One of the main desired capabilities of the smart grid is 'self-healing', which is the ability to quickly restore power after a disturbance. Due to critical outage events, customer demand or load is at times disconnected or shed temporarily. While deterministic optimisation models have been devised to help operators expedite load shed recovery by harnessing the flexibility of the grid's topology (i.e. transmission line switching), an important issue that remains unaddressed is how to cope with the uncertainty in generation and demand encountered during the recovery process. This study introduces two-stage stochastic models to deal with these uncertain parameters, and one of them incorporates conditional value-at-risk to measure the risk level of unrecovered load shed. The models are implemented using a scenario-based approach where the objective is to maximise load shed recovery in the bulk transmission network by switching transmission lines and performing other corrective actions (e.g. generator re-dispatch) after the topology is modified. The benefits of the proposed stochastic models are compared with a deterministic mean-value model, using the IEEE 118- and 14-bus test cases. Experiments highlight how the proposed approach can serve as an offline contingency analysis tool, and how this method aids self-healing by recovering more load shedding.
ASJC Scopus subject areas
- Control and Systems Engineering
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering