arXiv subjects

Fangxing Li

Publications and source records attributed to Fangxing Li.

At least 19 recordsLinked to original sources

arXiv2026

Topology-Aware Propagation-Based Assessment of Extreme-Weather Impacts on Distribution System Resilience

Extreme weather events and the increasing integration of distributed energy resources (DERs) introduce growing uncertainty and resilience challenges for distribution systems. Unlike conventional deterministic contingencies, weather-driven disruptions exhibit probabilistic and spatial-temporal characteristics, where outage consequences depend on both geographic exposure and feeder topology. Existing approaches commonly focus on deterministic outage analysis, while topology-aware operational impact assessment under forecast uncertainty remains limited. This paper proposes an event-conditioned uncertainty modeling and topology-aware impact propagation framework for distribution systems under torrential rain events. The proposed framework integrates probabilistic event-track modeling, branch-level fault screening, downstream impact propagation analysis, and operational impact assessment within a unified workflow. Finally, case studies on the IEEE 33-bus distribution feeder demonstrate that the proposed framework can distinguish geographic exposure from topology-dependent operational impacts and support progressive early-warning impact assessment under uncertain scenarios. Furthermore, the impact zones are visualized on the CURENT Large-scale Testbed (LTB)-AGVis platform.

eess.SY↗

arXiv2026

PFAgent: A Tractable and Self-Evolving Power-Flow Agent for Interactive Grid Analysis

Power system simulation workflows remain expert-intensive. Engineers must translate study intents into code or API calls, execute analyses, and interpret outputs. To automate this workflow, this paper presents PFAgent, a tractable and self-evolving power-flow agent for interactive grid analysis. PFAgent integrates four key capabilities: i) a tractable and interactive architecture for intent parsing, knowledge retrieval, tool execution, and structured reporting; ii) a self-evolution mechanism combining verification-driven refinement and human-in-the-loop feedback; iii) an AI-assisted evaluation and debugging loop that leverages conversational context, generated code, and execution errors for iterative fixing; and iv) an evaluation framework covering task success, convergence validity, numerical consistency, and explanation quality. Verification on IEEE benchmark systems shows that PFAgent can automate case change, analyze voltage violations, perform N-1 contingency analysis, generate plots and concise summaries, and return reproducible results with transparent execution logs. The proposed framework highlights a shift from conventional simulation tools to interactive, tractable, and self-evolving agents for power system analysis.

eess.SY↗

arXiv2024

Resilience-Oriented DG Siting and Sizing Considering Energy Equity Constraint

Extreme weather events can cause widespread power outages and huge economic losses. Low-income customers are more vulnerable to power outages because they live in areas with poorly equipped distribution systems. However, existing approaches to improve grid resilience focus on the overall condition of the system and ignore the outage experiences of low-income customers, which leads to significant energy inequities in resilience. Therefore, this paper explores a new resilience-oriented planning method for distributed generator (DG) siting and sizing, by embedding an additional energy equity constraint (EEC). First, the expected load shedding index (ELSI) is defined as the ratio of the load shedding to the original load, which quantifies the resilience-oriented energy equity. Then, the DG siting and sizing problem is formulated as a two-stage stochastic programming with the EEC. The first stage determines the optimal sites and sizes of DG units under investment constraints and EECs, while the second stage optimizes expected costs of unserved load. A subsidiary variable is introduced to ensure the model's solvability. Finally, numerical studies are performed on the IEEE 33-bus and 123-bus systems to verify the effectiveness of the proposed DG planning model in achieving energy equity. Three observations are presented as future guidelines for resilience-oriented DG planning.

eess.SY↗

arXiv2024

Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics

This paper presents, for the first time, a framework for Kolmogorov-Arnold Networks (KANs) in power system applications. Inspired by the recently proposed KAN architecture, this paper proposes physics-informed Kolmogorov-Arnold Networks (PIKANs), a novel KAN-based physics-informed neural network (PINN) tailored to efficiently and accurately learn dynamics within power systems. The PIKANs present a promising alternative to conventional Multi-Layer Perceptrons (MLPs) based PINNs, achieving superior accuracy in predicting power system dynamics while employing a smaller network size. Simulation results on a single-machine infinite bus system and a 4-bus 2- generator system underscore the accuracy of the PIKANs in predicting rotor angle and frequency with fewer learnable parameters than conventional PINNs. Furthermore, the simulation results demonstrate PIKANs capability to accurately identify uncertain inertia and damping coefficients. This work opens up a range of opportunities for the application of KANs in power systems, enabling efficient determination of grid dynamics and precise parameter identification.

