A New Modification for TLBO Algorithm to Placement of Distributed Generation

Taheri, Seyed Iman; Salles, Maurício B. C.

doi:10.1109/iccep.2019.8890101

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…Target functions included minimizing energy losses and operating costs as well as improving voltage profiles. In [14], the modified teaching-learning based optimization (MOTLBO) algorithm was used to optimize the DG units. In [15], DG optimization was performed by considering load and generation uncertainty using a genetic algorithm (GA).…”

Section: Meta-innovative Methodsmentioning

confidence: 99%

A Novel Analytical Approach for Optimal Placement and Sizing of Distributed Generations in Radial Electrical Energy Distribution Systems

et al. 2021

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Considering the strong influence of distributed generation (DG) in electric distribution systems and its impact on network voltage losses and stability, a new challenge has appeared for such systems. In this study, a novel analytical algorithm is proposed to distinguish the optimal location and size of DGs in radial distribution networks based on a new combined index (CI) to reduce active power losses and improve system voltage profiles. To obtain the CI, active power losses and voltage stability indexes were used in the proposed approach. The CI index with sensitivity analysis was effective in decreasing power losses and improving voltage stability. Optimal DG size was determined based on a search algorithm to reduce active power losses. The considered scheme was examined through IEEE 12-bus and 33-bus radial distribution test systems (RDTS), and the obtained results were compared and validated in comparison with other available methods. The results and analysis verified the effectiveness of the proposed algorithm in reducing power losses and improving the distribution system voltage profiles by determining the appropriate location and optimal DG size. In IEEE 12 and 33 bus networks, the minimum voltage increased from 0.9434 p.u and 0.9039 p.u to 0.9907 p.u and 0.9402 p.u, respectively. Additionally, the annual cost of energy losses decreased by 78.23% and 64.37%, respectively.

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Section: Meta-innovative Methodsmentioning

confidence: 99%

A Novel Analytical Approach for Optimal Placement and Sizing of Distributed Generations in Radial Electrical Energy Distribution Systems

et al. 2021

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“…The source of the distribution system data is presented in [74]. Table . Ref: [38]. We showed the voltage profile changes of the test distribution system with and without the integration of DERs in Fig.…”

Section: Simulation Results Of the First Scenariomentioning

confidence: 99%

“…The cost objective-function is the same as the first scenario, but the DERs types are changed in this scenario, so the cost objective-function is changed as following [38]:…”

Section: Objective Functionsmentioning

confidence: 99%

“…Eq. (38) shows the calculation model for the hosting capacity of the distribution system. We consider a min-max problem in this calculation model.…”

Section: Distribution System Hosting Capacity Modelmentioning

confidence: 99%

“…= min max ∑ =1 (38) The load variation, including (i.e., first variables) and (i.e., uncertain parameters), affects this hosting capacity model. In this regard, some scholars have presented two approaches, including load ability and voltage variation [32].…”

Section: Distribution System Hosting Capacity Modelmentioning

confidence: 99%

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Optimization strategies for decision-makers to increase the distributed energy resources penetration for a greener future.

Taheri¹

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Integrating distributed energy resources (DERs), specifically renewable distributed generation (DG), in the distribution system leads to energy management problems. These problems for electric utilities are varied based on different former improvements in distribution systems and their requirements. Therefore, this thesis presents different energy management scenarios and solutions for covering a wide range of optimization problems in the distribution systems related to integrating DERs.All these scenarios are in one direction under the 'placement of DER in the distribution system.' This thesis analyses the strategies, including four energy management optimization problems. The first and second scenarios are the single objective and multiobjective placement of DER in the distribution system. The thesis improves the first two scenarios by a multiobjective placement and sizing by considering the distribution system hosting capacity and the former installed DER (i.e., third scenario). Finally, the fourth scenario presents the optimal dispatching of DERs and microgrids for virtual power plant (VPP) Scheduling.These scenarios include the allocation of DG, such as fuel cells, wind turbines, and solar cells, in a distribution system to guide future energy management. In these scenarios, the applied optimization algorithms are the improved teaching-learningbased optimization (TLBO) algorithm with presenting two new modifications and two new hybrid optimization algorithms. The first and second scenarios use further modifications of TLBO for DER placement in the single objective and multiobjective problems. The third scenario applies a hybrid optimization algorithm combining TLBO and honey bee mating optimization (HBMO) algorithm. The hybrid algorithm of the fourth scenario is a combination of grid search algorithm and derivative-free optimization algorithm. TLBO, HBMO, grid search, and derivative-free optimization algorithms have been reported as the best optimization algorithms in this area. Thus, this thesis, by modifying and combining those algorithms, tries to design a better optimization algorithm matched to each scenario. Moreover, an IEEE standard distribution system, a 70-bus radial distribution system, is used to implement the proposed optimization algorithms based on the power system manager scenario and objective functions. IVThe results show the importance of the proposed algorithms to optimize objective functions. The proposed algorithms observed superiority presents the best accuracy and velocity in achieving the optimal solution among the other optimization algorithms tested. The proposed algorithms are new optimization algorithms obtained by modifying the original version of the TLBO or combine two optimization algorithms.TLBO modification is in teacher and learner phases with adding the proper optimization techniques such as quasi-opposition-based learning technique and mutation method. With the best optimal solutions, the electric utility can have an economic, technical, and environmental improvement in ...

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Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm

Taheri

Salles

Khan

2021

Int Trans Electr Energ Syst

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Summary The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching‐learning‐based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi‐opposition‐based‐learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability‐value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem.

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A New Modification for TLBO Algorithm to Placement of Distributed Generation

Cited by 7 publications

References 13 publications

A Novel Analytical Approach for Optimal Placement and Sizing of Distributed Generations in Radial Electrical Energy Distribution Systems

A Novel Analytical Approach for Optimal Placement and Sizing of Distributed Generations in Radial Electrical Energy Distribution Systems

Optimization strategies for decision-makers to increase the distributed energy resources penetration for a greener future.

Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm

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