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Metaheuristic Differential Evolution for Optimal Placement of Reactive Power Compensation Devices

Оптимальное размещение средств компенсации реактивной мощности с помощью метода дифференциальной эволюции

Metaheuristic Differential Evolution for Optimal Placement of Reactive Power Compensation Devices

Abstract

Reactive power compensation in high voltage power grids is and should be used for energy efficiency and reliability purposes. Several grid indicators must be maintained, from required voltage levels and permissible current loads to the rated power factors and electricity loss reduction. Optimizing the capacity and placement of compensation devices is a multivariable task that requires deep complex grid modeling and special mathematical optimizations given that the return on investment is of importance. This paper presents a method based on metaheuristic differential evolution for optimal placement of compensation devices based on maximizing the net present value criterion with adjustment for the allowed period-specific electricity loss and required substation bus voltage. DIgSILENT PowerFactory 2020 is the software used for simulation and calculations. PowerFactory is the leading integrated power system analysis software, which covers the full range of functionality from standard features to highly sophisticated and advanced applications, which provides unrivalled calculation accuracy. The proposed approach helps find the best placement and configuration for reactive power compensation devices from the a user-predefined model range; it also makes adjustments for the limitations of the grid and for the per-unit costs valid over any planning period, which is demonstrated on a 14-node IEEE

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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