arXiv subjects

Xingpeng Li

Publications and source records attributed to Xingpeng Li.

At least 19 recordsLinked to original sources

arXiv2026

Minimizing Grid Interconnection Capacity Requirements for AI Data Centers: A Developer-Side Planning Framework with Onsite Resources and Workload Flexibility

Securing grid interconnection capacity has become a bottleneck for AI data center projects and can take longer than constructing the facilities themselves. This mismatch can delay deployment for years, making early interconnection planning essential. This paper develops ICP-AI, an interconnection capacity planning framework from a data center developer's perspective. The framework minimizes grid import capacity under a prescribed onsite investment budget while jointly sizing photovoltaic (PV) and battery energy storage system (BESS) resources and scheduling deadline constrained workload flexibility. A secondary refinement fixes the minimum grid capacity and selects the minimum-investment PV-BESS portfolio among solutions that achieve that capacity. The framework is evaluated using monthly composite stress profiles across varying temporal assumptions, load shapes, flexible load fractions, and deferral windows. Results show that interconnection capacity reduction depends strongly on the planning environment: at a $100M budget, it is about 6% for the high load factor baseline, exceeds 10% under monthly average solar availability, and reaches 13.3% for a more diurnal load. At a $10M budget, 5% flexible load with a 1 h workload deferral window reduces BESS capacity from 15.30 to 4.87 MWh while increasing capacity reduction from 4.43% to 4.84%. To test sensitivity to temporal compression, the model is also solved over the full 8,760 h chronology, which preserves the main capacity and flexibility trends. Overall, ICP-AI quantifies the interconnection capacity and infrastructure substitution value of workload flexibility, providing an investment-interconnection frontier to support capital allocation and early project planning in constrained grid environments.

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arXiv2026

Optimal Assembly of Repurposed Lithium-Ion Battery Packs under Cell Heterogeneity and Screening Uncertainty

The growing supply of retired electric vehicle batteries presents an opportunity for second-life stationary energy storage, but assembling heterogeneous retired cells into reliable packs is challenging due to substantial variation in capacity, DC internal resistance (DCIR), and self-discharge. This paper proposes a robust optimization framework for cell-to-pack assembly of second-life batteries. A topology-screening stage first identifies minimum-cell series-parallel configurations satisfying inverter and energy requirements, reducing the dimensionality of the subsequent assignment problem. For each candidate topology, a mixed-integer linear program selects cells and assigns them along the series string, enforcing power, voltage, and energy requirements as hard constraints while minimizing a normalized, weighted sum of DCIR spread, capacity spread, and self-discharge imbalance. Additionally, measurement uncertainty in capacity and DCIR is modeled as bounded intervals to guarantee feasibility under worst-case parameter deviations. The framework is evaluated on four heterogeneous inventories for a 10 kW/10 kWh stationary backup application. The proposed method satisfies all feasibility requirements in every case, while single-metric sorting heuristics each fail on at least one inventory. Relative to the best single-metric baseline by objective value, it reduces the normalized mismatch objective by 76-87%, demonstrating that jointly optimizing cell matching with application-level feasibility requirements improves heterogeneous second-life pack assembly under screening uncertainty.

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arXiv2026

Violation-Informed Spatio-Temporal Adaptive Targeting Framework for EV-Driven Distribution System Expansion Planning

The rapid adoption of electric vehicles (EVs) can cause severe voltage drops and line current overloads in distribution networks, creating an urgent need for scalable expansion planning methods. This paper proposes a computationally efficient violation-informed spatio-temporal adaptive targeting (STAT) framework for EV-driven distribution system expansion planning. The framework first identifies potential voltage and current violations through a violation analysis model, and then mitigates them through a joint optimal expansion planning model that co-optimizes investment decisions for line reconductoring, shunt capacitors, and battery energy storage systems. To reduce computational burden, the proposed STAT-temporal criticality assessment (STAT-TCA) method extracts primitive stress events from annual operating data, derives an initial set of candidate planning horizons from signature-consistent segments, and selects a final transferable critical horizon set through cross-horizon validation based on optimization feasibility and cost. Meanwhile, the proposed STAT-adaptive spatial targeting (STAT-AST) method constructs device-specific spatial features for BESS and SC siting to retain compact yet high-impact candidate bus sets. Case studies on 33-bus and 240-bus distribution systems demonstrate that the proposed STAT framework can substantially reduce the temporal and spatial planning dimensions while preserving planning fidelity. Full-year validation further confirms that the resulting investment plans can eliminate EV-induced voltage and thermal violations while maintaining feasible BESS operations.

