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Certain edge failures in cyber–physical power systems (CPPSs) can cause cascading failures in the power system. Protecting these critical edges and essential equipment in the supporting communication network can avoid these failures. Here, based on complex network controllability regarding kinetic equations and considering the actual operating conditions of CPPSs, we filter out a class of transmission lines and a class of nodes in the power system communication network of the power system that show a significant impact on network controllability. We further validate the results of the filtering by examining the resulting network robustness. Through simulations, we observe that the power edges and communication nodes screened based on network controllability have the most significant impacts on system controllability. It is therefore crucial to protect these edges and nodes to achieve more stable power grid operation and prevent cascading failures. Finally, we validate our results using a case study on a city in Henan Province, China. Our simulations demonstrate that the addition of a communication system can enhance the controllability of the power system. • Different key components in both power grid and communication network are considered separately. • The impact of different coupling methods on the controllability of the system are evaluated. • The conclusions of this paper are validated in a real power grid. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time joint regulating reserve deployment (RRD) model of electric vehicles (EVs) and coal-fired generators (CGs) considering EV battery degradation. The superiority of the proposed model lies in the integration of the EVs' fast regulation speed and CGs' large regulation capacity. The whole optimization model is formulated under a two-stage Markov Decision Process (MDP) to correspond to the real-time RRD process. The user behavior characteristic and EV battery degradation are taken into account to stimulate the users to participate in RRD. Based on the formulated MDP, a piece-wise linear (PWL) function based scalable approximate dynamic programming (ADP) algorithm for different EV clusters is constructed to solve the real-time RRD model under uncertainties. A separable slope update method is proposed to update the slopes of PLF for different EV clusters with different user behaviors. The proposed ADP based real-time RRD algorithm (ADP-RTRRD) provides the approximate optimal real-time RRD policy with the empirical knowledge embedded through off-line training. Numerical simulation on a modified IEEE-39 bus system and Henan power grid in China verify the superiority of the proposed joint RRD model and the advantages of the proposed ADP-RTRRD algorithm. • Proposing the joint RRD model of EVs and CGs considering EV battery degradation. • Taking advantage of EVs' fast regulation speed and CGs' large regulation capacity. • Applying EV aggregation methods to simplify optimization complexity. • Reformulating the real-time RRD process under a two-stage MDP. • Proposing a scalable ADP algorithm to obtain approximate optimal solution. [ABSTRACT FROM AUTHOR]

• The evolution process of subsequent commutation failures (SCFs) is analyzed. • The influence of electrochemical energy storage (EES) power on SCFs is analyzed. • A coordinated power control strategy of EES to mitigate SCFs is proposed. • The influence of active power amplitude of EES on SCFs is discussed. Commutation failure is one of the most common faults in high voltage direct current (HVDC) transmission system. Subsequent commutation failures (SCFs) caused by commutation failure will cause large voltage and current swing. Several SCFs may cause DC transmission power interruption, which seriously threatens the safe and stable operation of AC/DC hybrid power grid. With the construction and commissioning of grid-side electrochemical energy storage (EES), it is possible to mitigate SCFs of adjacent HVDC transmission lines using EES with fast power response characteristics. In this paper, the influence mechanism of active and reactive power output of EES on commutation conditions is studied by combining the evolution of cascading outages and SCFs. It is found that reactive power mainly affects AC voltage and active power mainly affects DC current. Based on the mechanism analysis, a coordinated power control strategy for EES is presented. This strategy, combined with EES capacity constraints, can control EES active and reactive power output instructions adaptively according to the degree of AC failure at different stages of SCFs to fully release the transient support capacity of EES. Case studies are undertaken on the Henan multi-infeed UHVDC system and CIGRE-HVDC standard test system, respectively. The simulation results verify the correctness of the proposed EES power control strategy and the effectiveness of the strategy for SCFs mitigation in different systems, different fault types, and different fault degrees. [ABSTRACT FROM AUTHOR]

• Develop a dispatching scheme for DSUC incorporating multi-function demand response. • Design the LS and RC demand response to improve flexible ramping capability. • Accelerating solution method is proposed to reduce the computational burden. This paper proposes a two-stage data-driven unit commitment dispatching scheme, which coordinates the multi-function demand response of commercial air conditioning aggregators with thermal units to encounter the variations of load and wind power. In the proposed dispatching scheme, the flexible ramping capability of the thermal units is reflected in the coupling constraints between the ramping capacity and reserve capacity. To improve the flexibility of the whole system, multi-function demand response of commercial air conditioning aggregators is incorporated into the scheduling in both stages. In the day-ahead stage, load-shifting demand response reduces the ramping requirements by reshaping the load profile. In the real-time stage, reserve-capacity demand response releases the ramping capability by providing reserve for the rebalance of wind power fluctuation, which is represented by a data-driven uncertainty set. Since the dispatching scheme is formulated as a min–max-min problem, an accelerating solution method is developed to improve the computational efficiency, which combines the column-and-constraints generation algorithm and the specific property of the model. The simulation results tested on the modified IEEE 118-bus system and Henan power grid in China validate the effectiveness of the dispatching scheme and solution method in increasing flexible ramping capability, reducing wind power curtailment and load shedding, improving economic efficiency, and accelerating computation. [ABSTRACT FROM AUTHOR]