Generation Maintenance Scheduling for Generation Expansion Planning Using a Multi-Objective Binary Gravitational Search Algorithm

Iman Goroohi Sardou^1, , Mohammad Taghi Ameli¹ and Mehrdad Setayesh nazar¹

¹Faculty of Electrical and Computer Engineering, Shahid Beheshti University, Tehran, Iran

Cite this paper:
Iman Goroohi Sardou, Mohammad Taghi Ameli and Mehrdad Setayesh nazar. Generation Maintenance Scheduling for Generation Expansion Planning Using a Multi-Objective Binary Gravitational Search Algorithm. International Transaction of Electrical and Computer Engineers System. 2014; 2(2):44-55. doi: 10.12691/iteces-2-2-1

Abstract

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 conflicting 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.

Keywords:
generation maintenance scheduling generation expansion planning levelized reserve Gravitational Search Algorithm Genetic Algorithm multi-objective

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