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This presentation provides and overview of the Federated Architecture for Secure and Transactive Distributed Energy Resource Management Solutions (FAST-DERMS) project and progress to date as of December 2024.
Nuclear energy is the most reliable and environmentally sustainable energy source available today. In the United States (U.S.), nuclear-generated power accounts for approximately 20% of total electricity and over 55% of clean energy. New advanced reactors have enormous potential to help further decarbonize the energy market, enhance grid resiliency, create new jobs, and build a stronger economy. More than 50 new reactors are being developed in the U.S., and the federal government realizes an urgent need to deploy nuclear technologies to meet the country’s energy, environmental, and national security goals. As such, the U.S. Department of Energy (DOE) launched multiple programs to support advanced reactor deployment. The research described in this report explores implementation strategies for the Reliability and Integrity Management Program that directly supports the DOE goal to enable the near-term deployment of the advanced reactor technologies. The project is conducted under the Regulatory Development Program for advanced reactors sponsored by the DOE.
Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today's power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.
The electrical grid is facing unprecedented challenges due to the increasing penetration of inverter-based resources. Grid forming inverters (GFMIs) are a promising technology to address these challenges. Recently, the virtual oscillator based GFMI control is attracting more attention due to its superior performance over other control strategies. In this article, an adaptive control strategy is proposed to provide flexible operation and transition between grid-connected and islanded modes. The controller adapts the virtual oscillator's parameter values depending on the operation mode. It also provides a feedback signal using a measured frequency to account for any steady-state errors and to allow a seamless transition from grid-connected to islanded mode. Finally, to show the feasibility of the proposed controller, this article discusses the simulation results from the implementation of the controller on a single inverter system and on a group of inverters on a large practical utility feeder, the IEEE 13 node feeder, using the DIgSILENT simulation environment.
Nuclear energy is the most reliable and environmentally sustainable energy source available today. In the United States, nuclear-generated power accounts for approximately 20% of total electricity and over 55% of clean energy. New advanced reactors have enormous potential to help further decarbonize the energy market, enhance grid resiliency, create new jobs, and build a stronger economy. More than 50 new reactors are being developed in the United States, and the federal government realizes an urgent need to deploy nuclear technologies to meet the country’s energy, environmental, and national security goals. As such, the U.S. Department of Energy launched multiple programs to support advanced reactor deployment. The research described in this paper explores implementation strategies for the Reliability and Integrity Management Program that directly supports the U.S. Department of Energy goal to enable the near-term deployment of the advanced reactor technologies. The project is conducted under the Regulatory Development Program for advanced reactors sponsored by the U.S. Department of Energy.
As high performance computing technology progresses, computational simulations are becoming more advanced in their capabilities. In the computational aerosciences domain, single discipline steady-state simulations computed on a single grid are far from the state-of-the-art. In their place are complex, time-dependent multidisciplinary simulations that attempt to model a given geometry more realistically. The product of these multidisciplinary simulations is a massive amount of data stored in different formats, grid topologies, units of measure, etc., as a result of the differences in the simulated physical domains. In addition to the challenges posed by setting up and performing the simulation, additional challenges exist in analyzing computational results. Visualization plays an important role in the advancement of multidisciplinary simulations. To date, visualization has been used to aid in the interpretation of large amounts of simulation data. Because the human visual system is effective in digesting a large amount of information presented graphically, visualization has helped simulation scientists to understand complex simulation results. As these simulations become even more complex, integrating several different physical domains, visualization will be critical to digest the massive amount of information. Another important role for visualization is to provide a common communication medium from which the domain scientists can use to develop, debug, and analyze their work. Multidisciplinary analyses are the next step in simulation technology, not only in computational aerosciences, but in many other areas such as global climate modeling. Visualization researchers must understand and work towards the challenges posed by multidisciplinary simulation scenarios. This paper addresses some of these challenges, describing technologies that must be investigated to create a useful visualization analysis tool for domain scientists.
The development of a wholly superconducting turboelectric distributed propulsion system presents unique opportunities for the aerospace industry. However, this transition from normally conducting systems to superconducting systems significantly increases the equipment complexity necessary to manage the electrical power systems. Due to the low technology readiness level (TRL) nature of all components and systems, current Turboelectric Distributed Propulsion (TeDP) technology developments are driven by an ambiguous set of system-level electrical integration standards for an airborne microgrid system (Figure 1). While multiple decades' worth of advancements are still required for concept realization, current system-level studies are necessary to focus the technology development, target specific technological shortcomings, and enable accurate prediction of concept feasibility and viability. An understanding of the performance sensitivity to operating voltages and an early definition of advantageous voltage regulation standards for unconventional airborne microgrids will allow for more accurate targeting of technology development. Propulsive power-rated microgrid systems necessitate the introduction of new aircraft distribution system voltage standards. All protection, distribution, control, power conversion, generation, and cryocooling equipment are affected by voltage regulation standards. Information on the desired operating voltage and voltage regulation is required to determine nominal and maximum currents for sizing distribution and fault isolation equipment, developing machine topologies and machine controls, and the physical attributes of all component shielding and insulation. Voltage impacts many components and system performance.
