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Tonekābon, Iran

Rezaee Jordehi A.,Ayandegan University
Renewable and Sustainable Energy Reviews | Year: 2016

Distributed generation can be defined as power generation by small scale generating units that are installed at distribution systems. The penetration of distributed generation (DG) units in electric distribution systems is continually increasing. The process of finding optimal type, location and size of DG units is called "DG allocation". DG allocation is a hot area of research and represents a difficult problem in electrical power engineering. In this paper, the existing research works on DG allocation problem are reviewed from viewpoint of their used optimisation algorithms, objectives, decision variables, DG type, applied constraints and kind of uncertainty modelling. Based on the review of existing research works, the research gaps are identified and some helpful recommendations for future research on DG allocation will be provided. The author strongly believes that this paper can be helpful for researchers and engineers in the related field. © 2015 Elsevier Ltd. All rights reserved. Source


Jordehi A.R.,Ayandegan University
Renewable and Sustainable Energy Reviews | Year: 2015

Flexible alternating current transmission systems (FACTS) devices have proved to be very effective and viable in alleviating the problems of electrical transmission systems. The problem of finding optimal type, location and size of FACTS devices in power systems is called "FACTS allocation problem" and has widely attracted the attention of researchers in electrical power engineering. FACTS allocation problem represents a formidable, mixed integer, nonlinear and non-convex optimisation problem in electrical power engineering. Discovering near-global solutions in such a complex optimisation problem is very demanding, especially due to existence of multiple local optima. For solving this problem, diverse approaches have been proposed in the literature. Particle swarm optimisation (PSO) is considered as a famous, powerful and well-established metaheuristic optimisation algorithm and has been frequently utilised to solve FACTS allocation problem. In this paper, applications of PSO for solving FACTS allocation problem are deeply reviewed from perspective of the objectives, used basic PSO variant, parameter selection, multi-objective handling strategy, constraint handling strategy and discrete variable handling strategy. © 2015 Elsevier Ltd. All rights reserved. Source


Jordehi A.R.,Ayandegan University
Renewable and Sustainable Energy Reviews | Year: 2015

Distribution system optimisation is defined as satisfying the demand of the system in the most economical, reliable and environment-friendly way while all the related operational or geographical constraints are met. In the case that the future load of the system is intended to be met, the problem is also called "expansion planning" problem. In this paper, the existing researches on distribution system optimisation problem are reviewed from the viewpoint of their used optimisation algorithm, used objectives, used decision variables, load model, case study, planning type and planning period. This review found that although diverse optimisation algorithms have already been applied to distribution system optimisation problem, developing efficient algorithms with the ability of escaping from local optima and finding near-global solutions is required. In particular, development of diversity enhancement strategies for metaheuristics and applying them to this problem seems to be fruitful. This review also found that some aspects of distribution systems such as deregulation and demand side management have not been taken into account in modelling of distribution system optimisation problem. © 2015 Elsevier Ltd. All rights reserved. Source


Jordehi A.R.,Ayandegan University
Journal of Experimental and Theoretical Artificial Intelligence | Year: 2015

Seeker optimisation algorithm (SOA), also referred to as human group metaheuristic optimisation algorithms form a very hot area of research, is an emerging population-based and gradient-free optimisation tool. It is inspired by searching behaviour of human beings in finding an optimal solution. The principal shortcoming of SOA is that it is easily trapped in local optima and consequently fails to achieve near-global solutions in complex optimisation problems. In an attempt to relieve this problem, in this article, chaos-based strategies are embedded into SOA. Five various chaotic-based SOA strategies with four different chaotic map functions are examined and the best strategy is chosen as the suitable chaotic scheme for SOA. The results of applying the proposed chaotic SOA to miscellaneous benchmark functions confirm that it provides accurate solutions. It surpasses basic SOA, genetic algorithm, gravitational search algorithm variant, cuckoo search optimisation algorithm, firefly swarm optimisation and harmony search the proposed chaos-based SOA is expected successfully solve complex engineering optimisation problems. © 2015 Taylor & Francis. Source

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