The increase of extreme weather events with wide-area enduring adverse impact has caused enormous loss to power systems. Coordination of various emergency measures can effectively reduce the overall loss over the time-horizon of these events. However, coordination of preventive dispatch, emergency dispatch, and restorative dispatch in transmission systems under extreme weather conditions has not been thoroughly addressed. To fill this gap, we first propose a methodology to coordinate the above dispatch stages based on a novel hierarchical defender-attacker model (HDAM). In this model, various dispatch stages are modelled as defenders, whereas the extreme weather event is modelled as the attacker. Second, frequency security constraints, load shedding, and transmission line switching are modelled with the piecewise linear AC power flow in the HDAM. Particularly, an adaptive approximation of cosine function in the piecewise linear power flow equation is applied to greatly reduce the computational complexity. Third, the HDAM is recast as a two-stage robust optimization model with binary decision variables in the recourse stage and solved by the nested column-and-constraint generation algorithm. Then, the robust resilience trapezoid and coordinated strategy during extreme weather events are obtained by the proposed methodology. Moreover, by inference-based sensitivity analysis of the discrete line outage scenarios, the critical transmission lines and transformers are identified for pre-event hardening. Comprehensive case studies using IEEE benchmark systems and a Chinese 132-bus system validate that coordination of multiple dispatch stages is effective to enhance power system resilience against extreme weather events by reducing the overall loss.

The now ubiquitous use of information technology (IT) poses a crucial challenge to the cyber security of power grid operations, one that has aroused serious concerns from both industry and academia. State-of-the-art research either focuses on the vulnerability assessment of particular types of components or concentrates on the prevention and mitigation of cyber attacks from the power grid’s perspective. Complete causal chains connecting component vulnerabilities to cyber attacks causing malicious system-wide effects are unclear, which hinders cyber-attack prevention and the consolidation of affected components. To bridge this research gap, this survey aims to study two issues that need further investigation. On one hand, the causal relationship between power grid component vulnerabilities and resulting cyber attacks has not been thoroughly explored. On the other hand, the evolvement of cyber attacks, from initial attempt to resulting serious consequences has not been resolved. To study these two issues, we first analyze various stakeholders and associated information flows in diverse applications in power grid operation. Second, we summarize the root cause of cyber attacks in the vulnerability of communication protocols. Third, a multi-stage model is proposed to describe the cyber vulnerabilities, which reveals potential attacks and their evolvement on power grid components at each stage, assesses the associated impact on the entire power grid, and elucidates possible countermeasures. With the above efforts, this survey establishes a complete causal chain from component-wise vulnerability to system-wide impact assessment for enhancing cyber security, and potential research directions for enhancing power grid cyber security are identified.

Replay attack and false data injection attack (FDIA) are two common types of cyber-attacks against supervisory control and data acquisition systems, aiming to disrupt the normal operation of the power system by falsifying meter measurements. In this paper, we proposed a systematic methodology to defend hybrid attack with both replay attack and FDIA. Specifically, we propose a detection method applying random matrix theory to: (1) detect the hybrid attack on static state estimation, and (2) distinguish FDIA from replay attack as well as localize falsified measurements. Firstly, short-term forecast on load and renewable power generation is conducted to obtain the predicted measurements. Secondly, random variables are calculated by differentiating the forecasting measurements and real-time measurements. A random matrix is consequently constructed with the above random variables. Thirdly, hybrid attacks are detected by the changes of the linear eigenvalue statistics of the random matrix obtained by the sliding time window. More importantly, a novel multi-label classifier to distinguish replay attack from FDIA is designed to localize FDIA by combining SVD decomposition and eigenvalue analysis with convolutional neural network (SVD-CNN). Finally, comprehensive simulations on the IEEE 14-bus system and IEEE 57-bus system are provided to validate the performance of the proposed method. It is shown that the proposed detection method has strong detection ability by filtering measurement noise. Moreover, the proposed SVD-CNN improves the accuracy in FDIA localization.