Microgrids continue to proliferate, and they are transitioning away from using conventional generating resources to increasingly relying on inverter-based resources (IBRs) as the voltage and frequency leaders. It is crucial to evaluate the capability of IBRs to provide microgrid stability and resilience. Hardware-in-the-loop (HIL) experiments were conducted to de-risk the field deployment of the San Diego Gas & Electric Company Borrego Springs Microgrid, where a battery inverter was upgraded with grid-forming (GFM) capability to serve as the island leader. This paper presents the HIL experimental results from an HIL test bed that uses a power-hardware-in-the-loop (PHIL) interface with a power inductor that was previously developed for PHIL simulations of microgrids where the inverters need to switch modes, i.e., between grid-following and GFM as the microgrid transitions between grid-connected and islanded operation. This paper presents more details on the interface and HIL simulation results of the planned islanding and load steps in islanded operation to show the effectiveness of the inverters in managing the voltage and frequency.
Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.
This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Lastly, numerical experiments have been devised to verify the performance of the proposed method.
As the electricity generated by variable resources grows, system operators and variable resources have to manage challenging imbalances between forward and real-time markets. The Flexibility Auction is a novel approach for managing imbalances as it will allow resources with imbalance risk to hedge their production by buying flexibility options. The flexibility options are offered by grid-connected resources that can provide physical flexibility. This presentation provides an overview of the participants in the auction, the definition of flexibility options, and settlements.
The devastating impact of extreme weather-related events is increasingly evident on power grids, especially on distribution grids. The severity of their potential impact calls for 1) developing a suitable resilience assessment framework to capture the system performance and 2) assessing relevant mitigative strategies to lessen the impact of such events. This paper proposes a framework to identify grid vulnerabilities using the energy-at-risk concept to select, disconnect and isolate grid portions due to a resilience event. The proposed framework mainly consists of two steps; i) processing the utility's available infrastructure, i.e., a network model, possible switching combinations, and outage information for those combinations, as a graph-based database, and ii) implementing a novel optimal switching algorithm leveraging database and grid simulated metrics. These switching actions are generated to implement load curtailment in a rolling manner during anticipated grid scarcity conditions. In this study, a test case is created using two taxonomy feeders and is simulated against an extreme temperature event, e.g., a long, relatively cold, and prolonged freeze peak, thereby creating stress on the grid. It is demonstrated that the proposed framework allows utilities to predict the energy-at-risk during such resilience events and design suitable outage management strategies.
The contemporary electrical grid is characterized by its complexity and abundance of data. A control-rich environment supported by information and communication technologies within an Advanced Distribution Management System (ADMS) presents a viable and cost-effective option for utility companies aiming to implement advanced real-time analytical schemes for monitoring and remotely controlling distribution feeders. Modular platform-based approaches to distribution operations require a structured framework for acquiring field device measurements, performing analytics, converting the setpoint to the correct protocol, and sending it on the appropriate communications network to the field devices. We present the development and deployment of an application service to integrate an open-source standardsbased platform with an ADMS test bed with field devices using the Distributed Network Protocol (DNP3) for data exchange. The step-by-step procedure for establishing the DNP3-Master service on an open-source distribution platform is outlined, comprehensively explaining the Master setup process. Moreover, sample use case results highlight the capabilities of the DNP3- Master service setup. Results demonstrate the scalability and configurability of the DNP3-Master service, making it adaptable for integration with other relevant applications, thus providing potential opportunities for real-world field trials and real-time assessments.
Microgrids continue to proliferate, and they are transitioning away from using conventional generating resources to increasingly relying on inverter-based resources (IBRs) as the voltage and frequency leaders. It is crucial to evaluate the capability of IBRs to provide microgrid stability and resilience. Hardware-in-the-loop (HIL) experiments were conducted to de-risk the field deployment of the San Diego Gas & Electric Company Borrego Springs Microgrid, where a battery inverter was upgraded with grid-forming (GFM) capability to serve as the island leader. This paper presents the HIL experimental results from an HIL test bed that uses a power-hardware-in-the-loop (PHIL) interface with a power inductor that was previously developed for PHIL simulations of microgrids where the inverters need to switch modes, i.e., between grid-following and GFM as the microgrid transitions between grid-connected and islanded operation. This paper presents more details on the interface and HIL simulation results of the planned islanding and load steps in islanded operation to show the effectiveness of the inverters in managing the voltage and frequency.
