Enhancing grid management with offline and online feedback optimization
Sen Zhan defended his PhD thesis at the Department of Electrical Engineering on December 11th.
The shift toward renewable energy, electric heating, and transportation has transformed power distribution systems, presenting challenges like voltage fluctuations and congestion for distribution system operators (DSOs). Traditional grid expansion is costly and slow, making better use of distributed energy resources (DERs) like electric vehicles, heat pumps, batteries, and solar panels essential. This research of Sen Zhan demonstrates how optimization techniques, particularly Optimal Power Flow (OPF) can improve grid efficiency and hosting capacity. To address OPF鈥檚 real-time limitations, he introduces Online Feedback Optimization (OFO), a method leveraging real-time data for adaptive control, robust against uncertainties like fluctuating demand. Enhancements such as multi-timescale control and fairness-oriented design further improve resilience, planning, and equity. These innovations reduce the need for costly upgrades, supporting a sustainable and inclusive energy future.
Traditional OPF faces limitations in real-time applications due to the need for precise grid models and load data, which are not always available to DSOs. Moreover, solving optimization problems in real time can be too slow to respond to rapid changes in demand or generation. To address these challenges, Zhan investigated online feedback optimization (OFO), a newer approach that uses real-time measurements to adjust DER operations. Unlike OPF, OFO can operate effectively even without a complete grid model and is more robust to data inaccuracies and unexpected system disturbances. The thesis introduces enhancements to OFO, such as integrating fairness considerations to ensure that all users bear an equitable share of operational costs.
New distributed OFO method
The research also evaluates OFO's performance under various practical conditions, such as fluctuating loads and network inaccuracies. The results show that OFO generally performs well, though it struggles with certain issues, like communication delays and sudden changes in grid topology. Furthermore, a novel distributed OFO method is proposed. This design uses local communication between neighboring devices, reducing reliance on a centralized control system and enhancing resilience to single-point failures.
Multi-timescale control framework
In addition, the researcher addresses a key limitation of OFO: it lacks the ability to plan for future energy needs, which restricts the efficient use of DERs with storage capabilities, such as batteries. To improve this, a multi-timescale control framework is proposed, allowing DSOs to set longer-term operational goals while maintaining real-time flexibility. This dual approach combines the advantages of both offline and online optimization, offering a more comprehensive control strategy for DSOs.
Potential of advanced optimization techniques
Overall, this PhD research highlights the potential of advanced optimization techniques to transform power distribution management, paving the way for a more flexible and resilient energy system. It demonstrates how DER coordination can be improved through innovative methods, making local power grids more adaptable to the demands of a cleaner and increasingly electrified world.
Title of PhD thesis: . Promotor: Prof. Han Slootweg.