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DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Go slow to go fast? A review of the impacts of permitting on large-scale solar project development

State and local permitting challenges could impede the ability of large-scale solar (LSS) to meet growing electricity demand in the United States. Here, we review research that explores LSS permitting and its impacts on the pace and scale of LSS project development. Research on LSS permitting is relatively scarce, such that we support our review with research in the context of wind permitting, where appropriate. Further, few studies attempt to rigorously quantify the effects of permitting on the pace and scale of LSS project development. The available evidence allows us to identify various hypotheses and identify gaps for further research. Our review suggests that differences in permitting policies, regulations, and ordinances explain relatively little variation in LSS permitting and development outcomes across jurisdictions, except where jurisdictions implement rules designed to impede LSS. The evidence suggests LSS permitting challenges largely accrue during the implementation of permitting processes. Recent research suggests that community opposition to project development is a key driver of LSS permitting challenges, given that project opponents often use permitting processes to translate opposition into legal action. We call on future researchers to more concretely describe the LSS permitting challenge and to identify the specific actors responsible for implementing solutions.

Community opposition↗

DOE OSTI2025

Under-capacitated and over-powered? Rural austerity and asymmetrical negotiating relationships in US wind energy development

Though rural local governments are central actors in renewable energy development, local governments in the United States (US) remain systematically under-funded. This paper considers what the manifestations of austerity in local governments broadly and rural localities specifically mean for renewable energy development and for energy transitions. Drawing on a survey of 262 elected county officials with experience with wind energy in eight US states, this paper asks how local officials understand the impacts of wind development, how local governments are involved in wind energy negotiations, how the resources and expertise needed to navigate negotiations are distributed among counties, and analyze the relationship between local capacity, access to resources, and involvement in negotiations. We find that local officials express simultaneously affective and material concerns with the impacts of wind development and see negotiations with the developer as central to realizing local benefits. However, the expertise and staffing needed to negotiate with developers is less accessible to poorer or sparsely populated counties, and counties with lower overall revenues have narrower scopes of negotiation, and counties incre. Our results suggest that uneven rural capacity heightens an already asymmetrical relationship between localities and developers. In analyzing how infrastructure developments are shaped by relationships between localities and developers that are conditioned by austerity and (under)capacity, this paper contributes to and bridges scholarly discussions on rural austerity, rescaling, and renewable energy transitions. These results challenge conventional wisdoms around centralizing energy siting processes, contextualize popular and academic debates about opposition to renewable energy development, and highlight the need for rural reinvestment to realize meaningfully participatory energy developments.

Elmallah, Salma↗

DOE OSTI2025

Corrigendum to ‘Under-capacitated and over-powered? Rural austerity and asymmetrical negotiating relationships in US wind energy development’ [J. Rural Stud., 119 (2025) 1–14]

The authors regret that there is an incomplete sentence in the abstract of the article, and request that the portion “, and counties incre” be deleted from the abstract (found at the end of the sentence beginning with “However …”). The portion to be deleted is underlined and bolded below. The authors would like to apologise for any inconvenience caused. Current abstract: Though rural local governments are central actors in renewable energy development, local governments in the United States (US) remain systematically under-funded. This paper considers what the manifestations of austerity in local governments broadly and rural localities specifically mean for renewable energy development and for energy transitions. Drawing on a survey of 262 elected county officials with experience with wind energy in eight US states, this paper asks how local officials understand the impacts of wind development, how local governments are involved in wind energy negotiations, how the resources and expertise needed to navigate negotiations are distributed among counties, and analyze the relationship between local capacity, access to resources, and involvement in negotiations. We find that local officials express simultaneously affective and material concerns with the impacts of wind development and see negotiations with the developer as central to realizing local benefits. However, the expertise and staffing needed to negotiate with developers is less accessible to poorer or sparsely populated counties, and counties with lower overall revenues have narrower scopes of negotiation, and counties incre. Our results suggest that uneven rural capacity heightens an already asymmetrical relationship between localities and developers. In analyzing how infrastructure developments are shaped by relationships between localities and developers that are conditioned by austerity and (under)capacity, this paper contributes to and bridges scholarly discussions on rural austerity, rescaling, and renewable energy transitions. These results challenge conventional wisdoms around centralizing energy siting processes, contextualize popular and academic debates about opposition to renewable energy development, and highlight the need for rural reinvestment to realize meaningfully participatory energy developments.

