As the global demand for electricity surges, the integration of renewable energy sources (RES) into power grids is becoming increasingly vital. A recent study led by Raji Krishna from the School of Electrical Engineering at Vellore Institute of Technology Chennai, Tamil Nadu, India, presents an innovative approach to optimal energy management in hybrid microgrids. The research, published in ‘Results in Engineering,’ highlights the use of advanced forecasting techniques and optimization algorithms to address the inherent uncertainties associated with RES.
The study focuses on a hybrid AC-DC microgrid (HMG), which combines both alternating and direct current systems to enhance energy efficiency. The authors emphasize the importance of accurately forecasting uncertain parameters such as utility prices, electrical demand, and power generation from renewable sources. By employing the support vector machine (SVM) algorithm for forecasting, the researchers demonstrate its superiority over traditional methods like artificial neural networks (ANN). “Accurate forecasting is crucial for the reliability of hybrid microgrids, especially in the face of fluctuating renewable energy outputs,” Krishna notes.
Once the uncertain parameters are forecasted, the research introduces an improved Teaching and Learning-Based Optimization (ITLBO) algorithm to minimize generation costs over a 24-hour period. This two-phase approach—forecasting followed by scheduling—enables efficient power trading between the utility grid and the hybrid microgrid, tailored to meet load demands while keeping costs in check.
The implications of this research are significant for the commercial energy sector. By effectively managing energy resources and reducing operational costs, utilities and microgrid operators can enhance their competitiveness in an increasingly dynamic market. Krishna’s findings suggest that the ITLBO algorithm not only optimizes power dispatch but also positions microgrids as viable alternatives in the energy landscape. “The ability to minimize costs while maximizing efficiency could redefine how we approach energy management,” he adds.
The study’s results were validated using IEEE standard test systems, showcasing the ITLBO’s effectiveness compared to other meta-heuristic techniques, such as the traditional TLBO, Ant Lion Optimization (ALO), and Artificial Bee Colony (ABC) algorithms. This comparative analysis underscores the potential of the ITLBO algorithm to revolutionize energy management strategies in hybrid microgrids.
As the energy sector continues to evolve, this research paves the way for future developments in smart grid technology and renewable energy integration. By leveraging advanced forecasting and optimization techniques, the industry can move closer to achieving sustainable energy solutions that meet growing demands while minimizing environmental impact.
For further insights into this groundbreaking work, visit Vellore Institute of Technology Chennai.
