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DOE OSTI2024

Enhancing Power Grid Resilience with Causal Loops Diagram and Bayesian Networks

Enhancing power grid resilience through improved analysis and planning of Distributed Energy Resources is a key for power system planner. This paper explores the integration of Causal Loop Diagrams (CLDs) and Bayesian Networks (BNs) for enhancing resilience in power systems, focusing on Distributed Energy Resources (DER) planning. By automating CLD analysis in Python's matplotlib, we present a tool for rapid model validation and structural accuracy, crucial for power system planners. This hybrid approach utilizes BNs for inferential depth and CLDs for dynamic system modeling, offering a comprehensive framework for policy formulation and collaborative strategy development against disruptions. Here, we highlight the tool's capability to identify and analyze interconnected feedback loops, facilitating a deeper understanding of DER integration's impact on network resilience. This work aims to bridge quantitative analysis and qualitative insights, addressing the limitations of each method while providing a robust model for power system resilience assessment.

14 SOLAR ENERGY↗

DOE OSTI2022

Adversarial Sampling-Based Motion Planning

In this report there are many scenarios in which a mobile agent may not want its path to be predictable. Examples include preserving privacy or confusing an adversary. However, this desire for deception can conflict with the need for a low path cost. Optimal plans such as those produced by RRT* may have low path cost, but their optimality makes them predictable. Similarly, a deceptive path that features numerous zig-zags may take too long to reach the goal. We address this trade-off by drawing inspiration from adversarial machine learning. We propose a new planning algorithm, which we title Adversarial RRT*. Adversarial RRT* attempts to deceive machine learning classifiers by incorporating a predicted measure of deception into the planner cost function. Adversarial RRT* considers both path cost and a measure of predicted deceptiveness in order to produce a trajectory with low path cost that still has deceptive properties. We demonstrate the performance of Adversarial RRT*, with two measures of deception, using a simulated Dubins vehicle. We show how Adversarial RRT* can decrease cumulative RNN accuracy across paths to 10%, compared to 46% cumulative accuracy on near-optimal RRT* paths, while keeping path length within 16% of optimal. We also present an example demonstration where the Adversarial RRT* planner attempts to safely deliver a high value package while an adversary observes the path and tries to intercept the package.

42 ENGINEERING↗

DOE OSTI2021

Optimal Power System Black start using Inverter-Based Generation

Power system black start readiness is part of the system planning. Utility planners perform periodic studies to assess if their power system is capable of total restoration following a black out. Hydro and diesel generators are the most commonly used black start capable resources by power utilities. However, with increasing penetration of solar generation, inverter-based resources can be considered to provide black start capability. Since the solar inverters can be located at multiple locations throughout the power system, and in view of their unique characteristics, an optimal real-time capable plan is helpful for system operators for faster black start. Black start optimization is a multi stage mixed-integer non-linear optimization which is extremely hard to solve. In this paper, we propose an optimal black start methodology that is easier to solve and scalable in real-time. We demonstrate the proposed methodology on two test systems and illustrate how inverter-based resources can contribute and improve power system restoration.

power system restoration, blackstart, Inverter-bas↗

DOE OSTI2024

Newton-Raphson AC Power Flow Convergence Based on Deep Learning Initialization and Homotopy Continuation

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. In conclusion, the combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

Deep learning↗

DOE OSTI2021

Distributed Maneuver Planning With Connected and Automated Vehicles for Boosting Traffic Efficiency

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. Here, the framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. We present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Reconductoring Economic and Financial Analysis Tool (REFA) v1.0

REFA is designed to help transmission planners better understand the financial, environmental and economic benefits of reconductoring upgrades, using traditional or advanced conductors. More specifically, the tool enables grid planners and utilities to demonstrate the entire lifetime value of reconductoring projects, providing potential justifications for projects with higher Capex - the purchase of long-term physical or fixed assets used in a business's operations. Existing tools are designed to compare technical aspects of different conductor applications. They typically do not allow a cost-benefit analysis over the lifetime of the conductor's operations.