Elmallah, Salma↗

DOE OSTI2025

Governance and resilience as entry points for transforming food systems in the countdown to 2030

Due to complex interactions, changes in any one area of food systems are likely to impact—and possibly depend on—changes in other areas. Here we present the first annual monitoring update of the indicator framework proposed by the Food Systems Countdown Initiative, with new qualitative analysis elucidating interactions across indicators. Since 2000, we find that 20 of 42 indicators with time series have been trending in a desirable direction, indicating modest positive change. Qualitative expert elicitation assessed governance and resilience indicators to be most connected to other indicators across themes, highlighting entry points for action—particularly governance action. Literature review and country case studies add context to the assessed interactions across diets, environment, livelihoods, governance and resilience indicators, helping different actors understand and navigate food systems towards desirable change.

Schneider, Kate R. [Johns Hopkins Univ., Washingto↗

DOE OSTI2024

Streaming Readout and Data-Stream Processing With ERSAP

With the exponential growth in the volume and complexity of data generated at high-energy physics and nuclear physics research facilities, there is an imperative demand for innovative strategies to process this data in real or near-real-time. Given the surge in the requirement for high-performance computing, it becomes pivotal to reassess the adaptability of current data processing architectures in integrating new technologies and managing streaming data. This paper introduces the ERSAP framework, a modern solution that synergizes flow-based programming with the reactive actor model, paving the way for distributed, reactive, and high performance in data stream processing applications. Additionally, we unveil a novel algorithm focused on time-based clustering and event identification in data streams. The efficacy of this approach is further exemplified through the data-stream processing outcomes obtained from the recent beam tests of the EIC prototype calorimeter at DESY.

Vardan, Gyurjyan↗

DOE OSTI2026

Unlocking the benefits of transparent and reusable science for climate-risk management

People around the world seek climate-risk information to guide their decisions. For instance, projections about future flood risk inform where households choose to live, how lenders manage credit risks, and which communities receive federal funding. Yet data limitations and fundamental validation challenges raise important concerns about the reliability of such projections. The principles of transparency and reusability help address these concerns by enabling scrutiny of assumptions and methods, development of foundational data and tools, and consistent application of evaluation standards. While there is ongoing debate about how much transparency commercial climate-risk services should provide, many expect non-commercial actors to lead the way on operationalizing transparency and reusability to fulfill their knowledge-building role in the climate-risk ecosystem. However, despite prominent success stories, we find a substantial gap between principles and practice: only four percent of the most-cited peer-reviewed climate-risk studies in recent years fully share their data and code despite this being a widely accepted minimum standard for transparency. We highlight low-cost measures that non-commercial researchers can take now to improve transparency and reusability. We also emphasize that transformative progress requires substantial investment, cross-sector collaboration, and careful consideration of tradeoffs, data rights, and multiple perspectives on equity. We hope this perspective accelerates both immediate actions and longer-term conversations to improve the ability of science to effectively support timely, evidence-based, and sound climate-risk management.

Open Science↗

DOE OSTI2024

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

13 HYDRO ENERGY↗

DOE OSTI2026

Feature Engineering and Ensemble Methods for Imbalanced ICS Intrusion Detection: Pipeline Audit and Constrained Evaluation

