With increasing global concern about climate change and environmental pollution, clean power and zero-carbon transportation technologies have emerged as key solutions to address energy challenges. Electric vehicles (EVs) powered by renewable energy sources (RESs) are pivotal in reducing carbon emissions. However, the issue of power quality in RESs cannot be ignored. This paper first analyzes the effects of load, line resistance, and droop coefficient on voltage deviation coefficient and voltage unbalance factor in a bipolar DC microgrid. Subsequently, this paper proposes a distributed coordinated control of distributed RESs to mitigate the impact of large-scale EV integration on the power system. The proposed control approach ensures high power quality at various nodes while supporting RESs for EV charging. The control strategy adapts to dynamic load conditions, enhancing the integration of RESs. Furthermore, a small signal model is constructed to assess its impact on system stability. Finally, the MATLAB/Simulink and experimental platforms are established to validate the precision of the proposed distributed coordinated control scheme across different situations. This research advances zero-carbon transportation and smart grid development, laying a foundation for intelligent urbanization.

To realize the consumption of renewable energy such as wind power and photovoltaics in the power system, renewable energy integration system via modular multilevel converter (MMC)-based high voltage direct current (MMC-HVDC) has been widely applied. However, with the large-scale grid connection of renewable energy units, sub-synchronous oscillation (SSO) is prone to occur. Aiming at the problem, this paper proposes an SSO suppression strategy for renewable energy integration system via MMC-HVDC based on active disturbance rejection control (ADRC) theory. Using the direct drive permanent magnet synchronous generators (PMSG)-based wind farm integration system via MMC-HVDC as an example, firstly the topology and control system principles of the system are described, and a simulation model is built in PSCAD/EMTDC. Moreover, the SSO mechanism of the system is revealed by Nyquist stability criterion, and the major factors affecting the SSO of the system are simulated and analyzed. Subsequently, an additional sub-synchronous damping controller (ASSDC) is proposed based on ADRC theory. Compared to traditional additional damping controllers, the proposed controller considers disturbances of the system during the designing process and has stronger robustness. In addition, when faults happen, the speed of the system with ASSDC reaching a steady-state operating point rises by 33.7% as compared to the system without ASSDC. Finally, the effectiveness of the proposed suppression strategy is verified through simulation analysis.

In AC/DC hybrid power system, AC system failures and commutation valve trigger pulse disorder will lead to commutation failure, which may lead to DC voltage fluctuations, power transmission interruption and other serious consequences. In order to accurately and effectively identify the specific causes of commutation failures, a double deck traceability identification method is proposed in this paper. The surface identification based on wavelet entropy and affinity propagation (AP) algorithm can distinguish internal and external faults. The deep identification uses convolution neural network which can further lock the specific cause of commutation failures. In this paper, 1) the various factors leading to commutation failures are analyzed; 2) the fault feature space consists of the wavelet analysis components of DC voltage signal, and the AP algorithm is used to identify the surface source; 3) the DC current, AC voltage and current signals are added into the sample matrix of fault time-space, and the convolution neural network is used to identify the deep traceability. Finally, the accuracy of the method is verified by using the typical HVDC model.

In a power system with wind farms, the point of common coupling (PCC) usually suffers from voltage instability under large wind speed variations and the load impact. Using the internal converter of a doubly fed induction generator (DFIG)-based wind turbine to provide voltage support auxiliary service is an effective scheme to suppress the voltage fluctuation at PCC. To satisfy the reactive power demand of the connected grid, an active voltage coordinate control strategy with the hybrid energy storage system of the wind farm is proposed. The dynamic reactive power balance model is established to show the interaction between the reactive power limitation of the wind farm and the reactive power compensation demand of the grid. This indicates the initial conditions of the active voltage coordinate control strategy. According to the critical operating point and the operation state of the DFIG, the active and reactive power coordinate control strategy composed of active ω-β coordinate control and active β control is proposed to enhance the reactive power support capability and stabilize the grid voltage. To compensate the active power shortage, an auxiliary control strategy based on the hybrid energy storage system is introduced. The simulation results show that the proposed strategy can suppress the voltage fluctuation effectively and make full use of primary energy.

The stability of power system is impacted by the randomness of renewable generation output and it will further decline when the system is in high-order fault. Battery superconducting magnetic hybrid energy storage system (B-SMES) combines the ad-vantages of superconducting energy storage and battery energy storage, so it can simultaneously smooth the renewable generation output curve and reinforce the stability of power grid in extreme conditions. In this paper, a "planning-attack-defense" three-layer power grid planning model considering SMES characteristics and system robustness is proposed, which can optimize the construction location and capacity of SMES, and it is solved by Benders decomposition. The validity of the proposed model is verified in IEEE 24-RTS system.