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DOE OSTI2024

Real-time hybrid controls of energy storage and load shedding for integrated power and energy systems of ships

This paper presents an original energy management methodology to enhance the resilience of ship power systems. The integration of various energy storage systems (ESS), including battery energy storage systems (BESS) and super-capacitor energy storage systems (SCESS), in modern ship power systems poses challenges in designing an efficient energy management system (EMS). The EMS proposed in this paper aims to achieve multiple objectives. The primary objective is to minimize shed loads, while the secondary objective is to effectively manage different types of ESS. Considering the diverse ramp-rate characteristics of generators, SCESS, and BESS, the proposed EMS exploits these differences to determine an optimal long-term schedule for minimizing shed loads. Furthermore, the proposed EMS balances the state-of-charge (SoC) of ESS and prioritizes the SCESS’s SoC levels to ensure the efficient operation of BESS and SCESS. For better computational efficiency, we introduce the receding horizon optimization method, enabling real-time EMS implementation. Further, a comparison with the fixed horizon optimization (FHO) validates its effectiveness. Simulation studies and results demonstrate that the proposed EMS efficiently manages generators, BESS, and SCESS, ensuring system resilience under generation shortages. Additionally, the proposed methodology significantly reduces the computational burden compared to the FHO technique while maintaining acceptable resilience performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Fast and Accurate Frequency Response Estimation for Large Power System Disturbances Using Second Derivative of Frequency Data

Accurate Frequency Response Estimation (FRE) is critical to reliability risk management of large power system disturbances. This letter proposes a measurement-driven approach for FRE by calculating the second derivative of frequency from synchrophasor data. Compared with conventional methods, the proposed method has major advantages of low computational burden and high accuracy, which makes it appropriate for real-time FRE in bulk power systems. The effectiveness of this method is validated with actual synchrophasor data on 96 confirmed historical power system disturbances in the U.S. eastern interconnection.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Unalakleet Microgrid Optimization for Tribal Community Resilience

The Unalakleet Microgrid Optimization Project aimed to strengthen the reliability and efficiency of the isolated electric power system that serves the Tribal community of Unalakleet, Alaska. The community relies entirely on a local wind-diesel microgrid, consisting of four 475 kW diesel generators and six 100 kW wind turbines, to provide electricity to approximately 745 residents and Tribal facilities. Because Unalakleet is not on a road system and is located nearly 400 miles from the nearest major power grid, maintaining a resilient and efficient local energy system is critical. The scope of this project included upgrading a portion of the transmission line between the wind farm and the power plant to increase voltage and reduce line losses, along with modernizing the Supervisory Control and Data Acquisition (SCADA) system to improve monitoring, control, and data management of the power system. These upgrades were designed to increase wind energy utilization, reduce diesel fuel consumption by tens of thousands of gallons annually, and improve overall grid stability. By allowing more of the community’s electricity to be supplied by local renewable wind resources, the project was designed to lower operating costs, reduce dependence on imported fuel, and strengthen the long-term resilience of the power system. These improvements represent an important step toward the community’s long-term energy vision of expanding renewable generation, incorporating energy storage, and eventually achieving “diesels-off” operation, where essential Tribal loads are powered primarily by local renewable resources.

17 WIND ENERGY↗

DOE OSTI2024

Real Time-Optimal Power Flow-Based Distributed Energy Resource Management System (DERMS)

This project aims to promote lab-proven clean energy technology to commercially scalable versions of the technology, integrate the technology with broader systems, provide extended performance data, and validate the manufacturability and reliability of the technology. The lab-proven technology, RT-OPF DERMS, was developed and validated through previous U.S. Department of Energy-funded efforts, including Advanced Research Projects Agency-Energy funding under the Network Optimized Distributed Energy Systems program and Holy-Cross Energy High Impact Project. In the Advanced Research Projects Agency-Energy Network Optimized Distributed Energy Systems project, the RT-OPF DERMS was developed and implemented in multiple hardware platforms, demonstrating its performance and capabilities in the lab and field environments. The technology was also evaluated and matured via a participation in the U.S. Department of Energy I-Corps program, whose goal is to pair teams of researchers with industry mentors for an intensive 2-month training in which the researchers define technology value propositions, conduct customer discovery interviews, and develop viable market pathways for their technologies. These activities indicate the high technology maturity and Technology Readiness Level of the RT-OPF DERMS.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Electric Vehicle Managed Charging: Forward-Looking Estimates of Bulk Power System Value

