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DOE OSTI2022

ARPA-E PERFORM datasets

Time-coincident load, wind, and solar data including actual and probabilistic forecast datasets at 5-min resolution for ERCOT, MISO, NYISO, and SPP. Wind and solar profiles are supplied for existing sites as well as planned sites based on interconnection queue projects as of 2021. For ERCOT actuals are provided for 2017 and 2018 and forecasts for 2018, and for the remaining ISOs actuals are provided for 2018 and 2019 and forecasts for 2019. There datasets were produced by NREL as part of the ARPA-E PERFORM project, an ARPA-E funded program that aim to use time-coincident power and load seeks to develop innovative management systems that represent the relative delivery risk of each asset and balance the collective risk of all assets across the grid. For more information on the datasets and methods used to generate them see https://github.com/PERFORM-Forecasts/documentation.

5-min data↗

DOE OSTI2021

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

An Evaluation of the Economic and Resilience Benefits of a Microgrid in Northampton, Massachusetts

Recent developments and advances in distributed energy resource (DER) technologies make them valuable assets in microgrids. This paper presents an innovative evaluation framework for microgrid assets to capture economic benefits from various grid and behind-the-meter services in grid-connecting mode and resilience benefits in islanding mode. In particular, a linear programming formulation is used to model different services and DER operational constraints to determine the optimal DER dispatch to maximize economic benefits. For the resiliency analysis, a stochastic evaluation procedure is proposed to explicitly quantify the microgrid survivability against a random outage, considering uncertainties associated with photovoltaic (PV) generation, system load, and distributed generator failures. Optimal coordination strategies are developed to minimize unserved energy and improve system survivability, considering different levels of system connectedness. The proposed framework has been applied to evaluate a proposed microgrid in Northampton, Massachusetts that would link the Northampton Department of Public Works, Cooley Dickenson Hospital, and Smith Vocational Area High School. The findings of this analysis indicate that over a 20-year economic life, a 441 kW/441 kWh battery energy storage system, and 386 kW PV solar array can generate $2.5 million in present value benefits, yielding a 1.16 return on investment ratio. Results of this study also show that forming a microgrid generally improves system survivability, but the resilience performance of individual facilities varies depending on power-sharing strategies.

25 ENERGY STORAGE↗

DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Long-run Marginal CO2 Emission Rates Workbooks for 2020 Standard Scenarios Cambium Data

This dataset has been superseded by a new set of workbooks that can be found here: https://data.nlr.gov/submissions/183 These workbooks contain modeled estimates of long-run marginal CO2 emission rates (LRMER) for the contiguous United States. The LRMER is an estimate of the rate of emissions that would be either induced or avoided by a long-term (i.e., more than several years) change in electrical demand. It incorporates both the projected changes to the electric grid, as well as the potential for an incremental change in electrical demand to influence the structural evolution of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treats grid assets as fixed. In addition to year-over-year data, the Levelized LRMER worksheet within each workbook is set up to produce a levelized long-run marginal emission rate based on user-provided inputs. These levelized LRMER values are intended for analysts to use when estimating the emissions induced (or avoided) by a long-term change in end-use electricity demand. Three future scenarios are provided in separate workbooks: A Mid-case (i.e., business-as-usual), and two scenarios with relatively higher or lower renewable energy costs. For more details on these scenarios, see the Standard Scenarios 2020 Report ( https://www.nlr.gov/docs/fy21osti/77442.pdf ). For more data underlying each scenario, see the Standard Scenarios 2020 project (Cambium data) at https://cambium.nlr.gov/ . This data was produced as part of the Cambium project. For more details about the methodology, see the Cambium Documentation: Version 2020 ( https://www.nlr.gov/docs/fy21osti/78239.pdf ). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nlr.gov/analysis/cambium.html .

