In the ever-evolving landscape of energy management, a groundbreaking study has emerged that promises to revolutionize how microgrids handle peak load periods. Published in Platform, a Journal of Engineering, the research introduces a bi-level energy optimization framework designed to significantly reduce power losses and enhance grid efficiency through peak load shaving (PLS). At the helm of this innovative work is Mohd Fakhizan Romlie, an assistant professor from the Department of Electrical and Electronics Engineering at Universiti Teknologi PETRONAS in Malaysia.
Peak load periods are notorious for straining electrical networks, leading to substantial power losses and increased operational costs. Romlie’s study addresses this challenge head-on by strategically optimizing electricity supply from battery storage-based distributed generation (DG) during peak times. The key to this optimization lies in identifying peak and off-peak loads, a task that has historically been fraught with complexity due to the variability of electrical loads throughout the day and night.
The bi-level energy optimization framework developed by Romlie and his team operates in two distinct levels. The first level identifies peak load times (PLT), off-peak load times (OPLT), and no operation times (NOT) from daily time-varying load profiles. The second level involves scheduling energy supply to and from batteries using a sophisticated seven-stage battery dispatch controller. This dual-layer approach ensures that energy is dispatched efficiently, minimizing losses and maximizing grid performance.
To validate their framework, the researchers applied a genetic algorithm (GA) to solve the optimization problem for three different time-varying load profiles: industrial, residential, and commercial. The results were staggering. The proposed method achieved significant peak load reductions of 23.3% in industrial loads, 18.89% in residential loads, and 10.99% in commercial loads. These reductions translated into notable power loss reductions of 5.73%, 5.44%, and 2.45% respectively.
Romlie emphasized the practical implications of their findings, stating, “The ability to shave peak loads not only reduces power losses but also improves the overall efficiency of the grid. This can lead to substantial cost savings and enhanced reliability for energy providers and consumers alike.”
The study further validated the efficacy of the optimization framework by comparing it with results obtained using fixed values. The comparison showed that the proposed approach achieved maximum peak shaving in microgrids, confirming its superiority over traditional methods. The improvements in load factors and bus voltage profiles across all load profiles underscore the potential of this optimization framework in future energy management strategies.
As the energy sector continues to evolve, the need for innovative solutions to manage peak loads and reduce power losses becomes increasingly critical. Romlie’s research, published in Platform, a Journal of Engineering, offers a compelling blueprint for the future of microgrid management. By leveraging advanced optimization techniques and battery storage, energy providers can achieve greater efficiency, reliability, and cost-effectiveness, paving the way for a more sustainable and resilient energy infrastructure.
