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The fluctuation and intermittency of the wind and solar power outputs results in the regulation pressure of thermal units in power systems. The adjustable energy-intensive loads (such as the Electrolytic aluminium and Steel plants) have great potentialities to participate in demand response (DR) programs. This paper proposes an optimal scheduling method of the unit commitment (UC) with the consideration of the energy-intensive loads participating in wind and solar power consumption. It adopts the nonparametric kernel density estimation method to model the wind and solar power outputs. And then it uses Frank-Copula function to describe the correlation between the scenarios of wind and solar power outputs. A stochastic unit commitment (SUC) model introduces chance constrained theory of the reserve coefficient to describe time-variant scenarios, on the basis of the deviation between the typical and simulative scenarios. The simulation results based on IEEE 118-bus system shows that the energy-intensive load in the SUC model can flexibly adjust and respond to the change of wind and solar power output, reduce the impact of wind and solar power output’s uncertainty and promote the consumption of wind and solar power.

This paper proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

Few studies have focused on assessing the transient and steady-state voltage stability status of dynamic systems simultaneously, a concept referred to joint voltage stability assessment (JVSA) in this paper. Towards this end, this paper proposes a novel data-driven JVSA method considering load uncertainty. It combines multiple convolutional neural networks (multi-CNNs) and a novel variational Bayes (VB) inference for better JVSA accuracy. First, the multi-CNN model is utilized to fast estimate the maximum voltage deviations during the transient and steady-state process. Uncertain load scenarios and system topology under N-1 contingency with uncertain load scenarios are chosen as inputs of each CNN model. Second, estimated voltage deviations are put into the VB inference to automatically infer the transient and steady-state voltage stability status. To validate its effectiveness, numerical simulations are performed on the modified WECC 179-bus system by comparing with benchmark algorithms. It is demonstrated that the proposed data-driven JVSA method is more accurate and faster than the conventional VSA method.

Self-healing capability is one of the most critical factors for a resilient distribution system, which requires intelligent agents to automatically perform restorative actions online, including network reconfiguration and reactive power dispatch. These agents should be equipped with a predesigned decision policy to meet real-time requirements and handle highly complex $N-k$ scenarios. The disturbance randomness hampers the application of exploration-dominant algorithms like traditional reinforcement learning (RL), and the agent training problem under $N-k$ scenarios has not been thoroughly solved. In this paper, we propose the imitation learning (IL) framework to train such policies, where the agent will interact with an expert to learn its optimal policy, and therefore significantly improve the training efficiency compared with the RL methods. To handle tie-line operations and reactive power dispatch simultaneously, we design a hybrid policy network for such a discrete-continuous hybrid action space. We employ the 33-node system under $N-k$ disturbances to verify the proposed framework.

The utilization of aggregated demand-side flexibility via demand response (DR) has become a promising pathway for the integration of renewable energy resources in power systems. Nowadays, there are several management strategies for DR such as the price-based transactive control strategies. However, many of such existing price-based control strategies neglect the physics and operational constraints of the underlying distribution networks when computing the price, raising concerns regarding their theoretical and practical values. This paper studies this issue and investigates optimal DR (ODR) by incorporating the distribution locational marginal price (DLMP). In particular, we discuss DR in connection with DLMPs and propose a multiperiod bilevel optimization problem to find the ODR strategy. The objective is to minimize the peak load, load fluctuation, and payments of load aggregators. In addition, a robust bilevel ODR model is formulated to provide a robust ODR strategy while minimizing operating costs under the worst-case realization of uncertainties; this mitigates the impact of forecasting errors on renewable energy resources. Then, we propose an efficient solution approach by employing the KarushKuhnTucker conditions and strong duality. Simulation results are presented to illustrate the mutual impacts of the interaction between DR and DLMP and the benefits of the robust ODR strategy.

Winding faults are the most commonly occurring type of fault in offshore doubly-fed generator units. Such faults are difficult to detect and repair, and therefore higher fault identification accuracy is required. Most of the existing research analyzes stator and rotor winding faults separately, but due to the coupling characteristics between windings, it is necessary to analyze doubly-fed induction generator (DFIG) winding faults in a unified way. This paper proposes a general fault diagnosis method that can be applied to both the stator and rotor winding faults of DFIGs. First, a general mathematical model of a windings short circuit of a doubly-fed generator is established. Then, according to the mechanism of a turn-to-turn short circuit fault of a DFIG and the expression of a bilateral flux linkage between stator winding and rotor winding, a new fault feature-bilateral flux linkage difference vector (FLDV) is obtained by mathematical derivation. Finally, the recommended value for an experimental motor of feature quantity is set. The proposed method for inter-turn fault (ITF) diagnosis of stator and rotor windings provides technical support for the sustainable development of offshore wind power. A simulation and experiment prove that FLDV has high sensitivity and high reliability for windings ITF diagnosis.

This paper summarizes the technical activities of the IEEE Task Force on Solving Large Scale Optimization Problems in Electricity Market and Power System Applications. This Task Force was established by the IEEE Technology and Innovation Subcommittee to first review the state-of-the-art of the security-constrained unit commitment (SCUC) business model, its mathematical formulation, and solution techniques in solving electricity market clearing problems. The Task Force then investigated the emerging challenges of future market clearing problems and presented efforts in building benchmark mathematical and business models.

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interests active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which significantly enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is verified on the the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.