Short-term scheduling of thermal power systems using hybrid gradient based modified teaching–learning optimizer with black hole algorithm

• Published in: Electric power systems research Vol. 108; pp. 16 - 34
• Main Authors: Azizipanah-Abarghooee, Rasoul, Niknam, Taher, Bavafa, Farhad, Zare, Mohsen
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.03.2014; Elsevier
• Subjects: Applied sciences; Black hole algorithm; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Gradient-based method; Miscellaneous; Modified teaching–learning-based optimization; Power networks and lines; Prohibited operating zones; Short-term thermal generation scheduling; Valve-point effect; Gradient-based method; Short-term thermal generation scheduling; Black hole algorithm; Modified teaching–learning-based optimization; Prohibited operating zones; Valve-point effect; Short term; Performance evaluation; Costs; Optimization; Learning; Minimum time; Electric power; Inequality constraint; Valve; Gradient method; Scheduling; Algorithm; Algorithm performance; Thermal power; Electrical network; Wavelet transformation; Planning; Transmission loss; Modified teaching-learning-based optimization; On off effect
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2013.10.012

•Formulate short-term generation scheduling of thermal power systems.•Consider valve-point effects, prohibited operating zone and multi-fuel options.•Consider operating reserve, line flow limits and transmission losses in the formula.•Propose a new gradient-based MTLBO–BH.•Propose a novel self-adaptive wavelet mutation operator. Nowadays, a challengeable subject facing the electric power system operator is how to manage optimally the power generating units over a scheduling horizon of one day considering all of the practical equality, inequality and dynamic constraints. These constraints are comprised of load plus transmission losses balance, valve-point effects, prohibited operating zones, multi-fuel options, line flow constraints, operating reserve and minimum on/off time. In this regard, the proposed framework first presents a practical formulation for the short-term thermal generation scheduling (STGS). It has high-dimensional, high-constraints, non-convex, non-smooth and non-linear nature and needs an efficient algorithm to be solved. Then, a new optimization approach, known as gradient-based modified teaching–learning-based optimization combined with black hole (MTLBO–BH) algorithm, has been proposed to seek the optimum operational cost. Despite the superior characteristics of the MTLBO and BH algorithms, both of them suffer from the problem of entrapping in local optima. Consequently, the powerful combination of MTLBO and BH and a novel self-adaptive wavelet mutation operator for the organization of the new robust algorithm are proposed in this work. Although the MTLBO–BH algorithm has many properties, some problems still remain to be solved pleasantly. One of these problems is the produced further robust solution that the classical gradient-based technique can overcome it. Finally, performance of the suggested technique is tested on different thermal power systems.