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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Co-simulation Framework for Community-scale Building-grid Integration [SWR-21-75]

Distributed energy resources (DERs), including rooftop solar, energy storage, and flexible loads, are gaining popularity as costs decline and as building owners and utilities realize their benefits. DERs can improve distribution system efficiency, help prevent the need for expensive grid upgrades, and increase the resilience of local communities. However, they can also cause difficulties in grid operations and can require controls to achieve their benefits. To address this challenge, NREL researchers have developed a community-scale solution that assesses the impacts of DERs and their control strategies on a distribution system. The framework has been shown to reduce solar photovoltaic (PV) curtailment to 0%, mitigate the adverse impact of solar variability on the distribution voltage, and provide up to 5-day critical load support during emergency events. Utilizing 5 different modules representing the feeder, buildings, home energy management systems, an aggregator, and a utility controller, NREL expects this simulation technology to play a critical role in the continued integration of DERs. According to the Energy Information Administration (EIA), solar curtailments accounted for 94% of the total energy curtailed in the California Independent System Operator (CAISO) in 2020. By enabling Independent System Operators (ISOs) and utility operators to bring solar curtailments to 0%, the electrical grid can become less dependent on fossil-fueled power generation sources. NREL's co-simulation framework contains five major components: Distribution Feeder Model: describes the distribution feeder topology using OpenDSS, including the locations of all DERs. Residential Building Model: simulates a large number of buildings at a high resolution using OCHRETM. The model is equipped to control equipment based on signals from an external module. The model includes major household appliances such as HVAC and a water heater, non-dispatchable load models, a distributed PV system, and a home battery system. Home Energy Management System: optimizes the controls for the devices in a home using foreseeTM. The control can adjust based on the user preferences including cost, comfort, and convenience. In hierarchical control scenarios, where the houses follow signals from an aggregator, the home energy management system provides a flexibility band with a range of power and follows the dispatch signals received from aggregator. Community-Level Aggregator: solves for optimal energy dispatch based on the flexibility bands received from each home and the grid service signal received from the utility controller. Utility-Level Controller: provides grid signals for voltage control using Distributed Energy Resources (DERs), such as solar systems, in the community.

Balamurugan, Sivasathya Pradha↗
DOE OSTI2024

Brick Schema Standardized Plug Load Control Strategies for Load Reduction: Preprint

Plug loads comprise a significant percentage of commercial building energy consumption. Applying intelligent controls to turn off plug loads when unused can provide dynamic load reduction and flexibility, which are key traits of grid-interactive efficient buildings. This capability is important for equitable decarbonization as it can enable disadvantaged communities to electrify buildings without costly upgrades to electrical infrastructure. In this work, we present the effectiveness of various control strategies along with the operational lessons that informed their design. During a three-year period, we operated over 600 smart outlets in 12 university office buildings. The attached plug loads consisted primarily of printers, TVs, water dispensers, and copiers. After recording baseline power measurements for one year, we designed plug load control (PLC) strategies for each plug load type, use, and for different risk tolerance levels because PLC can potentially be disruptive to daily work. We used the Brick Schema to facilitate the management of plug load locations and other metadata. For advanced controls, we integrated the smart plugs with heating, ventilation, and air conditioning (HVAC) systems through the campus building automation system. We found static schedules to be the least disruptive and most predictable for occupants, resulting in 38% and 66% energy savings in two studies. For printers, print server-triggered PLC produced 86% savings, the highest of all strategies with minimal occupant impact. Scheduling of water dispensers and digital signage TVs produced 49% and 70% savings respectively with opportunities to improve performance with the use of HVAC occupancy data.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2020

Development and Implementation of Fault-Correction Algorithms in Fault Detection and Diagnostics Tools

A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.

field testing↗
DOE OSTI2022

Approaches for effective negative pressure isolation space control to minimize airborne transmission of contaminants in residential homes

Controlling airborne transmission of contaminants including respired viruses such as SARS-CoV-2 is necessary to protect occupants living in the same house with a contagious person. The effectiveness of interventions requiring minor efforts that create a negative pressure isolation zone (IZ) for a contagious person has yet to be systematically tested for residential homes. In this study, ASHRAE Standard 170, which offers guidance for negative pressure isolation space control in healthcare facilities, was used in developing practical and attainable recommendations for residential single-family homes. The relative effectiveness of several control strategies was evaluated through experimentally conducting 17 different test cases in a manufactured single-family house laboratory. These cases were designed based on various heating, ventilation and air-conditioning (HVAC) operating scenarios, intervention measures including closing the IZ door and/or sealing over supply and return air grilles in the IZ, and utilization of bathroom exhaust or portable window fans for pressure control. Four out of 17 test cases were identified as having the potential for strong containment with adequate depressurization in the IZ. The most effective IZ depressurization was achieved through continuously operating the exhaust fan in the bathroom attached to the IZ, by installing a portable window fan that extracted air out of the IZ, and a portable room air conditioner with the AC unit exhaust duct installed in one of the IZ windows.

