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DOE OSTI2025

Techno-Economic Analysis for the Addition of a Thermal Energy Storage System to a Central Plant

Increasing energy demand and rising peak loads present significant challenges for energy management in commercial and institutional settings. As climate change drives greater cooling needs, central plants must navigate the complex tradeoffs between operational efficiency, cost control, and grid stability. Thermal energy storage (TES) systems offer a viable solution by shifting energy consumption from peak to off-peak periods, thereby reducing peak demand, lowering utility expenses, and improving grid resilience. However, the success of TES implementation hinges on appropriate system sizing, effective control strategies, and alignment with local utility rate structures. This article presents a techno-economic analysis of integrating a chilled water TES system into the central plant at California State University, Dominguez Hills. Drawing on historical load profiles and utility tariffs, we assess three TES sizing approaches and their corresponding control strategies from both energy and economic perspectives. This article utilizes a model-based approach to assess the impact of TES sizing and control strategies on the techno-economic feasibility of integrating TES into an existing central plant. The models employed for this analysis were calibrated using 4 years of historical data. Here, the results demonstrated that utility tariffs and the campus's operational profiles dictate the most feasible sizing and control methods. The findings offer valuable insights for institutions and commercial building managers exploring sustainable energy solutions. By demonstrating how optimized TES strategies can improve operational efficiency while achieving financial savings, this study highlights the potential for TES to align performance with cost effectiveness in real-world applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Techno-Economic Analysis for the Addition of Thermal Energy Storage to a Campus With Existing Battery Storage

Rising global temperatures and increasing energy demands pose significant challenges for energy management, particularly in institutional and commercial settings. As cooling needs grow, campuses must balance operational efficiency, cost control, and grid stability. Energy storage solutions, such as thermal energy storage (TES) systems, offer a promising approach to shifting energy consumption from peak to off-peak periods, alleviating peak demand, reducing utility costs, and enhancing grid resilience. When integrated with existing battery energy storage systems (BESS), TES can further optimize load management and improve energy savings, especially in buildings with diverse energy needs. This article presents a techno-economic analysis of integrating a chilled water TES system into the central plant at California State University, Dominguez Hills, which already operates a BESS. We assess three TES sizing strategies—full storage, load leveling, and peak demand limiting—by modeling and simulations based on historical energy loads. Our findings show that we can control TES systems to complement BESS operation, with campus-level load leveling providing the greatest cost savings by reducing peak demands. Furthermore, the study also evaluates the long-term economic viability of TES, considering installation costs, energy savings, and payback periods under varying tariffs. This research offers practical guidance for institutions seeking to enhance energy resilience and reduce operational costs through energy storage solutions.

25 ENERGY STORAGE↗

DOE OSTI2026

Unified Modeling Architecture for Load Management in Extreme Heat: The New York City Case

Integration of renewable resources to meet growing energy demand is becoming a global priority under decarbonization mandates. This study contributes to ongoing efforts on this key subject by assessing the feasibility of using coastal-urban renewable energy resources, namely, offshore wind and rooftop photovoltaic systems, to meet electricity demand of New York City during the intense recent heat wave period of June 2025. A unified modeling framework, based on the urbanized weather research and forecasting model, is used to simulate climate, renewable resources, and energy demand variables. Findings show significant energy load mismatch of approximately 1150 GWh over the month, between the demand and the combined renewable generation outcome. Three storage integration scenarios are analyzed to mitigate the deficits, reducing said deficits by a minimum of approximately 9% over the duration of the month. This study provides a transferable modeling framework tool for evaluating renewable integration in dense urban environments that can be used by grid operators to support grid resilience during extreme heat events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

System and Component Development of Particle-Based Pumped Thermal Energy Storage

Reliable power supply from variable renewable resources requires energy storage at various scales to overcome resource intermittency. Long-duration energy storage (LDES, 10-100 hours) can improve dispatchability and grid reliability with increasing levels of renewable power supply. Thermal energy storage (TES) has siting flexibility and the ability to store a large capacity of energy, and thus has the potential to meet the LDES need and provide charging and discharging durations beyond the economic capacity of conventional batteries. TES technology has evolved from concentrating solar thermal power (CSP) generation and is recognized as an economic large-scale energy storage method. A standalone electric-thermal energy storage system supporting renewable integration of wind and solar power without CSP solar field can firm renewable generation and boost overall grid resilience and security.

