Controlled Evolution-Based Day-Ahead Robust Dispatch Considering Frequency Security with Frequency Regulation Loads and Curtailable Loads

With the extensive integration of volatile and uncertain renewable energy, power systems face significant challenges in primary frequency regulation due to instantaneous power fluctuations. However, the maximum frequency deviation constraint is inherently non-convex, and commonly used two-stage dispatch methods overlook causality, potentially resulting in infeasible day-ahead decisions. This paper presents a controlled evolution-based day-ahead robust dispatch method to address these issues. First, we suggest the convex relaxation technique to transform the maximum frequency deviation constraint to facilitate optimization. Then, an evolution-based robust dispatch framework is introduced to align day-ahead decisions with intraday strategies, ensuring both frequency security and power supply reliability. Additionally, a novel controlled evolution-based algorithm is developed to solve this framework efficiently. Case studies on a modified IEEE 14-bus system demonstrate the superiority of the proposed method in enhancing frequency security and system reliability.

💡 Research Summary

As the global energy landscape shifts toward renewable energy, the integration of highly volatile sources like solar and wind power presents a critical challenge to power system stability. The inherent intermittency of these resources leads to rapid power fluctuations, which can cause significant frequency deviations, potentially threatening the structural integrity of the entire electrical grid. This paper addresses the fundamental difficulty of maintaining frequency security during the day-ahead dispatch process, focusing on the complexities introduced by non-convex constraints and the lack of causality in traditional dispatch methods.

The authors identify two primary technical bottlenecks in current power system operations. First, the constraint governing the maximum frequency deviation is mathematically non-convex, making it extremely difficult to solve using standard optimization techniques without risking sub-optimal or infeasible results. Second, conventional two-stage dispatch frameworks—which separate day-ahead planning from real-time operation—often fail to account for the causal link between these two stages. This discrepancy means that a day-ahead schedule, while economically optimal on paper, may become physically impossible to implement when faced with real-time-induced fluctuations.

To overcome these hurdles, the paper proposes an innovative “controlled evolution-based day-ahead robust dispatch method.” The methodology employs a convex relaxation technique to transform the non-convex frequency deviation constraints into a more manageable convex form, thereby facilitating efficient and reliable optimization. Furthermore, the researchers introduce an evolution-based robust dispatch framework specifically designed to align day-ahead decision-making with intraday operational strategies. This ensures that the pre-planned power distribution is inherently robust and capable of handling real-time uncertainties.

A significant contribution of this research is the strategic integration of frequency regulation loads and curtailable loads into the optimization framework. By leveraging these flexible resources, the proposed method can actively mitigate frequency deviations caused by renewable energy volatility. To solve this complex optimization problem, the paper develops a novel controlled evolution-based algorithm. Experimental validations conducted on a modified IEEE 14-bus system demonstrate that the proposed approach significantly enhances both frequency security and power supply reliability compared to existing methods. This research provides a vital technological foundation for the stable and secure operation of future power grids characterized by high levels of renewable energy penetration.