eess.SY↗

arXiv2024

Coordination of Damping Controllers: A Novel Data-Informed Approach for Adaptability

This paper explores the novel concept of damping controller coordination, which aims to minimize the Total Action metric by identifying an optimal switching combination (on/off) of these controllers. The metric is rooted in power system physics, capturing oscillation energy associated with all synchronous generators in the grid. While coordination has shown promising results, it has relied on computing linear sensitivities based on the grid model. This paper proposes a data-informed framework to accurately estimate total action and subsequently determine an optimal switching combination. The estimation is provided by a multivariate function approximator that captures the nonlinear relationship between system-wide area measurements, the status of damping controllers, and the conditions of the disturbance. By enabling real-time coordination, electromechanical oscillations are reduced, enhancing power system stability. The concept is tested in the Western North America Power System (wNAPS) and compared with the model-based approach for coordination. The proposed coordination outperforms the model-based approach, demonstrating effective adaptability and performance in handling multi-mode events. Additionally, the results show significant reductions in low-frequency electromechanical oscillations even under various operating conditions, fault locations, and time delay considerations.

eess.SY↗

arXiv2023

Modeling the impact of extreme summer drought on conventional and renewable generation capacity: methods and a case study on the Eastern U.S. power system

The United States has witnessed a growing prevalence of droughts in recent years, posing significant challenges to water supplies and power generation. The resulting impacts on power systems, including reduced capacity and the potential for power outages, underscore the need for accurate assessment methods to ensure the reliable operation of the nation's energy infrastructure. A critical step is to evaluate the usable capacity of a regional power system's generation fleet, which is a complex undertaking and requires precise modeling of the effects of hydrological and meteorological conditions on diverse generating technologies. This paper proposes a systematic, analytical approach for assessing the impacts of extreme summer drought events on the available capacity of hydro, thermal, and renewable energy generators. More specifically, the systematic framework provides plant-level capacity derating models for hydroelectric, once-through cooling thermoelectric, recirculating cooling thermoelectric, combustion turbine, solar PV, and wind turbine systems. Application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system in the PJM and SERC regions yields insightful results. By examining the daily usable capacity of 6,055 at-risk generators throughout the study region, we find that in the event of the recurrence of the 2007 southeastern summer drought in the near future, the usable capacity of all at-risk power plants may experience a substantial decrease compared to a typical summer, falling within the range of 71% to 81%. The sensitivity analysis reveals that the usable capacity would experience a more pronounced decline under more severe drought conditions. The findings of this study offer valuable insights, enabling stakeholders to enhance the resilience of power systems against the potential effects of extreme drought in the future.

eess.SY↗

arXiv2023

DiME and AGVis: A Distributed Messaging Environment and Geographical Visualizer for Large-scale Power System Simulation

This paper introduces the messaging environment and the geographical visualization tool of the CURENT Large-scale Testbed (LTB) that can be used for large-scale power system closed-loop simulation. First, Distributed Messaging Environment (DiME) implements an asynchronous shared workspace to enable high-concurrent data exchange. Second, Another Grid Visualizer (AGVis) is presented as a geovisualization tool that facilitates the visualization of real-time power system simulation. Third, case studies show the use of DiME and AGVis. The results demonstrate that, with the modular structure, the LTB is capable of not only federal use for real-time, large-scale power system simulation, but also independent use for customized power system research.

eess.SY↗

arXiv2023

Decentralized and Coordinated Vf Control for Islanded Microgrids Considering DER Inadequacy and Demand Control

