arXiv subjects

Duncan Callaway

Publications and source records attributed to Duncan Callaway.

9 recordsLinked to original sources

arXiv2024

Effects of dynamic power electronic load models on power systems analysis using ZIP-E loads

Power grids are seeing more devices connected at the load level in the form of power electronics: e.g., data centers, electric vehicle chargers, and battery storage facilities. Therefore it is necessary to perform power system analyses with load models that capture these loads' behavior, which has historically not been done. To this end, we propose ZIP-E loads, a composite load model that has a ZIP load with a dynamic power electronic, or E, load model. We perform small signal and transient analysis of the IEEE WSCC 9 Bus test case with ZIP and ZIP-E load models. For small signals, we conclude that ZIP loads destabalize networks significantly faster than corresponding ZIP-E loads. In stable cases, transient results showed significantly larger oscillations for ZIP loads. Further, we find that a higher network loading condition is correlated with a higher sensitivity to load model choice. These results suggests that the constant power portion of the ZIP load has a large destabilizing effect and can generally overestimate instability, and that attention should be drawn to load model choice if operating near a stability boundary.

eess.SY↗

arXiv2024

Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. In this paper, we propose a data-driven surrogate model based on implicit machine learning -- specifically deep equilibrium layers and neural ordinary differential equations -- to learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.

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arXiv2023

Small-signal stability in inverter-dominated grids: exploring the role of gains, line dynamics, and operating conditions

In a power grid with growing penetrations of renewable energy sources, inverters play a larger role in the dynamic interactions among network components. However, much is yet to be studied regarding inverter-dominated grid stability. This has initiated a re-examination of long-established assumptions made when conducting power systems studies. In this work we study the small-signal stability characteristics of a power system comprised of inverter and synchronous machine sources, and consider the effect of inverter control gains, transmission line dynamics, and network operating conditions. We utilize PowerSimulationsDynamics.jl to perform this study on the IEEE WSCC 9 Bus test system. We vary inverter parameters across reported ranges in the literature, we consider three transmission line models of varying fidelity, and compare results across different operating conditions. We find that line dynamics are generally important for correctly assessing small-signal stability in networks with large penetrations of grid-following converters, and that gain tuning in these networks should utilize high fidelity line models.

eess.SY↗

arXiv2023

Transmission line dynamics on inverter-dominated grids: analysis and simulations

In this work, we study fast time scale dynamic interactions between inverters, synchronous machines, and transmission lines. The overlapping time scales between inverter controls and electromagnetic phenomena idfentified in recent years has necessitated a re-evaluation of assumptions made in power system dynamics studies. We utilize an open-source modeling platform to perform both small signal stability and dynamic time domain studies of networks containing inverters, machines, and loads. We use transmission line models of varying fidelity, including models with multiple segments and frequency dependence. Our results indicate that, for the cases we study, line dynamics are not important for characterizing small signal stability. However, while in many cases the high fidelity line models are unnecessary for dynamic simulations, there are some cases in which simpler models omit dynamics that could be important to the operation of inner inverter control loops.

eess.SY↗

arXiv2020

Computational Experiment Design for Operations Model Simulation

Computer simulations that demonstrate the valueof novel approaches are crucial to developing more flexibleand robust power systems operations with high penetrations ofrenewable energy at multiple geographic and temporal scales.However, optimization-based simulations that depend on forecastdata often face challenges in evaluating performance, reproducingresults, and testing under realistic simulation scenarios. In thispaper, we develop scientific computing best-practices for thevalidation and reproduction of power systems operational models.We then employ two case studies to demonstrate the proposedvalidation and reproduction framework.

eess.SY↗

arXiv2020

MPC-Based Fast Frequency Control of Voltage Source Converters in Low-Inertia Power Systems

A rapid deployment of renewable generation has led to significant reduction in the rotational system inertia and damping, thus making frequency control in power systems more challenging. This paper proposes a novel control scheme based on Model Predictive Control (MPC) for converter-interfaced generators operating in a grid-forming mode, with the goal of exploiting their fast response capabilities to provide fast frequency control service to the system. The controller manipulates converter power injections to limit the frequency nadir and rate-of-change-of-frequency after a disturbance. Both centralized and decentralized MPC approaches are considered and compared in terms of performance and practical implementation. Special attention is given to the decentralized controller by generating an explicit MPC solution to enhance computational efficiency and reduce hardware requirements. Simulation results obtained from a high-fidelity differential-algebraic equation model of the IEEE 39-bus system demonstrate the effectiveness of the proposed control schemes.

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arXiv2018

Linear Single- and Three-Phase Voltage Forecasting and Bayesian State Estimation with Limited Sensing

Implementing state estimation in low and medium voltage power distribution is still challenging given the scale of many networks and the reliance of traditional methods on a large number of measurements. This paper proposes a method to improve voltage predictions in real-time by leveraging a limited set of real-time measurements. The method relies on Bayesian estimation formulated as a linear least squares estimation problem, which resembles the classical weighted least-squares (WLS) approach for scenarios where full network observability is not available. We build on recently developed linear approximations for unbalanced three-phase power flow to construct voltage predictions as a linear mapping of load predictions constructed with Gaussian processes. The estimation step to update the voltage forecasts in real-time is a linear computation allowing fast high-resolution state estimate updates. The uncertainty in forecasts can be determined a priori and smoothed a posteriori, making the method useful for both planning, operation and post-hoc analysis. The method outperforms conventional WLS and is applied to different test feeders and validated on a real test feeder with the utility Alliander in The Netherlands.

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arXiv2018

Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning

The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near optimal performance and satisfaction of system constraints. A rate distortion framework enables the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. The methodology provides a natural extension to decide what nodes a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators, focusing on DERs that do not exhibit inter-temporal dependencies. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to achieve Distributed Energy Services in distribution networks.

cs.LG↗

arXiv2018

How much energy storage do modern power systems need?

The central question we seek to address in this paper is: How rapidly do the operating cost benefits of grid-scale energy storage decline as installed storage capacity increases? We use a 240-bus model based on the US Western Interconnection, first optimally locating storage in the network and then dispatching it in a unit commitment model with DC load flow. The model uses storage to provide frequency regulation, load following, and arbitrage for each hour of a study year, and we investigate a range of scenarios for fuel price and renewables penetration. We find that value from long-term energy shifting is negligible at all penetrations we investigate, but also that displacing fossil-fueled generators from providing reserves is initially very valuable. However, in most scenarios the value is negligible beyond 10 GWh of storage, or the equivalent of roughly 6 minutes of average demand in the system. Above penetrations of 4-8 GWh, storage operating cost benefits are less than estimated capacity values for storage. We also show that storage has the potential to increase overall carbon emissions in the electricity sector, even when it is not providing significant amounts of arbitrage and is preferentially providing regulation and load following services.

physics.soc-ph↗