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Long-run Marginal Emission Rates for Electricity - Workbooks for 2021 Cambium Data

These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. The LRMER is an estimate of the rate of emissions that would be either induced or avoided by a long-term (i.e., more than several years) change in electrical demand. It incorporates both the projected changes to the electric grid, as well as the potential for an incremental change in electrical demand to influence the structural evolution of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treats grid assets as fixed. The Levelized LRMER worksheet within each workbook is set up to produce a levelized long-run marginal emission rate based on user-provided inputs. These levelized LRMER values are intended for analysts to use when estimating the emissions induced (or avoided) by a long-term change in end-use electricity demand. There are two workbooks that supply the data at two different geographic resolutions: states and GEA regions (20 regions that are similar to, but not exactly the same as, the US EPA's eGRID regions). For more data underlying these emissions factors, see the Cambium 2021 project at https://cambium.nrel.gov/. For more details on the inputs into the scenarios available in the workbooks, see the Standard Scenarios 2021 Report (https://www.nrel.gov/docs/fy22osti/80641.pdf). This data was produced as part of the Cambium project. For more details about the methodology, see the Cambium Documentation: Version 2021 (https://www.nrel.gov/docs/fy22osti/81611.pdf). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nrel.gov/analysis/cambium.html.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Long-run Marginal Emission Rates for Electricity - Workbooks for 2022 Cambium Data

These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. A LRMER is an estimate of the rate of emissions that would be either induced or avoided by a change in electric demand, taking into account how the change could influence both the operation as well as the structure of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treat grid assets as fixed. Long-run marginal emissions rates are generally appropriate to use when trying to comprehensively estimate the impact of a long-lived (i.e., more than several years) intervention. There are two workbooks that supply the data at two different geographic resolutions: states and GEA regions (20 regions that are similar to, but not exactly the same as, the US EPA's eGRID regions). For more data underlying these emissions factors, see the Cambium 2022 project at https://scenarioviewer.nrel.gov/. For more details on input assumptions and methodology see the associated report (Cambium 2022 Scenario Descriptions and Documentation, https://www.nrel.gov/docs/fy23osti/84916.pdf). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nrel.gov/analysis/cambium.html.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

AMS History from 1950 to 2020

Presentation illustrates over 60 years of aerial measurements and establishment of Aerial Measuring System asset by the DOE. The presentation will be used as part of the training new AMS personnel. Presentation illustrates over 60 years of aerial measurements and establishment of Aerial Measuring System asset by the DOE. The presentation will be used as part of the training new AMS personnel.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

Cyber-attack detection and neutralization

The example embodiments are directed to a system and method for neutralizing abnormal signals in a cyber-physical system. In one example, the method includes receiving input signals comprising time series data associated with an asset and transforming the input signals into feature values in a feature space, detecting one or more abnormal feature values in the feature space based on a predetermined normalcy boundary associated with the asset, and determining an estimated true value for each abnormal feature value, and performing an inverse transform of each estimated true value to generate neutralized signals comprising time series data and outputting the neutralized signals.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Autonomous reconfigurable virtual sensing system for cyber-attack neutralization

An industrial asset may be associated with a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time representing current operation of the industrial asset. An abnormality detection computer may determine that at least one abnormal monitoring node is currently being attacked or experiencing a fault. A virtual sensing estimator may continuously execute an adaptive learning process to create or update virtual sensor models for the monitoring nodes. Responsive to an indication that a monitoring node is currently being attacked or experiencing a fault, the virtual sensing estimator may be dynamically reconfigured to estimate a series of virtual node values for the abnormal monitoring node or nodes based on information from normal monitoring nodes and appropriate virtual sensor models. The series of monitoring node values from the abnormal monitoring node or nodes may then be replaced with the virtual node values.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Retrofit-ability: A supplementary metric to inform energy efficiency policies and programs

Building Energy Use Intensity (EUI) has been commonly used to facilitate policy makers and utilities to design energy efficacy programs and help building managers prioritize investment in building upgrades. Buildings with higher EUIs are usually chosen as better candidates for retrofit. Our study of nearly one hundred buildings in Seattle’s Building Tune Up Accelerator program and thousands of buildings in the Asset Score database reveals that EUI (after weather/location/use type normalization) alone is not the best indicator of a building’s potential to save energy, especially for those buildings in the mid-range (30-70 percentile EUI). In this paper, we introduce the concept of “retrofit-ability” — a building’s real potential to reduce its energy use cost-effectively — as a supplementary metric to inform energy efficiency policy and investment. Currently, such potential analyses are predicted for a building stock using prototypical building models or statistical data. However, typical buildings or general historical data may not sufficiently represent building configurations in a portfolio and predict their saving potentials. We utilized machine learning to investigate how key building characteristics (such as envelope attributes, HVAC type, location, use type, etc.) affect a building’s improvement potentials and developed a low-cost method to quantify such potentials for a portfolio of buildings. The supplementary perspective provided by “retrofit-ability” highlights which building assets correlate most with a building’s energy savings potential across region, building use type and more.

