arXiv subjects

Zhaoyu Wang

Publications and source records attributed to Zhaoyu Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Situationally Aware Rolling Horizon Multi-Tier Load Restoration Considering Behind-The-Meter DER

Restoration in power distribution systems (PDSs) is well studied, however, most existing research focuses on network partition and microgrid formation, where load transfer is limited to adjacent feeders. This focus is not practical, as when adjacent feeders lack sufficient capacity, utilities may request support from more distant feeders in practice. Such a hirarchical restoration is complex, especially when involving changing system conditions due to cold load pickup and delayed reconnection of behind-the-meter DERs. To fill this research gap, a situationally aware multi-tier load restoration framework is proposed. Specifically, models are proposed to describe the multi-tier load restoration, including the multi-tier load transfer and substation transformer and feeder protection models. By introducing binary actional switching variables and load block transfer variables, the models effectively captures the dynamics of switches and multi-tier transfer process. To integrate situational awareness of evolving system conditions, the problem is formulated as a mixed-integer linear program (MILP) and then embedded within a rolling horizon optimization. Particularly, a set of safeguarded constraints are developed based on segment-level restoration reward bounds to mitigate the myopia of traditional rolling horizon optimization. The proposed safeguarded rolling strategy guarantees that each time step is lower bounded by a $(1-\varepsilon)$-fraction of its optimal restoration potential, thereby balancing short-term switching decisions with long-term restoration goals. Finally, cases studies on the modified IEEE 123-node test feeder validate the proposed multi-tier restoration framework.

eess.SY↗

arXiv2025

Enhanced superconductivity in PbTe-In hybrids

We report the realization of epitaxial indium thin films on PbTe nanowires. The film is continuous and forms an atomically sharp interface with PbTe. Tunneling devices reveal a hard superconducting gap. The gap size, 1.08 to 1.18 meV, is twice as large as bulk indium's ($\sim$ 0.5 meV), due to the presence of PbTe. A similar enhancement is also observed in the critical temperature of In on a PbTe substrate. Subgap peaks appear at finite magnetic fields. The effective $g$-factor (15-45) is notably enhanced compared to bare PbTe wires ($<$ 10) due to the presence of In, differing from Al-hybrids. Josephson devices exhibit gate-tunable supercurrents. The PbTe-In hybrid enhances the properties of both, the superconductivity of In and g-factors of PbTe, and thus may enable exotic phases of matter such as topological superconductivity.

cond-mat.mes-hall↗

arXiv2025

Grid Edge Intelligence-Assisted Model Predictive Framework for Black Start of Distribution Systems with Inverter-Based Resources

The growing proliferation of distributed energy resources (DERs) is significantly enhancing the resilience and reliability of distribution systems. However, a substantial portion of behind-the-meter (BTM) DERs is often overlooked during black start (BS) and restoration processes. Existing BS strategies that utilize grid-forming (GFM) battery energy storage systems (BESS) frequently ignore critical frequency security and synchronization constraints. To address these limitations, this paper proposes a predictive framework for bottom-up BS that leverages the flexibility of BTM DERs through Grid Edge Intelligence (GEI). A predictive model is developed for GEI to estimate multi-period flexibility ranges and track dispatch signals from the utility. A frequency-constrained BS strategy is then introduced, explicitly incorporating constraints on frequency nadir, rate-of-change-of-frequency (RoCoF), and quasi-steady-state (QSS) frequency. The framework also includes synchronizing switches to enable faster and more secure load restoration. Notably, it requires GEI devices to communicate only their flexibility ranges and the utility to send dispatch signals without exchanging detailed asset information. The proposed framework is validated using a modified IEEE 123-bus test system, and the impact of GEI is demonstrated by comparing results across various GEI penetration scenarios.

eess.SY↗

arXiv2025

Stochastic Black Start Resource Allocation to Enable Dynamic Formation of Networked Microgrids and DER-aided Restoration

