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Bri-Mathias Hodge

Publications and source records attributed to Bri-Mathias Hodge.

At least 19 recordsLinked to original sources

The consequences of high SMR operating costs in electricity markets

As US power markets contend with growing demand for firm generation, the nuclear industry has offered Small Modular Reactors (SMRs). However, how these concepts would fare in a rapidly evolving power grid is unclear, given the paucity of operational examples. Current literature, informed by substantial cost escalations for traditional nuclear plants, focuses on the investment costs SMRs need to achieve for private investment feasibility. However, this work finds that the operating and marginal costs of SMRs are more critical to economic feasibility in market environments. This work dispatches SMRs using a flexible operations model, considering revenue from two main electric markets, capacity and wholesale energy markets, with and without policy support. Manufacturer advertised costs for investment and operating costs are used, with fuel costs calculated from manufacturer provided design parameters. Results indicate that SMRs are uneconomical primarily because investment cost reductions are offset by increased marginal costs. As such, an environment of prices and subsidies beyond historic norms are necessary to attract private investment at manufacturer advertised cost benchmarks. Current SMRs are as profitable as advanced estimates of the AP1000 traditional nuclear reactor, and if investment costs escalate at the average rate for nuclear projects, they are similar to Vogtle 3 & 4. In projected future power markets, reductions in marginal cost may be more beneficial than those in investment costs.

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An Open Synthetic Test System for the Jordanian Transmission Grid

Open synthetic test systems are essential for reproducible power system research, yet the available cases represent almost exclusively North American and European grids. No open transmission test system exists for any Middle Eastern country, whose grids raise different questions such as single corridor fuel-supply resilience and high-renewable operation within small synchronous systems, and established benchmarks cannot demonstrate. This paper presents a synthetic test system for the Jordanian transmission grid, assembled entirely from public sources, where the real topology is reconstructed from a published diagram and restored to its 2018 energized state, with plant-level generation and renewable fleets, per-site hourly profiles, and loads calibrated to the values published by Jordan's grid operators. The case is validated through structural statistics against real-grid criteria, power flow and $N\!-\!1$ screening, an energy-weighted loss decomposition, in addition to a full-year production-cost run compared against the published per-plant energy, and cross-solver verification. The model reproduces the annual energy mix at technology level (largest producers within 4\%, system total within 0.1\%), and the released dataset includes the bus-identity key, all scripts, and a post-2019 scenario variant. This test case is designed to benchmark system-level resource adequacy, time-series dispatch and unit commitment, renewable integration, fuel-supply resilience scenarios, interconnection studies and steady-state studies.

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Nodal Frequency Stability-Constrained UC & ED for Renewable-Dominated Power Systems

In modern power systems with high shares of renewable, inverter-based resources (IBRs), frequency stability becomes more complex due to the fast dynamics of IBRs and frequency trajectories that vary significantly from bus to bus. In this paper, we present an optimization framework for unit commitment and economic dispatch with endogenous frequency stability constraints at each bus. Two approaches for mitigating excessively low instantaneous frequency values in the event of the largest generator contingency are proposed: 1) by introducing a constraint requiring more thermal generation, and 2) by constraining the maximum power output of the generator that had the largest power output in the incumbent solution. Both approaches proved effective in eliminating dispatch scenarios that resulted in instantaneous frequencies below 58 Hz, while the second approach minimized the difference in production cost values from the non-stability-constrained case. Overall, the results indicate that the proposed optimization framework is a more effective alternative to frequency stability-constrained unit commitment and economic dispatch (UC & ED) than those based on the center-of-inertia (COI) principle.

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Power Grid Infrastructure for AI Data Centers

This article addresses recent advances in artificial intelligence, which have set off an astounding race among technology frontiers to build large data centers. It provides insights into impacts of large data centers on the planning and operation of the power grid.

