Real-time emergency load shedding for power system transient stability control: A risk-averse deep learning method

Abstract Emergency load shedding is an effective and frequently used emergency control action for power system transient stability. Solving the full optimization models for load shedding is computational burdensome and thus slow react to the intense system variations from the increasing renewable energy sources and the more active demand-side behavior. Other sensitivity-based methods impair the control accuracy and may not guarantee global optimality. Artificial intelligence methods, as the data-driven approaches, have recently been well-recognized for its real-time decision-making capability to tackle the system variations. The existing artificial intelligence methods for emergency load shedding are based on shallow learning algorithms and can lead to both load under-cutting and over-cutting events. However, when the loads are under-cut, the power system will be exposed to a high risk of post-control instability that can propagate into cascading events, which incurs significantly higher cost than an over-cutting event. Being aware of such unbalanced control costs, this paper proposes a risk-averse deep learning method for real-time emergency load shedding, which trains deep neural network towards the reluctance to load under-cutting events, so as to avoid the huge control cost incurred by control failure. The case studies on two renewable power systems demonstrate that, compared to the state-of-the-art methods, the proposed risk-averse method can significantly improve the control success rate with negligible increase in prediction error, ending up with lower overall control cost. The results verify the enhanced control performance and the practical values of the proposed method.