Construction & Building Technology↗
DOE OSTI2020

RL-HEMS: Reinforcement Learning Based Home Energy Management System for HVAC Energy Optimization

Heating, ventilation, and air conditioning (HVAC) is one of the major energy consumers in the residential sector. It is important to be able to monitor and control the energy consumed to provide utility services such as load shaping while satisfying the comfort and economic constraints of the homeowner. The objective of this work is to create the optimal schedule for HVAC operation to reduce the cost while satisfying the home-owner and equipment’s constraints using a model-free Reinforcement Learning (RL)-based optimization. The specific goal is to find the right balance between reducing energy cost, consumption, and customer comfort level. Our research effort addresses this optimization problem using multiple components: the development of initial learning test-bed and implementation of RL techniques on a real home. This will enable the rapid evaluation of the RL techniques and provide an early baseline to train before implementation on site. The RL algorithm is designed to learn the energy use patterns and generate the optimized schedule for HVAC within an acceptable time-interval to satisfy the homeowner’s comfort and minimize the energy usage. Our preliminary results are promising and we have observed a 17% reduction in the total cost and a 15% reduction in the power utilization using our RL-based HVAC model—RL-HEMS.

Kotevska, Olivera↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Thermostat Control for Load Shifting in Large Offices

This documentation focuses on a single end-use savings shape measure - thermostat control for load shifting. The thermostat control for load shifting measure applies heating and cooling temperature setpoint adjustment for pre-conditioning before the peak window. The measure takes daily peak load schedule inputs generated by the method "Dispatch Schedule Generation" described in the "Supplemental Documentation: Dispatch Schedule Generation for Demand Flexibility Measures" to determine the start time of the peak window, and then adjusts the thermostat cooling or heating setpoints by a specified offset value from original schedules to precool or preheat the space , for a specified length of time before the start of peak window, and thus shifts load in the peak window to the pre-conditioning period. The measure is flexible and allows users to adjust the heating and cooling offset values and length of pre-conditioning period, but for this study, the load shifting strategy is applied as pre-cooling only (adjusting only cooling setpoints). The default adjustment for cooling setpoints is set to -1 degrees Celsius , and the default duration of pre-cooling is 1 hour. This measure is applicable to large offices equipped with electric HVAC system, which account for approximately 8.72% of the ComStock floor area. The thermostat control for load shifting measure demonstrates -1% daily peak demand reduction and 0% total site energy savings (0 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

CRADA Number NFE-19-07846 with Grid Fruit, LLC (CRADA Final Report)

Cooperative Research and Development Agreement (CRADA) NFE-19-07846 between Oak Ridge National Laboratory (ORNL) and Grid Fruit, LLC (Grid Fruit) focused on simulating, monitoring, and adjusting controls of commercial refrigerator and freezer systems to provide load flexibility and demand response services from these machines. Viewing these medium and low-temp coolers as thermal masses, the volumes of chilled air in these coolers make them time-shiftable loads ripe for deployment at optimal times. Grid Fruit is a startup company developing methods to schedule the chilling cycles (e.g., in “build load” and “shed load” grid events) to provide energy efficiency, peak shifting, and other benefits both to the grid and the broader environment. These benefits can be magnified by coordinating chillers with HVAC, lights, computers, and other loads. Grid Fruit completed simulations and then selected and ordered controls hardware to prove the technology were successful, but the diversity of chiller models in the field without digital controls, as well as limited market demand at present, make further exploration necessary before commercialization.

42 ENGINEERING↗
DOE OSTI2024

Energy Analysis of Combi Heat Pump System Configurations for Space Conditioning and Domestic Hot Water Heating in Residential Buildings

