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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Feedback Optimization of Incentives for Distribution Grid Services

Energy prices and net power injection limitations regulate the operations in distribution grids and typically ensure that operational constraints are met. Nevertheless, unexpected or prolonged abnormal events could undermine the grid's functioning. During contingencies, customers could contribute effectively to sustaining the network by providing services. Herein this paper proposes an incentive mechanism that promotes users' active participation by essentially altering the energy pricing rule. The incentives are modeled via a linear function whose parameters can be computed by the system operator (SO) by solving an optimization problem. Feedback-based optimization algorithms are then proposed to seek optimal incentives by leveraging measurements from the grid, even in the case when the SO does not have a full grid and customer information. Numerical simulations on a standard testbed validate the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids

27 ARPA - Advanced Research Projects Agency-Energy↗

DOE OSTI2020

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids: Preprint

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. Different types of DERs such as Photovoltaics (PV), energy storage, electric vehicles (EVs), etc. have varying effects on the grid based on how and where they are deployed. Again, the DER deployment and its impact are network-dependent, while the traditional electric grids were not designed to host DERs. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper investigates the impacts of integrating a mix of DERs in the utility distribution network in Colorado, U.S. Firstly, BTM DERs at the residential scale including distributed PV; battery energy storage systems (BESS); heating, ventilation, and air-conditioning (HVAC) load; electric water heater (EWH) load; and EVs are modeled. The impacts of integrating these resources on the distribution network are evaluated by conducting time-series simulations for different scenarios considering different days to capture the worst-case conditions. Monte Carlo simulations are conducted to generate the realistic EV charging profile. The voltage issues, substation transformer loadings, and critical nodes in the network are identified with the incorporation of uncoordinated EV charging loads and other DER in the network. This analysis helps visualize the DER impact on the grid, identify the challenges, and provides an insight into the new distribution management and control needs to enable reliable and resilient distribution grid operations. Additionally, further analysis is performed to estimate the daily residential electricity cost with the inclusion of DERs under time-of-use tariffs. The result shows the daily residential electricity cost reduced by 20:7% on average with DERs compared to the case without DERs.

27 ARPA - Advanced Research Projects Agency-Energy↗

DOE OSTI2022

Hybrid-RL-MPC4CLR (Hybird-Reinforcement-Learning-Model-Predictive-Control-for-Reserve-Policy-Assisted-Critical-Load-Restoration-in-Distribution-Grids)

Hybrid-RL-MPC4CLR was developed as a hybrid controller for active distribution grid critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. The RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while the MPC models grid operations incorporating the RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. The developers formulated the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The software is developed using various software packages in Python. The MPC's optimal power flow (OPF) model is implemented using the Pyomo package, the RL simulation environment is implemented using the MPC simulation with various scenarios of renewable energy and load demand profiles and power outage beginning times, based on the OpenAI Gym framework. The RL agent training is performed using the RLlib Ray package. The RL algorithm is trained offline using historical forecasts of renewable generation and load demand profiles. Simulation analysis and performance tests are conducted using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery.

Eseye, Abinet Tesfaye↗

DOE OSTI2023

Designing and Operating Resilient Electric Distribution Grids [Slides]

In the past, electricity was produced by power plants on the transmission system and distributed to customers through substations. Now, distributed energy resources and dynamic loads are penetrating distribution grids, with their own energy production, resilience, and reliability challenges separate from the traditional model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

On the Verification of Deep Reinforcement Learning Solution for Intelligent Operation of Distribution Grids

Capabilities of deep reinforcement learning (DRL) in obtaining fast decision policies in high dimensional and stochastic environments have led to its extensive use in operational research, including the operation of distribution grids with high penetration of distributed energy resources (DER). However, the feasibility and robustness of DRL solutions are not guaranteed for the system operator, and hence, those solutions may be of limited practical value. This paper proposes an analytical method to find feasibility ellipsoids that represent the range of multi-dimensional system states in which the DRL solution is guaranteed to be feasible. Empirical studies and stochastic sampling determine the ratio of the discovered to the actual feasible space as a function of the sample size. In addition, the performance of logarithmic, linear, and exponential penalization of infeasibility during the DRL training are studied and compared in order to reduce the number of infeasible solutions