Wang, Na↗

DOE OSTI2021

Integration of Simulated and Real Distributed Acoustic Sensor Measurements to Develop AI Enhanced Intelligent Sensing Operations

Distributed acoustic sensors (DAS) have shown remarkable success in monitoring critical civil, energy and transportation assets over long distances and under harsh environmental conditions. Integration of artificial intelligence (AI) technologies with DAS can extend their operational capabilities from conventional tasks (e.g., vibration frequency detection) to more advanced intelligent tasks like anomaly detection. However, these AI technologies often require large labeled/ annotated DAS measurements datasets of the assets in both normal and anomalous operating conditions. This data acquisition task can be both time consuming and costly. This work develops a generative adversarial network based unsupervised domain adaptation framework to build intelligent DAS operational capabilities.

Venketeswaran, Abhishek↗

DOE OSTI2021

Accessibility and Availability of Gas Stations for Liquid Fuel Supply During Severe Weather Events

Energy infrastructure is a pivotal lifeline with wider inter-dependencies. Electric power is essential to the continued functioning of critical assets; liquid fuels and other physical primary energy commodities that are used to supply the local energy needs of these assets during extreme events when electricity infrastructure is compromised. During Superstorm Sandy, the disruption of fuel supplies in the Tri-state area necessitated increasing situational awareness about oil and natural gas (ONG) systems to redirect fuel supplies to first responders and for restoration efforts. During winter storm events over the past several years, the demand for ONG for heating and power generation has skyrocketed in the north-east, which subsequently had caused ONG price to hit their highest level in years. ONG is a major energy source for restoration and recovery activities. However, very little, if any, situational awareness information about ONG supply and demand is available to those coordinating emergency efforts. Often this lack of information is due to the decentralized nature of the ONG supply chain and proprietary data maintained by private entities. To understand the impact of fuel availability on response and recovery efforts, this research focuses on (i) determining the accessibility to gas stations for liquid fuel supply across space and time, and (ii) examining the spatio-temporal variation of fuel availability and demand during extreme weather events. The broader scope of the project focuses on providing spatio-temporal situational awareness information about ONG availability and demand across the fuel supply chain for positioning of resources for response activities.

Kar, Bandana↗

DOE OSTI2021

Transformative remedial action scheme tool (TRAST)

Techniques and apparatuses are described that enable transformative Remedial Action Scheme (RAS) analyses and methodologies for a bulk electric power system, including methods of designing, reviewing, revising, testing, implementing, verifying, or validating a RAS. An improved RAS improves operation of the power system, including performance, reliability, control, and asset utilization. The example methodologies discussed—also referred to as a transformative Remedial Action Scheme tool (TRAST)—provide an end-to-end solution for adaptively setting RAS parameters based on realistic and near real-time operation conditions to improve power grid reliability and grid asset utilization, by leveraging utility data analysis and employing dynamic simulations and machine learning to significantly simplify and shorten the entire RAS process.

Fan, Xiaoyuan↗

DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗

DOE OSTI2022

Use of Systems Engineering in Repurposing Coal-Fired Power Plants with Malta Pumped Thermal Energy Storage System

