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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Model-Predictive Hierarchical-Control Framework for Aggregating Residential DERs to Provide Grid Regulation Services

This paper develops a hierarchical control frame-work to aggregate and to manage behind-the-meter distributed energy resources (DERs), which will be ubiquitous in future distribution systems. In the proposed framework, firstly, each controller in the hierarchy determines the flexibility of the DERs such that the obtained flexibility is feasible with respect to its operational purview. For example, the operational purview of a home energy management system may only consider consumer comfort preferences, while that for an aggregator or a grid controller may consider network voltage management as well. Based on the feasible flexibility, optimal setpoints for the DERs is then determined by the hierarchical controllers to help the distribution power network in voltage regulation, coordination issues with existing transmission-level conventional generators, etc. Therefore, the proposed strategy, which is based on model-predictive control, can be effectively utilized by the distribution network to coordinate several DERs to provide grid regulation services. Numerical simulations performed on the IEEE 37-bus test system demonstrate the efficacy of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Assessing Cost-Optimal Battery Energy and Solar-Plus-Storage Systems for Federal Customers: A Nationwide Assessment: Preprint

Prior research has identified that the financial viability of behind-the-meter battery energy storage systems is heavily dependent on technology cost, utility rate structure, energy consumption patterns, and co-deployment with synergistic technologies like solar photovoltaics (PV), among other factors. This study builds on existing research by comprehensively evaluating the economics of battery energy storage systems (BESS) and solar-plus-storage systems for a reference office building at 755 reference sites under 834 utility rates, and four storage capital cost scenarios. Results indicate that even with dramatic cost reductions, BESS is likely to be cost-effective only under utility rates that include demand charges, and possibly include time-of-use (TOU) pricing as well. Even under these utility rates, BESS systems provide marginal savings, and office building operators are unlikely to deploy the technology for cost savings alone. Solar-plus-storage systems provide more savings than BESS and allow for larger economic storage capacities. Solar-plus-storage provides compelling savings opportunities at baseline prices, and even at capital costs 25% higher than baseline. Solar-plus-storage is most effective where there are demand charges and energy pricing schemes include TOU pricing, or where electricity is expensive (at least $0.30/kWh). Our case studies illustrate that the presence of demand charges, even at similar energy costs, can be the deciding factor in BESS and solar-plus-storage viability. The findings and maps from this assessment may be of use to planners, building owners, and developers looking for potentially economic storage opportunities.

battery energy storage systems↗
DOE OSTI2026

Utility Managed Distributed Energy Resources Intelligent Community - UDERMS iCommunity (Final Technical Report)

The Utility Managed Distributed Energy Resources Intelligent Community (UDERMS iCommunity) project, led by PacifiCorp and supported by the U.S. Department of Energy’s Building Technologies Office (Award No. DEEE0009782), aimed to demonstrate an integrated, utility-managed “behind-the-meter” distributed energy resource (DER) program connecting multifamily, commercial, institutional, and industrial buildings to deliver grid services and improve energy efficiency.

14 SOLAR ENERGY↗
DOE OSTI2024

Simulation-Guided Decision-Making for Enhancing Energy Resilience in a Remote Alaskan Community

The increasing frequency and severity of extreme weather events underscore the need to bolster the resilience of energy infrastructure in remote coastal communities exposed to climate hazards. In recent years, considerable effort has been made to harden the grid infrastructure of remote communities through investment in energy storage, advanced metering, renewable generation and energy-efficient loads. However, simulation-based studies are still needed to identify appropriate locations for investment and determine the adequacy of existing infrastructure in supporting new resources. Preparing the required models may often be challenging due to insufficient metering, disparate data sources and workforce limitations. This paper describes modeling efforts undertaken to represent, within a unified co-simulation platform: (a) the electric distribution network and (b) the thermal behavior of a community medical center, in the remote community of Cordova, Alaska. With the help of case-studies, it is shown how the developed simulation platform can help the local utility in making decisions regarding capital investment aimed at enhancing community resilience.