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arXiv2026

Learning-Assisted Day-Ahead Energy Scheduling for Frequency-Secure Inverter-Dominated Grids with Grid-Forming Battery Energy Storage Systems

As grid-forming (GFM) battery energy storage systems (BESS) are increasingly deployed to enhance power system inertial response and frequency stability, incorporating their frequency support capabilities into day-ahead energy scheduling (DAES) is essential for achieving both frequency security and operational efficiency. However, accurately determining frequency metrics in grids with coexisting GFM inverters and synchronous generators requires electromagnetic transient (EMT) simulations, which are computationally prohibitive for direct embedding in grid operational optimization models. To bridge the gap between modeling accuracy and computational efficiency, a learning-assisted DAES (LA-DAES) framework is proposed in this work. By leveraging a surrogate model to represent the frequency support dynamics of GFM BESS, the proposed framework ensures frequency security with a reasonable solve time. Comparative results demonstrate that, relative to analytical frequency-constrained DAES, the proposed LA-DAES framework more accurately captures grid frequency metrics and improves the utilization of GFM BESS.

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arXiv2026

Hosting Capacity Assessment and Enhancement for Edge Data Centers in Active Distribution Networks

With the increasing demand for edge computing and AI-driven workloads, integrating small and medium-sized edge data centers into distribution networks has become increasingly important. This paper investigates the hosting capacity of distribution networks for data center integration and identifies the key physical mechanisms that limit the maximum allowable data center load. The baseline analysis shows that data center hosting capacity varies significantly across candidate buses due to network topology and electrical distance. Three dominant limiting mechanisms are identified: current-constrained locations, voltage-constrained locations, and mixed-constrained locations where both current loading and voltage deviation jointly affect hosting capacity. To increase the hosting capacity, this study evaluates multiple flexible resources, including battery energy storage systems (BESS), dispatchable distributed generators (DDG), and static synchronous compensators (STATCOM). Numerical results demonstrate that these resources provide complementary benefits through active power support, sustained local generation, and reactive power compensation, effectively expanding data center hosting capacity in distribution systems.

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arXiv2026

Data Center Spatio-Temporal Load Flexibility in Security-Constrained Unit Commitment for Enhanced Grid Efficiency and Reliability

Data center electricity consumption reached 4.4% of U.S. total in 2023 and is projected to grow to 6.7--12% by 2028, imposing increasing stress on transmission networks while representing a largely untapped source of controllable demand-side flexibility. This paper proposes a modular security-constrained unit commitment (SCUC) framework that coordinates flexible data center workloads with system-level scheduling to reduce renewable curtailment, alleviate congestion, and lower operating costs. Three mixed-integer linear programming (MILP) models are formulated: the Data Center Spatial model (DC-S), enabling instantaneous workload redistribution across geographically distributed sites; the Data Center Temporal model (DC-T), permitting each site to shift its deferrable load across time while preserving the daily energy balance; and the Data Center Spatio-Temporal model (DC-ST), jointly activating both mechanisms and spanning the largest feasible operating region. Case studies on a modified IEEE 24-bus reliability test system show that DC-ST eliminates all base-case and post-contingency transmission violations at a flexibility ratio of 40%, and reduces renewable curtailment by up to 84.4% at 30% relative to the inflexible baseline. Sensitivity analysis further reveals that moderate flexibility levels of 20%--30% already capture most of the achievable benefits, supporting practical deployment with limited operational burden on data center operators.

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arXiv2026

Optimal Loss Reduction in Distribution Networks Using Conservation Voltage Reduction and Network Topology Reconfiguration

Conservation voltage reduction (CVR) and network topology reconfiguration (NTR) are widely employed to improve distribution system performance; however, existing approaches largely treat them independently, overlooking their coupled impact on load demand, voltage profiles, and power flow distribution, thereby limiting their overall effectiveness. This paper proposes a coordinated optimization framework for day-ahead operational planning of distribution networks, integrating CVR and NTR to enhance overall network efficiency and reduce active power losses in radial distribution networks. The problem is formulated as a mixed-integer conic programming model incorporating AC power flow constraints, voltage-dependent load representation, and radiality constraints. CVR is implemented to achieve load reduction through coordinated voltage control, while NTR redistributes line loading via optimal switching of controllable branches. The proposed framework is validated on the IEEE 33 and 123-bus distribution systems under varying load conditions. Results demonstrate that the coordinated approach consistently outperforms independent strategies, achieving up to 20.6% reduction in active power losses while maintaining voltage compliance and improving branch loading uniformity. These findings confirm that coordinated optimization provides an effective and scalable solution for enhancing efficiency in modern distribution networks.