Extended outages in distributed systems (DSs) dominated by distributed energy resources (DERs) require innovative strategies to efficiently and securely deploy black start (BS) resources. To address the need, this paper proposes a two-stage stochastic resource allocation method within synchronizing dynamic microgrids (MGs) for black start (SDMG-BS), enabling risk-averse and adaptive restoration across various scenarios while ensuring frequency security. Virtual synchronous generator (VSG)-controlled grid-forming inverters (GFMIs) equipped with primary frequency governors (PFGs) are modeled as BS resources. Their frequency response is characterized by three transient indices, which are deployed as frequency dynamic constraints on load pick-up events to ensure frequency stability during the BS process. SDMG-BS framework facilitates location-independent synchronization among restored MGs and with the transmission grid (TG) with the help of smart switches (SSWs). The model incorporates scenario-based stochastic programming to address multi-source uncertainties, including season-dependent operational conditions and unpredictable TG outage durations, ensuring a resilient allocation plan. The proposed approach is validated on a modified IEEE 123-node feeder with three study cases designed across sixteen uncertainty scenarios.

eess.SY↗

arXiv2025

Data-driven quantification and visualization of resilience metrics of power distribution system

This paper presents a data-driven approach for quantifying the resilience of distribution power grids to extreme weather events using two key metrics: (a) the number of outages and (b) restoration time. The method leverages historical outage records maintained by power utilities and weather measurements collected by the National Oceanic and Atmospheric Administration (NOAA) to evaluate resilience across a utility's service territory. The proposed framework consists of three stages. First, outage events are systematically extracted from the outage records by temporally and spatially aggregating coincident component outages. In the second stage, weather zones across the service territory are delineated using a Voronoi polygon approach, based on the locations of NOAA weather sensors. Finally, data-driven models for outage fragility and restoration time are developed for each weather zone. These models enable the quantification and visualization of resilience metrics under varying intensities of extreme weather events. The proposed method is demonstrated using real-world data from a US distribution utility, located in Indianapolis, focused on wind- and precipitation-related events. The dataset spans two decades and includes over 160,000 outage records.

eess.SY↗

arXiv2025

Fast Feeder Reconfiguration via Mesh Adaptive Direct Search in Black-Box Distribution System Environments

Feeder reconfiguration is a critical operational strategy in power distribution systems. However, existing optimization approaches typically rely on explicit mathematical formulations and analytical models, which are often infeasible in practical utility environments characterized by heterogeneous, proprietary, and black-box simulation modules. To address this challenge, this paper proposes a fast feeder reconfiguration framework based on Mesh Adaptive Direct Search (MADS). The proposed approach requires only performance metric evaluations through simulation modules used for power flow, protection, and voltage regulation analysis. A bi-objective formulation is adopted to jointly minimize active power loss and operational constraint violations. A Pareto-based frontier filter is integrated into the MADS algorithm to efficiently guide the search toward high-quality configurations while systematically pruning dominated solutions. The approach adaptively refines the search space around promising candidates using local polling strategies and convergence aware updates. Case studies on the IEEE-123 node test feeder demonstrate that the proposed approach achieves near-optimal configurations with significantly fewer evaluations compared to heuristic methods.

eess.SY↗

arXiv2025

Model Predictive Black Start for Dynamic Formation of DER-Led Microgrids with Inrush Current Impacts

Black start (BS) of the distribution system (DS) with high penetration of distributed energy resources (DERs) requires advanced control frameworks to ensure secure and efficient restoration. This paper proposes a model predictive black start (MPBS) framework incorporating an inrush current feasibility module to dynamically generate real-time feasible and optimal restoration sequences. Short-term forecasts of DER output and transmission grid (TG) availability are utilized to construct adaptive cranking paths. The inrush current feasibility module analytically estimates the transient inrush current caused by energizing no-load distribution transformers (DTs). To mitigate excessive inrush current and avoid potential misoperations of protection devices, an emergency operation-inspired voltage control strategy and a switch blocking mechanism are developed. The proposed inrush model is validated against electromagnetic transient (EMT) simulations in PowerFactory with estimation accuracies exceeding 90 %. Case studies on a modified IEEE 123-node feeder demonstrate that the MPBS framework prevents misoperations of fuses and reclosers, reduces unnecessary DER energy consumption, and enhances load restoration efficiency during DER-led BS processes.