Combi heat pump systems, also referred to multifunctional variable refrigerant flow heat recovery (MF-VRFHR) systems, are specifically designed for residential applications to manage both space conditioning and domestic hot water (DHW). They have attracted attention due to their potential for energy conservation through heat recovery. The incorporation of a hot water tank introduces various system configurations, each characterized by distinct pros and cons related to energy efficiency, system stability, and maintenance. Despite this, a critical gap exists as the specific energy performance remains unquantified under diverse operational modes (e.g., heating mode and heat recovery mode). This paper aims to bridge this gap by conducting a comprehensive comparative analysis of two prevalent system configurations while considering feasible proposed control logics. Configuration 1 integrates a separate hot water tank and a refrigerant-to-water heat exchanger (HEX), also known as a Hydro Kit while Configuration 2 incorporates a refrigerant-wrapped hot water tank. To facilitate this analysis, we developed high-fidelity system models for both configurations in Modelica, capturing system dynamics and detailed control sequences effectively. These system models were built upon the TIL library for HVAC equipment components and the Buildings library for residential building thermal load calculations. The validation of the simulation testbed utilized data from experiments conducted in the PNNL lab home for Configuration 1. To establish the simulation testbed for Configuration 2, we extended the modeling setup derived from Configuration 1. This extension specifically involved substituting the separate hot water tank and Hydro Kit with a refrigerant-wrapped hot water tank of similar sizing sourced from an actual product. The simulation analysis of heating-only and heat recovery modes reveals that Configuration 2 not only saves energy and maintains warmer tank temperatures but also demonstrates faster water heating capabilities. This is attributed to decreased energy loss and improved heat transfer. The study encompasses a wide range of scenarios, considering diverse thermal loads and water usage patterns across heating and heat recovery modes. Overall, the comprehensive results indicate that Configuration 2 achieves energy savings ranging from 3.5% to 12.2% compared to Configuration 1, depending on factors such as water usage patterns, thermal loads, and operational modes.

Configuration, Comparison, Multi-functional, Resid↗
DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources: Preprint

Integration of a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problem requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (controllable DERs) including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperformed other benchmark controllers regarding computation time without compromising operational performance.

DER↗
DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources

Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (controllable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗
DOE OSTI2022

Empower Wall: Active insulation system leveraging additive manufacturing and model predictive control

Buildings are one of the largest energy consumers worldwide, using large amounts of energy during their construction and for climate control during operation. Active insulation systems (AIS) have been shown to reduce the energy needed for climate control in buildings by dynamically regulating the heat transferred between a building’s interior and exterior. Infrastructure-scale additive manufacturing (AM) has the potential to reduce the resources needed for building construction. Combining these two technologies into a single building envelope would create a path towards more sustainable buildings. A test was conducted for the Federal Energy Management Program (FEMP) Energy Exchange training and trade show, in August 2021, to investigate a new building envelope design, termed the Empower Wall, that utilized an AIS and was constructed using AM. Model predictive control was implemented to manage operation of the Empower Wall in concert with the existing HVAC system. Finally, the prototype system demonstrated that the Empower Wall lowered total energy consumption and reduced the cost of energy used.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources

Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (control-lable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗
DOE OSTI2022

Micro-Environmental Control System (Final Report)

This project developed an innovative micro-environmental control system (µX) that enables office buildings to reduce energy used for heating and cooling by 15% or more. The µX is a compact, quiet, and ergonomic device that is designed to be installed under an office workstation; it is designed to maintain occupant comfort when room thermostat setpoints are incremented by 4°F or more (warmer in the summer and cooler in the winter) to save energy. When ambient room temperatures are outside of the usual comfort range, the µX maintains occupant comfort by delivering personalized cooling or heating locally to each office worker. The µX provides personalized cooling using a micro vapor compression system that includes a new high-performance micro-scroll compressor and a novel thermal storage unit. The vapor compression system operates at night to freeze a phase-change material (PCM). During the workday, the cooling stored in the PCM is released as a cool breeze of air to make occupants more comfortable. The micro-scroll compressor was developed specifically for this application; it is smaller than any of its type, minimizing the amount of power needed. In heating mode, the µX maintains occupant comfort using a foot heating mat with an infrared reflective box. The µX R&D project was conducted by Syracuse University in collaboration with United Technologies Research Center, Air Innovations, Bush Technical, and Cornell University. Over the course of the initial three-year project, the team developed and evaluated four versions of the unit, advancing the concept to Technology Readiness Level 6. The capabilities of individual proof-of-concept prototypes were verified in tests that were conducted with: 1) units in psychrometric chambers, 2) an instrumented manikin in a laboratory, and 3) human subjects in laboratories that simulate office environments. The tests verified that the µX prototypes met or exceeded all performance targets required to enable office buildings to reduce energy used for heating and cooling by 15% or more by maintaining occupant comfort when thermostat setpoints are incremented by 4°F or more.

