters. This would highlight the importance of developing a robust model that protect the schedule against changes in the values of these parameters, and ensure an acceptable, near optimal solution as well. In this paper, we would model the uncertainties in load and production cost factors, based on a fuzzy framework. The UMS problem is modeled and solved as a dynamic non-cooperative fuzzy game. The results show the effectiveness of the proposed approach.