When and where electric vehicle charging occurs has significant implications for power systems supporting widespread electric vehicle deployment with high shares of wind and solar generation. Numerous studies have estimated the value of scheduling or otherwise managing electric vehicle charging in such power systems. This study improves on those earlier works by leveraging detailed simulation models for electric vehicle adoption, electric vehicle use, electric vehicle charging, and bulk power system operations; and linking them with methods for describing charging flexibility at both the individual vehicle and aggregate levels. This study closely analyzes electric vehicle managed charging (EVMC) performance along the dimensions of flexibility type (within-charging session or within-week scheduling), dispatch mechanism (direct load control or one of several price-based mechanisms), and participation rate, under the assumptions of ubiquitous chargers and all trips completed on time. The study is located in a passenger light-duty vehicle adoption scenario with 100% electric vehicle sales by 2035, and in an envisioned 2038 New England power system for which within-region generation is 84% clean.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Two-Stage Optimization Framework for Detecting and Correcting Parameter Cyber-Attacks in Power System State Estimation

One major tool of Energy Management Systems for monitoring the status of the power grid is State Estimation. Since the results of state estimation are used within the energy management system, the security of the state estimation process is most important. The focus research in this area is on detecting False Data Injection attacks on measurements. While this is important, State Estimation also rely on database that are used to describe the relationship between measurements and systems' states. This paper presents a two-stage programming framework to detect and correct attacks in the parameters of the measurement model used by the state estimation process in the Energy Management System. In the first stage, an estimate of the line parameters ratios are obtained. In the second stage, the estimated ratios from stage I are used in a Bi-Level model for obtaining a final estimate of the measurements' model parameters. Hence, the presented framework does not only unify the detection and correction in a single optimization run, but also provide a monitoring scheme for the SE database that is typically considered static. In addition, in the two stages, linear programming framework is preserved. For validation, the IEEE 118 bus system is used for implementation. The results of this paper illustrate the effectiveness of the proposed model for detecting attacks in the database used in the state estimation process.

state estimation, two-stage optimization, cyber-ph↗

DOE OSTI2020

Comparison of Thermal Management Approaches for Integrated Traction Drives in Electric Vehicles

The continuous push to increase power densities of electric vehicle (EV) traction drive systems necessitates combining electric motor and power electronics into one unit. A single, compact traction drive unit with fewer interconnecting components also facilitates fast, automated assembly of electric vehicles, driving production costs down and enabling wider adoption of EVs. There are a number of challenges associated with the integration of power electronics with the electric machine, including thermal management of the combined traction drive system. However, one important benefit of integration from the thermal management system perspective is the potential for using a single fluid loop instead of two separate cooling systems for the electric machine and the power electronics/inverter. This paper reviews several integration approaches and, employing finite element analysis (FEA), compares thermal management solutions for the combined electric machine and power electronics systems. Namely, three different scenarios are modeled: (1) independent component (motor and power electronics) cooling, which is compared to the combined cooling system approach for (2) radially and (3) axially integrated power electronics modules into the motor enclosure. Temperature distributions for selected thermal loads and thermal resistances from the key heat-generating components to the cooling fluid are compared for each scenario.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

SolarSTARTS: Solar-Assisted State-Aware and ResilienT infrastructure System

This final technical report provides a description and results of the design, development, testing, and validation of the Automated Resilience Management System (ARMS) solution, which integrates different systems to collect, store and process relevant information from the power distribution system to automatically manage the operation of multiple energy assets to enhance the resilience of the power grid against high-impact physical incidents (e.g., hurricanes) and cyber threats. The developed solution comprises a suite of novel AI-based algorithms designed to first detect, locate, and classify anomalies by collecting and analyzing data from multiple monitoring and control devices