eess.SY↗

arXiv2025

Painlevé transcendents in the defocusing mKdV equation with non-zero boundary conditions

We consider the Cauchy problem for the defocusing modified Korteweg-de Vries (mKdV) equation with non-zero boundary conditions \begin{align} &q_t(x,t)-6q^2(x,t)q_{x}(x,t)+q_{xxx}(x,t)=0, \nonumber &q(x,0)=q_{0}(x)\to \pm 1, \ \ x\rightarrow\pm\infty, \nonumber \end{align} which can be characterized using a Riemann-Hilbert problem through the inverse scattering transform. Using the $\bar\partial$-generalization of the Deift-Zhou nonlinear steepest descent approach, combined with the double scaling limit technique, we obtain the long-time asymptotics of the solution of the Cauchy problem for the defocusing mKdV equation in the transition region $|x/t+6|t^{2/3}< C$ with $C>0$. The asymptotics can be expressed in terms of the solution of the second Painlevé transcendent.

math-ph↗

arXiv2025

Gate modulation and interface engineering on Coulomb blockade in open superconducting islands

Mesoscopic Coulomb blockade (MCB) is recognized as a phase-coherent variant of the conventional Coulomb blockade that arises in systems with open contacts. In open quantum dots, MCB is enhanced by a decrease in background conductance. This occurs because the reduction in coupling strength between the quantum dot and the outer reservoir renders the system more closed, thereby facilitating the emergence of conventional Coulomb blockade. In this work, we demonstrate that the MCB in open superconducting islands exhibits an different correlation with coupling strength compared to open quantum dots. Specifically, a decrease in background conductance may result in a weakening of the MCB. This observation indicates that the MCB in superconducting islands originates from the presence of superconducting-normal interfaces.

cond-mat.mes-hall↗

arXiv2025

Reducing disorder in PbTe nanowires for Majorana research

Material challenges are the key issue in Majorana research where surface disorder constrains device performance. Here, we tackle this challenge by embedding PbTe nanowires within a lattice-constant-matched crystal. The wire edges are shaped by self-organized growth instead of lithography, resulting in nearly-atomic-flat facets along both cross-sectional and longitudinal directions. Quantized conductance is observed at zero magnetic field with channel lengths maximally reaching 1.7 $μ$m, significantly surpassing the state-of-the-art of III-V nanowires (an order-of-magnitude improvement compared to InSb). Coupling PbTe to a Pb film unveils a flat interface spanning microns and a large superconducting gap of 1 meV. Our result not only represents a stride toward meeting the stringent low-disorder requirement for Majoranas, but may also open the door to various hybrid quantum devices requiring a low level of disorder.

cond-mat.mes-hall↗

arXiv2025

Anisotropy of PbTe nanowires with and without a superconductor

We investigate the anisotropic behaviors in PbTe and PbTe-Pb hybrid nanowires. In previous studies on PbTe, wire-to-wire variations in anisotropy indicate poor device control, posing a serious challenge for applications. Here, we achieve reproducible anisotropy in PbTe nanowires through a substantial reduction of disorder. We then couple PbTe to a superconductor Pb, and observe a pronounced deviation in the anisotropy behavior compared to bare PbTe nanowires. This deviation is gate-tunable and attributed to spin-orbit interaction and orbital effect, controlled by charge transfer between Pb and PbTe. These results provide a guidance for the controlled engineering of exotic quantum states in this hybrid material platform.

