This paper proposes a low-carbon operation model for an energy hub (EH) that combines the distributionally robust optimization (DRO) method with the Stackelberg game. Firstly, a bilevel single-leader-multi-follower Stackelberg game model is presented where the EH is the leader while users and electric vehicles (EVs) are regarded as two followers. Then, the Kullback-Leibler (KL) divergence-based DRO model is developed to deal with the uncertainty of renewable generation (RG) in the EH. Besides, Karush–Kuhn–Tucker (KKT) conditions, strong duality theory, and big- M approach are combined to transform the bilevel model into a single-level model. The reformulated single-level operation model is incorporated into the KL-based DRO approach. Furthermore, since the crafted column and constraint generation (C&CG) algorithm can prevent possible numerical problems caused by the exponential function and accelerate the solution speed, the crafted C&CG algorithm with linearization for the upper-level slave problem is proposed to iteratively solve the KL-based DRO integrated with Stackelberg game. Finally, numerical case studies are conducted with all simulation results confirming the effectiveness of the proposed model and method.

The modeling of dynamics in energy devices and pipeline networks reflects the real states of multi-energy flows, which is significant for realizing accurate optimal dispatch of integrated energy system (IES). In this paper, an approach with data-driven dynamic energy hubs (DDEH) and thermal dynamics of pipeline networks (TDPN) is proposed to describe the energy dynamic response process of IES. Based on the efficiency characteristics of energy conversion and storage devices, data-driven deep neural network (DNN) is adopted to excavate input-output relationship of the variable efficiency devices, which helps establish DDEH in time domain. To address the problem of nonlinear introduced by DNN, the nonlinear activation function in DDEH is equivalently converted into mixed integer linear model. At the same time, the TDPN is developed by bilateral characteristic line method (BCLM), which quantifies the time delay and loss of pipeline networks. TDNP demonstrates the network transportation dynamics in optimal dispatch, and the virtual energy storage effect of pipeline networks are analyzed. Case study from a community IES verifies the proposed approach effectively improve dynamic modeling accuracy of devices and pipeline networks, and have superiority in providing precise, reasonable and highly efficient optimal dispatch scheme.

Voltage sag is one of the most prominent power quality problems in electric power system. Accurate estimation of voltage sag parameters is an important reference for electric utilities and users to evaluate power quality. In this paper, a novel algorithm based on modified S transform with digital prolate spheroidal window (DPSW) is proposed to achieve accurate detection of voltage sag. The DPSW and its time-frequency characteristics are firstly detailed. Then, the construction and performance of modified S transform with DPSW are given. Since DPSW has superior time-frequency distribution, characteristics of voltage sag signal, such as sag depth, duration, and phase jump, can be obtained by analyzing the two-dimensional complex matrix after the modified S transform with DPSW. Simulation and experimental results verify the accuracy and validity of the proposed algorithm.

Although the pulse charging strategy has many advantages, it is still controversial. The paradox is that it prolongs the charging time to eliminate the polarization voltage by the pulse interval, and the ac component in pulse current will increase the ohmic losses. In this article, an optimal charging strategy with a complementary pulse current of lithium-ion is proposed to address and alleviate these issues. For the pulse frequency, the optimization can be achieved through the electrochemical impedance spectroscopy (EIS) analysis to reduce the battery’s ac impedance. For the pulse amplitude, based on the battery’s internal resistance characteristics, minimize the ohmic losses to obtain the optimal amplitude. For the pulse duty cycle, it is adjusted to achieve a state of charge (SOC) balancing in the early charging stage, and the battery’s terminal voltage balancing in the later stage. Based on the optimized charging strategy, a pulse charger concept is proposed, which can flexibly control the pulse charging currents and use complementary pulses to completely eliminate the prolonged charging time caused by the pulse interval. In the end, an experimental platform for pulse chargers of lithium-ion batteries is designed, and the theoretical analysis is verified by the comparative experiments.

Distributed feeder automation system (DFAs), as a promising protection technology for power distribution system (PDS) with distributed generation, its vulnerability to cyberattacks and hybrid attacks (contains both physical and cyberattacks) is gradually recognized and haunts utilities, creating potential risks for its large-scale applications. This paper proposes a novel bilevel resilient control solution (BRCS) deployed to DFAs without hardware burden. Two key modules are developed: 1) Lightweight distributed cyberattack detection module (DCDM), deployed into agents of DFAs, based on unsupervised learning to realize the quick detection and reporting of cyberattacks; 2) Robust centralized fault section localization module (CFSLM), installed in DFAs’ host workstation located in control center, achieving the correctly fault section localization and the high dimensionally awareness of attack events in cyberattack and hybrid attack scenarios. By adopting BRCS, outages and load losses caused by cyberattacks can be 100% avoided, and faults caused by physical attacks can be correctly isolated at once. Finally, the effectiveness and performance of the proposal are verified and conducted by the real two-feeder test platform with DFAs. In this process, a digital high-dimensional awareness and control unit is created against cyber and hybrid attacks, contributing to the system-level application of risk management and resilient control.

The three-phase three-wire neutral-point-clamped shunt active power filter (NPC-SAPF), which most adopts classical closed-loop feedback control methods such as proportional-integral (PI), proportional-resonant (PR) and repetitive control, can only output 1st–25th harmonic currents with 10–20 kHz switching frequency. The reason for this is that the controller design must make a compromise between system stability and harmonic current compensation ability under the condition of less than 20 kHz switching frequency. To broaden the bandwidth of the compensation current, a Lyapunov stability theory-based control strategy is presented in this paper for NPC-SAPF. The proposed control law is obtained by constructing the switching function on the basis of the mathematical model and the Lyapunov candidate function, which can avoid introducing closed-loop feedback control and keep the system globally asymptotically stable. By means of the proposed method, the NPC-SAPF has compensation ability for the 1st–50th harmonic currents, the total harmonic distortion (THD) and each harmonic content of grid currents satisfy the requirements of IEEE Standard 519-2014. In order to verify the superiority of the proposed control strategy, stability conditions of the proposed strategy and the representative PR controllers are compared. The simulation results in MATLAB/Simulink (MathWorks, Natick, MA, USA) and the experimental results obtained on a 6.6 kVA NPC-SAPF laboratory prototype validate the proposed control strategy.