Industries are becoming increasingly connected and are more vulnerable to cyberattacks due to the widened attack surface. Industrial Control Systems (ICS) are among the most critical sectors that malicious actors can target, as such attacks can cause significant operational disruption and physical damage. It is imperative to detect such attacks as early as possible. This paper evaluates constraint-conditioned optimistic performance estimates for traditional ML models in ICS intrusion detection (i.e., estimates obtained under contiguous, non-shuffled temporal evaluation without test-set alteration, but with pre-split feature engineering that may introduce temporal leakage, due to dataset constraints). Our findings are threefold. First, we quantify how iterative feature engineering affects tree-based ensemble performance and examine how pipeline decisions (split strategy, sampling scope, and cleaning policy) can inflate or reduce reported IDS results under constraint-bound evaluation. Second, we compare intrinsic class-imbalance handling across ensemble models. Third, under our current pipeline constraints (including pre-split feature engineering), CatBoost achieves the best performance on Water Storage Tank (accuracy: 0.9831, class-1 F1: 0.9682), while Light- GBM achieves the best performance on Gas Pipeline (accuracy: 0.9618, class-1 F1: 0.9086).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Deep Multi-Agent Reinforcement Learning for Real-World Signalized Traffic Corridor Control

Signalized traffic control problem has been addressed recently with deep Reinforcement Learning (RL) approaches involving diverse state, action, and reward structures. While significant progress has been noted in the literature, open challenges still remain in the areas of adaptive signal phase timing, coordination in a multi-intersection corridor setting, and consideration of real-world traffic conditions. In the context of deep RL-based problem framing, extensions are needed that enable adaptive signal phase timings in an intersection agent's action space, computationally efficient information sharing among neighboring signalized intersection agents along a corridor, and experimentation in realistic simulation environments. In this paper, we develop a deep Advantage Actor Critic (A2C) multi-agent RL (MARL) approach capturing the research extensions above and apply it within a real-world calibrated Aimsun Next traffic corridor simulation model based on traffic data from the City of Coral Gables, Florida. For a multi-intersection corridor control setting, our numerical simulation experiments with a decentralized A2C MARL algorithm applied at different time periods led to a total average corridor travel delay reduction (expressed in seconds/mile averaged over vehicles) from 4.9% to 19.9% compared to state-of-the-art actuated control.

Shuvo, Salman S. [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2025

Agentic AI and the Cyber Arms Race

Here, in this article, we examine the implications for cyberwarfare and global politics as agentic artificial intelligence becomes more powerful and enables the broad proliferation of capabilities only available to the most well-resourced actors today.

Cybersecurity↗

DOE OSTI2024

Federated Deep Reinforcement Learning for Decentralized VVO of BTM DERs

The future of grid control requires a hybrid approach combining centralized and decentralized methods to fully utilize the potential of smart edge devices with artificial intelligence (AI) capabilities. This paper aims to develop and evaluate a federated deep reinforcement learning (FDRL) framework for decentralized adaptive volt-var optimization (VVO) of behind-the-meter (BTM) distributed energy resources (DERs). First, this paper models a single deep reinforcement learning (DRL) agent using the Markov Decision Process (MDP) framework for decentralized adaptive VVO of BTM DERs. Two DRL algorithms, soft actor-critic (SAC) and twin-delayed deep deterministic policy gradient (TD3), are compared for their effectiveness in optimizing VVO. Results show that TD3 outperforms SAC, achieving a 71.3% improvement in mean reward. Finally, the DRL agent is deployed within the FDRL framework, using the Flower platform, to enhance learning, provide adaptive control, and ensure data privacy for BTM DERs.

Ravi, Abhijith↗

DOE OSTI2025

Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning

In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to identify valid input spaces in high-dimensional, highly constrained systems while minimizing the total runtime of offline simulations. The simulations adapt their runtime based on the likelihood that a given training input will be informative to the classifier. Furthermore, we introduce a method for using the trained SAC model to predict whether a desired system input is likely to violate constraints, along with a technique to adjust the input as necessary. Additionally, we explore the potential of this model to detect faults or adversarial attacks within the system. The effectiveness of our approach is demonstrated through various simulations of challenging classification problems and a constrained quadrotor model.