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A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment

Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve this through solving the task of Unit Commitment (UC),ca high dimensional large-scale Mixed-integer Linear Programming (MILP) problem that is strictly and heavily governed by the grid physical constraints. As grid integrate variable renewable sources, and new technologies such as long duration storage in the grid, UC must be optimally solved for multi-day horizons and potentially with greater frequency. Therefore, traditional MILP solvers increasingly struggle to compute solutions within these tightening operational time limits. To bypass these computational bottlenecks, this paper proposes a novel framework utilizing a transformer-based architecture to predict generator commitment schedules over a 72-hour horizon. Also, because raw predictions in highly dimensional spaces often yield physically infeasible results, the pipeline integrates the self-attention network with deterministic post-processing heuristics that systematically enforce minimum up/down times and minimize excess capacity. Finally, these refined predictions are utilized as a warm start for a downstream MILP solver, while employing a confidence-based variable fixation strategy to drastically reduce the combinatorial search space. Validated on a single-bus test system, the complete multi-stage pipeline achieves 100\% feasibility and significantly accelerates computation times. Notably, in approximately 20\% of test instances, the proposed model reached a feasible operational schedule with a lower overall system cost than relying solely on the solver.

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Empirical Assessment of Time-Series Foundation Models For Power System Forecasting Applications

Accurate forecasting of electric load and renewable generation is essential for reliable and cost effective power system operations. Recent advances in transformer based and foundation machine learning models, driven by large scale pretraining, increased available data and computation, in addition to architectural innovations, have shown promise in time series forecasting across multiple domains. However, their application to power system forecasting tasks remains largely underexplored. This work presents a comprehensive, empirical benchmark of state of the art time series foundation models, transformer architectures, and deep learning baselines for solar, wind, and load forecasting using the high resolution ARPAE PERFORM dataset for the Electric Reliability Council of Texas (ERCOT) grid. Eight core capabilities are assessed, including zero shot performance, fine tuning efficiency, multivariate input and output handling, horizon sensitivity, generalization to unseen sites, probabilistic forecasting, and context window effects. Models evaluated include TimesFM, Chronos Bolt, MoiraiL, MOMENT, Tiny Time Mixer, Temporal Fusion Transformer, PatchTST, TimeXer, LSTM, and CNN. The manuscript aims to provide clear guidance on when foundation models can provide enhanced renewable and load forecasting capabilities and when other approaches remain the more practical choice for power system operations.

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Cross-Atlantic Research Agenda for Scalable Grid Architectures and Distributed Flexibility

Electric power systems are rapidly evolving into deeply digital, cyber-physical infrastructures in which large fleets of distributed energy resources must be coordinated as system-level flexibility across multiple spatial and temporal scales. Despite growing distributed energy resource deployment, existing grid and market architectures lack scalable, interoperable mechanisms to reliably translate device-level flexibility into grid-aware services, creating risks to reliability, affordability, and resilience at high penetration. We propose that scalable and reliable coordination of distributed energy resource-based flexibility in future power systems is fundamentally an architectural problem that can be addressed through laminar cyber-physical design using minimal, standardized interoperability interfaces that link device autonomy with system-level objectives. To assess this claim, we present and discuss a layered cyber-physical systems architecture and explicate its implementation through standards-based interfaces, Flexibility Functions, hierarchical control, and case studies spanning U.S. and Danish regulatory, market, and operational contexts. Empirical evidence from New York's Grid of the Future proceedings, Danish Smart Energy Operating System pilots, and operational aggregator deployments demonstrates that such architecture enables predictable, grid-aware flexibility while preserving device autonomy, interoperability, reliability, and quality of service. These results support a cross-Atlantic research agenda centered on joint testbeds, harmonized interoperability mechanisms, and coordinated policy experiments to accelerate the deployment of resilient, scalable, and flexible clean energy systems.

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DAE Index Reduction for Electromagnetic Transient Models

Electromagnetic transient (EMT) models are index-2 differential-algebraic equations when they include certain topologies and are formulated with modified nodal analysis. Such systems are difficult to numerically integrate, a challenge that is currently addressed by applying model approximations or reformulating with index-reduction algorithms. These algorithms exist in general-purpose software tools, but their reliance on symbolic representation makes them computationally prohibitive for large network-wide EMT models. This paper derives and presents two modular index-reduced subsystem models that allow EMT models to be integrated with standard solvers, without approximations or symbolic algorithms. Both subsystems include a transformer, one isolated and one machine-coupled. We measure the computational performance of constructing EMT models with up to 1152 buses using the custom subsystem models and the symbolic algorithms. The custom approach reduces memory usage and runtime of model construction by several orders of magnitude compared to the general approach, shifting the bottleneck from construction to integration.

