A review on optimal sizing and siting of distributed generation system: Integrating distributed generation into the grid

Gupta, Peeyoosh; Pandit, Manjaree; Kothari, D. P.

doi:10.1109/poweri.2014.7117648

Cited by 11 publications

(7 citation statements)

References 66 publications

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“…However, due to environmental concerns and the shortage of conventional power plants, there is a need to develop models that can consider both types of power generators including conventional and renewable energy power generators, that is, solar power plants and wind turbines. Researchers like Gupta et al [35] discussed the integration of DGs into the present supply chain and Khatod et al [36] contributed to handling the uncertainties associated with load and renewable resources (wind and solar) to overcome issues in the continuous supply of power and discussed the optimal placement of photovoltaic arrays (PVAs) and wind turbine generators WTGs in a radial distribution system. Further, Mena et al [37] proposed a framework for the optimal size and location of the distributed renewable generation units (DG) and also considered the uncertainties in renewable resources availability, components failure and repair events, load and grid power supply.…”

Section: Literature Reviewmentioning

confidence: 99%

Power Distribution Network Expansion and Location Optimization of Additional Facilities: A Case Study

et al. 2021

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Planning the power distribution network is critical and challenging; the main challenges include the multiple costs involved, selecting the appropriate locations of different nodes of the network at minimal cost, and minimizing the cost of energy loss for both the primary and secondary networks. Literature on the power distribution network presents different approaches, however, lacks to address the several issues of the complex power distribution networks and many aspects are yet to be explored; for example, the uncertain cost of energy loss. This study intends to address the gaps in the literature by proposing a four-phased approach. In doing so, first, an integer linear programming model is formulated with the objective of cost minimization. Secondly, fuzzy variables are used to tackle the parameters with uncertainty; cost of energy loss. In the third phase, a fine-tuned genetic algorithm (FT-GA) that uses the Taguchi Orthogonal Array is introduced to solve the mathematical model. It is worth mentioning that during the design of the experiment, the input parameters are crossover rate, elite count, and population size. In the last phase, a pragmatic approach is adopted and a Pakistan-based case study is used to validate the proposed model and its implication in real-life scenarios. The results exhibit that our proposed approach outperforms traditional methods like the genetic algorithm (GA) and inter-point methods in terms of fitness function value, number of generations, and computational time. This research contributes at both theoretical and managerial levels and may help decision-makers to design networks more efficiently and cost-effectively in Pakistan, Asia, and beyond.

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Section: Literature Reviewmentioning

confidence: 99%

Power Distribution Network Expansion and Location Optimization of Additional Facilities: A Case Study

et al. 2021

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“…If DG cannot be installed properly, neither the above benefits can be achieved nor detrimental effects can be reduced 9,32,33 . Considerable efforts have been made to tackle challenges involved in DG planning from different perspectives, including devices selection, modelling, problem formulation, and algorithms 34,35 . As shown in Figure 2, general steps of DG planning can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

“… Step 3 : Select the most suitable optimization algorithm based on mathematical properties of the formulated problem: In most cases, DG planning is to be formulated as a high‐dimension and non‐convex problem. Analytical methods considering power losses or power flow are widely utilized thanks to the merits of easy convergence and applicability for commercial solvers 33‐36 . However, they heavily rely on an accurate model developed on a set of assumptions or simplifications, making the results not suitable in practice.…”

Section: Introductionmentioning

confidence: 99%

“…Analytical methods considering power losses or power flow are widely utilized thanks to the merits of easy convergence and applicability for commercial solvers. [33][34][35][36] However, they heavily rely on an accurate model developed on a set of assumptions or simplifications, making the results not suitable in practice. By contrast, numerical algorithms such as linear programming (LP), 37 mixed non-linear programming (MINLP), [38][39][40][41][42] dynamic programming (DP) 43 reduce the reliance on accurate modelling which are easy to be implemented.…”

Section: Introductionmentioning

confidence: 99%

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Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

Yang

Chen

et al. 2020

Int J Energy Res

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Summary Distributed generation (DG) has attracted significant attention due to its great potential for enhancing economical and technical performance of power systems and reducing dependence on fossil fuels. Optimal sizing and placement are critical for stimulating such potential, about which a considerable number of models and algorithms have been proposed in past literature. This paper attempts to undertake a comprehensive review on optimal sizing and placement of DG via a systematic methodology procedure, including definition and classifications of DG, modelling and problem formulation with different technical and economic criteria, and summary of optimization algorithms. Common features and distinctive characteristics of both models and methods are identified, followed by evaluations and comparisons based on their practical performance in various test systems. Selection of DG techniques with respect to application scenarios, indispensable and optional considerations in DG planning models, and pros and cons of algorithms are listed in tables for a clearer understanding. Lastly, a total of 107 algorithms are addressed, which are classified into five categories. Particular, hybrid methods can deal with complex engineering problems with multiple objective functions and constraints most effectively and robustly. Future research trends are also highlighted with the aim of providing a comprehensive and state‐of‐the‐art survey for researchers, engineers, and other stakeholders.

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“…However, further objectives have also been addressed, such as to minimize line loadings (Rau and Wan 1994;Abou El-Ela et al 2010), and to maximize the DGs capacity with respect to the technical constraints Keane and O'Malley (2005). A review on optimal sizing and siting of distributed generation could be found in Gupta et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Optimized Technical and Economic Evaluation of the Connection Point of Distributed Generators

Pereira

Padilha‐Feltrin

Rueda–Medina

2015

J Control Autom Electr Syst

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In most studies, the problem of allocation and sizing of the distributed generation is addressed considering that the distributed generators (DGs) could be installed in any bus of the system. Thus, these studies bring the optimal or best allocation place for the DGs without considering that in a real implementation not all buses are valid candidates due to restrictions in the system, such as environmental, social, and geographical aspects, for example. In this sense, the problem treated in this work consists of evaluating the allocating point of DGs in distribution systems based on technical and economic aspects, assuming that they are not necessarily located near the optimal or best allocation bus of the system. Two interests are investigated: on one hand, the problem 1, formulated as a multi-objective optimization (MO) problem and defined from the point of view of the independent energy producer (IEP) which owns the DGs; and, on the other hand, the problem 2, also formulated as a MO problem, but defined from the point of view of the distribution system operator (DSO). The results for the two problems were a set of solutions that established a conflict between the IEP and the DSO because the alternative that proved the most promising for the IEP was the less attractive for the DSO. The set of solutions found through the used solution technique constitutes a flexible environment that allows the IEP and the DSO to evaluate each proposed solution and decide on the one that is closest to the interests of both parties.

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A review on optimal sizing and siting of distributed generation system: Integrating distributed generation into the grid

Cited by 11 publications

References 66 publications

Power Distribution Network Expansion and Location Optimization of Additional Facilities: A Case Study

Power Distribution Network Expansion and Location Optimization of Additional Facilities: A Case Study

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

Optimized Technical and Economic Evaluation of the Connection Point of Distributed Generators

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