This paper proposes a decentralized and coordinated voltage and frequency (Vf) control framework for islanded microgrids, with full consideration of the limited capacity of distributed energy resources (DERs) and Vf dependent load. First, the concept of DER inadequacy is illustrated with the challenges it poses. Then, a decentralized and coordinated control framework is proposed to regulate the output of inverter based generations and reallocate limited DER capacity for Vf control. The control framework is composed of a power regulator and a Vf regulator, which generates the supplementary signals for the primary controller. The power regulator regulates the output of grid forming inverters according to the real time capacity constraints of DERs, while the Vf regulator improves the Vf deviation by leveraging the load sensitivity to Vf. Next, the static feasibility and small signal stability of the proposed method are rigorously proven through mathematical formulation and eigenvalue analysis. Finally, a MATLAB Simulink simulation demonstrates the functionalities of the control framework. A few goals are fulfilled within the decentralized and coordinated framework, such as making the best use of limited DERs capacity, enhancing the DC side stability of inverter based generations, and reducing involuntary load shedding.

eess.SY↗

arXiv2023

Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision

Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A high-level research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of three fusing approaches, i.e., model identification and parameter tuning, supplementary signal generation, and controller substitution, with the existing control framework. Finally, the fundamental challenges associated with adopting MFRL in microgrid control and corresponding insights for addressing these concerns are fully discussed.

eess.SY↗

arXiv2022

Virtual Inertia Scheduling for Power Systems with High Penetration of Inverter-based Resources

This paper proposes a new concept called virtual inertia scheduling (VIS) to efficiently handle the high penetration of inverter-based resources (IBRs). VIS is an inertia management framework that targets security-constrained and economy-oriented inertia scheduling and generation dispatch of power systems with a large scale of renewable generations. Specifically, it schedules the proper power setting points and reserved capacities of both synchronous generators and IBRs, as well as the control modes and control parameters of IBRs to provide secure and cost-effective inertia support. First, a uniform system model is employed to quantify the frequency dynamics of the IBRs-penetrated power system after disturbances. Based on the model, the s-domain and time-domain analytical responses of IBRs with inertia support capability are derived. Then, VIS-based real-time economic dispatch (VIS-RTED) is formulated to minimize generation and reserve costs, with a full consideration of dynamic frequency constraints and derived inertia support reserve constraints. The virtual inertia and damping of IBRs are formulated as decision variables. To address the non-linearity of dynamic constraints, deep learning-assisted linearization is employed to solve the optimization problem. Finally, the proposed VIS-RTED is demonstrated on a modified IEEE 39-bus system. A full-order time-domain simulation is performed to verify the scheduling results.

eess.SY↗

arXiv2022

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market, but incorporates characteristics of the distribution system, such as high R/X ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This paper first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.

eess.SY↗

arXiv2022

Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience

Multi-microgrid formation (MMGF) is a promising solution to enhance power system resilience. This paper proposes a new deep reinforcement learning (RL) based model-free on-line dynamic multi-MG formation (MMGF) scheme. The dynamic MMGF problem is formulated as a Markov decision process, and a complete deep RL framework is specially designed for the topology-transformable micro-grids. In order to reduce the large action space caused by flexible switch operations, a topology transformation method is proposed and an action-decoupling Q-value is applied. Then, a CNN based multi-buffer double deep Q-network (CM-DDQN) is developed to further improve the learning ability of original DQN method. The proposed deep RL method provides real-time computing to support on-line dynamic MMGF scheme, and the scheme handles a long-term resilience enhancement problem using adaptive on-line MMGF to defend changeable conditions. The effectiveness of the proposed method is validated using a 7-bus system and the IEEE 123-bus system. The results show strong learning ability, timely response for varying system conditions and convincing resilience enhancement.

eess.SY↗

arXiv2021

Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning with Region-of-Interest Active Sampling

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interests active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which significantly enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is verified on the the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

eess.SY↗

arXiv2021

Deep Learning based Model-free Robust Load Restoration to Enhance Bulk System Resilience with Wind Power Penetration

This paper proposes a new deep learning (DL) based model-free robust method for bulk system on-line load restoration with high penetration of wind power. Inspired by the iterative calculation of the two-stage robust load restoration model, the deep neural network (DNN) and deep convolutional neural network (CNN) are respectively designed to find the worst-case system condition of a load pickup decision and evaluate the corresponding security. In order to find the optimal result within a limited number of checks, a load pickup checklist generation (LPCG) algorithm is developed to ensure the optimality. Then, the fast robust load restoration strategy acquisition is achieved based on the designed one-line strategy generation (OSG) algorithm. The proposed method finds the optimal result in a model-free way, holds the robustness to handle uncertainties, and provides real-time computation. It can completely replace conventional robust optimization and supports on-line robust load restoration which better satisfies the changeable restoration process. The effectiveness of the proposed method is validated using the IEEE 30-bus system and the IEEE 118-bus system, showing high computational efficiency and considerable accuracy.