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Synchronous Condensers: Enhancing Stability in Power Systems with Grid-Following Inverters

Large-scale integration of inverter-based resources into power grids worldwide is challenging their stability and security. This paper takes a closer look at synchronous condensers as a solution to mitigate stability challenges caused by the preponderance of grid-following inverters. It finds that while they are not grid-forming assets themselves, they could enhance grid stability. Throughout this paper, different facets of power system stability and their underlying phenomena are discussed. In addition, instances of instability and mitigation strategies using synchronous condenser are demonstrated using electromagnetic transient simulations. The analysis in this paper highlights the underlying mechanism by which synchronous condensers enhance angular stability, frequency response, and voltage stability. Moreover, it underscores the criticality of their choice of location by demonstrating the destabilizing behavior that could be initiated by the interactions of synchronous condensers.

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Selective State-Space Models for Koopman-based Data-driven Distribution System State Estimation

Distribution System State Estimation (DSSE) plays an increasingly-important role in modern power grids due to the integration of distributed energy resources (DERs). The inherent characteristics of distribution systems make classical estimation methods struggle, and recent advancements in data-driven learning methods, although promising, exhibit systematic failure in generalization and scalability that limits their applicability. In this work, we propose MambaDSSE, a model-free data-driven framework that incorporates Koopman-theoretic probabilistic filtering with a selective state-space model that learn to infer the underlying time-varying behavior of the system from data. We evaluate the model across a variety of test systems and scenarios, and demonstrate that the proposed method outperforms machine learning baselines on scalability, resilience to DER penetration levels, and robustness to data sampling rate irregularities. We further highlight the Mamba-based SSM's ability to capture long range dependencies from data, improving performance on the DSSE task.

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Optimal County-Level Siting of Data Centers in the United States

Data centers are growing rapidly, creating the pressing need for the development of critical infrastructure build out to support these resource-intensive large loads. Their immense consumption of electricity and, often, freshwater, continues to stress an already constrained and aging power grid and water resources. This paper presents a comprehensive modeling approach to determine the optimal locations to construct such facilities by quantifying their resource use and minimizing associated costs. The interdisciplinary modeling approach incorporates a number of factors including the power grid, telecommunications, climate, water use, and collocated generation potential. This work establishes the base model whose functionality is shown through several test cases focusing on carbon-free generation collocation on a county-level in the United States. The results suggest that while capital costs are the biggest driver, having a longer future outlook and allowing more variable generation collocation influences the model to choose sites with higher renewable potential.

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Implications of Grid-Forming Inverter Parameters on Disturbance Localization and Controllability

The shift from traditional synchronous generator (SG) based power generation to generation driven by power electronic devices introduces new dynamic phenomena and considerations for the control of large-scale power systems. In this paper, two aspects of all-inverter power systems are investigated: greater localization of system disturbance response and greater system controllability. The prevalence of both of these aspects are shown to be related to the lower effective inertia of inverters and have implications for future widearea control system design. Greater disturbance localization implies the need for feedback measurement placement close to generator nodes to properly reject disturbances in the system while increased system controllability implies that widearea control systems should preferentially actuate inverters to most efficiently control the system. This investigation utilizes reduced-order linear time-invariant models of both SGs and inverters that are shown to capture the frequency dynamics of interest in both all-SG and all-inverter systems, allowing for the efficient use of both frequency and time domain analysis methods.

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Learning a Generalized Model for Substation Level Voltage Estimation in Distribution Networks

Accurate voltage estimation in distribution networks is critical for real-time monitoring and increasing the reliability of the grid. As DER penetration and distribution level voltage variability increase, robust distribution system state estimation (DSSE) has become more essential to maintain safe and efficient operations. Traditional DSSE techniques, however, struggle with sparse measurements and the scale of modern feeders, limiting their scalability to large networks. This paper presents a hierarchical graph neural network for substation-level voltage estimation that exploits both electrical topology and physical features, while remaining robust to the low observability levels common to real-world distribution networks. Leveraging the public SMART-DS datasets, the model is trained and evaluated on thousands of buses across multiple substations and DER penetration scenarios. Comprehensive experiments demonstrate that the proposed method achieves up to 2 times lower RMSE than alternative data-driven models, and maintains high accuracy with as little as 1\% measurement coverage. The results highlight the potential of GNNs to enable scalable, reproducible, and data-driven voltage monitoring for distribution systems.

