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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Experimentation in Exploring Photovoltaic Inverter Dynamics Under Different Irradiance Levels Through a Data-Driven Approach

As conventional direct connections of synchronous generators are being phased out, inverter-based resources (IBRs) with grid support functions are increasingly being integrated into power systems. This transition requires the development of accurate dynamic models for IBRs to predict how power systems will adapt to varying levels of IBRs penetration, establish grid code requirements, and ensure compliance. Here, this study introduces an active probing signal-based data-driven modeling technique to accurately derive the dynamics model of a smart photovoltaic inverter operating in Volt-Watt and Freq-Watt modes, in compliance with the IEEE 1547–2018 standard. The paper focuses on investigating how the dynamics of the PV inverter model respond to fluctuations in solar irradiance, utilizing real-time digital simulator experimentation. The experimental analysis demonstrates that the amplitude of dynamics fluctuates with changes in irradiance across both operational modes and confirms the active power’s dependence on irradiance levels. Furthermore, the nature of inverter dynamics varies distinctly between the different modes of activation. Critically, our findings indicate that dynamic models require DC-gain adjustments to accommodate contrasting irradiance levels, highlighting a negative gradient linear relationship between the DC-gain of each model and the irradiance.

14 SOLAR ENERGY↗
DOE OSTI2025

DistOPF: Advanced Solutions for Distribution Optimal Power Flow Analysis - DistOPF v0.2 Documentation

To achieve an affordable and reliable energy system, research on power distribution system is often focused on integration of distributed generators, energy storage solution, EV charging, smart meters, and other advanced assets that may benefit from or require more advanced control and optimization techniques. Despite this focus on advanced distribution system topics, early researchers and grid scientists often start from scratch when developing optimization programs for power distribution systems. This report introduces DistOPF, a Python package that consolidates years of research into a versatile and modular tool. DistOPF provides researchers with essential capabilities to solve distribution system Optimal Power Flow (OPF) problems using standard network models. Additionally, it offers a platform to benchmark both new and existing algorithms against established test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

An Analysis of Post Attack Impacts and Effects of Learning Parameters on Vulnerability Assessment of Power Grid

Due to the increasing number of heterogeneous devices connected to electric power grid, the attack surface increases the threat actors. Game theory and machine learning are being used to study the power system failures caused by external manipulation. Most of existing works in the literature focus on one-shot process of attacks and fail to show the dynamic evolution of the defense strategy. In this paper, we focus on an adversarial multistage sequential game between the adversaries of the smart electric power transmission and distribution system. We study the impact of exploration rate and convergence of the attack strategies (sequences of action that creates large scale blackout based on the system capacity) based on the reinforcement learning approach. We also illustrate how the learned attack actions disrupt the normal operation of the grid by creating transmission line outages, bus voltage violations, and generation loss. This simulation studies are conducted on IEEE 9 and 39 bus systems. The results show the improvement of the defense strategy through the learning process. The results also prove the feasibility of the learned attack actions by replicating the disturbances created in simulated power system.

attack impacts↗
DOE OSTI2021

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms. In this paper, a new method, called the pruned physics-aware neural network (P2N2), is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one- year smart meter data of load consumptions for three-phase power flow is developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS in terms of data redundancy and estimation accuracy.

distribution system state estimation↗
DOE OSTI2023

AI-Based Optimal Design and Controls Can Greatly Reduce Carbon Emissions and Enhance Resilience in Residential Communities in Cold Climates

Net-zero energy residential communities are crucial for achieving decarbonization goals, but the high-penetration photovoltaic (PV) in those communities is posing challenges to the distribution grid. Traditional design and operation of net-zero communities rely on rule-of-thumb methods and may not work in complex scenarios. Artificial intelligence and machine learning methods can optimally size PV for net-zero energy, identify user preferences and usage patterns, and fully unlock the potential of distributed energy resources to address distribution grid issues.

artificial intelligence↗
DOE OSTI2022

Improved Line Outage Detection in Transmission Systems with Few PMUs

Unlike transmission systems, distribution systems historically lack enough measurements, making their real-time monitoring almost impossible. Recent deployment of diverse types of devices such as phasor measurement units (PMUs), smart meters, solar inverters and weather information sensors opens up new ways of monitoring these systems, with the assistance of customized machine learning (ML) applications. The paper describes a grid-model-informed machine learning (ML) tool which integrates heterogeneous data streams and creates synchronous measurement snapshots to be used by a hybrid robust state estimator (SE) which provides not only accurate state estimates but also real-time feedback for ML model refinement. Improved monitoring performance due to the use of developed computational framework is experimentally observed by simulated scenarios on an electric utility’s distribution system.