Netter, Josh [Georgia Institute of Technology, Atl↗

DOE OSTI2024

Using Co-Simulation to Model Interconnect-Scale Power Systems from Loads to Generators

Co-simulation is a modeling technique that allows analysts to combine simulation tools and their corresponding models to exchange data during run-time, allowing the creation of larger and more complex models across heterogeneous domains. HELICS is a co-simulation platform developed over the past six years that has been shown to be effective for these multi-domain analysis. Recently, a HELICS-based analysis was completed where the ERCOT electrical interconnect in the United States was modeled in high detail from bulk power system generation to individual customer loads. This model was used to evaluate a flat-rate and transactive energy tariff with integrated wholesale and retail real-time and day-ahead energy markets. This modeling allows detailed analysis showing how the operations of the power system under these tariffs impact all actors in the power system, from individual customers to bulk power system operators.

co-simulation, HELICS, transactive energy system, ↗

DOE OSTI2024

Deep Reinforcement Learning for Microgrid Cost Optimization Considering Load Flexibility

This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load, addressing practical energy billing formation by power distribution utilities. Key contributions include an innovative reward function to mitigate sparse reward challenges and a mixed control strategy for discrete and continuous variables, ensuring radial network topology and minimizing power loss. We evaluate the proposed method on the model of a real microgrid located in Southern California, U.S.. The SAC-DRL model is tested to demonstrate its efficacy in reducing grid dependence, optimizing resource use, and minimizing costs. The results highlight the potential of DRL in modern energy systems, offering a sustainable and economically efficient solution for energy management in microgrids.

deep reinforcement learning↗

DOE OSTI2024

Enabling DER visibility using a distributed dissemination network

The electrical grid is currently undergoing a series of rapid transformational changes that have resulted in the introduction of new actors and operational schemes that have fragmented the data and control planes. To help address the issue, this paper describes the implementation of a sensor-oriented, distributed data dissemination network that seeks to eliminate data silos. The implementation is based on the DGSS architecture previously described in [1]. The developed product seeks to facilitate the seamless integration of multi-operator, multi-origin, multi-domain sensor data by using a distributed systems approach. The proposed solution decouples the sensor’s data streams from the application-specific infrastructure and migrates them into a software-defined databus that can be configured to suit the end application’s demands. To further validate DGSS capabilities, a DER oriented use case has been developed.

Sensor Dissemination Networks, Enhanced DER visibi↗

DOE OSTI2025

A Sequential Model Predictive and Deep Reinforcement Learning-Based Controller for Distribution System Outage Mitigation under Hurricane Events

This paper proposes a proactive outage mitigation framework for power distribution networks to withstand hurricane-induced disruptions. It leverages Model Predictive Control (MPC) to identify safe lines for proactive switching during hurricanes, minimizing the risk of cascading failures and voltage violations. The switching strategies optimized by MPC are sequentially integrated with a Deep Reinforcement Learning agent using the Advantage Actor-Critic algorithm, enabling dynamic line switching to maximize connected buses and minimize voltage violations in real time. Using a probabilistic hurricane model, the framework predicts line failures and adapts to varying conditions to enhance grid resilience. Simulations on the IEEE 123-bus system demonstrate its effectiveness in maintaining high connectivity and minimizing disruptions. Real-time testing with an RTDS confirms the practicality and reliability of the proposed approach.

Selim, Alaa [University of Connecticut]↗

DOE OSTI2024

Modeling Grid Data Flows for Transmission and Distribution Operations: Review, Design, Next Steps

Operational scenarios of the power grids grow multifold to accommodate the diverse needs of both the utilities and end consumers, and the various other stakeholders in-between. To comprehensively model and apply analytics to support objectives and business functions of grid sectors, a reliable approach to characterize and design data flows is crucial. The flows bridge business functions with communications protocols, stakeholders such as the grid actors, and data interfaces comprising different data objects. Additionally, constraints applied to the flow such as cybersecurity, trust, privacy, and ownership among others intersect these entities, requiring the delineation of their interactions under different scenarios. This paper aims to not only highlight relevant research in the space of grid data flows, but also proposes, for the transmission-distribution sector, a novel modeling approach that marries the aforementioned entities: objectives, business functions, data interfaces, communication protocols, data stakeholders, and flow constraints. It elaborates on the design philosophy and the significance of each entity within the model and applies it to an example function of fault location, isolation and service restoration (FLISR). Finally, the next steps to extend the application of this data flow model for other practical operational scenarios are discussed.

Sundararajan, Aditya [ORNL] (ORCID:000000033577854↗