Shiwei Xia

Synchrophasor-based sub-synchronous oscillation (SSO) parameter identification is effective for monitoring SSOs, while its performance can be significantly affected by measurement noise, posing serious challenges to reliable identification. This study proposes an improved identification scheme that combines adaptive empirical Fourier decomposition (AEFD) with the Prony method to enable accurate and simultaneous estimation of sub/super-synchronous modes. First, the AEFD method is applied to oscillation signals, effectively decomposing the signals into sub-synchronous and super-synchronous modal components. Particularly, the sparsity index (SI) is introduced to determine the number of oscillation modes contained in the signal. Subsequently, the Prony method is employed on the decomposed components to extract modal parameters. The proposed method effectively suppresses modal aliasing and improves noise robustness of the empirical wavelet transform by employing an improved spectrum segmentation technique and a zero-phase filter bank, thereby enhancing the extraction accuracy of the estimation results. Through comparisons with existing methods and simulated case studies, it is verified that the proposed method performs exceptionally well in terms of accuracy, mode mixing suppression and noise robustness, demonstrating its superiority and effectiveness in the extraction of sub/super-synchronous oscillation parameters.

High penetration of distributed renewables in the distribution grid has adverse effects on nodal voltage, network loss, and system stable operation. Essentially, the cause of these phenomena lies in the difficulty of the distribution network's reactive power to dynamically regulate and adapt to the varying power flow. Therefore, a multi-timescale reactive power optimization model for distribution networks considering network reconfiguration and the demand response (DR) of air conditioning (AC) systems is proposed in this paper. In the day-ahead reactive power optimization timescale, the On-Load Tap Changers (OLTCs) and capacitor banks (CBs) are modeled with a limited number of daily switching times, afterward are optimized with the network reconfiguration to alleviate power grid risk and minimize network loss, voltage deviation and operation cost of the distribution grid. Based on the day-ahead optimized operation statuses of OLTCs and CBs, in the intraday reactive power optimization timescale, the active and reactive power output of SVCs, SVGs, and photovoltaic (PV) inverters are flexibly controlled to achieve the minimum network loss and voltage deviations. To solve the nonlinear multi-timescale optimization model quickly and accurately, the adaptive immune particle swarm optimization (AIPSO) hybrid with embedded K-means strategy is designed to obtain the optimal tap positions of OLTCs and CBs as well as the optimized PV inverters' power output. Case studies based on a modified IEEE33-bus and standard IEEE118-bus distribution network validate the effectiveness of the proposed multi-timescale reactive power optimization strategy.

Synchrophasor-based identification of low-frequency oscillation is an effective means for monitoring the security and stability of power systems, while the loss of identification accuracy due to measurement noise remains a major obstacle. This article proposes an improved fast independent component analysis (improved FastICA) algorithm to identify oscillation modes (oscillation frequency and damping ratio) from noise-contaminated synchronized measurements. To improve the ability of conventional FastICA to extract modes from noise-polluted synchrophasor, nonlinear matching pursuit (NMP) is introduced to replace the negative entropy calculation of mono-frequency modes in FastICA. NMP enables FastICA to identify oscillation frequency and damping ratio even under strong noise conditions, thereby significantly improving identification accuracy. The performance of the proposed method is evaluated on synthetic signals and numerical simulation signals from the IEEE 16-machine system. The results of the identified modes in all cases, as well as comparison results from conventional FastICA, empirical mode decomposition (EMD), and variational mode decomposition (VMD), all combined with Hilbert transform (HT), confirm the accuracy, robustness, and efficiency of the proposed method for oscillation mode identification.

The spatiotemporal energy-shifting and moving flexibility of mobile energy storage (MES) can be explored to effectively support the operation security and resilience of distribution network. With this regard, a discrete multilayer spatiotemporal coupled network is first established to model the power charging/discharging, energy-shifting and moving-trajectory coupling of MESs; then a two-stage optimal operation strategy of distribution network simutaneously considering the operation flexibility of MESs and distributed generators (DGs) is proposed. In the first stage, the optimized connecting points of MES and optimal power output of DGs are optimized to achieve the minimum distribution network load shedding cost during fault; in the second stage, MES moving trajectory and charging/discharging power, as well as the power output of DGs are collaboratively optimized to minimize the comprehensive operation cost of the distribution network in post-fault. Finally, the effectiveness of the proposed two-stage optimal operation strategy considering MES flexibility was verified by the IEEE 33-bus network. Compared to scheme 2 with location-fixed energy storage and scheme 3 with DGs, the proposed approach reduced the load shedding costs by 4.5% and 2.2%, with the distribution network comprehensive operation costs decreased by 3.7% and 10%, respectively.

This paper presents a strategic method for optimizing energy distribution in highway cluster microgrids. It employs a model that synchronizes the energy usage of shared power‐exchanging vehicles (SPEVs) with their spatial and temporal dynamics, while prioritizing the operational safety of the microgrids. The strategy begins with applying the DistFlow power flow equation to define the microgrid's voltage and line capacity limits, aligning the energy use of SPEVs and battery packs both temporally and spatially. A comprehensive cost analysis is then conducted, covering aspects like line losses, unit start–stop expenses, and carbon emission costs. Following this, we develop an energy‐scheduling model tailored for highway microgrids, which upholds operational safety and promotes energy sharing among the grid. A case study illustrates the effectiveness of this strategy, showing it not only optimizes SPEV routes and power outputs from distributed generators and battery packs but also achieves cost‐effective, efficient, and eco‐friendly energy use in highway microgrids.