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arXiv2026

Net Load Forecasting with Renewable Capacity Features: A Comparison of Direct and Indirect Neural Network Methods

Renewable energy adoption has increased significantly in recent years. However, the growing penetration of renewable generation that depends on the weather introduces additional variability, making accurate net load forecasting increasingly challenging. In this study, long short-term memory (LSTM) and fully connected neural network (FCNN) models are employed to forecast net load one hour ahead using direct and indirect approaches. In the direct approach, net load is predicted directly, whereas in the indirect approach, total load, wind generation, and solar generation are predicted separately and then used to derive net load. Installed renewable generation capacity is also incorporated as an input feature to capture changes in renewable generation capability over time. A 24-hour input window and a chronological 90%/5%/5% training, validation, and test split are employed, with preprocessing fitted exclusively on the training data. On 1,754 common test timestamps, the direct FCNN slightly outperforms the indirect FCNN, whereas the indirect LSTM achieves the best overall performance, with a MAPE of 2.46%, an RMSPE of 3.59%, and an R2 of 98.7%. These results demonstrate that component-wise forecasting can improve LSTM-based net load prediction when the characteristics of renewable generation and installed capacity information are represented appropriately.

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arXiv2026

WAKE-NET: A 3D-Wake-Aware Economic Turbine Layout and Cabling Optimization Framework for Multi-Capacity Multi-Hub-Height Wind Farms Serving Grid-Scale and Industrial Power Systems

The global transition towards renewable energy has accelerated the deployment of utility-scale wind farms, increasing the need for accurate performance and economic assessments. Although wind energy offers substantial potential for carbon emission reduction, investment decisions are highly sensitive to predicted annual energy production and economic profitability. Conventionally wind farm analyses often estimate turbine power output based solely on incoming wind conditions, neglecting wake interactions between turbines. These wake effects can significantly reduce downstream turbine performance, leading to overestimation of energy yield and financial returns. This study proposes WAKE-NET, a 3D wake-aware optimization framework that integrates turbine layout optimization, turbine capacity selection, cable routing, and hub height diversification within a unified profit-driven formulation. Unlike traditional approaches that assume a uniform hub height and turbine capacities or ignore wake dynamics, the proposed framework accounts for wake-induced power losses during optimization. A benchmark wake-ignorant model is also evaluated to quantify the impact of neglecting wake interactions. Results indicate that the wake-ignorant optimization can significantly overestimate annual profits, while the use of multiple hub heights and capacities reduce wake overlap and improve spatial utilization. Overall, the findings demonstrate that wake-aware optimization coupled with hub height and capacity diversification provides more reliable energy yield prediction and economic assessment, offering valuable guidance for large-scale wind farm planning and investment.

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arXiv2026

Diagnosis-Driven Co-planning of Network Reinforcement and BESS for Distribution Grid with High Penetration of Electric Vehicles

While the rapid proliferation of electric vehicles (EVs) accelerates net-zero goals, uncoordinated charging activities impose severe operational challenges on distribution grids, including exacerbated peak loads, thermal overloading, and voltage violations. To overcome the computational intractability of jointly optimizing grid infrastructure reinforcements and Battery Energy Storage System (BESS) installations, this paper proposes a novel three-stage Diagnosis-Driven Co-Planning (DDCP) framework. The methodology integrates a Violation Detection and Quantification (VDQ) model to systematically identify system breaches, and a Violation Mitigation-Based Planning (VMBP) model for optimal BESS allocation. Specifically, Stage I of the DDCP framework diagnoses critical bottleneck lines that render standalone BESS solutions infeasible; Stage II executes targeted physical upgrades exclusively on these bottlenecks; and Stage III finalizes the optimal BESS deployment on the updated network topology. Furthermore, this study quantifies the EV hosting capacity thresholds before and after BESS integration across varying EV adoption rates and base voltages. Finally, a comprehensive comparative analysis evaluates four mitigation approaches: the VDQ-driven cable upgrade (VCU) model, the VMBP model, system-wide voltage uprating, and the proposed DDCP framework. The results demonstrate that the DDCP framework not only resolves the complex joint-optimization hurdle but also achieves superior techno-economic performance in addressing high-EV-penetration challenges.

