Co-Simulation of Transmission and Distribution Systems—From Modeling to Software Development
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This work presents a review of open-source electric power transmission and distribution systems analysis tools suitable for use by industry professionals and academic researchers. Due to the high complexity of the electric grid, there exist numerous tools and extensive research pertaining to nearly every aspect of the design, operation, and control of transmission and distribution networks. In addition to the commercial tools, a wide range of free, open-source tools, models, and data usable by the scientific community for related research have been developed by different organizations, including both international and US universities and national laboratories. However, due to the absence of a catalog of available tools, models and data, researchers often lack a knowledge of existing capabilities and may develop duplicative software and tools. Increasing awareness of these available resources seeks to accelerate their broader use, leading to more efficient and standardized grid analysis. This review paper (which is part of a larger survey effort that studied over 400 tools in the transmission, distribution, buildings, and electric vehicles space) outlines selected open-source resources that have been developed in power transmission and distribution systems research. It is anticipated that this work can serve as a guide for industry and academic researchers alike, ensuring that research efforts are well-channeled.
The Energy Exascale Earth System Model (E3SM) Project is an ongoing, state-of-the-science Earth system modeling, simulation, and prediction project that targets efficient utilization of U.S. Department of Energy’s (DOE) supercomputers to meet the science needs of the nation and the mission needs of DOE. This work focuses on our early evaluation of the A64FX architecture on Fugaku supercomputer using E3SM benchmarks. We will present results that track hardware trends, facilitate architecture comparison and the specific impact on our workload using an atmospheric model benchmark. We have two variants of the code written in Fortran and C++/Kokkos respectively which were used to collect data on a variety of CPU and GPU platforms. Furthermore, we have conducted a comparative evaluation of the compilers on the A64FX architecture and found GNU to be the best performer for our workload. Our experience so far indicates that Fugaku/A64FX shows promising energy efficiency (performance/Watt) with further performance gains possible through architecture-aware optimization efforts.
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Potential issues of front-end converters of wireless power transfer system modules for extreme fast charging are discussed and analyzed in this study in order to provide some recommendations to defend against attacks on electric vehicles and charging systems. Compared to conventional low-power charging systems, the impact of a cyber-attack might be more detrimental in high-power / fast charging systems since the fault energy levels would be inherently higher both on the grid- and vehicle- side converters. In order to analyze the potential issues that might be a result of cyber-attacks, the negative scenarios are reviewed in this study which include interfering with the grid-side controllers, establishing fake communications between the vehicles and the charging stations, and interfering with the battery management system functionalities. A 100-kW stationary wireless power transfer system with a series-series resonant compensation network is used as a representative system in the analysis. Potential damages and the fault energy levels for selected fault scenarios are investigated. The system is simulated to verify the analysis results. On the basis of the discussed worst-case study, a set of hardware design-level solutions are recommended in this study to provide cyber protection.
This paper presents a test bed that utilities, vendors, and researchers can use to evaluate the performance of existing and emerging advanced distribution management systems (ADMS) or specific applications of such systems. The test bed provides a realistic laboratory test setting, including detailed modeling of the power system, controller and/or power hardware, and industry-standard communication protocols. The test bed can further represent the current state of a specific utility's distribution system or the anticipated state with increased penetrations of distributed energy resources, such as rooftop photovoltaic systems, residential battery energy storage systems, electric vehicles, and loads with smart controls. The paper describes the design and implementation of the test bed. We present two use cases of the test bed and preliminary results for these two use cases. We also discuss upcoming and potential future use cases. Results from test bed experiments are intended to accelerate industry deployment of ADMS technologies and advance development of ADMS capabilities.
