Optimal siting and sizing of distributed generation in radial distribution system using a novel student psychology-based optimization algorithm

Balu, Korra; Mukherjee, V.

doi:10.1007/s00521-021-06185-2

Cited by 49 publications

(32 citation statements)

References 49 publications

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“…A Distributed Generator (DG) is a small electricity-generating unit that plays an essential role in improving the power market because of its compact size, high efficiency, low operating costs, and safety [ 1 ]. The DGs are smaller in size, ranging from a few kilowatts to 100 Megawatts [ 2 ]. In addition, DGs are directly integrated to the consumer side, unlike the centrally located power generating units situated far away from load centers and resulting in higher transmission losses [ 3 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

et al. 2022

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The installation of Distributed Generation (DG) units in the Radial Distribution Networks (RDNs) has significant potential to minimize active power losses in distribution networks. However, inaccurate size(s) and location(s) of DG units increase power losses and associated Annual Financial Losses (AFL). A comprehensive review of the literature reveals that existing analytical, metaheuristic and hybrid algorithms employed on DG allocation problems trap in local or global optima resulting in higher power losses. To address these limitations, this article develops a parallel hybrid Arithmetic Optimization Algorithm and Salp Swarm Algorithm (AOASSA) for the optimal sizing and placement of DGs in the RDNs. The proposed parallel hybrid AOASSA enables the mutual benefit of both algorithms, i.e., the exploration capability of the SSA and the exploitation capability of the AOA. The performance of the proposed algorithm has been analyzed against the hybrid Arithmetic Optimization Algorithm Particle Swarm Optimization (AOAPSO), Salp Swarm Algorithm Particle Swarm Optimization (SSAPSO), standard AOA, SSA, and Particle Swarm Optimization (PSO) algorithms. The results obtained reveals that the proposed algorithm produces quality solutions and minimum power losses in RDNs. The Power Loss Reduction (PLR) obtained with the proposed algorithm has also been validated against recent analytical, metaheuristic and hybrid optimization algorithms with the help of three cases based on the number of DG units allocated. Using the proposed algorithm, the PLR and associated AFL reduction of the 33-bus and 69-bus RDNs improved to 65.51% and 69.14%, respectively. This study will help the local distribution companies to minimize power losses and associated AFL in the long-term planning paradigm.

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Section: Introductionmentioning

confidence: 99%

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

et al. 2022

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“…Teachers often ignore the use of feedback methods such as confirmation and clarification, especially in the process of improving students' language ability (Kitayama, 2017 ). Because the feedback means of understanding and verification can easily lead to unidirectional communication in the classroom, only the information input from the teacher to the student does not achieve the purpose of the student's meaningful language output (Balu and Mukherjee, 2021 ). Teachers need to be aware of the shortcomings of purely understanding and verification feedback methods, and consciously increase feedback methods such as confirmation and verification, to change the traditional one-way grammar-centered classroom interaction environment, and to provide students with sufficient language input while also having enough language opportunities to output opportunities, thereby improving their language output and communication skills (Verner et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

The Construction of Interactive Teaching Quality Monitoring System From the Perspective of Psychology

Chen¹

2022

Front. Psychol.

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The article first proposes a reflection on the status quo of classroom teaching in public universities, selects a large number of educational scenes, and analyzes these selected educational concepts in detail from a theoretical perspective. Through the establishment of a teaching quality monitoring system based on careful observation and analysis, several major problems in public university classrooms have been discovered: poor classroom interaction mode, single classroom interaction mode, low classroom interaction efficiency, and inefficient classroom interaction feedback. Specific manifestations include the alienation of teacher–student relationship, insufficient student coverage, lack of interaction channels, lack of interaction context, only formalized teacher–student interaction, low impact, and simplification of teacher–student interaction. Summarizing the causes of classroom interaction problems, the article summarizes three factors: teachers, students, and the macro environment. In the experiment, 85% of the students thought that the teacher–student relationship was relatively ordinary. Among the 11 students who participated in the experiment, 60% of the students were very active in interactive classroom discussions. Therefore, based on the analysis of problems and factors, the article puts forward an optimization strategy to create a classroom interaction atmosphere, improve classroom interaction mode, improve classroom interaction efficiency, and strengthen classroom interaction feedback. In particular, it promotes the integration of teacher–student relationship, emphasizes the role of students, improves the level of classroom interaction, creates conditions for classroom interaction, improves teacher–student interaction control ability, enhances student interactive speech ability, strengthens error correction and feedback, and increases multiple feedback methods.

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“…The authors in [18] used two algorithms such as student psychology-based optimization algorithm and HHO algorithm to solve optimal DG placement problem with cost analysis, and tested on IEEE 33 and 69-bus, and Brazil 136-bus RDNs.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Voltage constraint-1 means that any bus voltage after DG integration cannot get worse than before DG placement. In voltage constraints-2, the voltage values of all buses after DG integration must be between 0.95 and 1.05 pu [2], [13], [18].…”

Section: Voltage Constraintsmentioning

confidence: 99%

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Pürlü

Türkay

2022

IEEE Access

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In this paper, two metaheuristic methods, genetic algorithm and particle swarm optimization, are proposed to determine the optimal locations, sizes and power factors of single and double distributed generation units. In line with the 2050 carbon neutral goal, the aim was to integrate renewable distributed energy sources such as photovoltaic panels and wind turbines into the distribution system with a high penetration level. In contrast to most studies based on constant loads and dispatchable generations, an application considering the seasonal uncertainties of generation and consumption was performed to minimize the annual energy losses and voltage deviations of the distribution network. In addition, dispatchable, controllable and fuel-based conventional resources were allocated to compare the contributions of renewable resources. These seasonal case studies with various constraints were applied to IEEE 33-bus radial distribution network. To verify the feasibility and robustness of the proposed algorithms, case studies for peak loads were created and compared with the literature studies. While all distributed generation sources were operated at both unity and optimum power factor in all case studies, zero power factor and leading power factor scenarios were examined for a peak load only. Photovoltaic applications without energy storage technologies have not been very efficient because of the uneven daily distribution of solar irradiance, especially insufficient irradiation in the evening and excessive irradiation at noon. However, wind energy applications are more reliable and feasible, because the wind speed distribution is relatively more uniform than that of solar irradiation, both seasonally and daily. In all cases, operating distributed generation sources at the optimal power factor provided better results than those operating at unity power factor. As a result, wind turbines operating at optimal power factors have been found to contribute better than photovoltaic systems and are almost as good as conventional sources with controllable power output. While both proposed algorithms yielded better results than those in the literature, particle swarm optimization was better than genetic algorithm in terms of providing the best solution, faster convergence and shorter running time.

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Optimal siting and sizing of distributed generation in radial distribution system using a novel student psychology-based optimization algorithm

Cited by 49 publications

References 49 publications

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

The Construction of Interactive Teaching Quality Monitoring System From the Perspective of Psychology

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

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