concentrating solar thermal power↗

DOE OSTI2025

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗

DOE OSTI2020

Degradation of Commercial Lithium-Ion Cells as a Function of Chemistry and Cycling Conditions

Energy storage systems with Li-ion batteries are increasingly deployed to maintain a robust and resilient grid and facilitate the integration of renewable energy resources. However, appropriate selection of cells for different applications is difficult due to limited public data comparing the most commonly used off-the-shelf Li-ion chemistries under the same operating conditions. This article details a multi-year cycling study of commercial LiFePO 4 (LFP), LiNi x Co y Al 1–x–y O 2 (NCA), and LiNi x Mn y Co 1–x–y O 2 (NMC) cells, varying the discharge rate, depth of discharge (DOD), and environment temperature. The capacity and discharge energy retention, as well as the round-trip efficiency, were compared. Even when operated within manufacturer specifications, the range of cycling conditions had a profound effect on cell degradation, with time to reach 80% capacity varying by thousands of hours and cycle counts among cells of each chemistry. The degradation of cells in this study was compared to that of similar cells in previous studies to identify universal trends and to provide a standard deviation for performance. All cycling files have been made publicly available at batteryarchive.org, a recently developed repository for visualization and comparison of battery data, to facilitate future experimental and modeling efforts.

25 ENERGY STORAGE↗

DOE OSTI2025

Physics-Based Analysis of Cell Imbalances and Aging in Lithium-Ion Battery Modules and Packs

Lithium-ion battery (LIB) packs are a key solution for grid-scale energy storage, enabling grid resilience and supporting critical infrastructure. LIB modules and packs experience current imbalances and uneven cell aging due to various design and operational factors, and require a battery management system (BMS) to continuously monitor and control. In this context, a physics-based modeling framework for LIB modules and packs (liionpack) was enhanced to identify design and control strategies that minimize current imbalance and improve module/pack operation. Simulations of an 8-cell parallel-connected module demonstrate that reducing current imbalance leads to more uniform cell aging and improved module/pack-level degradation predictions. The analysis shows that current imbalance are affected by the electrical resistances. Terminal location significantly affects imbalance, with opposite-end terminal connections at intermediate branches minimizing the imbalance, and the pack circuit construction influences the accuracy of physics-based analysis at the pack scale. This framework enables design optimization of modules and packs through a fast and easy evaluation of pack performance and aging, and supports the development of aging-informed balancing strategies compatible with BMS implementation. Thereby, offering practical pathways to improve reliability and cycle life predictions in large-scale battery energy storage systems.

Ayalasomayajula, Surya Mitra [Oak Ridge National L↗

DOE OSTI2024

gcm_eval (Global Climate Model Evaluation) [SWR-24-37]

The interplay between energy, climate, and weather is becoming more complex as our changing climate continues to affect the weather we experience which in turn drives changes in the ever increasing share of renewable energy generation and energy demand. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and often subjective process. There is no single perfect climate model or dataset for all applications. In this repository, we include software and the corresponding assessments of various global climate models (GCMs) from the Coupled Model Intercomparison Project (CMIP6), evaluating their skills with respect to the historical climate and comparing of their future projections of climate change. We focus on variables that directly affect the energy system including the representation of extreme values that can drive grid resilience events. The objective of this repository is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and datasets in subsequent work.