Heleno, Miguel↗

DOE OSTI2024

Cyber100 Compass [SWR 23-64]

Cyber100 Compass ("Compass") is a unique risk assessment framework that will enable grid system planners to understand and mitigate cybersecurity risk for grids transitioning to high levels of renewable generation, including 100%. The idea for Compass was developed by NREL based on past work on high-renewable grids and a series of discussions with DOE. Compass is part of Cyber100, a portfolio of proposed research activities that would greatly expand understanding of cybersecurity for high-renewable grids. Compass is a desktop application designed with a user-friendly interface. The tool gathers information from users, conducts probabilistic backend calculations, and outputs a series of visualizations to help users understand and analyze their cybersecurity risks based on the unique features of their future grid. Compass will take as inputs the values for different conditions and produce a risk score of the resulting grid. By trying different configurations, system planners can compare the resultant risks against their own risk tolerance and decide which system-of-system controls to implement as they transition toward a 100% renewable grid.

Martin, Maurice↗

DOE OSTI2025

BAMBAM (The Behavior and Advanced Mobility Big Access Model) [SWR-25-123]

Access modeling toolkit for Rust built on the RouteE Compass energy-aware route planner. The Behavior and Advanced Mobility Big Access Model (BAMBAM) is a mobility research platform for scalable access modeling. The process begins with a grid defined at some spatial granularity (e.g., census block or 1 km grid) and a variety of travel configurations. For each grid cell and configuration, the platform executes constrained searches, uses the results to index points of interest (POI), and aggregates the findings to the grid level. It provides researchers access through R or Python running on HPC. The platform automates the import and merging of datasets from a variety of sources including data.gov (with automatic merging of Tiger/Lines geometries), OpenStreetMaps, OvertureMaps, and GTFS. It is built upon RouteE Compass, a scalable, energy-aware route planner written in Rust, extended to model multiple travel modes.

Fitzgerald, Robert [National Renewable Energy Labo↗

DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗

DOE OSTI2026

Cooperative Automated Cohort Driving on Connected Infrastructure, Arterial Roadways, and Highways: Final Project Demonstration and System-of-Systems Model Correlation

This project seeks to synergize vehicle automated driving and connectivity data to improve mobility and energy efficiency of groups of mixed vehicles operating in close proximity (vehicle cohort) on various infrastructure. A custom cellular communication network links vehicles operating as a cohort with infrastructure to a centralized system-of-systems digital twin with an AI-based optimal behavior planner. The data contained in this set are from final testing and technology demonstrations to U.S. Department of Energy staff at the American Center for Mobility. The data contain single-lane, single-light scenarios; multi-lane, multi-light arterial scenarios; and limited-access highway scenarios. All test cases were derived from simulations and replicated on the test track. The project employed two and four light-duty vehicles with connectivity and drive automation for the testing. The baseline scenario without connectivity was run under the control of the system-of-systems centralized planner but operating each vehicle with an intelligent driver model controlling the velocity, lane utilization, and vehicle gap. This was to ensure the highest compatibility with the simulation in terms of dynamic behavior. The connected cohort case utilized AI optimization to perform coordinated and cooperative control for energy, as well as safe, comfortable behavior for the cohort. The dataset is appropriately named with unconnected and connected designations, with comparisons sharing the same run index number. The included PowerPoint and PDF files describe the test setup and provide an overview of results from the project. ![image](de-EE0009209_March_2023_Data_Arterial_Scenario_Results.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Deploying Intra-hour Uncertainty Analysis Tools to ABB’s GridView - CRADA 445

The quickly-changing generation resource mix in the US grid, with large additions of variable resources, retirements of traditional thermal generation, distributed generation and demand response, are creating new challenges on the traditional operation of both the generation and transmission systems. The ability to perform intra-hour high fidelity production cost modeling (PCM) is needed to allow grid operators and grid planners to integrate high penetration (more than 50%) of variable energy resources, and to make prompt well informed decisions in market operations and planning. In the last decade, PNNL has developed several stand-alone tools to enable grid operators and planners to understand the impact of high variable generation on their systems. These tools have been used is studies such as: (1) Evaluation the benefits of WECC balancing authorities coordination under high variable generation penetration , (2) Benefits of Energy Imbalance Market in the North West Power Pool and (3) Duke Energy and NV Energy solar integration studies. ABB’s GridView is a widely used commercial PCM tool. It is the tool used by WECC and their stakeholders to develop the WECC PCM planning model on bi-annual basis. This proposal is focused on the integrating of PNNL intra-hour uncertainty analysis tools to GridView.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Empirical Estimates of Transmission Value using Locational Marginal Prices [Slides]