cond-mat.mes-hall↗

arXiv2025

Long-time Asymptotics for the Ablowitz-Ladik system with present of solitons

We investigate the soliton resolution and Painlevé asymptotics for the focusing Ablowitz-Ladik system with the initial data in a discrete weighted $\ell^2$ space. First, we establish the global well-posedness of this initial-value problem, which is further reformulated as a Riemann-Hilbert problem with higher-order poles. Using Fredholm theory, the Riemann-Hilbert problem with the jump contour consisting of three circles centered around the origin is uniquely solved. Then, by performing a $\bar\partial$-nonlinear steepest descent method to the Riemann-Hilbert problem, we obtain the asymptotic approximation to the solution of the focusing Ablowitz-Ladik system for large time in different space-time regions of the $(n,t)$-half plane. In the sectors $\{(n,t): n /(2t) <-M_0 \}$ and $\{(n,t): n /(2t) >M_0 \}$, where $M_0$ is a positive constant, the leading order asymptotics is dominated by the solitons; while in the sector $\{(n,t): |n /(2t) -1 <M_0^{-1} \}$, the long-time asymptotics is influenced by both the solitons and the oscillations; In the two transition zones $\{(n,t): |n /(2t)+1|t^{2/3} <C \}$ and $\{(n,t): |n /(2t)-1|t^{2/3} <C \}$ with $C$ being a positive constant, we find the Painlevé-type asymptotics which can be expressed in terms of the solution of the second Painlevé transcendents.

math.AP↗

arXiv2024

DERs-Aided Blackstart and Load Restoration Framework for Distribution Systems Considering Synchronization and Frequency Security Constraints

Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids, sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolated microgrids. In DER-aided blackstart, the continuous operation of these microgrids is uncertain due to the finite energy capacity of commonly used blackstart units, such as battery energy storage (BES)-based grid-forming inverters (GFMIs). To address this issue, this article proposes a holistic blackstart and restoration framework that incorporates synchronization between microgrids and the entire DS with the transmission grid (TG). To support synchronization, we leveraged virtual synchronous generator-based control for GFMIs to estimate their frequency response to load pick-up events using only initial/final quasi-steady-state points. Subsequently, a synchronization switching condition was developed to model synchronizing switches, aligning them seamlessly with a linearized branch flow problem. Finally, we designed a bottom-up blackstart and restoration framework that considers the switching structure of the DS, energizing/synchronizing switches, DERs with grid-following inverters, and BES-based GFMIs with frequency security constraints. The proposed framework is validated in IEEE-123-bus system, considering cases with two and four GFMIs under various TG recovery instants.

eess.SY↗

arXiv2024

Coulomb blockade in open superconducting islands on InAs nanowires

Electrons in closed systems can exhibit Coulomb blockade (CB) oscillations due to charge quantization. Here, we report CB oscillations in aluminum superconducting islands on InAs nanowires in the open regime. The Al island is connected to the source/drain leads through two contacts: One is fully transmitting while the other is tuned into the tunneling regime. This device configuration is typical for tunneling spectroscopy where charging energy is generally considered negligible. The oscillation periods are 2$e$ or 1$e$, depending on the gate settings. A magnetic field can induce the 2$e$ to 1$e$ transition. Our result is reminiscent of the "mesoscopic Coulomb blockade" in open quantum dots caused by electron interference.

cond-mat.mes-hall↗

arXiv2024

The Cauchy problem for the Degasperis-Procesi Equation: Painlevé Asymptotics in Transition Zones

The Degasperis-Procesi (DP) equation \begin{align} &u_t-u_{txx}+3κu_x+4uu_x=3u_x u_{xx}+uu_{xxx}, \nonumber \end{align} serving as an asymptotic approximation for the unidirectional propagation of shallow water waves, is an integrable model of the Camassa-Holm type and admits a $3\times3$ matrix Lax pair. In our previous work, we obtained the long-time asymptotics of the solution $u(x,t)$ to the Cauchy problem for the DP equation in the solitonic region $\{(x,t): ξ>3 \} \cup \{(x,t): ξ<-\frac{3}{8} \}$ and the solitonless region $\{(x,t): -\frac{3}{8}<ξ< 0 \} \cup \{(x,t): 0\leq ξ<3 \}$ where $ξ:=\frac{x}{t}$. In this paper, we derive the leading order approximation to the solution $u(x,t)$ in terms of the solution for the Painlevé \uppercase\expandafter{\romannumeral2} equation in two transition zones $\left|ξ+\frac{3}{8}\right|t^{2/3} 0$ lying between the solitonic region and solitonless region. Our results are established by performing the $\bar \partial$-generalization of the Deift-Zhou nonlinear steepest descent method and applying a double scaling limit technique to an associated vector Riemann-Hilbert problem.

