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Because of the control complexity of voltage source converters (VSCs), transient stability analysis of multi-VSC parallel systems is challenging, and there are still no effective methods to solve this problem. Inspired by the decoupling principle and combined with the normal form method, an innovative coupling-factor-based nonlinear decoupling (CFND) method is proposed. According to the coupling factors that can be used to evaluate the nonlinear coupling degree among different state variables, the CFND method approximately transforms a high-order nonlinear multi-VSC parallel system into multiple decoupled low-order modes. Thus, the transient stability of the original high-order multi-VSC system can be reflected indirectly by the mature inversing trajectory method and the phase plane method. The CFND method has universality, flexibility, and insensitivity to system order, and no need to construct corresponding Lyapunov functions for different nonlinear systems, breaking through the inherent limitations of traditional analysis methods. Furthermore, this paper derives a reduced-order large-signal model and the corresponding truncated model for a single VSC grid-connected system. The effectiveness of the reduced-order model is verified through simulation waveforms and ROAs partitioning. Subsequently, a generalized model of the multi-VSC grid-connected system is developed. Finally, taking the grid-connected system with three VSCs as an example, the proposed CFND method is used to analyze typical operation cases, and the conclusions of transient stability analysis are verified using the hardware-in-loop (HIL) experiments. [ABSTRACT FROM AUTHOR]

Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access. [ABSTRACT FROM AUTHOR]

The cost reduction of hydrogen refueling stations (HRSs) is very important for the popularization of hydrogen vehicles. This paper proposes an optimized operation algorithm based on hydrogen energy demand estimation for on-site hydrogen refueling stations. Firstly, the user's hydrogen demand was estimated based on the simulation of their hydrogenation behavior. Secondly, mixed integer linear programming method was used to optimize the operation of the hydrogen refueling station to minimize the unit hydrogen energy cost by using the peak–valley difference of the electricity price. We then used three typical scenario cases to evaluate the optimized operation method. The results show that the optimized operation method proposed in this paper can effectively reduce the rated configuration of electrolyzer and storage tank for HRS and can significantly reduce the unit hydrogen energy cost considering the construction cost compared with the traditional method. Therefore, the optimization operation method of a local hydrogen production and hydrogen refueling station proposed in this paper can reduce the cost of a hydrogen refueling station and accelerate the popularization of hydrogen energy vehicles. Finally, the scope of application of the proposed optimization method and the influence of the variation of the electricity price curve and the unit cost of the electrolyzer are discussed. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed renewable energy resources causes voltage fluctuations in distribution networks. The controllable active and reactive power resources such as energy storage (ES) systems and electric vehicles (EVs) in active distribution networks play an important role in mitigating the voltage excursions. This paper proposes a two-timescale hybrid voltage control strategy based on a mixed-integer optimization method and multi-agent reinforcement learning (MARL) to reduce power loss and mitigate voltage violations. In the slow-timescale, the active and reactive power optimization problem involving capacitor banks (CBs), on-load tap changers (OLTC), and ES systems is formulated as a mixed-integer second-order cone programming problem. In the fast-timescale, the reactive power of smart inverters connected to solar photovoltaic systems and active power of EVs are adjusted to mitigate short-term voltage fluctuations with a MARL algorithm. Specifically, we propose an experience augmented multi-agent actor-critic (EA-MAAC) algorithm with an attention mechanism to learn high-quality control policies. The control policies are executed online in a decentralized manner. The proposed hybrid voltage control strategy is validated on an IEEE testing distribution feeder. The numerical results show that our proposed control strategy is not only sample-efficient and robust but also effective in mitigating voltage fluctuations. [ABSTRACT FROM AUTHOR]