Hosseini, Mohammad Mehdi↗

NASA NTRS1983

Application of the method of lines for solutions of the Navier-Stokes equations using a nonuniform grid distribution

The feasibility of the method of lines for solutions of physical problems requiring nonuniform grid distributions is investigated. To attain this, it is also necessary to investigate the stiffness characteristics of the pertinent equations. For specific applications, the governing equations considered are those for viscous, incompressible, two dimensional and axisymmetric flows. These equations are transformed from the physical domain having a variable mesh to a computational domain with a uniform mesh. The two governing partial differential equations are the vorticity and stream function equations. The method of lines is used to solve the vorticity equation and the successive over relaxation technique is used to solve the stream function equation. The method is applied to three laminar flow problems: the flow in ducts, curved-wall diffusers, and a driven cavity. Results obtained for different flow conditions are in good agreement with available analytical and numerical solutions. The viability and validity of the method of lines are demonstrated by its application to Navier-Stokes equations in the physical domain having a variable mesh.

Abolhassani, J. S.↗

DOE OSTI2021

Efficient Phasor-Based Dynamic Volt/VAr and Volt/Watt Analysis of Large Distribution Grid with High Penetration of Smart Inverters

As the penetration of power-electronics based smart inverters (SIs) is increasing in distribution grids, it adds computational challenges in solving dynamic models of large-scale distribution feeders. Voltage and reactive power (Volt/VAr), and voltage and active power (Volt/Watt) dynamics have been analyzed at slower time scales akin to the control of legacy grid devices. However, smart inverters, being power-electronics based devices, can provide dynamic active/reactive power support at a faster time scale, which necessitates Volt/VAr and Volt/Watt dynamics to be analyzed at a faster time scale. The existing dynamic models are overly detailed and computationally intractable for distribution feeders with a large number of inverters. In this context, this proposed work aims towards developing a computationally tractable, scalable, and accurate phasor-based model for dynamic Volt/VAr and Volt/Watt analyses of large distribution systems with high penetration of smart inverters. Case studies demonstrate that the proposed phasor-based model sufficiently captures the Volt/VAr and Volt/Watt dynamics, and is computationally faster by one order of magnitude compared to the average model and by two orders of magnitude compared to the detailed switching model. Case studies also demonstrate the efficacy and scalability of the proposed model in analyzing Volt/VAr and Volt/Watt dynamics of large-scale power networks with hundreds of SIs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Distributed Ledger Technology for Fault Tolerant Distribution Grid Operations

This paper explores the potential of distributed ledger technology (DLT) to improve fault-tolerant grid operations by leveraging its core features as an immutable, decentralized ledger, a distributed, consensus-based agreement process, and a distributed state-replication engine. Distribution power systems deliver electricity to millions of customers; however, they are susceptible to various threats that can result in customer interruptions. These include faults caused by adverse weather conditions, natural disasters, vegetation growth, equipment failure, and malicious attacks. To minimize the effects of these faults, fault-handling approaches rely on network knowledge to isolate affected areas and reconnect unaffected areas, reducing the number of affected customers while maintaining safety. Here, we present a trusted data-sharing architecture that enables independent, distributed actors to reconstruct the pre-fault system state by enabling distributed resources to make appropriate decisions with limited network/system information. Although the process requires some data sharing between switch-delimited areas, the approach limits the amount of private information shared, preserving customers' privacy and business-sensitive information. We include three use cases that form a foundation for third parties to develop functional solutions that can eventually be deployed in the field. The gross error detection method used within switch-delimited areas can identify sensor errors and accurately detect circuit breaker states. The evaluation of possible reconnection while preserving data ownership resulted in a voltage magnitude difference smaller than 0.001% from the OpenDSS power flow solution that has full system knowledge, which is below the expected power flow tolerance. The approach offers a promising opportunity for improving fault-tolerant distribution grid operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Cybersecurity Workforce Training for SMR Integration into Distribution Grids: A Competency Framework and Containerized Hands-On Lab for the SMR/DER/Microgrid Boundary