14 SOLAR ENERGY↗

DOE OSTI2020

Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands

With the rapid increase of variable renewable energy sources in power systems, how to manage and price the uncertainty of renewable resources’ power outputs is becoming an urgent issue. Current market designs considering the uncertainties are mainly based on the probabilistic scenario set of demand and renewable energy resources power outputs. This consideration makes market designs vulnerable to three significant challenges when put into practice. First, the accurate probability distribution of renewable generation is hard to obtain in real-time. Second, it is challenging to clear the market timely with many scenarios to guarantee accuracy. Third, generation cost recovery cannot be guaranteed for some scenarios. To overcome these challenges, this paper proposes a locational uncertainty marginal price model to price the uncertainty explicitly based on a scenario-free stochastic market-clearing model. Instead of using the probabilistic scenario set, the uncertainty of renewable energy sources and loads is modeled with distributionally-robust chance constraints. The correlation of uncertainties can be endogenously modeled in both the market-clearing and the locational uncertainty marginal price formation. Furthermore, this paper proves that generation cost recovery, revenue adequacy, and partial market equilibrium can be achieved using the locational uncertainty marginal price model. Numerical results from both the small and large systems simulations validate that the generation cost recovery is maintained no matter the generation participates in uncertainty mitigation or not. The transmission congestion surplus is also allocated appropriately among loads, renewable energy sources, and financial transmission right owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Managing Increased Electric Vehicle Shares on Decarbonized Bulk Power Systems

Transportation electrification and power sector decarbonization - the convergence of these two trends present a complex planning problem requiring realistic, region-specific modeling and analysis to ensure that both can happen rapidly and cost effectively. This project, funded through the U.S. Department of Energy's Vehicle Technologies Office, models the evolution of the U.S. bulk power system (through 2050) in response to large-scale EV charging across all on-road vehicle segments. We will assess the opportunity and value of demand-side flexibility (i.e., "smart" charging) for reducing energy costs, increasing renewable generation shares, and managing future EV loads on the bulk power system. Overall, this study aims to provide an improved understanding of least-cost solutions for managing EV load growth on the grid and will make high-resolution EV load data sets publicly available for further analysis.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2022

Pathways to the Next-Generation Power System With Inverter-Based Resources: Challenges and recommendations

Managing the stability of today's electric power systems is based on decades of experience with the physical properties and control responses of large synchronous generators. Today's electric power systems are rapidly transitioning toward having an increasing proportion of generation from nontraditional sources, such as wind and solar (among others), as well as energy storage devices, such as batteries. In addition to the variable nature of many renewable generation sources (because of the weather-driven nature of their fuel supply), these newer sources vary in size - from residential-scale rooftop systems to utility-scale power plants - and they are interconnected throughout the electric grid, both from within the distribution system and directly to the high-voltage transmission system. Most important for our purposes, many of these new resources are connected to the power system through power electronic inverters. Collectively, we refer to these sources as inverter-based resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