The inherent variability and uncertainty in distributed energy resources can presents myriad challenges to the planning and operations of power systems. These risks are poised to become larger as the penetration of renewable energy sources rises in the power generation mix. Hybrid solar-wind energy systems are able to mitigate some of these risks by their complementary resource availability. Surface solar and wind fields are coupled and correlated in both space and time. Appropriately estimating the hybrid solar wind energy system requires simulating the spatio-temporal structure of these fields that can be produced for each time horizon. We introduce a novel joint spatio-temporal stochastic differential equation (SPDE) approach that captures the spatio-temporal dynamics of solar and wind fields and their joint dependency over a domain for each time step. In the case study on Colorado, we consider nonstationary three-level hierarchical spatio temporal models for both hourly solar irradiance data and wind speed data in Colorado. Dependence between the solar irradiance data and wind speed data is captured by a shared spatio-temporal random effect. Our approach performs well in terms of the prediction score criterion.
Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that have led to and are expected to increase the occurrence of carbon cycle extremes. Carbon cycle extremes represent large anomalies in the carbon cycle that are associated with gains or losses in carbon uptake. Carbon cycle extremes could be continuous in space and time and cross political boundaries. Here, we present a methodology to identify large spatiotemporal extremes (STEs) in the terrestrial carbon cycle using image processing tools for feature detection. We characterized the STE events based on neighborhood structures that are three-dimensional adjacency matrices for the detection of spatiotemporal manifolds of carbon cycle extremes. We found that the area affected and carbon loss during negative carbon cycle extremes were consistent with continuous neighborhood structures. In the gross primary production data we used, 100 carbon cycle STEs accounted for more than 75% of all the negative carbon cycle extremes. This paper presents a comparative analysis of the magnitude of carbon cycle STEs and attribution of those STEs to climate drivers as a function of neighborhood structures for two observational datasets and an Earth system model simulation.
A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severe voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.
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Here, we present a method and tool for evaluating the placement of Distributed Energy Resources (DER) on distribution circuits in order to control voltages and power flows. Our previous work described Phasor-Based Control (PBC), a novel control framework where DERs inject real and reactive power to track voltage magnitude and phase angle targets. Here, we employ linearized power flow equations and integral controllers to develop a linear state space model for PBC acting on a three-phase unbalanced network. We use this model to evaluate whether a given inverter-based DER configuration admits a stable set of controller gains, which cannot be done by analyzing controllability nor by using the Lyapunov equation. Instead, we sample over a parameter space to identify a stable set of controller gains. Our stability analysis requires only a line impedance model and does not entail simulating the system or solving an optimization problem. We incorporate this assessment into a publicly available visualization tool and demonstrate three processes for evaluating many control configurations on the IEEE 123-node test feeder (123NF).
In an effort to better understand the impacts of high penetrations of photovoltaic generators on distribution systems, Arizona Public Service and its partners have begun work on a multi-year project to develop the tools and knowledge base needed to safely and reliably integrate high penetrations of utility- and residential-scale photovoltaics (PV). Building upon the APS Community Power Project --Flagstaff Pilot, this project will analyze the impact of PV on a representative feeder in northeast Flagstaff. To quantify and catalog the effects of the estimated 1.3 MW of PV that will be installed on the feeder (both smaller units at homes as well as large, centrally located systems), high-speed weather and electrical data acquisition systems and digital 'smart' meters are being designed andinstalled to facilitate monitoring and to build and validate comprehensive, high-resolution models of the distribution system. These models will be used to analyze the impacts of the PV on distribution circuit protection systems (including anti-islanding), predict voltage regulation and phase balance issues, and develop volt/var control schemes. This paper continues from a paper presented at the2011 IEEE PVSC conference that introduces the project and describes some of the preliminary consideration, as well as project plans and early results. This paper gives a status update of the project and presents selected results from Phase 2 of the project. It discusses baseline feeder modeling, load allocation, data acquisition, utility-scale PV integration, preliminary model validation, andplans for future phases.
This paper explores the detection and localization of cyber-attacks on power systems, focusing on comparing conventional machine learning (ML) and deep learning methods, and graph neural network (GNN)-based techniques. We assess the detection accuracy of these approaches and their potential to pinpoint the locations of specific buses under attack. Given the demonstrated success of GNNs in other time series anomaly detection applications, we aim to evaluate their performance within the context of power systems cyber-attack. Utilizing the IEEE 68-bus system, we simulated four types of attacks to test the selected approaches. Our results indicate that GNN-based methods outperform conventional machine learning and deep learning models in detection. Additionally, GNNs show promise in accurately localizing attacks for simple scenarios, although they still face challenges in more complex cases.