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arXiv2026

Deep Neural Network-Enhanced Frequency-Constrained Optimal Power Flow with Multi-Governor Dynamics

To ensure frequency security in power systems, both the rate of change of frequency (RoCoF) and the frequency nadir (FN) must be explicitly accounted for in real-time frequency-constrained optimal power flow (FCOPF). However, accurately modeling sys-tem frequency dynamics through analytical formulations is chal-lenging due to their inherent nonlinearity and complexity. To address this issue, deep neural networks (DNNs) are utilized to capture the nonlinear mapping between system operating condi-tions and key frequency performance metrics. In this paper, a DNN-based frequency prediction model is developed and trained using the high-fidelity time-domain simulation data generated in PSCAD/EMTDC. The trained DNN is subsequently transformed into an equivalent mixed-integer linear programming (MILP) form and embedded into the FCOPF problem as additional con-straints to explicitly enforce frequency security, leading to the proposed DNN-FCOPF formulation. For benchmarking, two alternative models are considered: a conventional optimal power flow without frequency constraints and a linearized FCOPF in-corporating system-level RoCoF and FN constraints. The effec-tiveness of the proposed method is demonstrated by comparing the solutions of these three models through extensive PSCAD/EMTDC time-domain simulations under various loading scenarios.

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arXiv2025

Frequency-Dynamics-Aware Economic Dispatch with Optimal Grid-Forming Inverter Allocation and Reserved Power Headroom

The high penetration of inverter-based resources (IBRs) reduces system inertia, leading to frequency stability concerns, especially during synchronous generator (SG) outages. To maintain frequency dynamics within secure limits while ensuring economic efficiency, frequency-constrained optimal power flow (FCOPF) is employed. However, existing studies either neglect the frequency support capability and allocation of grid-forming (GFM) IBRs or suffer from limited accuracy in representing frequency dynamics due to model simplifications. To address this issue, this paper proposes a deep learning (DL)-based FCOPF (DL-FCOPF) framework. A DL model is first developed as a predictor to accurately estimate frequency-related metrics: the required reserved headroom and allocation of GFM IBRs, the rate of change of frequency and frequency nadir. After being trained with data obtained from electromagnetic transient simulations, the DL model is reformulated and incorporated into FCOPF. Case studies conducted on two test systems demonstrate the effectiveness of the proposed approach. Compared with the traditional OPF and linear FCOPF benchmarks, the DL-FCOPF can optimally coordinate SGs and IBRs with minimum cost, achieving desired frequency response, within an acceptable computing time. Further-more, sensitivity analyses are conducted to identify the most suit-able structure and linearization approach of the DL-based frequency predictor.

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arXiv2025

Data-Driven EV Charging Load Profile Estimation and Typical EV Daily Load Dataset Generation

Widespread electric vehicle (EV) adoption introduces new challenges for distribution grids due to large, localized load increases, stochastic charging behavior, and limited data availability. This paper proposes two data-driven methods to estimate residential EV charging profiles using real-world customer meter data from CenterPoint Energy serving the Houston area. The first approach applies a least-squares estimation to extract average charging rates by comparing aggregated EV and non-EV meter data, enabling a statistical method for starting and ending charge times. The second method isolates EV load from meter profiles and applies a kernel density estimation (KDE) to develop a probabilistic charging model. Both methods produce a distinct "u-shaped" daily charging profile, with most charging occurring overnight. The validated profiles offer a scalable tool for utilities to better anticipate EV-driven demand increases and support proactive grid planning.

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arXiv2025

ADMM Penalty Parameter Evaluation for Networked Microgrid Energy Management

The alternating direction method of multipliers (ADMM) is a powerful algorithm for solving decentralized optimization problems including networked microgrid energy management (NetMEM). However, its performance is highly sensitive to the selection of its penalty parameter \r{ho}, which can lead to slow convergence, suboptimal solutions, or even algorithm divergence. This paper evaluates and compares three district ADMM formulations to solve the NetMEM problem, which explore different methods to determine appropriate stopping points, aiming to yield high-quality solutions. Furthermore, an adaptive penalty heuristic is also incorporated into each method to analyze its potential impact on ADMM performance. Different case studies on networks of varying sizes demonstrate that an objective-based ADMM approach, denominated as OB-ADMM, is significantly more robust to the choice of \r{ho}, consistently yielding solutions closer to the centralized optimal benchmark by preventing premature algorithm stopping.