he electric sector across North America is facing a transition. Both economics and policy decisions have pointed towards a broad retirement of fossil assets across markets. Owners are facing the problem of how to evolve the base of the electric sector from fossil asset to greener alternative. Coal-fired power plants built the modern electricity grid. Their rotating machinery are the beating heart of the grid, providing essential resiliency and reliability services. Power plant retirements are disruptive to plant workforces and cause outsized impacts on surrounding communities. The transition from thermal power plants that use rotating machinery to generate electricity (e.g., coal- and gas-fired plants) to variable, inverter-based generation (e.g., solar and wind) is affecting the reliability of the electric grid. Grid operators and national regulators have issued warnings about known and anticipated risk. In 2021, Malta Inc. was awarded a Department of Energy (DOE) grant to study how to integrate a Malta 100MW Pumped Heat Energy Storage (PHES) system with a retiring coal-fired power plant to meet emissions requirements, retain plant workforces, preserve communities, and maintain grid reliability. This presentation provides a summary of this study, focusing on how systems engineering approach was used to arrive at a proposed design concept that met multiple objectives and requirements. There will be three main parts for this presentation. The first part of the presentation will focus on how different systems engineering was applied for this work. In particular, the following areas: stakeholder engagement, site selection process, developing requirements and use cases for the integrated system, defining the system and its boundary, coming up with different system architecture/option, performing a techno-economic analysis to compare the different options and down selection of the preferred option, will be discusses. For these areas, discussion on the decisions on how much breadth and depth to go into each area will be provided. These discussions provide good insights into how to apply systems engineering. The second part of the presentation will provide a deeper dive into the two recommended integration options that repurpose coal-fired power plants with Malta PHES system. The comparison of the two options and general guidance of how to choose an option will be provided. This is particularly useful for utilities who are facing coal-plant retirements. The two options will be compared based on its performance (such as power output, efficiency), complexity, and cost. The social impact on local communities of the two options will also be discussed. The final part of the presentation will discuss the impact that this work has had, including Malta Inc. being invited to the White House to discuss progress and outcomes of this work with the Interagency Working Group on Coal and Power Plant Communities and Economic Revitalization. In summary, this presentation aims to provide a showcase of how sy

25 ENERGY STORAGE↗

DOE OSTI2022

Ushering in the New Age of Laboratories: Smart Labs in Practice; Preprint

Ventilation is the first line of defense against airborne hazards produced during research activities in laboratories. A vital component to maintaining healthy, safe, indoor air quality, laboratory ventilation systems are often victim to ineffective operation, posing a risk to an organization's most important asset - the researchers. Furthermore, system inefficiencies can lead to up to 50% wasted energy. By providing a framework to improve the safety and energy efficiency through optimized ventilation and operations, the Smart Labs Toolkit guides laboratory stakeholders through a straight-forward, holistic approach to achieving a dynamic Smart Labs program. A Smart Labs program employs a combination of physical, administrative, and management techniques to plan, assess, optimize, and manage high-performance laboratories. Grounded in the Smart Labs methodology, the National Renewable Energy Laboratory (NREL) implemented a successful Smart Labs program to oversee the design, construction, maintenance, and operations of its laboratories. To accomplish this effort, NREL's key stakeholders created an internal partnership to align NREL's existing laboratories with Smart Labs principles and solidify organizational roles for the safe and efficient operation of laboratory assets. The program provides the groundwork for decarbonization strategies centered around building operation. This paper outlines best practices employed by NREL to develop a cross-cutting Smart Labs team, garner managerial support, and effectively communicate of goals around safety and energy. Strategies include specific Smart Labs best practices, such as implementing a Laboratory Ventilation Risk Assessment - a systematic process for identifying risk due to airborne hazards and informing dynamic, demand-based ventilation to optimize safety and efficiency.

decarbonization↗

DOE OSTI2022

If One GEB is Good, a Community of GEBs is Better

Energy efficient, connected, grid-interactive, smart and flexible buildings are key to decarbonization, lowering energy use and improving the nation’s electricity grid. The U.S. Department of Energy’s Connected Communities initiative works to demonstrate how coordinated groups of highly efficient buildings combined with other distributed energy resources (DERs), such as electric vehicle (EV) charging, batteries, storage, demand response and photovoltaic (PV) generation can reliably and cost-effectively serve as grid assets by strategically deploying efficiency and demand flexibility while reducing carbon emissions. In 2021, DOE competitively awarded $61 million to a diverse portfolio of 10 pilot projects to promote grid-interactive efficient buildings (GEBs) working together to reliably and cost-effectively serve as grid assets while decarbonizing. Two of the main tenets of the program are measuring the communities’ energy and carbon performance and understanding how to replicate project successes in other communities. This paper begins with a discussion of what Connected Communities are (including a brief history) and their many benefits, including reduced carbon emissions and increased building efficiency and demand flexibility. Next, it provides an overview of the 10 projects, highlighting the diversity of approaches to measure success and replicate the projects: geographic locations; building types; utility, regulatory, market environments; and building vintages that will be used to test the ability of buildings to serve as grid resources. It concludes with a discussion of anticipated project impacts and the metrics that will be used to evaluate the Connected Communities projects.

Nemtzow, David↗