Distribution systems, graph learning, machine lear↗
DOE OSTI2021

Blockchain-Based Smart Contracts Use for Photovoltaic Energy Trade Transactions

In recent decades, interest in renewable energy via solar energy has increased, especially in California and Nevada. In a region that can produce enough solar power for self-use, prosumers must use or sell surplus energy. Solar energy self-consumption is essential in modern energy transactions in the grid. This paper proposes an approach towards grid services that includes photovoltaic hardware to store the excess energy in battery storage. Such energy can then be sold to power companies while the transaction is handled using blockchain. Software, security architecture and data flow requirements based on the Ethereum Blockchain are included. Furthermore, this solution protects the private information of seller and buyer and secures transactions.

14 SOLAR ENERGY↗
DOE OSTI2021

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks: Preprint

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classical state estimation algorithms. In this paper, a new method so-called pruned physics-aware neural network (P2N2) is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one-year smart meter data of load consumptions for threephase power flow is developed to generate the measurement and voltage state data. The IEEE 123 node system is selected as the test network to benchmark the proposed algorithm against the classical weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS, in terms of data redundancy and estimation accuracy.

distribution systems state estimation↗
DOE OSTI2024

Survey and Gap Prioritization of U.S. Electric Vehicle Charge Management Deployments

The goal of this study was to survey and characterize the scope of current technical and programmatic knowledge pertaining to EV charge management technologies and practices in the US and relevant international jurisdictions. This characterization of existing field demonstrations and knowledge derived were used to determine gaps in the SCM demonstration landscape. Addressing these gaps through research and demonstration could increase confidence in the U.S. that load management and EV charge control could achieve overarching societal benefits. A survey of charge management deployments and input from stakeholders was completed to determine the state-of-the-art of smart charge management (SCM) where SCM is defined as controlling the amount of power exchanged between chargers and EVs to meet customers' charging needs while also responding to external power demand or pricing signals to provide load management, resilience, or other benefits to the customer and electric grid. The survey was the basis of the gap analysis in this report and determines which areas are well understood, with high confidence, and which areas need further investigation. Existing examples of EV charge management are characterized here to determine aspects that are ready for widespread deployment and have been demonstrated in the field. These include demonstration studies, pilots, programs, and EV-specific tariffs. In all, 110 examples of charge management were characterized. The data sources were public literature and utility filings as well as targeted interviews. In addition, 43 interviews with stakeholders were conducted with a consistent set of questions used in each interview. This study prioritized gaps in demonstrated SCM capabilities based on 1) Urgency of the particular use-case to offset traditional grid assets, 2) Impact, extensibility, and scaling of results across the entire spectrum of 3000+ utility service territories including projected technical and market potential for a given grid service, and 3) Value of federal funding in addressing the gap, including potential to leverage and/or add scope to existing field demonstrations funded by other non-federal funding mechanisms.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements is the foundation for analyzing system vulnerability to natural hazards and connecting loads with end users and their communities. However, power system models and geospatial data for power grid assets may have been developed asynchronously without close coordination. Creating a direct mapping between the two may be a challenging task, considering heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect power system model elements and geospatial data for transmission network, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.

Resilience, geospatial data, grid transmission net↗
DOE OSTI2024

Nova Analysis: Holistically Valuing the Contributions of Residential Efficiency, Solar and Storage

Policies to address climate change and grid modernization, in combination with cost reductions and technological advancements in energy efficiency (EE) and distributed energy resources (DER), are driving rapid deployment of building electrification and energy efficiency retrofits, rooftop solar photovoltaics (PV), smart thermostats, smart water heaters, and battery energy storage. In residential buildings, there are multiple stakeholders (occupant, utility, aggregator, society at large) that are each focused on different value streams. This project uses a suite of metrics intended to all of these value streams to try to holistically analyze the benefits that come from solar, storage, and energy efficiency. To demonstrate the value of these metrics, a semi annual study was performed simulating hundreds of homes across the U.S. under several different upgrade scenarios.

