In this paper, a two-stage Bayesian learning-based approach is proposed to enable online dynamic security assessment (DSA) and preventive control in power systems. To develop a reliable data mining-based model for DSA, the Bayesian neural network is learned by Bayes by Backprop to detect the security status in real-time and to inform the system operators with the confidence of the prediction. For dynamic insecurity prevention, the security-constrained optimal power flow model incorporating the Bayesian neural network based security constraints is proposed, which is solved by the derivation-free Bayesian optimization. Case studies on the IEEE 39-bus system show that the Bayesian neural network is able to detect the security status of the power system correctly and reliably and is able to avoid the over-fitting problem compared with the traditional point estimation neural network. Also, Bayesian optimization is able to solve the non-analytical SCOPF model efficiently to mitigate the dynamic insecurity by preventive generation rescheduling.

The uncertain nature of renewable energy resources (RERs) and fast demand response lead to recurring voltage violations in the power systems, which causes frequent transformer tap shifting and capacitor switching. Therefore, this paper resorts to the static var compensators (SVCs) to manage the bus voltages based on the multi-agent deep reinforcement learning (MA-DRL) algorithm. The proposed scheme includes several system agents and SVC agents to collaboratively adjust the injected reactive power to restrict the bus voltages within the normal range. All the agents are trained centrally and executed separately, which requires minimum communication cost. The IEEE 14-bus system, IEEE 300-bus system, and China 157-node urban power grid are used to verify the effectiveness of the proposed method.

Total transfer capability (TTC) is an effective indicator to evaluate the transmission limit of the interconnected systems. However, due to the large-scale wind power integration, operation conditions of a power system may change rapidly, yielding time-varying characteristics of TTC. As a result, the traditional time-consuming transient stability constrained TTC model is unable to assess the online transmission margin. In this paper, we propose an online measurement-based TTC estimator using the nonparametric analytics. It consists of three major steps: the probabilistic data generation approach, the composite feature selection method and the group Lasso regression-based training scheme. Specifically, we present a probabilistic data generation approach to take into account the uncertainties of the day-ahead generation scheduling and reduce the number of redundant or infeasible data. Then, the composite feature selection is presented to reduce the dimension of the generated data without losing relevant information. Finally, these selected features are trained by the group Lasso regression to learn the correlation between the TTC and the online measurements. Once real-time measurements are available, the TTC can be assessed immediately through this learned inherent correlation. Extensive numerical results carried out on the modified New England test system demonstrate the feasibility of the proposed TTC estimator for online applications.

Larger-scale stochastic power systems urge the development of real-time alternating current optimal power flow, artificial intelligence (AI) thus becomes an alternative. However, traditional AI only imitates experiences, and cannot follow in-depth physics. This may cause an undesired nongeneralizability and topology intractability. To address this issue, a physics-guided graph neutral network (PG-GNN) is proposed. The PG-GNN firstly capture the physical constraints by a dual Lagrangian. Besides, the branch features of power grids are fully exploited to allow the PG-GNN to master tremendous topological patterns. To further manage the out-of-distribution topology, stability property of the PG-GNN is proved, then upon this evidence, an online transfer learning is proposed to allow the PG-GNN to fast master the unexpected topology. Numerical tests on benchmarks show that, the proposed method holds well topology-transferability, enables near or even better solutions than conventional optimizer, but merits much more than 100 times efficiency.

Real-time topology estimation in distribution grid with high penetration of distributed energy resources remains a challenging task due to the insufficient high-precision measurements and frequent topology variations. This paper proposes a real time distribution system topology estimation approach building on the graph theory and Bayesian networks (BNs) with sparse measurements. The graph theory develops the topology graph bank to effectively leverage the prior knowledge of topology models, including the topology structure and the switching relationship between different topologies. This allows the development of the Bayesian networks for topology tracking using real-time voltage and power injection measurements. A novel discrete method considering the similarity of data correlation information is proposed for the optimal placement of μPMUs to ensure the performance of topology estimation. Numerical results on the IEEE 33-node and 123-node systems show that the BN-based topology estimation model has better performance against incomplete information, i.e., missing data, than other alternatives.

In this article, a novel approach that considers the time-varying load restoration capability is proposed for operational reliability assessment of distribution networks. To evaluate the operational reliability, two indices are firstly defined as the minimal load loss under the worst-case fault contingency in the upcoming time interval. To search for the optimal remedial actions for load restoration, a sequential network reconfiguration (NR) model is then proposed considering the reconfigurability of feeders and capability of emergent power support of energy storage systems (ESSs). Since this model is a complex nonlinear mixed integer programming model, an improved greedy searching-based algorithm is proposed to compute the optimal remedial actions and to further evaluate the risk of load loss. Comparative case studies are presented in this article to demonstrate that the load level, the expectation of the state-of-charge of ESS and the failure duration have the impacts on the system operational reliability. Besides, the proposed sequential NR model can be used to enhance the reliability of distribution networks by enabling an effective sequential load transfer strategy under emergent conditions. Unlike traditional reliability assessment, the proposed method provides the operators with the real-time awareness of the operational reliability level considering the real-time condition of distributed resource equipped feeders and worst-case emergency constraints.