Microgrid, energy storage, resilience↗
DOE OSTI2020

An intelligent energy router for managing behind-the-meter resources and assets

With increase in distributed energy resources (DERs) and smart loads, each energy resource and load need a separate power conversion system leading to complex coordination and interaction, reduced energy conversion efficiency, coordinating compliance to grid standards (IEEE 1547) from multiple sources, reduced security. Also, multiple vendors with legacy system designs and proprietary communications interfaces result in redundancy and increase in cost of power electronics systems. This paper presents an energy router concept for buildings applications which provides autonomous power flow between sources and loads with a novel agent-based software interface.

Chinthavali, Madhu Sudhan↗
DOE OSTI2021

Probabilistic Resilience of DER Systems - A Simulation Assisted Optimization Approach

Energy systems resilience is becoming increasingly important as the frequency of major grid outages increases. In this work, we present a methodology to optimize a behind-tlie-meter distributed energy resource system to sustain a site's critical loads during a pre-defined outage period. With the fixed system design, we then propose an outage simulation approach to estimate the resilience potential of the DER system to sustain loads beyond the fixed outage period - a yearlong resilience performance analysis. We apply statistical analysis to assess the system's resilience performance over a broader parametric problem space on an hourly, monthly, and yearly basis. We demonstrate the impact of the pre-defined outage period on the resilience performance through a case study. Results show that the probability of surviving a random outage of a given duration changes from 20% to 95% when the outage is modeled for a weekday instead of a weekend for the given load-profile.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2021

Probabilistic Resilience of DER Systems - A Simulation Assisted Optimization Approach

Energy systems resilience is becoming increasingly important as the frequency of major grid outages increases. In this work, we present a methodology to optimize a behind-the-meter distributed energy resource system to sustain a site's critical loads during a pre-defined outage period. With the fixed system design, we then propose an outage simulation approach to estimate the resilience potential of the DER system to sustain loads beyond the fixed outage period - a yearlong resilience performance analysis. We apply statistical analysis to assess the system's resilience performance over a broader parametric problem space on an hourly, monthly, and yearly basis. We demonstrate the impact of the pre-defined outage period on the resilience performance through a case study. Results show that the probability of surviving a random outage of a given duration changes from 20% to 95% when the outage is modeled for a weekday instead of a weekend for the given load-profile.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

RODeO (Revenue Operation and Device Optimization Model) [SWR 20-67]

The Revenue, Operation, and Device Optimization (RODeO) model explores optimal system design and operation considering different levels of grid integration, equipment cost, operating limitations, financing, and credits and incentives. RODeO is a price-taker model formulated as a mixed-integer linear programming (MILP) model in the GAMS modeling platform. The objective is to maximizes the net revenue for a collection of equipment at a given site. The equipment includes generators (e.g., gas turbine, steam turbine, solar, wind, hydro, fuel cells, etc.), storage systems (batteries, pumped hydro, gas-fired compressed air energy storage, long-duration systems, hydrogen), and flexible loads (e.g., electric vehicles, electrolyzers, flexible building loads). The input data required by RODeO can be classified into three bins: 1) utility service data, which refers to retail utility rate information (meter cost, energy and demand charges), 2) electricity market data, which include energy and reserve prices, 3) other inputs, which refer to additional electrical demand, product output demand, technological assumptions, financial properties, and operational parameters.