math.AP↗

arXiv2024

Soliton resolution and asymptotic stability of $N$-solitons to the Degasperis-Procesi equation on the line

The Degasperis-Procesi (DP) equation \begin{align} &u_t-u_{txx}+3κu_x+4uu_x=3u_x u_{xx}+uu_{xxx}, \nonumber \end{align} serving as a model delineating the propagation of shallow water waves, stands as a completely integrable system and admits a $3\times3$ matrix Lax pair. In this manuscript, we study the soliton resolution and large time behavior of solutions to the Cauchy problem of the DP equation with generic initial data in Schwarz space. Employing the $\bar \partial$-generalization of the Deift-Zhou nonlinear steepest descent method, we deduce different long time asymptotic expansions of the solution $u(x,t)$ in two distinct types of space-time regions. This result verifies the soliton resolution conjecture and asymptotic stability of $N$-soliton solutions for the DP equation.

math-ph↗

arXiv2024

ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B. They represent our most capable models that are trained with all the insights and lessons gained from the preceding three generations of ChatGLM. To date, the GLM-4 models are pre-trained on ten trillions of tokens mostly in Chinese and English, along with a small set of corpus from 24 languages, and aligned primarily for Chinese and English usage. The high-quality alignment is achieved via a multi-stage post-training process, which involves supervised fine-tuning and learning from human feedback. Evaluations show that GLM-4 1) closely rivals or outperforms GPT-4 in terms of general metrics such as MMLU, GSM8K, MATH, BBH, GPQA, and HumanEval, 2) gets close to GPT-4-Turbo in instruction following as measured by IFEval, 3) matches GPT-4 Turbo (128K) and Claude 3 for long context tasks, and 4) outperforms GPT-4 in Chinese alignments as measured by AlignBench. The GLM-4 All Tools model is further aligned to understand user intent and autonomously decide when and which tool(s) touse -- including web browser, Python interpreter, text-to-image model, and user-defined functions -- to effectively complete complex tasks. In practical applications, it matches and even surpasses GPT-4 All Tools in tasks like accessing online information via web browsing and solving math problems using Python interpreter. Over the course, we have open-sourced a series of models, including ChatGLM-6B (three generations), GLM-4-9B (128K, 1M), GLM-4V-9B, WebGLM, and CodeGeeX, attracting over 10 million downloads on Hugging face in the year 2023 alone. The open models can be accessed through https://github.com/THUDM and https://huggingface.co/THUDM.

cs.CL↗

arXiv2024

Input Convex Neural Network-Assisted Optimal Power Flow in Distribution Networks: Modeling, Algorithm Design, and Applications

This paper proposes an input convex neural network (ICNN)-Assisted optimal power flow (OPF) in distribution networks. Instead of relying purely on optimization or machine learning, the ICNN-Assisted OPF is a combination of optimization and machine learning. It utilizes ICNN to learn the nonlinear but convex mapping from control variables to system state variables, followed by embedding into constrained optimization problems as convex constraints. Utilizing a designed ICNN structure, a fast primal-dual gradient method is developed to solve the ICNN-Assisted OPF, with the chain rule of deep learning applied to accelerate the algorithmic implementation. Convergence and optimally properties of the algorithm design are further established. Finally, different distribution network applications are discussed and proposed by means of the ICNN-Assisted OPF.

eess.SY↗