This study focuses on one potential benefit of transmission infrastructure—congestion relief. It explores historical grid conditions from 2012 through the first half of 2022, and evaluates the marginal value of transmission in facilitating trade within and across regional boundaries by calculating differences in observed nodal wholesale power prices. The study finds that wholesale power prices exhibit stark geographic differences that, in many cases, are stable over time. Many regional and interregional transmission links have significant potential economic value from reducing congestion and expanding opportunities for trade. In fact, many links have hourly average pricing differences in 2021 that exceeded $\$15$/MWh—equivalent to $130 million per year for a 1000 MW link. The value of transmission is correlated with overall energy prices and varies by region and year. Critically, extreme conditions and high-value periods play an outsized role in the value of transmission, with 50% of transmission’s congestion value coming from only 5% of hours. Transmission planners run the risk of understating the benefits of regional and interregional transmission if extreme conditions and high-value periods are not adequately considered. These periods are natural features of actual market operations. As such, the study highlights the need for planners to more-comprehensively assess the value of transmission under both normal and extreme conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Travel Patterns and Characteristics of Elderly Population in New York State: 2017 Update

According to US Census Bureau, the elderly population (individuals 65 years and older) has grown by over a third during the past decade (2010 to 2019), and by 3.2% from 2018 to 2019. It is essential for policymakers and planners to understand transportation issues associated with the elderly to meet their increasing travel demands. These issues include transportation and mobility of the elderly population, factors impacting their travel behavior, and transportation safety. In this study, Oak Ridge National Laboratory was tasked by the New York State Department of Transportation (NYSDOT) to conduct a detailed examination of travel behaviors and identify patterns and trends of its elderly residents. The National Household Travel Survey (NHTS) was used as the primary data source to analyze subjects and address questions such as: Are there differences in traveler demographics between the elderly population and those of younger age groups who live in various New York State (NYS) regions, e.g., New York City (NYC), other urban areas of NYS, or other parts of the country? How do they compare with the population at large? Are there any regional differences (e.g., urban versus rural)? Do any unique travel characteristics or patterns exist within the elderly group? How did these patterns change over time? In addition to the analysis of NHTS data, roadway travel safety concerns associated with elderly travelers were also investigated. Specifically, data on crashes involving the elderly (including drivers, passengers, and pedestrians) as captured in the Fatal Analysis Reporting System database was analyzed to examine elderly drivers and elderly pedestrian travel safety issues in NYS. This study report provides a summary of travel behavior and social-demographic characteristics of NYS elderly residents. These statistics could be used to examine equity issue concerning elderly New Yorkers, as well as to evaluate how well their mobility needs are being met. With a deeper understanding of issues and needs that this special population group is facing, policymakers and transportation planners would be able to make informed decisions on transportation investments and design services that could better address them.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Multi-Timescale Integrated Dynamics and Scheduling for Solar (MIDAS-Solar) (Final Technical Report)

Solar photovoltaic (PV) installations have experienced unprecedented growth in the United States. PV will become not only an energy producer but also a necessary provider of ancillary services at multiple timescales. Conventional methods to simulate power systems operations - such as long-term production simulation (which typically considers schedules from hours to minutes by using an optimization framework) and short-term transient studies (which simulate dynamics from seconds to sub-seconds using state variables and differential equations) - are not sufficient for studying the multiple-timescale variation of solar generation and its impact on system reliability. Long-term system economics and short-term system dynamics are highly coupled, particularly when the penetration level of renewable generation is extremely high, because the uncertainty and variability of solar generation will impact both power system steady-state and dynamic performance. This project helps meet and exceed the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office goal of systems integration by directly addressing this stability and reliability challenge for power grid planning and operation. We have developed a temporally comprehensive, closed-loop simulation model, named Multi-timescale Integrated Dynamics and Scheduling (MIDAS), that seamlessly simulates power system operations from economic scheduling (day-ahead to hours) to dynamic response analysis (seconds to sub-seconds). For schedules with very high levels of inverter-based resources (IBRs), up to and including 100%, the stability of grid controls has been evaluated through electromagnetic transient (EMT) simulations and power-hardware-in-the-loop (PHIL) simulations of key transient events at key schedule points. Specifically, MIDAS provides: 1) a closed-loop simulation framework for simulating timescales from economic scheduling to dynamic stability analysis; 2) machine learning-based stability assessment; 3) EMT modeling and analysis for large-scale power systems; 4) MIDAS PHIL test bed. We worked with Hawaii Electric Companies to apply the MIDAS study framework to a Maui grid study. The entire island's transmission system was modeled in detail - from a yearly scheduling model, to a second-level frequency dynamic model, down to a sub-second-scale EMT model to address critical stability issues. The project demonstrated how MIDAS can help system planners and operators assess system reliability and stability while the power grid is marching toward a high-renewable, high-IBR future. In this Maui grid study, we found that 100% instantaneous IBR operation is achievable in EMT simulation and PHIL testing, and grid planners and operators might need new analysis/simulation tools to assess grid reliability and stability in the scheduling stage. MIDAS will bring Maui and other systems closer to 100% clean and stable energy futures. (In this study, we examined transient stability. Other topics necessary for 100% IBR operation, such as protection and resource adequacy, were not examined.)

