Optimal capacitor banks placement in distribution grids using NSGA II and Harmonic Resonance Chart

Onaka, J. H. D.; Lima, Athila Santos de; Manito, Allan Rodrigo Arrifano; Bezerra, Ubiratan Holanda; Tostes, Maria Emília de Lima; Carvalho, Carminda Célia Moura de Moura; Soares, Thiago Mota; Mendes, D. C.

doi:10.1109/ichqp.2016.7783363

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…Post-optimization constraints may also be applied as employed by Onaka et al for the IEEE 34-bus test system to consider total harmonic distortion (THD) [5]. Optimal solutions are determined initially through the non-dominated sorting genetic algorithm II (NSGA-II) for two objectives in which the maximum bus voltage violations and total system costs considering capacitor installation as well as active power losses at both fundamental and harmonic frequencies are to be minimized.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A. Eajal et al for the IEEE 13-bus test system through a discrete particle swarm optimization (PSO) with a single objective of minimizing total system cost considering active power losses and capacitor installation [6]. A THD constraint is applied during the optimization instead of in the postoptimization stage as in [5].…”

Section: Literature Reviewmentioning

confidence: 99%

“…It was found that typical objectives for capacitor planning generally include the minimization of active power losses and investment [3], [4], [6]. Constraints were also sometimes applied concurrently with the optimization or afterward upon the resulting Pareto set to ensure power system operational compliance for proposed capacitor configurations by limiting voltage violations and THD [5]- [7], [15].…”

Section: Figurementioning

confidence: 99%

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Optimal Capacitor Placement and Rating for Large-Scale Utility Power Distribution Systems Employing Load-Tap-Changing Transformer Control

et al. 2023

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Significant opportunity for savings in energy and investment through improved performance of power distribution systems exists in the optimal placement and rating of capacitors, a conventionally costeffective and popular reactive power compensating technology. A novel optimal capacitor planning (OCP) procedure is proposed for large-scale utility power distribution systems, which is exemplified on an existing utility circuit of approximately 4,000 buses. An initial sensitivity analysis is employed to intelligently reduce OCP computation time and maintain quality of optimal configurations. Three optimization objectives are considered, including the minimization of total system active power losses, standard deviation of node voltages, and investment in total capacitor power rating. Eight multi-objective optimization methods that employ the non-dominated sorting algorithm III (NSGA-III) concept are compared to determine individual merits. Differences between the methods include the incorporation of a penalty constraint for voltage violations and the automatic readjustment of load-tap-changing (LTC) transformer tap settings for proposed capacitor re-configurations concurrently within the optimizer, which ensures peak system performance and fair comparison to the reference case. A multi-step model conversion process was developed with OpenDSS to enable the OCP procedure to be generally applicable to real large-scale utility circuits. OCP is performed for three example sub-circuits served by a substation with a 48MW, 9Mvar peak load, which represents the most extreme case and offers the best opportunity for savings. Example configurations from the resulting Pareto sets through a pseudo-weight vector approach are also analyzed through a systematic procedure of comparison between the most extreme configuration types to inform configuration selection.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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Optimal Capacitor Placement and Rating for Large-Scale Utility Power Distribution Systems Employing Load-Tap-Changing Transformer Control

et al. 2023

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“…In [26], a nondominated sorting genetic algorithm (NSGA-II) is implemented to solve network reconfiguration, optimal placement of the distributed generator and capacitor; however, the parameters should be adjusted well to not fall into local optima. In [27], NSGA II is also proposed to solve the proposed issue taking into account the total harmonic distortion constraint; however, there are some solutions on the Pareto front, which exceed the security boundaries in terms of voltage resonance. In [28], a comparison is made between NSGA II and Strength Pareto Evolutionary Algorithm (SPEA 2) to solve this issue taking into account the limitations of harmonic distortion.…”

Section: Introductionmentioning

confidence: 99%

Capacitor Allocation Using Multiobjective Water Cycle Algorithm and Fuzzy Logic

El-Saeed

Abdel-Gwaad

Farahat

2022

ELEKTRON ELEKTROTECH

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Radial distribution systems are susceptible to a lack of voltage profile and increase system losses, particularly at the distant ends of the distribution feeder. This manuscript proposes an approach to solve the optimal capacitor placement problem in radial distribution networks to minimize system losses, improve the voltage profile of all buses, promote total voltage stability, and improve net savings. The optimal capacitor placement problem is solved in two stages. Firstly, normalized loss sensitivity factor and voltage magnitude are used as inputs to build fuzzy expert rules to arrange the most candidate buses for capacitor placement. Secondly, a multiobjective water cycle algorithm is applied to determine the optimal sizes and locations of capacitors within the predefined search space using fuzzy expert rules. The multiobjective function is formulated with operational constraints considering fixed and switched capacitors. To validate the effectiveness of this methodology, it is demonstrated on IEEE 33 and IEEE 94-bus radial distribution networks. Clearly, the findings show the improvement in the voltage profile and static voltage stability, the significant reduction in system losses, as well as the enhancement in overall savings. Furthermore, a comprehensive evaluation is also carried out by comparing the numerical results with other methods such as interior point algorithm, a combination fuzzy real coded genetic algorithm method, water cycle algorithm for IEEE 33-bus system and artificial bee colony algorithm for IEEE 94-bus system which prove the viability and effectiveness of the proposed methodology.

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