arXiv subjects

Buxin She

Publications and source records attributed to Buxin She.

9 recordsLinked to original sources

arXiv2026

Security-Constrained Operation of IBR-Dominated Power Systems: Static and Dynamic Security Across Preventive and Corrective Decisions

Inverter-based resources (IBRs) couple power system operation to fast dynamics and controller-dependent responses. Their configurable capabilities are reshaping the formulation and coordination of security-constrained operation. This paper presents a two-axis view: static versus dynamic security and preventive versus corrective decision timing. Preventive scheduling is extending from static post-contingency feasibility toward dynamic security, while corrective operation spans equilibrium-based corrective actions and trajectory-based control. A generic formulation represents this change and makes the preventive--corrective tradeoff explicit. Existing formulations, methods, and capability representations are reviewed and synthesized within this framework. The surveyed work yields two findings. First, IBR capability can expand the feasible set or relieve security constraints, reducing operating cost or improving security performance. Second, shared capability and constraints across formulations determine whether that capability remains operationally deliverable. These findings motivate future research in IBR capability characterization and quantification, joint scheduling, and scalable solution frameworks.

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arXiv2026

Techno-Economic Boundary Analysis of Small Modular Reactor Cogeneration for Hyperscale Data Center IT and Cooling Loads

Hyperscale data centers are adding firm, high-utilization demand faster than grids can serve it, renewing interest in colocating them with small modular reactors. Such a plant could earn revenue in two ways, selling low-carbon power and diverting steam to absorption chillers that serve a cooling load accounting for 20-40% of facility electricity use, but neither revenue stream has been priced across the conditions that must coincide. Here we co-optimize reactor dispatch, steam extraction, absorption cooling and grid exchange hourly for a 200 MW$_\mathrm{e}$ data center in the Electric Reliability Council of Texas (ERCOT) region, across 109 runs spanning capital, market, policy, financing and cooling efficiency. At 2023 mid-range reactor capital, the nuclear configurations cost 49-62% more than grid supply even with the Section 45Y production tax credit. The viable region opens near \$5,000 kW$_\mathrm{e}^{-1}$, and nth-of-a-kind capital makes them 77-89% cheaper in 2023, though between parity and 34% more expensive in the low-price 2024 market. A carbon price of \$53-64 tCO$_2^{-1}$ closes the mid-range gap under hourly export crediting. Absorption cooling is dispatched in response to hourly electricity prices and supplies 38% of annual cooling, at an added cost of \$9.2 million yr$^{-1}$ relative to the reactor-only plant; that gap closes at an installed absorption cost of \$60 kW$_\mathrm{c}^{-1}$ at baseline efficiency and \$570 kW$_\mathrm{c}^{-1}$ on a legacy-efficiency campus, against surveyed commercial prices of \$450-1,200 kW$_\mathrm{c}^{-1}$. Together these results delineate the capital, market and policy conditions under which colocated reactor cogeneration is competitive with grid procurement, and the range over which each condition moves the outcome.

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arXiv2026

Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework

Artificial intelligence (AI) is increasingly central to power and energy systems, supporting modeling, forecasting, optimization, and control. Yet most existing works emphasize specialized applications and offer little reusable material for newcomers or interdisciplinary learners, who increasingly rely on large language models rather than building their own. This gap points to a need for engineering-grounded AI (EGAI), in which AI workflows follow established engineering and power-system domain rules rather than acting as task-agnostic black boxes. Motivated by a community survey of researchers and practitioners, which shows 92% report at least one barrier before running an AI model and 94% want a power-specific hands-on course. This paper presents a framework consisting of open, executable module library that lowers the entry barrier for AI in power systems. The modules follow a progressive difficulty ladder that maps core AI concepts onto representative power-system tasks: (i) foundational deep neural network (DNN) templates for function approximation and load-curve fitting; (ii) a domain-coupled convolutional neural network (CNN) power-flow surrogate for a 5-bus system; and (iii) frontier modules on DNN-assisted optimization, deep reinforcement learning (DRL) for battery storage control, and physics-informed neural networks (PINNs) for the swing equation. All modules are released as Jupyter notebooks that run locally or on Google Colab and are delivered through an IEEE online course and IEEE Power & Energy Society (PES) webinar series. The webinar drew more than 590 live attendees, which is among the ten most-attended IEEE PES webinars, and over 344 repository visits within two weeks, reinforcing the survey-based motivation.

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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.

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arXiv2025

Control Co-Design Under Uncertainty for Offshore Wind Farms: Optimizing Grid Integration, Energy Storage, and Market Participation

Offshore wind farms (OWFs) are set to significantly contribute to global decarbonization efforts. Developers often use a sequential approach to optimize design variables and market participation for grid-integrated offshore wind farms. However, this method can lead to sub-optimal system performance, and uncertainties associated with renewable resources are often overlooked in decision-making. This paper proposes a control co-design approach, optimizing design and control decisions for integrating OWFs into the power grid while considering energy market and primary frequency market participation. Additionally, we introduce optimal sizing solutions for energy storage systems deployed onshore to enhance revenue for OWF developers over time. This framework addresses uncertainties related to wind resources and energy prices. We analyze five U.S. west-coast offshore wind farm locations and potential interconnection points, as identified by the Bureau of Ocean Energy Management (BOEM). Results show that optimized control co-design solutions can increase market revenue by 3.2\% and provide flexibility in managing wind resource uncertainties.

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arXiv2024

Configuration and EMT Simulation of the 240-bus MiniWECC System Integrating Offshore Wind Farms (OWFs)

As offshore wind farms (OWFs) become increasingly prevalent in Northern California and Southern Oregon, they introduce faster dynamics into the Western Electricity Coordinating Council (WECC) system, reshaping its dynamic behavior. Accordingly, electromagnetic transient (EMT) simulation is essential to assess high frequency dynamics of the WECC system with integrated OWFs. Against this background, this paper presents the integration of detailed dynamic models of OWFs into a 240-bus miniWECC system in PSCAD software. The sequential initialization technique is employed to facilitate the smooth initiation of a large-scale system in an EMT simulation. The performance of the configured model is assessed under wind speed variations and grounded faults, demonstrating the effectiveness of the miniWECC system with OWFs. This system serves as a valuable basic use case for validating the fast dynamic performance of future WECC systems with high penetration of wind energy.

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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.

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arXiv2022

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.

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arXiv2022

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.

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