| Abstract: |
| Considering the escalating usage of renewable energy and rising frequency of extreme meteorological events, the risk of emergency load shedding (ELS) in power grids due to faults is increasing. The existing ELS strategies fail to provide users with advance warnings and blackout preparation time. To address this issue, an innovative strategy called warning and delayed load shedding is proposed in this study. In this approach, when it becomes necessary to shed load for emergency control, users are immediately notified with a power outage warning. Subsequently, energy storage and other regulatory resources are employed to substitute for load shedding, thus postponing the execution of the load shedding command. This delay equips users with the ability and time to respond and prepare. To implement this strategy, the operational principles supported by energy storage and backup power are further discussed. Five performance indexes are utilized to evaluate the delayed load shedding capability. Moreover, the delayed load shedding switch function and energy storage power balance equation are constructed to determine the relationship between energy storage, backup power sources, and load shedding time. Subsequently, two optimized load shedding models supported by energy storage are established, i.e., maximum and flexible delayed models. For comparison, improvements are made to the conventional load shedding model without delay by incorporating energy storage. An IEEE 30-node network with energy storage is used to test the three load shedding models. Accordingly, the evaluation indexes are calculated and compared. The results of the performance indexes and comparative analysis validate the effectiveness of the proposed methods, indicating that by using energy storage, users can be notified with advance power outage warnings and preparation time. |
| Key words: Delayed load shedding, load shedding evaluation index, load shedding warning, emergency load shedding, unscheduled outage. |
| DOI：10.23919/PCMP.2024.000090 |
|
| Fund:This work is supported by the National Natural Science Foundation of China (No. 52077017). |
|