EVSE Characterization: V2G EVSE Comparison

As part of the U.S. Department of Energy EVs@Scale consortium Next-Generation Profiles project, results and analysis from the characterization of high-power conductive and wireless charging infrastructure are presented. This characterization is conducted over a wide range of direct current (DC) current and DC voltage operation for nominal test conditions and off-nominal test conditions. Test plans and procedures were developed to define the test configurations and requirements, measurement parameters, and test procedures used throughout testing. Results from a 2024 study conducted on electric vehicle supply equipment (EVSE) characterization by the Idaho National Laboratory (INL) include two bi-directional vehicle-to-grid (V2G) capable EVSEs. These EVSE are referred to as V2G-EVSE9 and V2G-EVSE10. Laboratory testing is conducted at nominal test conditions to characterize the power transfer capabilities, efficiency, power factor, and other power quality metrics of the two DC EVSEs capable of V2G bi-directional power transfer. Results from testing show the performance is consistent for V2G-EVSE9 and V2G-EVSE10 when comparing charging to discharging performance, except for V2G-EVSE10 for power transfer when operating above 70% of the rated DC current. V2G-EVSE10 efficiency is >98% while charging and <91% while discharging at the same operating conditions, near maximum-rated current, at 300VDC. In contrast, V2G-EVSE9 results are consistent for charging and discharging. This EVSE is nearly 96% efficient while charging or discharging when operating over 50% of rated AC power. V2G EVSE performance is also characterized during off-nominal AC grid conditions involving AC voltage deviation (426 VAC to 518 VAC), AC frequency deviation of +2% (58.8 Hz to 61.2 Hz), and AC voltage harmonics injection. Many test conditions have little-to-no impact on performance characteristics of the two EVSEs; however, there are a few notable findings with significant power transfer capability impacts. AC voltage harmonics injection resulted in negative impacts on power quality attributes for both EVSEs, but with no impact on power transfer capability. Off-nominal AC voltage and frequency conditions resulted in unstable or lack of power transfer capability for both EVSEs. V2G-EVSE9 is unable to transfer power when AC voltage is >300V L-N. V2G-EVSE10 is unable to transfer power when AC frequency deviation exceeds +0.8%. V2G energy management system transient response and latency are quantified during laboratory testing. V2G-EVSE9 and V2G-EVSE10 utilize cloud-based V2G energy management systems that command the power transfer level between the EVSE and EV. The latency and response characteristics of the entire systems (web-based user interface, V2G energy management system, cellular communications, and EVSE response) are quantified through laboratory testing for V2G-EVSE9 and V2G-EVSE10. V2G-EVSE9 latency ranges from 0.8 to 1.8 seconds, whereas V2G-EVSE10 latency ranges from 3.4 to 8.8 seconds. The ramp rate to a change in power transfer request also differs between the two EVSEs. V2G-EVSE10 ramp rate ranges from 50% to -250% of rated AC power per second, whereas V2G-EVSE9 rate ranges from 95% to -95% of rated AC power per second. At the highest rate of change in power transfer, V2G-EVSE10 can change from full charge power to full discharge power in less than one second. The V2G EVSE characterization presented in this report provides valuable insights and results for use by numerous entities. This includes modeling and simulation organizations, decision makers, fleet planning, industry stakeholders, and many others involved with the development and deployment of electrified transportation technologies. Additional high-power DC chargers, bidirectional chargers, and inductive power transfer EVSE characterization results are anticipated from additional EVSE brands and models, which will be detailed in future publications in support of the U.S. Department of Energy EVs@Scale consortium Next-Gen Profiles project.

25 ENERGY STORAGE↗

DOE OSTI2020

Indicator-directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems

Modern high-performance and warehouse computing centers show strong interest in minimizing system power consumption while satisfying customers’ quality of service (QoS). Dynamic voltage and frequency scaling (DVFS) is effective for achieving this goal. Nevertheless, automating the process online and making it transparent to users must address three major challenges: (1) Complexity — today’s hardware components (e.g., CPUs, GPUs, memory, network, etc.) can be configured in several or dozens of frequency/voltage states for satisfying divergent system demands. Given their combination and the emergence of heterogeneity, searching the optimal configuration in the design space online can be timing consuming. (2) QoS guarantee — user-defined objectives such as power constraint and performance target must be monitored, predicted and ensured at the best effort. (3) Adaptability — various known and unknown workloads run on systems. Workloads characteristics should be quickly determined and configurations dynamically adjusted in accord with workloads and QoS. In this work, we focus on applications exhibiting an interesting feature – iterative or periodic, which is common among conventional HPC and emerging machine learning workloads. We propose an online dynamic power-performance (ODPP) management framework to dynamically adjust GPU DVFS configurations to meet performance and power objectives and constraints, without any code annotation or intrusion. Particularly, ODPP extracts the performance and power indicators for applications from their resources utilization profiles in a short episode. It further automatically constructs an accurate model that infers from the indicators how the application's performance and power vary with GPU core and memory frequencies. Aided with the model, for both seen and unseen applications, ODPP can quickly determine the most appropriate DVFS configuration for their execution. We evaluate ODPP on an NVIDIA GPU using multiple exascale computing (ECP) and deep learning applications.

