High penetration of wind generation will increase the requirement for fast frequency response services as currently wind plants do not provide inertial response. Although the importance of inertia reduction has been widely recognized, its impact on the system scheduling has not been fully investigated. In this context, this paper proposes a novel mixed integer linear programming (MILP) formulation for stochastic unit commitment that optimizes system operation by simultaneously scheduling energy production, standing/spinning reserves and inertia-dependent fast frequency response in light of uncertainties associated with wind production and generation outages. Post-fault dynamic frequency requirements, rate of change of frequency, frequency nadir and quasi-steady-state frequency are formulated as MILP constraints by using the simplified model of system dynamics. Moreover the proposed methodology enables the impact of wind uncertainty on system inertia to be considered. Case studies are carried out on the 2030 Great Britain system to demonstrate the importance of incorporating inertia-dependent fast frequency response in the stochastic scheduling and to indicate the potential for the proposed model to inform reviews of grid codes associated with fast frequency response and future development of inertia-related market.

The power system inertia is gradually decreasing with the growing share of variable renewable energy (VRE). This may jeopardize the frequency dynamics and challenges the secure operation of power systems. In this paper, the concept of frequency security margin is proposed to quantify the system frequency regulation ability under contingency. It is defined as the maximum power imbalance that the system can tolerate while keeping frequency within the tolerable frequency range. A frequency constrained unit commitment (FCUC) model considering frequency security margin is proposed. Firstly, the analytical formulation of system frequency nadir is derived while considering both the frequency regulation characteristics of the thermal generators and the frequency support from VRE plants. Then, the frequency security margin is analytically formulated and piecewise linearized. A novel FCUC model is proposed by incorporating linear frequency security constraints into the traditional unit commitment model. Case studies on a modified IEEE RTS-79 system and HRP-38 system are provided to verify the effectiveness of the proposed FCUC model. The impacts of VRE penetration on system frequency security are analyzed using frequency security margin.

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct the health index. In this paper, a novel data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RULs of bearings. More concretely, raw vibrations of training bearings are first processed using the Hilbert-Huang transform to construct a novel nonlinear degradation energy indicator which can be used as the training label. The CNN is then employed to identify the hidden pattern between the extracted degradation energy indicator and the raw vibrations of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted through using a ϵ-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by performance test on other bearings undergoing different operating conditions.

The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and software vulnerabilities, communication issues, and personnel errors, etc. Therefore, enhancing the cyber-resiliency of DER-based smart grid - the ability to survive successful cyber intrusions - is becoming increasingly vital and has garnered significant attention from both industry and academia. In this survey, we aim to provide a comprehensive review regarding the cyber-resiliency enhancement (CRE) developments of the DER-based smart grid, present a holistic CRE framework, and thoroughly discuss the research directions of the next-generation CRE methods. Firstly, an integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasises on vulnerability identification and impact analysis. Then, the defense-in-depth strategies encompassing prevention, detection, mitigation, and recovery are comprehensively surveyed, systematically classified, and rigorously summarized. A holistic CRE framework is subsequently proposed to incorporate the five key resiliency enablers. Finally, challenges and future directions are discussed in details. The overall aim of this survey is to illustrates the recent development of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid.

The undergoing transition from conventional to converter-interfaced renewable generation leads to significant challenges in maintaining frequency stability due to declining system inertia. In this paper, a novel control framework for Synthetic Inertia (SI) provision from Wind Turbines (WTs) is proposed, which eliminates the secondary frequency dip and allows the dynamics of SI from WTs to be analytically integrated into the system frequency dynamics. Furthermore, analytical system frequency constraints with SI provision from WTs are developed and incorporated into a stochastic system scheduling model, which enables the provision of SI from WTs to be dynamically optimized on a system level. Several case studies are carried out on a Great Britain 2030 power system with different penetration levels of wind generation and inclusion of frequency response requirements in order to assess the performance of the proposed model and analyze the influence of the improved SI control scheme on the potential secondary frequency dip. The results demonstrate that the inclusion of SI provision from WTs into Unit Commitment (UC) can drastically impact the overall system costs.