2019
DOI: 10.1002/2050-7038.12244
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Optimal planning in active distribution networks considering nonlinear loads using the MOPSO algorithm in the TOPSIS framework

Abstract: Summary In recent years, harmonic distortion has sharply increased because of injecting harmonic components caused by using nonlinear loads in the distribution system. Uncontrolled harmonic distortion can bring damage to equipment of the power system, reduce the power system efficiency, and interrupt protection and measurement devices. This parameter can be managed using various resources and control programs in active distribution networks. This paper presents active distribution networks (ADNs) with multiple… Show more

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Cited by 7 publications
(3 citation statements)
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“…CRITIC method, as an objective assignment method, is based on the comparative intensity of evaluation indicators and the conflict between indicators to comprehensively measure the objective weights of indicators. In order to exclude human subjectivity and improve the accuracy of the weights, the CRITIC method is chosen to calculate the weights of the evaluation indicators, and the TOPSIS model [32] is used to calculate the comprehensive evaluation index of the program. The specific steps of the CRITIC-TOPSIS evaluation model are shown in the right part of Fig.…”
Section: Critic-topsis Evaluation Modelmentioning
confidence: 99%
“…CRITIC method, as an objective assignment method, is based on the comparative intensity of evaluation indicators and the conflict between indicators to comprehensively measure the objective weights of indicators. In order to exclude human subjectivity and improve the accuracy of the weights, the CRITIC method is chosen to calculate the weights of the evaluation indicators, and the TOPSIS model [32] is used to calculate the comprehensive evaluation index of the program. The specific steps of the CRITIC-TOPSIS evaluation model are shown in the right part of Fig.…”
Section: Critic-topsis Evaluation Modelmentioning
confidence: 99%
“…The system reliability, stability, and power quality are thus, improved substantially. Therefore, to obtain improvements in system's attributes, the multi-objective optimization methods for problems including operational cost, siting and sizing of distributed energy resources (DERs), Carbon Dioxide (CO 2 ) emission, total power loss, voltage deviation, demand-side management, charging-discharging of BSS, total harmonic distortion, and system reliability are also established [13][14][15][16][17].…”
Section: Literature Surveymentioning
confidence: 99%
“…Energy optimal scheduling in a park can be summed up as a complex optimization problem with multi‐dimensional variables and multiple constraints. At present, researchers widely use meta‐heuristic algorithms to solve multi‐objective optimization problems, 17,18 such as the Non‐Dominated Sorting Genetic Algorithm (NSGA), the Non‐dominated Sorting Genetic Algorithm based on the elite strategy (NSGA‐II) 19 and multi‐objective particle swarm optimization algorithms (MOPSO) 20 . Compared with NSGA, the NSGA‐II has the advantages of low calculation complexity, short calculation time, good convergence, and distribution 19,21 .…”
Section: Introductionmentioning
confidence: 99%