UNIT COMMITMENT PROBLEM WITH RAMP RATE CONSTRAINT USING NEW HYBRIDIZED ADAPTIVE OPTIMIZATION METHOD | ||||
| ERJ. Engineering Research Journal | ||||
| Article 1, Volume 38, Issue 4, October 2015, Page 241-250 PDF (517.65 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/erjm.2015.66846 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Ehab E. Elattar | ||||
| Department of Electrical Engineering, Faculty of Engineering, Menoufiya University, | ||||
| Abstract | ||||
| Solving unit commitment (UC) problem is one of the most critical tasks in electric power system operations. Therefore, proposing an accurate method to solve this problem is of great interest. The original bacterial foraging (BF) optimization algorithm suffers from poor convergence characteristics for larger constrained optimization problems. In addition, the stopping criterion used in the original BF increases the computation burden of the original algorithm in many cases. To overcome these drawbacks, a hybridized adaptive bacterial foraging and genetic algorithm (HABFGA) approach is proposed in this paper to solve the unit commitment problem with ramp rate constraint. The HABFGA approach can be derived by combining adaptive stopping criterion, BF algorithm and genetic algorithm, so that the drawbacks of original BF algorithm can be treated before employing it to solve the complex UC problem. To illustrate the effectiveness of the HABFGA approach, several standard and real test systems with different numbers of generating units are used. The results of HABFGA approach are compared with the results obtained using other published methods employing same test systems. This comparison shows the effectiveness and the superiority of the proposed method over other published methods. | ||||
| Keywords | ||||
| Unit Commitment; ramp-rate constraint; adaptive bacterial foraging; hybridized adaptive bacterial foraging and genetic algorithm | ||||
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