In a significant advancement for local energy communities (LECs), researchers have unveiled a groundbreaking method for optimizing flexibility provision that could reshape the energy landscape. The study, led by An Thien Huu Nguyen from the Department of Electrical Engineering at Eindhoven University of Technology, introduces a chance-constrained optimization (CCO) approach that leverages logit-based regression to enhance the efficiency of decentralized power management.
LECs are becoming increasingly vital in the transition toward sustainable energy systems, enabling self-consumption and the sharing of resources among community members. However, the unpredictable nature of distributed energy resources and the charging demands of electric vehicles have posed significant challenges in predicting flexibility provision performance. Traditional methods, such as stochastic optimization (SO), have struggled to adequately address these uncertainties, often resulting in computationally intensive processes that hinder timely decision-making.
Nguyen’s research addresses these challenges head-on. “Our method not only streamlines the computational process but also effectively correlates forecast errors with network issues, such as congestion and voltage violations,” he explains. By deriving a closed-form expression for probabilistic constraints, the CCO approach significantly reduces the risks associated with voltage violations, limiting them to below 5% compared to 20% with conventional methods.
The implications of this research are far-reaching. By improving the prediction capabilities of LECs, this innovative approach enables communities to optimize their energy resources more effectively, leading to reduced operational costs and enhanced reliability of energy supply. In numerical simulations conducted on a modified IEEE 33-bus network connected to two LECs in Bunnik, Netherlands, the CCO method demonstrated remarkable efficiency, outperforming traditional profile-based SO and network-aware SO by factors of 8 and 165, respectively, in terms of computational time.
This advancement not only benefits local communities but also holds significant commercial potential for the energy sector. As utilities and energy providers increasingly seek ways to integrate decentralized systems and enhance grid resilience, the ability to predict and manage flexibility provision effectively will be invaluable. “This research paves the way for more agile and responsive energy systems that can adapt to the dynamic nature of energy consumption and generation,” Nguyen adds.
As the energy sector continues to evolve, the methodologies developed in this study could serve as a cornerstone for future innovations, allowing for a more sustainable and efficient energy ecosystem. The findings are published in ‘IEEE Access’, a journal that highlights cutting-edge research in electrical engineering and technology. For more information about Nguyen’s work, you can visit the Department of Electrical Engineering at Eindhoven University of Technology.