Buster, Grant↗

DOE OSTI2020

Low-Income Energy Affordability: Conclusions From A Literature Review

This paper examines the persistent problem of high energy burdens among low-income households, based on a review of more than 180 publications that pointed to several promising opportunities to address energy affordability including inclusive solar programs, leveraged health care benefits, and behavioral economics. using an equity and affordability lens. Even after decades of weatherization and bill-payment programs, low-income households, on average, continue to spend a higher share of their income on electricity and natural gas bills than any other income group. Energy burden for low-income households is not declining, and it remains persistently high, particularly in the South, in rural America, among minority households, and those with children and elderly residents. On a per household basis, utility companies spend less on energy-efficiency programs for low-income households than for other income groups. In addition, government and utility programs that promote rooftop solar power, electric vehicles, and home energy storage are largely inaccessible to low-income households. Our review identifies promising opportunities to address energy affordability including inclusive solar programs, leveraged health care benefits, behavioral economics, data analytics, advanced information and communication technologies, and grid resiliency. Scalable approaches require linking implementing agencies, programs and policies to tackle the complex web of causes and impacts on low-income households with high energy burdens.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Early-Time (E1) High-Altitude Electromagnetic Pulse Effects on Transient Voltage Surge Suppressors

Determining the effectiveness of surge and pulse protection devices in the United States power grid against effects of a High-Altitude Electromagnetic Pulse (HEMP) is crucial in determining the present state of grid resilience. Transient Voltage Surge Suppressors (TVSS) are used to protect loads in substations from transient overvoltages. Designed to mitigate the effects of lightning, their response to a HEMP event is unknown and was determined. TVSSs were tested in two unique configurations using a pulser that generates pulses in the tens of nanoseconds scale to determine their protective capability as well as to determine their self-resilience against HEMP pulses. Testing concluded that TVSS devices adequately protect against microsecond scale pulses like lightning but do not protect against pulses resembling HEMP events. It suggests that TVSS devices should not be relied upon to mitigate the effects of HEMP pulses.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Connected Loads – Grid Connected Appliances: Commercial Refrigeration System Fault Detection and Diagnostics

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

High-Resolution Wind Resource Data Set of the Greater Puerto Rico Region

In February 2022, the U.S. Department of Energy and six national laboratories launched the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100). PR100 aims to provide a comprehensive analysis of possible pathways for Puerto Rico's energy future, with a goal of 100% renewable energy by 2050. As a part of the renewable energy potential assessment in this project, we developed 20 years (2001-2020) of data using a numerical weather prediction (NWP) model for onshore and offshore wind resource assessment for the Puerto Rico. The research steps in developing the long-term wind resource data sets based on the NWP model were: 1. Model wind resource based on the Weather Research and Forecasting (WRF) model. 2. Develop WRF model configurations for Puerto Rico. 3. Test WRF with 11 different physics parameterizations for planetary boundary layer (PBL). 4. Assess WRF output from the different PBL schemes against observations. 5. Select a final model configuration which can produce the modeled wind speed with sufficient accuracy. 6. Produce 20 years wind resource data sets for Puerto Rico region. In the first stage of our framework for developing wind resource data, we developed a WRF model configuration using two nested domains (9 km and 3 km) to cover Puerto Rico and U.S. Virgin Islands and downscale the ERA5 reanalysis data (0.25 degrees x 0.25 degrees; hourly interval) to a 3-km domain. For the second stage, we implemented one-year simulations focused on using 11 different PBL physics parameterizations to find a combination of WRF physics parameterizations that could provide accurately modeled wind speed for Puerto Rico. We also analyzed the sensitivity of the modeled wind speed to PBL schemes for onshore and offshore locations. The WRF output resulting from the 11 WRF experiments using different PBL parameterizations were evaluated against observations obtained from the National Data Buoy Center (NDBC) as well as at hub height for a location for which measurements were available. A final model setup selected through the validation with observational data was used to produce 20 years of data with 3-km spatial and 5-minute temporal resolution. The WRF model output was post-processed to include wind profiles and basic atmospheric variables in a format that can be easily used for downstream modeling. The 20 years of wind resource data will be made available through NREL and support the estimation of wind energy development costs for the PR100 study.