eess.SY↗

arXiv2021

Effective Parallelism for Equation and Jacobian Evaluation in Power Flow Calculation

This letter investigates parallelism approaches for equation and Jacobian evaluations in large-scale power flow calculation. Two levels of parallelism are proposed and analyzed: inter-model parallelism, which evaluates models in parallel, and intra-model parallelism, which evaluates calculations within each model in parallel. Parallelism techniques such as multi-threading and single instruction multiple data (SIMD) vectorization are discussed, implemented, and benchmarked as six calculation workflows. Case studies on the 70,000-bus synthetic grid show that equation evaluations can be accelerated by ten times, and the overall Newton power flow advances the state of the art by 20%.

eess.SY↗

arXiv2021

Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems

Self-healing capability is one of the most critical factors for a resilient distribution system, which requires intelligent agents to automatically perform restorative actions online, including network reconfiguration and reactive power dispatch. These agents should be equipped with a predesigned decision policy to meet real-time requirements and handle highly complex $N-k$ scenarios. The disturbance randomness hampers the application of exploration-dominant algorithms like traditional reinforcement learning (RL), and the agent training problem under $N-k$ scenarios has not been thoroughly solved. In this paper, we propose the imitation learning (IL) framework to train such policies, where the agent will interact with an expert to learn its optimal policy, and therefore significantly improve the training efficiency compared with the RL methods. To handle tie-line operations and reactive power dispatch simultaneously, we design a hybrid policy network for such a discrete-continuous hybrid action space. We employ the 33-node system under $N-k$ disturbances to verify the proposed framework.

eess.SY↗

arXiv2021

Trilevel Scheduling Model Considering Residential Demand Flexibility of Aggregated HVACs and EVs under Distribution LMP

Residential loads, especially heating, ventilation, and air conditioners (HVACs) and electric vehicles (EVs) have great potentials to provide demand flexibility which is an attribute of Grid-interactive Efficient Buildings (GEB). Under this new paradigm, first, EV and HVAC aggregator models are developed in this paper to represent the fleet of GEBs, in which the aggregated parameters are obtained based on a new approach of data generation and least-squares parameter estimation (DG-LSPE), which can deal with heterogenous HVACs. Then, a tri-level bidding and dispatching framework is established based on competitive distribution operation with distribution locational marginal price (DLMP). The first two levels form a bilevel model to optimize the aggregators payment and to represent the interdependency between load aggregators and the distribution system operator (DSO) using DLMP, while the third level is to dispatch the optimal load aggregation to all residents by the proposed priority list-based demand dispatching algorithm. Finally, case studies on a modified IEEE 33-Bus system illustrate three main technical reasons for payment reduction due to demand flexibility: load shift, DLMP step changes, and power losses. They can be used as general guidelines for better decision-making for future planning and operation of demand response programs.

eess.SY↗

arXiv2021

Transmission-and-Distribution Frequency Dynamic Co-Simulation Framework for Distributed Energy Resources Frequency Response

The rapid deployment of distributed energy resources (DERs) in distribution networks has brought challenges to balance the system and stabilize frequency. DERs have the ability to provide frequency regulation; however, existing dynamic frequency simulation tools-which were developed mainly for the transmission system-lack the capability to simulate distribution network dynamics with high penetrations of DERs. Although electromagnetic transient (EMT) simulation tools can simulate distribution network dynamics, the computation efficiency limits their use for large-scale transmission-and-distribution (T&D) simulations. This paper presents an efficient T&D dynamic frequency co-simulation framework for DER frequency response based on the HELICS platform and existing off-the-shelf simulators. The challenge of synchronizing frequency between the transmission network and DERs hosted in the distribution network is approached by detailed modeling of DERs in frequency dynamic models while DER phasor models are also preserved in the distribution networks. Thereby, local voltage constraints can be respected when dispatching the DER power for frequency response. The DER frequency responses (primary and secondary)-are simulated in case studies to validate the proposed framework. Lastly, fault-induced delayed voltage recovery (FIDVR) event of a large system is presented to demonstrate the efficiency and effectiveness of the overall framework.

eess.SY↗