14 SOLAR ENERGY↗
DOE OSTI2022

Implementing multi-settlement decentralized electricity market design for transactive communities with imperfect communication

Recent advances in information and communication technologies and smart metering, provides strategic opportunities for ``prosumers" to reform their conventional energy practices towards more consumer-centric economies. From an operational perspective, managing power distribution networks is becoming more difficult with such active grid-edge systems providing limited to no visibility or control. Transactive Energy (TE) has been emerging as a key enabler towards effectively and efficiently integrating prosumers into competitive electricity markets. This work presents a transactive implementation of community-centric markets. A co-simulation framework is developed for evaluating the proposed market structure with high-fidelity models. Case studies on the IEEE-123 node test system demonstrate that community-centric transactive markets can enable communities of prosumers to operate collaboratively as grid-edge systems. The potential benefits of implementing community-centric TE systems are also illustrated.

Mukherjee, Monish↗
DOE OSTI2021

Residential Demand Side Aggregation of Privacy-Conscious Consumers

The increasing adoption of smart meters has led to growing concerns regarding privacy risks stemming from the high resolution measurements. This has given rise to privacy protection techniques that physically alter the consumer's energy load profile, masking private information by using localised devices, e.g. batteries or flexible loads. Meanwhile, there has also been increasing interest in aggregating the distributed energy resources (DERs) of residential consumers to provide services to the grid. In this paper, we propose an online distributed algorithm to aggregate the DERs of privacy-conscious consumers to provide services to the grid, whilst preserving their privacy. Results show that the optimisation solution from the distributed method converges to one close to the optimum computed using an ideal centralised solution method, balancing between grid service provision, consumer preferences and privacy protection. More importantly, the distributed method preserves consumer privacy, and does not require high-bandwidth two-way communications infrastructure.

ancillary services↗
DOE OSTI2026

V1G Frequency Regulation: Algorithm Development, Validation & Analysis at Scale

Researchers at Argonne National Laboratory developed and validated a high-fidelity digital twin of a smart charging (V1G) ecosystem to model the participation of up to 1,000 unique electric vehicles (EVs) in the PJM frequency regulation market. Utilizing a discrete-event framework, the simulation models complex interactions, from dynamic grid signals (updated every 2 seconds) to individual EV charging dynamics. The simulation incorporates multiple EV models created from real-world lab test data. Researchers tested multiple control algorithms to balance the dual objectives of maximizing aggregator’s revenue and driver charging needs. Results demonstrate that aggregated EVs function as a controllable, highly effective grid resource, achieving high PJM Performance Scores (80–90%). Additionally, an optimized, market-aware bidding strategy was identified as key to profitability. The platform was shown to provide drivers with an average charging discount of nearly 50%. The algorithm was further validated in the lab using production EVs and charging stations to compare simulation results with real-world performance.

Manne, Nithin↗
DOE OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Design and Evaluation of a Model-Free Frequency Control Strategy in Islanded Microgrids with Power-Hardware-in-the-Loop Testing

The deterioration of inertial and primary frequency response of islanded microgrids has been a crucial issue for the proper operation of such microgrids. Several approaches, including inertia emulation strategies, have been proposed to improve the primary frequency response. However, the demonstrations of most inertia emulation research works are based on simulation-only testing which hinder their applicability in the field. In this paper, a novel model-free control (MFC) strategy for frequency response support in a diesel-wind microgrid system is presented, and it is tested for evaluation using a power-hardware-in-the-Ioop (PHIL) platform. The PHIL testing is one step closer to field testing since it allows real-time verification and accounts for the nonlinear grid dynamics, noise, and time delays that are usually ignored in simulation-only environments. The proposed MFC strategy is fully distributed, has low computational requirements, and can be easily implemented in resource constrained devices such as smart inverters. A PHIL microgrid platform consisting of two type-3 wind turbine generators and one diesel generator is used to test the proposed MFC strategy.

Ferrari Maglia, Max↗