Small modular reactors (SMRs) and microreactors are entering the U.S. distribution grid as synchronous generation on feeders designed for loads and inverter-based distributed energy resources (DERs). No existing cybersecurity training program addresses this intersection of nuclear operations, DER management, and operational technology security. As subcontractor to Iowa State University on the CyDERMS Center, Argonne analyzed the relevant standards and training landscape, translated the resulting gaps into a twelve-objective competency framework across distribution-operator and graduate-analyst role tracks, and built a containerized training lab using a ∼400-bus composite grid model behind a realistically simulated Modbus TCP SCADA stack. The analysis isolates the balance-of-plant / energy-management-system (BOP/EMS) boundary as the critical jurisdictional seam where, as of March 2026, neither NRC nor NERC CIP cleanly claims cybersecurity responsibility for distribution-connected SMRs. The framework maps each objective across NIST CSF 2.0, ISA/IEC 62443, NIST NICE Task–Knowledge–Skill statements, and NRC RG 5.71 awareness-and-training controls. The training lab implements operator-recognition assessment scenarios spanning grid-side disturbances and telemetry-layer anomalies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

EV Hosting Capacity Analysis on Distribution Grids: Preprint

Increasing electric vehicle (EV) charging loads can increase the magnitude and duration of conventional peaks in demand profiles and even shift them significantly, causing operational violations in the distribution grid. It is important to develop tools to quantify the impacts of injecting large number of EV charging loads and determine the available capacity of the existing distribution feeder for the safe operation of the grid. Such tools would enable utilities to better prepare for grid operations in the near future while exploring the impact and effectiveness of strategies such as peak pricing and smart charging in managing these loads. This paper evaluates the hosting capacity of some real-world feeders to accommodate EV charging loads, including extreme fast-charging (xFC) options.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

EV Hosting Capacity Analysis on Distribution Grids

The increasing trend in electric vehicle (EV) adoption can cause challenges to traditional electric grid operations if utilities are not equipped with tools and methods to effectively manage these fleets. Growing EV charging loads will alter the magnitude and duration of conventional peaks in demand profiles and even significantly shift them, potentially causing operational violations in the distribution grid. This paper presents the development and results of an EV hosting capacity tool to quantify the impacts of injecting large numbers of EV charging loads and to determine the available capacity of existing distribution feeders to continue providing reliable and affordable grid operations. Tools like the hosting capacity analysis would enable utilities to better prepare for grid operations in the near future while exploring the impact and effectiveness of strategies to manage these loads, such as peak pricing and smart charging. This paper evaluates the hosting capacity of some real-world feeders to accommodate EV charging loads, including extreme fast-charging options.

distribution grid↗

DOE OSTI2026

Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization

We develop a new methodology to select scenarios of DER adoption most critical for distribution grids. Anticipating risks of future voltage and line flow violations due to additional PV adopters is central for utility investment planning but continues to rely on deterministic or ad hoc scenario selection. We propose a highly efficient search framework based on multi-objective Bayesian Optimization. We treat underlying grid stress metrics as computationally expensive black-box functions, approximated via Gaussian Process surrogates and design an acquisition function based on probability of scenarios being Pareto-critical across a collection of line- and bus-based violation objectives. Our approach provides a statistical guarantee and offers an order of magnitude speed-up relative to a conservative exhaustive search. Case studies on realistic feeders with 200-400 buses demonstrate the effectiveness and accuracy of our approach.