100% Renewables↗

DOE OSTI2023

Principles for Equitable Transmission Planning

Energy justice is gaining traction in the planning processes that govern the generation and supply of electric power in the U.S., with a growing number of federal and state authorities applying it as a framework to ensure that the benefits of a clean energy future extend to all levels of society. However, the principles of energy justice have not yet been integrated into electric transmission planning processes, which are designed to meet established reliability criteria and have not historically considered externalities like social inequities. This paper presents a review of transmission planning requirements in the U.S. and existing literature on energy justice, then synthesizes them to propose a framework for incorporating energy justice principles into transmission planning processes to yield more equitable outcomes. By applying procedural and recognition justice principles to create more extensive, inclusive, and diverse public participation in the planning process, planners can gain insight into—and begin to correct—the inequities created by previous planning decisions. Equipped with this understanding and informed by affected communities, planners can improve the distributional outcomes of the planning process and identify opportunities for energy system investments that will restore communities and place them on more equitable footing.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Africa Battery Energy Storage Systems (BESS) Capacity Building Li-Ion Battery Degradation and Performance [Slides]

This presentation is intended for power system engineers, operators, and planners who work on isolated power systems that have (or may soon have) high levels of power generation coming from wind turbines, solar farms, and battery energy storage, which are collectively referred to as inverter-based resources. It describes the basics of lithium-ion battery energy storage systems and lithium-ion battery degradation. Battery degradation at the cell-level is explained, and then the structure and degradation of battery energy storage systems is also explained. This is intended to help power system engineers, operators, and planners to understand and effectively utilize battery energy storage systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Explained: Causes of Three Recent Major Blackouts and What Is Being Done in Response

While the power grid is designed to have an adequate supply of electricity generating capacity under hot and cold conditions well above or below average, weather in recent years has approached the bounds of anticipated conditions, in both magnitude and duration. Extreme weather can result in unanticipated levels of electricity demand and correlated power plant outages driven by extreme temperatures. How power system planners account for the changing role of wind and solar also plays an important role in the power grid's ability to supply enough electricity during extreme weather. In response to recent significant outages, power system planners have been focusing on ensuring methods to evaluate reliability during periods of "net peak demand" consider availability of renewable resources and capture the impact of extreme weather on power plants of all types. In particular, there is significant emphasis on the ability of natural gas-fueled power plants to operate during periods of extreme cold temperatures. The impact of extreme weather on the need to change planning processes is demonstrated in this fact sheet that discusses three significant outages that occurred in recent years.

power grid↗

DOE OSTI2024

Advance Local Mobility Through Energy Efficient Mobility Systems Technologies

Everyone deserves reliable, affordable, and safe transportation to connect people to jobs, healthcare, education, and recreation. Our transportation systems are interconnected, multimodal networks working together to move people and goods. These systems are dynamic and are being reshaped by factors such as population trends, new technologies, shifting labor models, economic forces, and changing climate. Energy efficient mobility systems (EEMS) technologies can help transportation planners ensure changes in our transportation systems are equitable and sustainable by improving energy efficiency, travel time, and affordability, as well as overall access to mobility. Transportation planners and decision makers can use the following EEMS tools and strategies to advance local mobility.

ADVANCED PROPULSION SYSTEMS↗