In a significant stride towards optimizing renewable energy-powered microgrids, a team of researchers has developed a real-time cost management system that integrates electric vehicle (EV) charging and discharging control. The study, led by Swati Sharma, was recently published in the *Journal of Electrical Engineering*, offering a promising solution to the growing challenges faced by public grids due to the increasing number of EVs.
The research introduces a novel hybrid algorithm that combines particle-swarm optimization (PSO) and grey wolf optimization (GWO) to dynamically manage energy flow and EV charging schedules. This innovative approach ensures both immediate response to disturbances and long-term optimization, maintaining microgrid stability. “Our model not only enhances cost-effectiveness but also fosters energy efficiency,” Sharma explained, highlighting the dual benefits of the system.
The study was conducted using MATLAB 2022a and tested on a medium-scale microgrid IEEE-37 Node system via a real-time digital simulator (RTDS). The results were impressive, with the hybrid PSO-GWO algorithm reducing costs by $152.47 and execution time by 0.81 seconds compared to other metaheuristic algorithms. Notably, about 36.85% of the load was absorbed by EVs, with surplus power fed back to the main grid.
The implications of this research are far-reaching for the energy sector. As the adoption of EVs continues to rise, the strain on public grids is expected to increase. This real-time cost optimization model offers a viable solution to manage this strain effectively. “This comprehensive approach affirms the efficacy of hybrid PSO-GWO in real-time microgrid management,” Sharma noted, underscoring the potential of the algorithm in future applications.
The study’s findings could shape future developments in microgrid management, particularly in integrating renewable energy sources and EV charging infrastructure. By optimizing energy flow and reducing costs, this research paves the way for more sustainable and efficient energy solutions. As the energy sector continues to evolve, such innovations will be crucial in meeting the growing demand for clean and reliable energy.
In summary, Sharma’s research presents a cutting-edge solution to the challenges posed by the proliferation of EVs, offering a glimpse into the future of microgrid management. The hybrid PSO-GWO algorithm’s superior performance and cost-saving capabilities make it a promising tool for the energy sector, driving towards a more sustainable and efficient energy landscape.