We introduce a safe extremum-seeking (Safe ES) algorithm which achieves the minimization of an unknown objective function while ensuring that an unknown, yet measured, control barrier function (CBF) remains above an arbitrarily small negative value for all time. In other words, “practical safety” is maintained during the entire period of convergence to the constrained extremum. Our design is based on quadratic program (QP) CBF style filters for safety, which is applied in an average and estimated sense. Using nonsmooth analysis tools, we guarantee semiglobal practical asymptotic (SPA) stability of the global constrained optimum, practical convergence to the safe set if starting in a condition violating the CBF, and practical safety for all time—semiglobally—if starting in safe set. The safety result of the paper is analogous with modern notions of SPA stability, guaranteeing that, for any small violation of safety, there exist design coefficients which guarantee that such a small violation is not exceeded. The paper outlines a set of sufficient conditions on the barrier and objective functions, and by way of a Lyapunov argument, we demonstrate that nonconvex constrained optimization problems can be solved. We present these results in the setting of a static map and a dynamical system. A simulation example illustrates the results.
Variability in power generation at multiple consumer owned renewable energy sources can cause random voltage fluctuations, which may result in voltage violations at multiple nodes in a power distribution system. Traditional methods of voltage control are ineffective to address fast voltage variations. New dynamic methods of voltage control using end user devices can significantly benefit from the knowledge of the dominant influencer of voltage fluctuation (DIVF). This paper introduces information theoretic metrics to identify DIVF for every node in the power distribution system. A node can be DIVF due to its location in the distribution system and its capacity to generate and change its power. Use of traditional power flow algorithms to identify the DIVF is computationally complex which limits their use in real-time applications. In this work, information theoretic indicators of the DIVF are computed analytically in a computationally efficient manner, and their effectiveness to identify the DIVF is tested using the IEEE 69 node test system. From simulations, it is illustrated that differential entropy, Kullback-Leibler distance and Frechet distance are effective indicators of the DIVF and can successfully identify the most influential nodes providing the foundation for intelligent voltage control.
Efficient management and coordination of distributed energy resources with advanced automation schemes requires accurate distribution system modeling and monitoring. Big data from smart meters and photovoltaic (PV) micro-inverters can be leveraged to calibrate existing utility models. This paper presents computationally efficient distribution system parameter estimation algorithms to improve the accuracy of existing utility feeder radial secondary circuit model parameters. The method is demonstrated using a real utility feeder model with advanced metering infrastructure (AMI) and PV micro-inverters, along with alternative parameter estimation approaches that can be used to improve secondary circuit models when limited measurement data is available. Lastly, the parameter estimation accuracy is demonstrated for both a three-phase test circuit with typical secondary circuit topologies and single-phase secondary circuits in a real mixed-phase test system.
Abstract not provided
This paper proposes a power-smoothing scheme for a variable-speed wind turbine generator (WTG) that can smooth out the WTG's fluctuating power caused by varying wind speeds, and thereby keep the system frequency within a narrow range. The proposed scheme employs an additional loop based on the system frequency deviation that operates in conjunction with the maximum power point tracking (MPPT) control loop. Unlike the conventional, fixed-gain scheme, its control gain is modified with the rotor speed. In the proposed scheme, the control gain is determined by considering the ratio of the output of the additional loop to that of the MPPT loop. To improve the contribution of the scheme toward maintaining the frequency while ensuring the stable operation of WTGs, in the low rotor speed region, the ratio is set to be proportional to the rotor speed; in the high rotor speed region, the ratio remains constant. The performance of the proposed scheme is investigated under varying wind conditions for the IEEE 14-bus system. The simulation results demonstrate that the scheme successfully operates regardless of the output power fluctuation of a WTG by adjusting the gain with the rotor speed, and thereby improves the frequency-regulating capability of a WTG.