Zou, Pengfei↗

DOE OSTI2021

DC-Saturated Continuously Variable Series Reactors (CVSRs) for Power Flow Control in Power Transmission Systems

This paper proposes a novel continuously variable series reactor (CVSR) based on a dc current controller (DCC) to manage power flow in transmission systems. There are three major contributions. First, the three-dimensional electromagnetic interaction has been comprehensively analyzed to extend the understanding beyond the conventional 2D relationship. Second, a high-fidelity reluctance model of the CVSR with an improved DCC model is proposed and implemented. To overcome the fundamental concern for the system modeling, the DCC has been modeled as an ideal current source in parallel with an output impedance. Additionally, the induced back-EMF can be precisely projected which provides critical design guidelines for the DCC. Third, inspired by the theoretical analysis and modeling, a reliable high power DCC converter is designed accordingly to interface with kV-level back-EMF and supply kA-level dc current for a 115 kV/1500 A CVSR. Experiments are conducted in a practical transmission demonstration system. When the ac current in the transmission system varies from zero to 1500 A, experimental results show that the proposed CVSR can continuously regulate the reactance from 1.6 Ω to 5 Ω, validating the effectiveness of the proposed system design and modeling methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Consensus-Based Approach for Active Power Control and Reserve Estimation in Distributed PV Systems

With the increased adoption of distributed energy resources (DER), they are expected to contribute to power system reliability services and enhance power system stability. This paper presents a distributed consensus control approach for the real-time active power reserve estimation and power management in distributed PV (photovoltaic) systems. The proposed method estimates the cumulative active power reserve from numerous PV generators using only the sparse communication network for a DER aggregator to provide active power regulation services. The real-time active power reserve estimation using the proposed approach can be used by DER aggregators to manage curtailment in distributed PV systems to maintain stipulated reserves and further to provide power system frequency regulation. Using the proposed approach, the DER aggregator only requires measurements at the feeder substation and it does not require information from individual DER units, thereby improving system resiliency. The proposed distributed consensus control method is validated on IEEE 123 testbed using PSCAD simulations.

active power reserve↗

DOE OSTI2022

Consensus-Based Approach for Active Power Control and Reserve Estimation in Distributed PV Systems: Preprint

active power reserve↗

DOE OSTI2020

Real-Time Distributed Control of Smart Inverters for Network-level Optimization

The limitations of centralized optimization methods in managing electric power distribution systems operations have led to the distributed paradigm of computing and decision-making. Unfortunately, the existing distributed optimization algorithms are limited in their applicability to managing fast varying phenomena such as those resulting from highly variable Distributed Energy Resource (DER) generation patterns. They require a large number of communication rounds (in the order of 10 2 to 10 3 ) among the computing agents to solve one instance of the optimization problem. Related real-time distributed control methods are equally limited in their applications to power distribution systems with fast-changing DER generation; they require hundreds of rounds of communication and thus are slow in tracking the network-level optimal solutions. In this paper, we propose a novel distributed voltage controller that provides a fast-tracking of rapidly varying DER generation profiles while simultaneously converging to network-level optimal solutions within a few communication rounds. The proposed control algorithm leverages the radial topology of the system, which reduces the required communication rounds to reach the network-level optimum solution by order of magnitude. The novelty lies in carefully reducing the electrical network model from the perspective of each distributed controller and enabling appropriate data sharing among upstream and downstream nodes to achieve fast convergence. The simulation results demonstrate the effectiveness of the proposed approach in minimizing the feeder losses while maintaining the node voltage within the pre-specified limits.

voltage control, optimization, reactive power, inv↗

DOE OSTI2025

Evaluating the Impact of Managed EV Charging for Reliable Operation of Bulk Power Systems with High Non-Dispatchable Generation

The growth of electric vehicles (EVs) and variable-generation (VG) sources introduces new challenges for power-system operations. This study introduces a modeling framework and evaluates five EV charging strategies under projected 2040 grid conditions in the Evergy service territory with high non-dispatchable generation. Using realistic EV behavior and generation models, their impacts on system peak demand, ramp rate, and reserve capacity are evaluated. Results show that only the peak-avoidance strategy effectively reduces system peak demand, while decentralized strategies-particularly cost based dynamic charging-can exacerbate peaks due to synchronized user behavior. However, ramp-rate minimization strategy significantly reduce the stress on dispatchable generation achieving the lowest maximum absolute ramp rate (MARR) (56.81 MW) and lowest reserve requirement (2.39 GW). In contrast, unmanaged and TOU random strategies increase the stress on dispatchable generation sources with increased MARR and reserve requirements. These findings highlight the importance of coordinated, system-aware managed charging strategies to ensure reliable and affordable grid operation in the presence of EVs and VG sources.

14 - SOLAR ENERGY↗