Mixed Biogeography-Based Optimization for GENCOs' Maintenance Scheduling in Restructured Power Systems. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Fetanat, Abdolvahhab; Shafipour, Gholamreza
• Is Part Of: Applied artificial intelligence. Volume 32:Issue 1(2018)
• Page Start: 65
• Page End: 84
• Publication Date: 2018-01-02
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2018.1448158
• Languages: English
• ISSNs: 0883-9514

Abstract

ABSTRACT: Power industry restructuring has brought new challenges to the generation unit maintenance scheduling problem. Maintenance scheduling establishes the outage time scheduling of units in a particular time horizon. In the restructured power systems, the decision-making process is decentralized where each generating company (GENCO) tries to maximize its own benefit. Therefore, the principle to draw up the unit maintenance scheduling is different from the traditional centralized power systems. The objective function for GENCOs is to minimize his maintenance investment loss. Therefore, he hopes to put its maintenance on the weeks when the market-clearing price is lowest so that maintenance investment loss descends. This paper addresses the unit maintenance scheduling problem of GENCOs in restructured power systems. The problem is formulated as a mixed integer programming problem, and it is solved by using an optimization method known as biogeography-based optimization (BBO). BBO is simple to implement in practice and requires a reasonably small amount of computing time and a small amount of data communication. BBO has been tested by applying it to a GENCO with three generating units. This model consists of an objective function and related constraints, e.g., maintenance window, generation capacity, load and network flow. The simulation result of this method is compared with a classic method. The outcome is very encouraging and proves that BBO is powerful for minimizing GENCOs' objective function.