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arXiv2025

Grid Operational Benefit Analysis of Data Center Spatial Flexibility: Congestion Relief, Renewable Energy Curtailment Reduction, and Cost Saving

Data centers are facilities housing computing infrastructure for processing and storing digital information. The rapid expansion of artificial intelligence is driving unprecedented growth in data center capacity, with global electricity demand from data centers projected to double by 2026. This growth creates substantial challenges for power transmission networks, as large concentrated loads can cause congestion and threaten grid reliability. Meanwhile, the intermittent nature of solar and wind generation requires flexible resources to maintain grid reliability and minimize curtailment. This paper assesses whether data center spatial flexibility-the ability to migrate computational workloads geographically-can serve as a grid resource to address these challenges. An optimal power flow model is developed to co-optimize generation dispatch, security reserves, and flexible data center loads. Case studies on a modified IEEE 73-bus system show that inflexible data center placement can lead to severe transmission violations, with line overloads reaching 30.1%. Enabling spatial flexibility mitigates these violations in the studied scenarios and restores system feasibility. This flexibility also reduces solar curtailment by up to 61.0% by strategically reallocating load to solar-rich areas. The results suggest that spatial flexibility offers a viable approach to defer transmission upgrades and enhance renewable utilization.

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arXiv2025

A Reliability-Cost Optimization Framework for EV and DER Integration in Standard and Reconfigurable Distribution Network Topologies

The rapid growth of electric vehicle (EV) adoption poses operational and economic challenges for power distribution systems, including increased line loading levels and network congestions. This may require potential infrastructure reinforcement and expansion. As a fast inexpensive alternative solution, network topology reconfiguration (NTR) offers a practical means to redistribute power flows, reduce operational costs, and defer infrastructure upgrades. This paper presents a linear programming framework to evaluate the impact of varying EV penetration on operational costs under four configurations: standard distribution network (SDN), SDN with NTR (SDNTR), SDN with distributed energy resources (SDN-DER), and SDNTR with DERs (SDNTR-DER). Numerical simulations are conducted on the IEEE 33-bus system. The analysis demonstrates that integrating DERs reduces operational costs, while NTR further enhances system flexibility, enabling higher EV penetration levels without compromising feasibility. The combined SDNTR-DER approach offers the most cost-effective and reliable pathway for accommodating future EV growth while mitigating the need for immediate infrastructure upgrades.

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arXiv2025

BESS Siting and Sizing to Mitigate EV Charging-Induced Voltage Violations: A Planning Space Reduction Approach

The rapid growth of electric vehicle (EV) charging can cause persistent voltage violations in distribution networks and increase the need for grid-side voltage support. This paper develops a proactive voltage management framework for planning battery energy storage systems (BESS) to mitigate EV charging-induced voltage violations. Annual EV charging profiles are generated through Monte Carlo simulation using probability distributions derived from real charging events and are incorporated into an annual voltage assessment to identify stressed operating conditions. An optimal expansion planning model then determines installation locations and capacities at minimum investment cost. To improve computational tractability, a planning space reduction (PSR) strategy combines critical period selection to reduce the chronological horizon with candidate bus screening to reduce the BESS siting space. The resulting investment decisions are evaluated through full-year operational validation on the IEEE 33-bus, IEEE 69-bus, and a 240-bus distribution system. Results show that PSR reduces solution time by up to 99.2% while matching the exhaustive benchmark in the 33-bus and 69-bus systems and limiting the objective deviation to 0.55% in the 240-bus system. The optimized BESS deployments eliminate the identified annual voltage violations and further reduce electricity purchase costs and network energy losses under increasing EV penetration.

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arXiv2025

A Scenario-based Stochastic Model of using BESS-based Virtual Transmission Lines in Day-Ahead Unit Commitment

The rapid increase in renewable energy sources (RES) implementation in the power system creates more severe network congestion, which may reduce grid operation efficiency and cause renewable curtailment. Deterministic optimization for the unit commitment shows that battery energy storage system (BESS)-based Virtual Transmission Line (VTL), as an alternative to physical transmission lines, can offer a quick solution for congestion relief, reduced operational costs, and lowered renewable curtailment. This paper aims to evaluate the benefits of VTL when considering Renewable Energy Sources uncertainty. Particularly, this work proposes a scenario-based stochastic security-constrained unit commitment model considering VTL, referred to as SSCUC-VTL. It incorporates the forecast error of RES into the commitment decision for systems with VTL. The performance of applying the VTL strategy is compared to that of adding a new physical transmission line and a standalone BESS. A case study has been conducted on an enhanced IEEE 24-bus test system. The simulation results demonstrate that VTL provides 23% more operational cost reduction than the physical transmission line, and up to 67% more congestion relief than the standalone BESS in a power system with solar and wind generation.

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