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Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions

Solving the nonlinear AC optimal power flow (AC OPF) problem remains a major computational bottleneck for real-time grid operations. In this paper, we propose a residual learning paradigm that uses fast DC optimal power flow (DC OPF) solutions as a baseline, and learns only the nonlinear corrections required to provide the full AC-OPF solution. The method utilizes a topology-aware Graph Neural Network with local attention and two-level DC feature integration, trained using a physics-informed loss that enforces AC power-flow feasibility and operational limits. Evaluations on OPFData for 57-, 118-, and 2000-bus systems show around 25% lower MSE, up to 3X reduction in feasibility error, and up to 13X runtime speedup compared to conventional AC OPF solvers. The model maintains accuracy under N-1 contingencies and scales efficiently to large networks. These results demonstrate that residual learning is a practical and scalable bridge between linear approximations and AC-feasible OPF, enabling near real-time operational decision making.

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Computationally Efficient Analytical Models of Frequency and Voltage in Low-Inertia Systems

In this paper, low-order models of the frequency and voltage response of mixed-generation, low-inertia systems are presented. These models are unique in their ability to efficiently and accurately model frequency and voltage dynamics without increasing the computational burden as the share of inverters is increased in a system. The models are validated against industry-grade electromagnetic transient simulation, compared to which the proposed models are several orders of magnitude faster. The accuracy and efficiency of the low-inertia frequency and voltage models makes them well suited for a variety of planning and operational studies, especially for multi-scenario and probabilistic studies, as well as for screening studies to establish impact zones based on the dynamic interactions between inverters and synchronous generators.

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Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment

This paper introduces a novel approach to the power system security assessment using Multi-Task Learning (MTL), and reformulating the problem as a multi-label classification task. The proposed MTL framework simultaneously assesses static, voltage, transient, and small-signal stability, improving both accuracy and interpretability with respect to the most state of the art machine learning methods. It consists of a shared encoder and multiple decoders, enabling knowledge transfer between stability tasks. Experiments on the IEEE 68-bus system demonstrate a measurable superior performance of the proposed method compared to the extant state-of-the-art approaches.

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Analytical Models of Frequency and Voltage in Large-Scale All-Inverter Power Systems

Low-order frequency response models for power systems have a decades-long history in optimization and control problems such as unit commitment, economic dispatch, and wide-area control. With a few exceptions, these models are built upon the Newtonian mechanics of synchronous generators, assuming that the frequency dynamics across a system are approximately homogeneous, and assume the dynamics of nodal voltages for most operating conditions are negligible, and thus are not directly computed at all buses. As a result, the use of system frequency models results in the systematic underestimation of frequency minimum nadir and maximum RoCoF, and provides no insight into the reactive power-voltage dynamics. This paper proposes a low-order model of both frequency and voltage response in grid-forming inverter-dominated power systems. The proposed model accounts for spatial-temporal variations in frequency and voltage behavior across a system and as a result, demonstrates the heterogeneity of frequency response in future renewable power systems. Electromagnetic transient (EMT) simulations are used to validate the utility, accuracy, and computational efficiency of these models, setting the basis for them to serve as fast, scalable alternatives to EMT simulation, especially when dealing with very large-scale systems, for both planning and operational studies.

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Autonomous Grid-Forming Inverter Exponential Droop Control for Improved Frequency Stability

This paper introduces the novel Droop-e grid-forming power electronic converter control strategy, which establishes a non-linear, active power--frequency droop relationship based on an exponential function of the power output. A primary advantage of Droop-e is an increased utilization of available power headroom that directly mitigates system frequency excursions and reduces the rate of change of frequency. The motivation for Droop-e as compared to a linear grid-forming control is first established, and then the full controller is described, including the mirrored inversion at the origin, the linearization at a parameterized limit, and the auxiliary autonomous power sharing controller. The analytic stability of the controller, including synchronization criteria and a small signal stability analysis, is assessed. Electromagnetic transient time domain simulations of the Droop-e controller with full order power electronic converters and accompanying DC-side dynamics, connected in parallel with synchronous generators, are executed at a range of dispatches on a simple 3-bus system. Finally, IEEE 39-bus system simulations highlight the improved frequency stability of the system with multiple, Droop-e controlled grid-forming inverters.

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