Guerra Fernandez, Omar Jose↗
DOE OSTI2024

Solar-Plus-Storage Program Design: Frameworks and Examples [Slides]

The Columbia River Treaty Tribes in the Pacific Northwest - the Nez Perce, Umatilla, Warm Springs, and Yakama - hold treaty-reserved fishing rights for the Columbia River, the largest river in North America flowing into the Pacific Ocean. The four Tribes, through the Columbia River Inter-Tribal Fish Commission (CRITFC), prepared a vision for a more harmonized energy and water system in their 2022 Energy Vision for the Columbia River Basin. In it, the four Tribes envision a future where the Columbia Basin electric power system supports healthy and harvestable fish and wildlife populations, protects Tribal treaty and cultural resources, and provides clean, reliable, and affordable electricity. To help realize the Energy Vision, CRITFC received technical assistance from the National Renewable Energy Laboratory (NREL) through the Communities Local Energy Action Program (LEAP) pilot with the goal of ensuring that the Tribes are fully informed and prepared to integrate their interests into regional power system planning. This resource aims to provide an overview of program and policy design frameworks for behind-the-meter (BTM) energy storage and solar-plus-storage programs and examples from across the United States. This information is intended to build CRITFC's understanding of potential policies and program designs that could support the deployment of solar photovoltaics (PV) and energy storage in the Pacific Northwest.

14 SOLAR ENERGY↗
DOE OSTI2026

Energy, power, and infrastructure demands from electrifying airport ground support equipment at United States airports

As the airline industry seeks to reduce costs and transition to clean energy, electric ground support equipment is emerging as a favorable option. The integration of electric ground support equipment into airport operations requires careful planning for vehicle deployment, charging infrastructure, and grid impacts. We develop a flexible, bottom-up modeling framework to assess energy and infrastructure needs across more than 300 U.S. airports. Our analysis estimates site-specific power and energy demand, equipment counts by type, charger requirements, and costs. Here we quantify the magnitude of new electrical loads created by the electrification of airport ground support equipment, finding that peak power demand at the largest airports can reach up to 20 megawatts, with annual electricity consumption approaching 51,000 megawatt-hours. We further show that behind-the-meter battery energy storage systems and solar photovoltaic systems can reduce peak load and lower total system costs by as much as 10 million dollars.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Data-Driven Approach to Transactive Energy Systems with Commercial Buildings

A microgrid with solar, storage, and responsive load resources has been implemented and tested on an urban academic campus. Through modeling and simulation, a consensus transactive energy mechanism has been implemented, with each resource participating as a virtual battery. Most owners of large buildings don't have the information and expertise to develop and validate suitable models of their buildings using available tools. To mitigate this adoption barrier, a data-driven building model has been implemented and validated. It uses 5-minute weather data, 3-second revenue meter data, energy audit information, and a load reduction test conducted by the building owner.

Buildings, data-driven modeling, deep learning, en↗
DOE OSTI2022

A Review of Behind-the-Meter Solar Generation Modeling and Forecasting

Solar photovoltaic systems largely integrated within the distribution grid are operated 'behind-the-meter' and power generation cannot be directly monitored by most utilities. The increasing penetration of behind-the-meter solar photovoltaic systems can deter efficient network and market operations due to variability and uncertainty in net load, which is exacerbated by limited visibility and the difficulty in analyzing the hosting capacity. Risk introduced by behind-the-meter solar contributions may hinder reliable and secure grid operations due to biased system monitoring and forecasts. Accurate behind-the-meter estimations, together with capacity and specification forecasts, thus play a key role in balancing supply and demand and this article reviews the pertinent literature, identifying key characteristics and predictive methods for efficient behind-the-meter solar photovoltaic generation. Forecasting is central to methods herein. The fundamental characteristics of behind-the-meter solar forecasting, including which methods are applicable for scenario-driven use cases, are driven by the metrics most useful for system-wide performance evaluation. To this aim, the literature is reviewed with a focus on forecasting applications for aggregate, regional behind-the-meter generation useful to bulk system and utility operations. As distinguished from net load forecasting, subtleties in these coincident tasks are explored before concluding with recommendations for current practice and future implementations.

behind-the-meter↗
DOE OSTI2021

Improving the economics of battery storage for industrial customers: Are incentives enough to increase adoption?