17 WIND ENERGY↗

DOE OSTI2022

Connected Loads – Grid Connected Appliances: Deployment IoT Solution for Fault Detection and Diagnostics

As one of the most energy-intensive end-uses in the commercial buildings sector, supermarkets consume around 50 kWh/ft 2 ( or 537.6 kWh/m 2 ) of electricity annually, or more than 2 million kWh of electricity per year for a typical store. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40–60% of its total electricity usage and is equivalent to about 2–3% of the total energy consumed by commercial buildings in United States, or around 0.5 quadrillion Btu (or 0.53 quadrillion KJ). Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Current supermarket refrigeration systems rely on high global warming potential hydrofluorocarbon refrigerants. Reducing refrigerant usage or using environment friendly alternatives can result in significant climate benefits. Transcritical CO2 refrigeration systems have attracted more attention in recent years because of their zero-carbon emission advantages compared with traditional refrigerants. These systems are widely used in commercial buildings such as supermarkets. The refrigeration system can also be adapted to handle flexible building loads and be integrated into grid response transactive control to balance the supply and demand of the electric grid. Even minor improvements in the efficiency and operational reliability of supermarket refrigeration systems can create significant value in terms of saving energy, improving food quality, protecting the environment, reducing carbon footprint, and enhancing electric grid resilience. For decarbonization, the new administration has set a target to reduce greenhouse gas emissions by 50– 52% by 2030 and targeting a carbon-neutral economy by 2050. For electrification, the goal is to achieve 100% clean electricity by 2035. Such decarbonization and electrification in the building sector require that energy consumption in buildings be reduced significantly. Therefore, the building sector must continuously adopt new technologies to achieve its energy and carbon emission goals. One of the most fundamental technologies is the Internet of Things (IoT). IoT has proven to be an effective solution for the building domain, including building information/energy modeling, smart buildings, etc. Although much progress has been made in the development of IoT-based building energy systems, there is still a lack of reliable, scalable, and affordable IoT-based automated fault and degradation diagnostics (AFDDs) solutions. Such solutions would enable deployment of advanced algorithms into real systems to archive the projected energy benefits. This study reviews existing IoT solutions developed for building energy– related application and developed a simple but effective AFDD IoT deployment solution, including developing a suitable IoT architecture and conducting easy and scalable deployment by leveraging a common cloud-based IoT service.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

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 OSTI2023

Virtual Power Plants and Energy Justice

The Federal Energy Regulatory Commission's (FERC) Order 2222, issued in September 2020, removes barriers for distributed energy resources (DERs) to participate in wholesale energy markets by allowing DERs to aggregate and participate in wholesale markets as a single entity, known as a virtual power plant (VPP). VPPs can provide balancing, reliability, and resiliency grid services and can participate in capacity, energy and ancillary services markets. They can also increase customer energy access and lower electricity bills. This report focuses on VPP business models, including considerations of energy justice (EJ). Through an analysis of the VPP value chain, business models, programs, and pilots, several VPP applications are identified and grouped by their ability to have quantitatively measurable or monetized benefits. Benefits and barriers specific to underserved communities are outlined, and VPP programs with an intentional focus on underserved communities are compared to those without such a focus.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

PR100: Estimated Medium- and Heavy-Duty Electric Vehicle Adoption and Load Estimation in Puerto Rico through 2050

The 2-year Puerto Rico Grid Resilience and Transition to 100% Renewable Energy Study analyzed stakeholder-driven pathways to Puerto Rico’s clean energy future. Outputs relating to electricity demand modeling were partially informed by estimates of electric vehicle adoption across all classes of medium- and heavy-duty vehicles (MHDVs), and the ensuing charging loads. To create these estimates, the team developed a transportation model for MHDVs in Puerto Rico to estimate the amount and geospatial distribution of energy used. Charging schedules for the different end uses of MHDVs were then used to construct electric load shapes assuming a portion of those vehicles would be replaced by battery electric counterparts. Study results showed that, by 2050, electric vehicles may constitute roughly 50% of the MHDV population in Puerto Rico. The resulting electrical demand curve attributable to MHDV charging showed that, for solar energy-based electrical systems with limited energy storage, this demand may create challenges unless appropriately managed either on the demand or supply side.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