Alstom Grid Inc’s (ALSTOM Grid) Research Design and Development (RD&D) project, “Microgrid RD&D and Testing for PIDC and PWD” was conducted in partnership with Philadelphia Industrial Development Corporation (PIDC) in its role as owner’s representative and manager of a vibrant commercial and industrial community, involving critical loads in one of the nation’s largest unregulated, non-military electric distribution systems. PIDC needed to develop solutions to address the planned considerable growth in Distributed Energy Resources (DER) Combined Heat Plan (CHP), Renewables, Distributed Generation (DG), Demand Response (DR) and Storage. In supporting the corporate objective, PIDC needed a new class of control systems for achieving enhanced energy resilience of their critical infrastructure operation during adverse conditions together with carbon emission reduction and optimization of the overall system operation economics through system energy efficiency during normal and emergency operating conditions. Additionally, PIDC anticipated the need to support a new class of commercial agreements with tenants, such as those for Urban Outfitters, for guaranteed one hundred percent (100%) grid resilience and electric power supply in case of utility outage conditions. The project was designed to address the challenges for the commercial & industrial (C&I) communities. But more importantly, the project included development of scalable and replicable solutions intended to target a multitude of the nation’s electric distribution communities. The project researched and developed a fully comprehensive prototype consisting of microgrid operation and control functions including islanding, synchronization and reconnection, protection, voltage, frequency, and power quality management, dispatch, and system resiliency. The project provided the foundation required to significantly enhance the overall national objectives set by the DOE for energy resilience, emission reduction and system energy efficiency improvement, including protection of critical infrastructure and public resources.
Distributed energy resource (DER) systems feature complex, data-driven communications networks that require careful system coordination and constant vigilance to ensure that grid assets are secure. Because DERs are an important component of the decarbonization strategy, agencies need to secure energy data that could implicate issues of national security if compromised. To help federal energy managers assess, monitor, and manage cybersecurity while achieving decarbonization, the National Renewable Energy Laboratory's (NREL's) Distributed Energy Resource Cybersecurity Framework (DER-CF) offers a comprehensive, web-based assessment tool focusing on cyber governance or policies, technical management, and physical security. The DER-CF currently presents users with a series of pertinent cybersecurity questions that are used to generate a site-specific report and recommendations. This paper outlines a plan to integrate the DER-CF with another key asset-NREL's cyber range-to visualize cybersecurity resilience and compliance and to enhance the usability and accessibility of the DER-CF for federal facility energy managers and planners. This integration will result in a visualization environment to interpret and interact with compliance data. Its development will include regular conversations with stakeholders to assess the effectiveness of these efforts, refine the visualization capability, and ensure its value to our partners.
The author has identified the following significant results. Of the sampling techniques considered, a combination soil mapping and area sampling offered the most practical method for gathering soils data. Using the dot grid count, a relative percentage composition of soils can be calculated for each spectral class. From these percentages, a legend describing the dominant soils and inclusions can be developed. Interval drainage class seemed to be correlated with magnitude. For every parent material area, the more poorly drained soils had a lower magnitude of reflectance. Soil spectral classes seemed to be predominantly one internal drainage class.
This paper presents an approach to numerically simulate the inherently unsteady rime ice accretion problem on a two-dimensional airfoil and elucidate the associated variations under different icing conditions. The airflow field and the water impingement on the airfoil are obtained based on an Eulerian two-phase model. A dynamic mesh strategy is employed to unsteadily account for the changes in the ice profile and its impact on the air and droplet flow by continuously reconstructing the computational grid at each time-step through smoothing and layering mechanisms. All main icing modules including the airflow field, droplet trajectory, icing thickness profile, and mesh management are fully coupled within the same computational framework without resorting to any external tools. Classical icing theory is employed to model the rime ice roughness, and it is assumed that the ice accretes in a direction normal to the airfoil surface. The governing Reynolds-averaged Navier–Stokes (RANS) conservation equations along with the energy and continuity equations are solved to produce the velocity and temperature fields. A convective film heat transfer coefficient is computed based on the surface heat flux and a recovery temperature which takes into account the dissipative heat release in the boundary layer in the vicinity of the airfoil surface. With the implemented strategy and calculating the convective heat transfer coefficient, the water film thickness is also calculated along with the ice shape. Furthermore, the model is validated by comparing the local collection efficiency distribution and ice shape with experimental data, and the results show that the implemented approach provides acceptable predictions of ice accretion profiles and rates.