arXiv subjects

Peng Sang

Publications and source records attributed to Peng Sang.

5 recordsLinked to original sources

arXiv2026

Steady-State Equivalent Circuit Model for Data Center Loads

Planners currently represent data centers as aggregate constant-PQ or ZIP loads in steady-state interconnection and contingency studies. These aggregate models are computationally convenient. However, they obscure the electrical relationship between computational workloads, server utilization, and grid-side demand. They ignore the internal power-electronic conversion stages of IT loads and assume homogeneous workload distributions across the compute clusters. This hides operating-point-dependent converter losses and efficiency variations. We propose a steady-state equivalent-circuit model (ECM) for data centers, which explicitly builds circuit models for IT loads, power supply units, cooling, and auxiliary systems. For power supply units, the equivalent circuit model explicitly represents internal power-electronic conversion stages. For IT loads, we develop a utilization-dependent server power model, and we combine it with loss-aware ECMs of power supply units. This approach captures the grid-side impact of heterogeneous workload distributions while preserving compatibility with conventional power-flow analysis. We evaluate this data center ECM in large-scale transmission power flows, using Monte Carlo simulations under heterogeneous and homogeneous cluster utilization. In comparison with the fixed-efficiency constant-PQ model, the ECM predicts that the most stressed line exceeds its thermal limit in about 30% of Monte Carlo samples. The results further show that homogeneous server utilization overstates line-loading variability by 17%-46% relative to heterogeneous server utilization, depending on the intra-cluster workload correlation.

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arXiv2025

Analysis of Circuit-based Per-Panel Diode Model of Photovoltaic Array

Solar photovoltaic systems are increasing in size and number on the grid. In regions with high penetration, such as California, PV systems serve multiple functions, including peak shaving and demand response. Therefore, the criticality of PV systems to grid operations calls for accurate models. The current practice is to represent the PV array, composed of multiple PV panels, with an aggregated single-diode model (SDM). The highly abstract model has a limited ability to capture real-world behaviors, such as partial shading and hotspots. Thus, we develop a circuit-based per-panel PV array model that uses a single diode model for each panel and interconnects them to form an array. This approach bridges the tradeoff between cell-level physics and control-dependent system-level behavior. We establish conditions for mathematical equivalence between the proposed per-panel array circuit model and the aggregated single-diode array model. We generate empirical evidence by running simulations using parameters derived from real-world PV panels. Results indicate that the proposed per-panel array model can represent the electrical behavior of the array under non-ideal conditions, such as partial shading, more accurately. With maximum power point tracking control, the proposed model is 21.2% more accurate when estimating the real power output of an array under a partial shading scenario and 8.1% more accurate under a hot spot scenario.

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arXiv2025

PowerChain: A Verifiable Agentic AI System for Automating Distribution Grid Analyses

Rapid electrification and decarbonization are increasing the complexity of distribution grid (DG) operation and planning, necessitating advanced computational analyses to ensure reliability and resilience. These analyses depend on disparate workflows comprising complex models, function calls, and data pipelines that require substantial expert knowledge and remain difficult to automate. Workforce and budget constraints further limit utilities' ability to apply such analyses at scale. To address this gap, we build an agentic system PowerChain, which is capable of autonomously performing complex grid analyses. Existing agentic AI systems are typically developed in a bottom-up manner with customized context for predefined analysis tasks; therefore, they do not generalize to tasks that the agent has never seen. In comparison, to generalize to unseen DG analysis tasks, PowerChain dynamically generates structured context by leveraging supervisory signals from self-contained power systems tools (e.g., GridLAB-D) and an optimized set of expert-annotated and verified reasoning trajectories. For complex DG tasks defined in natural language, empirical results on real utility data demonstrate that PowerChain achieves up to a 144/% improvement in performance over baselines.

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arXiv2024

Circuit-Theoretic Joint Parameter-State Estimation of Utility-Scale Photovoltaic, Battery, and Grid Systems

Solar PV and battery storage systems have become integral to modern power grids. Therefore, bulk grid models in real-time operation must include their physical behavior accurately for analysis and optimization. AC state estimation is critical to building real-time bulk power systems models. However, current ACSE techniques do not include detailed physics and measurements for battery and PV systems. This results in sub-optimal estimation results and subsequent less accurate bulk grid models for real-time operation. To address these challenges, we formulate a circuit-theoretic AC state estimator with accurate PV and battery systems physics and corresponding measurements. First, we propose an aggregated equivalent circuit model of the solar PV, battery, and traditional grid components. Next, we add measurements from PV and battery systems to the traditional measurement set to facilitate accurate estimation of the overall grid model. Finally, we develop a circuit-theoretic joint parameter-state estimation algorithm that can accurately estimate grid, PV, and battery system states and is robust against erroneous parameters. To demonstrate the efficacy of the proposed framework, we estimate the states of 10k node transmission networks with hundreds of battery+PV-tied systems. We compare the accuracy against the estimation of stand-alone grid, battery, and PV systems.

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arXiv2024

Circuit-theoretic Joint Parameter-State Estimation -- Balancing Optimality and AC Feasibility

AC State Estimation (ACSE) is widely recognized as a practical approach for determining the grid states in steady-state conditions. It serves as a fundamental analysis to ensure grid security and is a reference for market dispatch. As grid complexity increases with rapid electrification and decarbonization, there is a growing need for more accurate knowledge of the grid operating state. However, existing ACSE algorithms have technical gaps. Critically, current ACSE algorithms are susceptible to erroneous system parameters, which are assumed to be fixed in traditional approaches. In this paper, we build a novel circuit-theoretic joint parameter-state estimation algorithm to address this limitation. The innovative algorithm builds an analogous equivalent circuit of the grid with states and certain parameters unknown. It solves a circuit-constrained optimization to estimate the most likely grid states and parameters given a set of measurements. Further, it quantifies the goodness of the estimated output by formulating tight convex envelopes around the original non-convex problem to quantify the quality of estimates. We compare the various proposed approaches on systems with up to 2869 nodes while demonstrating a tradeoff between solution optimality and model fidelity.

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