Carbonatites and related alkaline rocks host most REE resources. Phosphate minerals, e.g., apatite and monazite, commonly occur as the main REE-host in carbonatites and have been used for tracing magmatic and mineralization processes. Many carbonatite intrusions undergo metamorphic and/or metasomatic modification after emplacement; however, the effects of such secondary events are controversial. In this study, the Miaoya and Shaxiongdong carbonatite-alkaline complexes, in the South Qinling Belt of Central China, are selected to unravel their magmatic and hydrothermal remobilization histories. Both the complexes are accompanied by Nb-REE mineralization and contain apatite and monazite-(Ce) as the major REE carriers. Apatite grains from the two complexes commonly show typical replacement textures related to fluid metasomatism, due to coupled dissolution-reprecipitation. The altered apatite domains, which contain abundant monazite-(Ce) inclusions or are locally surrounded by fine-grained monazite-(Ce), have average REE concentrations lower than primary apatite. These monazite-(Ce) inclusions and fine-grained monazite-(Ce) grains are proposed to have formed by the leaching REE from primary apatite grains during fluid metasomatism. A second type of monazite-(Ce), not spatially associated with apatite, shows porous textures and zoning under BSE imaging. Spot analyses of these monazite-(Ce) grains have variable U-Th-Pb ages of 210–410 Ma and show a peak age of 230 Ma, which is significantly younger than the emplacement age (440–430 Ma) but is roughly synchronous with a regionally metamorphic event related to the collision between the North China Craton and Yangtze Block along the Mianlue suture. However, in situ LA-MC-ICP-MS analyses of those grains show that they have initial Nd values same as those of magmatic apatite and whole rock. We suggest these monazite-(Ce) grains crystallized from the early Silurian carbonatites and have been partially or fully modified during a Triassic metamorphic event, partially resetting U-Pb ages over a wide range. Mass-balance calculations, based on mass proportions and the REE contents of monazite-(Ce) and apatite, demonstrate that the quantity of metasomatized early Silurian monazite-(Ce) is far higher than the proportion of monazite-(Ce) resulting from the metasomatic alteration of the apatite. Therefore, Triassic metamorphic events largely reset the U-Th-Pb isotopic system of the primary monazite-(Ce) and apatite but only had limited or local effects on REE remobilization in the carbonatite-alkaline complexes in the South Qinling Belt. Such scenarios may be widely applicable for other carbonatite and hydrothermal systems.

This paper proposes a method to establish an optimal dynamic coordinated condition-based maintenance strategy that considers harsh external conditions, for example, harsh weather conditions. Component deterioration is modeled as a Markov process based on physical characteristics, with the effects of harsh external conditions represented as probabilistic models. The proposed model involves interactions between different maintenance strategies on various components, as well as influences on the operation of the entire system. The optimal maintenance strategies are obtained by optimizing the proposed model with the cost to go, including system reliability cost and maintenance cost. This proposed model is solved using a backward induction algorithm associated with a search space reduction approach developed to reduce the simulation time. Two IEEE systems and one actual system validate the proposed model. The results show that this optimal maintenance strategy model that considers harsh external conditions provides insight for scheduling appropriate maintenance activities.

Rapid growth of renewable energy sources (RES) in the generation capacity mix poses substantial challenges on the operation of power systems in various time scales. Particularly in the intra-hour time scale, the interplay among variability and uncertainty of RES, unexpected transmission/generation outages, and short dispatch lead time cause difficulties in generation-load balancing. This paper proposes a method to quantify the intra-hour flexibility region. A robust security-constrained multiperiod optimal power flow model is first constructed to quantify the frequency, magnitude, and intensity of insufficient flexibility. The randomness of RES is captured by uncertainty sets in this model. The N-k contingency, spinning reserve, and corrective control limit constraints are included. This model is then cast into a two-stage robust optimization model and solved by the column-and-constraint generation method. The emergency measures with a least number of affected buses are derived and subsequently assessed by the post-optimization sensitivity analysis. Finally, the operational flexibility region is determined by continuous perturbation on the RES penetration level and the forecast error. The IEEE 14-bus system and a realistic Chinese 157-bus system are used to demonstrate the proposed method.