article · Applied Sciences
Modern power distribution networks rely heavily on flexible alternating current transmission systems to maintain stable operations. Among these, distribution static compensators provide essential reactive power compensation, harmonic reduction, and voltage regulation, but their effectiveness depends heavily on proper placement and sizing. An improved grey wolf optimisation algorithm addresses this challenge by incorporating a dimension learning-based hunting strategy to prevent premature convergence and sustain search diversity. Applied to determine the optimal allocation and capacity of compensators, the algorithm accounts for operating constraints alongside the stochastic fluctuations of electricity demand, wind generation, and solar power. Validation on standard 33-, 69-, and 85-bus distribution test networks indicates that the method surpasses existing techniques in cutting power losses, elevating voltage profiles, and reinforcing voltage stability.
Integrating variable wind and solar resources into power grids creates unpredictable fluctuations that can destabilise voltage and increase energy losses. Better placement and sizing of grid compensators allows network operators to absorb renewable power more smoothly. This ensures electrical networks remain reliable and efficient, reducing costly transmission waste while supporting the transition towards cleaner energy sources.
The method is applicable to electrical distribution network operators and utility planning teams seeking to optimise capital investments in grid-stabilising hardware. As an algorithmic planning tool, it could be incorporated into commercial power system simulation and planning software packages. Because the approach has been evaluated only on standard benchmark bus networks, it remains at an early, computer-modelled stage of development prior to utility-scale deployment.
AI-generated from the published abstract. Always read the original work before citing.
Over the last decade, flexible alternating current transmission systems (FACTS) have been crucial in ensuring optimal power distribution within modern power systems. A vital component of FACTS devices is the distribution static compensator (DSTATCOM), which is essential for maintaining a reliable power supply. It is commonly used for reactive power compensation, voltage regulation, and harmonic reduction. Determining the appropriate size and placement of DSTATCOMs is vital to ensuring their efficiency. This study introduces the improved gray wolf optimizer (I-GWO), a refined version of the classical gray wolf optimization (GWO) method. The I-GWO incorporates a dimension learning-based hunting (DLH) strategy to preserve population diversity, balance exploration and exploitation, and prevent the premature convergence of classical GWO. In this research, the I-GWO was applied to determine the optimum allocation and sizing of the DSTATCOMs, considering system constraints, including those presented by the intermittent and stochastic nature of the load and renewable energy resources, specifically wind and solar energy. The suggested approach was successfully tested on 33-, 69-, and 85-bus distribution systems and then compared with existing studies. The results demonstrated the I-GWO-based approach’s superiority in terms of reducing power losses, improving voltage profiles, and enhancing voltage stability.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/app14020556
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
