eng
Science and Education Publishing
International Transaction of Electrical and Computer Engineers System
2014-02-23
2
2
44
55
10.12691/iteces-2-2-1
ITECES2014221
article
Generation Maintenance Scheduling for Generation Expansion Planning Using a Multi-Objective Binary Gravitational Search Algorithm
Iman Goroohi Sardou
i_goroohi@sbu.ac.ir
1
Mohammad Taghi Ameli
1
Mehrdad Setayesh nazar
1
Faculty of Electrical and Computer Engineering, Shahid Beheshti University, Tehran, Iran
Generation maintenance scheduling (GMS) is an important and effective part of Generation expansion planning (GEP) problem. Preventive-maintenance schedules need to be optimized to trade-off between two con?icting objectives, reducing the overall cost and improving the reliability. This paper presents a multi-objective binary gravitational search algorithm (BGSA) for solving GMS problem of generation systems as a sub-problem of the main GEP problem. In the proposed method, a fuzzy membership function is defined for each term in the objective function. There are three objective functions in this problem. The first objective function is leveling reserve capacity when unit maintenance outages are considered. The second and the third objectives which are also objectives of the main GEP problem, are to minimize the operation and maintenance (O&M) cost and the reliability index of Expected Energy Not Supplied (EENS). As GMS problem is a sub-problem of the main GEP problem, it is solved for a typical solution of the main GEP problem. The proposed method is applied to solve GMS problem for 4-bus test system from Grainger & Stevenson and IEEE-RTS 24-bus test system for a planning horizon of one year and two years, respectively. To verify the capability of the proposed BGSA based method, a binary genetic algorithm (BGA) method is also implemented to solve GMS problem and then the results are compared.
http://pubs.sciepub.com/iteces/2/2/1/iteces-2-2-1.pdf
generation maintenance scheduling
generation expansion planning
levelized reserve
Gravitational Search Algorithm
Genetic Algorithm
multi-objective