25 ENERGY STORAGE↗
DOE OSTI2020

Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration

Model predictive control (MPC) has been proven in simulations and pilot case studies to be a superior control strategy for large buildings. MPC can utilize the weather and occupancy schedule forecasts, together with the system model, to predict the future thermal behavior of the building and minimize the overall energy use and maximize thermal comfort. However, these advantages come with the cost of increased modeling effort, computational demands, communication infrastructure, and commissioning efforts. Thus a typical approach is to, often rapidly, simplify the building modeling and MPC optimization problem while paying a price of not reaching the full performance potential. It has been shown that by employing accurate physics-based models, MPC performance can be notably increased closer to its theoretical performance bound. However, implementation of such high-fidelity MPC in real buildings remains a challenge, resulting in a lack of successful field test studies. This work presents the methodology and field test demonstration of a computationally efficient implementation of the white-box MPC in an office building in Belgium. The detailed model of the building is based on first-principle physical equations. The deployment and supervision of MPC operation in a practical setting are supported by an automated cloud-based communication infrastructure. The motivating factor behind the cloud-based architecture is its compatibility with a commercially appealing control as a service concept. The building is equipped with a ground source heat pump (GSHP) and thermally activated building structures (TABS), where the combination of both is also known as GEOTABS. From a control perspective, GEOTABS buildings are particularly challenging systems due to large scale, complex heating, ventilation and air conditioning (HVAC) system, and slow dynamics with time delays. On the other hand, there is an increased potential for energy savings due to the high thermal mass, which acts as thermal storage. The MPC operation is demonstrated during the challenging transient seasons (switching between heating and cooling), and its performance is compared to a traditional rule-based controller (RBC). We provide a proof of concept of real MPC operation for the most difficult seasons with notable GSHP energy use savings equal to 53.5% and thermal comfort improvement by 36.9%. Other MPC applications found in the literature describe tests for only cooling or only heating, and up to now only for a black-box or a grey-box approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Implementation and test of an automated control hunting fault correction algorithm in a fault detection and diagnostics tool

Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Laboratory testing methods to evaluate the reliability of occupancy sensors for commercial building applications

The energy performance of commercial buildings is greatly influenced by occupants which are highly variable and among the most unpredictable components of a building's operation. While most building control systems use fixed, predetermined occupancy schedules, these fixed occupancy levels can be quite different from actual occupancy. This can cause unnecessary energy consumption, particularly from heating, ventilation, and air conditioning (HVAC) and lighting systems which are responsible for approximately 60% of commercial buildings' energy use. The use of occupancy counting sensor systems integrated with building management system controls is one method that can be used to improve the energy-consuming performance of buildings. However, there is no standardized universal methodology and metrics to evaluate their reliability. The aim of this research is to develop a uniform evaluation methodology to assess the reliability of occupancy counting sensor systems in a controlled laboratory environment. The developed testing methodology includes both “typical” scenarios representing the occupancy scenarios of a typical commercial building, and “failure” testing scenarios which represent a range of potential scenarios that may impact a sensor system's reliability. These methods were then implemented in a case study to evaluate the performance of two novel occupancy counting sensor systems (i.e., door-centric, and camera-based). Results suggest that typical testing results can be used to compare the overall performance of the occupancy counting sensor systems; however, failure testing is also important to understand the weaknesses of the sensor system in order to select the suitable one for the intended use of the commercial building. In addition, the proposed methodology includes a modified confusion matrix which enables the ability to identify if failures are caused by over or under counting occupants and to what extent this occurs over the testing period.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Membrane-based Ionic Liquid Absorption System for Ultra-Efficient Dehumidification and Heating

The project team – comprising the University of Florida (UF), GTI Energy, and Modine Manufacturing – sought to develop a highly efficient heat-powered absorption cycle with combined dehumidification and heat pumping. This DOE-supported project advanced the technology from a TRL of 3 to 6, culminating in the development of a 1,000 CFM system. Key innovations implemented in the system addressed the low efficiency, size, cost, and reliability challenges of conventional absorption cycles. Specifically, the system was enabled by: (1) a semi-open cycle that allowed simultaneous dehumidification and heat recovery, (2) non-crystallizing ionic liquids (ILs) that enabled “double-effect” operation at elevated temperatures without the need for costly control equipment, (3) a compact membrane-based absorber that confined the IL and directly cooled it to achieve low dew points, and (4) a novel, highly integrated desorber–condenser assembly that reduced size, weight, and cost. The technology was developed with commercial HVAC applications in mind, particularly for separate sensible and latent cooling (SSLC), an innovation aimed at achieving independent and more efficient humidity control.

42 ENGINEERING↗
DOE OSTI2022

High-Power and Flexible Low-Frequency AC Transmission for Renewable Integration

This project develops a steady-state, dynamic, and production cost models of a multi-terminal, high-power, flexible low-frequency ac (LF-HVac) transmission system with grid-forming converters. Through both numerical analysis and EMTP simulation, the project goal is to demonstrate the feasibility of such an innovative and transformative LF-HVac system to support bulk integration of large-scale, cheap, and steady renewable resources from remote locations such as offshore wind farms to meet recent clean energy goals from DOE. The proposed adoption of low-frequency ac transmission aims to improve power transfer capability for remote large-scale renewable integration. In addition, the control for power electronics converters provides grid-support functions to improve the flexibility and resilience the existing 60-Hz HVac system. Future projects on this topic can be supported by both Energy Efficiency and Renewable Energy (DOEEE) and Office of Electricity Delivery & Energy Reliability (DOEOE) offices.

24 POWER TRANSMISSION AND DISTRIBUTION↗