As adoption of behind-the-meter battery energy storage increases across the United States, implementation continues to lag in the industrial sector. This analysis considers two manufacturing facilities with potential for load shifting to reduce peak demand. Although both facilities have load profiles that demonstrate great potential for regular and programmed demand reduction during peak hours, battery energy storage was deemed prohibitively expensive. A review of several existing utility and state-level policies and incentives determined that few may be rightsized for the industrial customer class. Furthermore, this analysis further considers multiple incentive structures and finds that although incentives increase viability of energy storage, developers must also consider optimization, unique load profiles, and use case to effectively increase adoption of battery energy storage by industrial customers.

25 ENERGY STORAGE↗
DOE OSTI2023

Impacts of Dispatch Strategies and Forecast Errors on the Economics of Behind-the-Meter PV-Battery Systems

To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncer- tainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.

batteries↗
DOE OSTI2024

Community Solar Subscription Credit Considerations and Case Study

Together New Orleans (TNO) requested technical assistance through the US Department of Energy's (DOE's) National Community Solar Partnership (NCSP). The National Community Solar Partnership is a coalition of community solar stakeholders working to expand access to affordable community solar to every U.S. household and enable subscribers and their communities to realize meaningful benefits, such as reduced energy burden, increased resilience, community ownership, and equitable workforce development. TNO asked for a subject matter expert from NCSP to review the Entergy New Orleans (ENO) proposed revised Rate Schedule for Community Solar Generating Facilities. TNO requested that the proposed methodology for subscription credits for applicable residential and non-residential rate schedules be reviewed to determine the expected credit rate. The aim behind the analysis is to provide TNO and engaged stakeholders with an informed understanding of the proposed rate schedule before making decisions on the appropriate rate design for community solar (CS) subscriptions. This report is an exploration of CS subscription credit rate calculation considerations using the CS program in New Orleans as a case study. The report provides a framework for modeling CS credit rates in addition to topics that may be helpful to address when undertaking program design or rule making.

14 SOLAR ENERGY↗
NASA NTRS2012

HEART Aerothermodynamic Analysis

This paper presents an assessment of the aerothermodynamic environment around an 8.3 meter High Energy Atmospheric Reentry Test (HEART) vehicle. This study generated twelve nose shape configurations and compared their responses at the peak heating trajectory point against the baseline nose shape. The heat flux sensitivity to the angle of attack variations are also discussed. The possibility of a two-piece Thermal Protection System (TPS) design at the nose is also considered, as are the surface catalytic affects of the aeroheating environment of such configuration. Based on these analyses, an optimum nose shape is proposed to minimize the surface heating. A recommendation is also made for a two-piece TPS design, for which the surface catalytic uncertainty associated with the jump in heating at the nose-IAD juncture is reduced by a minimum of 93%. In this paper, the aeroshell is assumed to be rigid and the inflatable fluid interaction effect is left for future investigations.

Mazaheri, Alireza↗
NASA NTRSDate not supplied

Comparison Study of Machine Learning Techniques to Predict Flight Energy Consumption for Advanced Air Mobility

This paper addresses the need to predict the flight energy consumption of aerial vehicles in the presence of wind using machine learning techniques. The presented work is critical to achieving sustainable and efficient operations for Advanced Air Mobility (AAM) and to evaluating the readiness of the ground-supporting energy infrastructure, e.g., electric grid and AAM portals. The flight energy consumption is described using the "energy per meter" (EPM) metric. We present a comparison study of influential machine learning techniques in predicting EPM using real-world flight test data. We presented new results of using the Decision Tree, Random Forest, and linear regression techniques, along with our previous results using the Recurrent Neural Network and Feed Forward Neural Network techniques. The comparison results show that the Linear Regression method outperforms other methods on the basis of the Mean Squared Error and error variance.

Machine Learning↗
DOE OSTI2023

Impacts of Dispatch Strategies and Forecast Errors on the Economics of Behind-the-Meter PV-Battery Systems

To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncertainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.

batteries↗