A recent study led by Sandeep Gogula from the Department of Electrical and Electronics Engineering at JNTU Kakinada in Andhra Pradesh, India, has introduced an innovative approach to optimizing the placement and sizing of Distributed Generating Units (DGUs) in radial distribution systems. Published in the International Journal of Cognitive Computing in Engineering, this research leverages a nature-inspired algorithm known as the multi-objective Harris Hawks optimization (HHO) algorithm, which mimics the cooperative hunting strategies of Harris Hawks.
DGUs are small-scale power sources located near consumer load centers, designed to enhance energy reliability and integrate renewable energy sources. However, their effectiveness can be hampered by issues such as power losses, voltage imbalances, and harmonic distortions. Conventional optimization methods have often struggled to address these challenges adequately. Gogula’s research aims to fill this gap by providing a more effective optimization strategy that not only reduces power losses but also improves voltage profiles in electrical networks.
The HHO algorithm stands out due to its unique mechanism of utilizing the supportive actions of hawks during hunting, which can be translated into a collaborative search for optimal solutions in power distribution. The fitness function used in this optimization process considers both the voltage profile at each bus and the active power losses across distribution branches.
In practical terms, the results from testing the HHO algorithm on two benchmark distribution systems (69 bus and 118 bus) were promising. The optimization of seven DGUs in the 118 bus radial distribution system led to a significant reduction of active power losses by 68.78%, dropping losses to 405.32 kW, while also achieving a minimum voltage of 0.9723 Vs. This suggests that implementing this optimization approach could lead to more efficient energy distribution systems, ultimately benefiting both utility companies and consumers.
The commercial implications of this research are substantial. As energy markets increasingly shift towards decentralized generation and renewable energy integration, the ability to optimize the placement and size of DGUs becomes critical. Utilities can leverage this research to enhance their grid reliability and efficiency, leading to cost savings and improved service quality. Furthermore, this optimization technique could be particularly valuable for regions looking to expand their renewable energy capacity while minimizing the environmental impact of energy production.
Gogula emphasizes the significance of the study, stating, “The unique hunting process of Harris Hawks allows us to search for better quality solutions, which is crucial in optimizing multi-objective fitness functions in power systems.” This highlights the innovative nature of the research and its potential to transform how energy distribution is managed.
As the energy sector continues to evolve, the findings from this study could play a pivotal role in shaping more resilient and efficient power distribution networks, paving the way for a sustainable energy future.
