Optimal Threshold for Self-adaptive Reactive Power Optimization Based on Event-triggered Algorithm

An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy, which aims to strike a balance between optimization performance and control load while ensuring continuous optimization. First, evaluation indicators are introduced to comprehensively analyze the impact of power fluctuations on the objective function and system voltage at both the system-wide and local levels. Based on these indicators, a multi-stage centralized optimization (MCO) is selectively applied, addressing system state deviations to achieve optimal operating states while maintaining a voltage security margin to ensure system safety. Then, distributed optimization (DO) is carried out at each bus with a renewable energy source or random load integration to accommodate short-term uncertainties using a self-adaptive reactive power algorithm. The optimal threshold value for event-triggered DO is calculated to balance control burden and optimization effectiveness. Utilizing the local state deviation evaluation indicator, unnecessary DOs are skipped when minor power fluctuations occur at the local level. Finally, following the linear superposition principle, event-triggered DOs executed at all distributed controllers collectively constitute the self-adaptive optimization strategy for the entire system. A case study on the IEEE New England 39-bus power system illustrates the effectiveness of the proposed strategy.