Mulkin, Olivier↗

DOE OSTI2021

Simplified Transactive Distribution Grids for Bulk Power System Mechanism Development

As distributed energy resources and smart devices become omnipresent in the electrical power grid, transactive energy control mechanisms are evolving. From real-time to day-ahead markets, these transactive energy algorithms involve more and more agents, whose behavior is going to affect the transmission and generation network. In order to study the interaction between the wholesale and retail energy markets, extensive co-simulations are performed. To be able to redesign, evaluate, and verify new control algorithms, the simulations need to provide results in a fast and reliable manner. This work has built and tested a transactive distribution grid model, the DSO-Stub, meant to offer a configurable distribution retail market while ensuring the computational burden is not significantly increased.

Transactive Energy, , Distribution System Operator↗

DOE OSTI2022

Survey and Benchmark of Benefits of High Voltage SiC Applications in Medium Voltage Power Distribution Grids

The main objective of this survey and benchmark study is to identify and document potential high-impact applications in today's and future medium voltage (MV) distribution grids that will significantly benefit from high voltage (HV) (i.e. > 3.3 kV) SiC-based power electronics equipment. In particular, it is desirable to identify applications that can utilize the high switching speed and high control bandwidth enabled by SiC-based equipment. Specifically, the benefits of using SiC are quantified through the benchmark analysis and design using simulation for the selected high-impact applications. In addition, to provide grid support services, it is necessary to consider grid requirements in the grid-connected converter's design. Therefore, the impact of grid requirements on the HV SiC-based grid-connected converter is evaluated.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Decoding Resilience by Modeling Outage and Restoration Processes in Distribution Grids: A Pittsburgh Case Study

Climate-induced extreme weather events, such as floods and heatwaves, pose significant challenges to the resilience of urban power distribution grids. This paper examines the outage and restoration dynamics of Pittsburgh's power grid during the flood event in April 2024 and three heatwaves in June, July, and August 2024. We introduce a comprehensive modeling framework that integrates outage and restoration processes, enabling the quantification of resilience metrics, including total customer-hours of power outage, maximum residual values, and restoration durations across various ZIP codes. Our analysis highlights spatial disparities in outage impacts and restoration efficiencies, with ZIP Code 15222 experiencing the highest cumulative disruptions, while ZIP Code 15217 shows lower susceptibility. By linking statistical trends with weather events, this study underscores the critical need for targeted infrastructure upgrades and advanced restoration strategies. Furthermore, the proposed framework offers valuable insights for planning and managing resilient power systems in the face of increasing climate stress.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Reinforcement Learning for Distribution Grid Optimization (PyCIGAR) v0.1

PyCIGAR is a python software package that merges off-the-shelf reinforcement learning libraries (RLLib and Ray) with electric power distribution system simulation tools (OpenDSS and a custom power flow solver built by LBL). PyCIGAR enables the training of neural networks to optimize the behavior of different components in the electric distribution grid, such as control systems in photovoltaic rooftop solar inverters and electric battery storage systems. The software package has been used to train neural networks to update settings in photovoltaic rooftop solar inverter control systems to mitigate cyber attacks on other solar photovoltaic rooftop devices.

Arnold, Daniel↗

DOE OSTI2023

Adaptive Control of Distributed Energy Resources for Distribution Grid Voltage Stability

Volt-VAR and Volt-Watt functionality in photovoltaic (PV) smart inverters provide mechanisms to ensure system voltage magnitudes and power factors remain within acceptable limits. However, these control functions can become unstable, introducing oscillations in system voltages when not appropriately configured or maliciously altered during a cyberattack. In the event that Volt-VAR and Volt-Watt control functions in a portion of PV smart inverters in a distribution grid are unstable, the proposed adaptation scheme utilizes the remaining and stably-behaving PV smart inverters and other Distributed Energy Resources to mitigate the effect of the instability. The adaptation mechanism is entirely decentralized, model-free, communication-free, and requires virtually no external configuration. Here we provide a derivation of the adaptive control approach and validate the algorithm in experiments on the IEEE 37 and 8500 node test feeders.

24 POWER TRANSMISSION AND DISTRIBUTION↗