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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics

Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2021

Extreme Miller cycle with high intake boost for improved efficiency and emissions in heavy-duty diesel engines

This study experimentally investigates the impact of extreme Miller cycle strategies paired with high intake manifold pressures on the combustion process, emissions, and thermal efficiency of heavy-duty diesel engines. Well-controlled experiments isolating the effect of Miller cycle strategies on the combustion process were conducted at constant engine speed and load (1160 rpm, 1.76 MPa net IMEP) on a single cylinder research engine equipped with a fully-flexible hydraulic valve train system. Late intake valve closing (LIVC) timing strategies were compared to a conventional intake valve profile under either constant cylinder composition, constant engine-out NO x emission, or constant overall turbocharger efficiency (η TC ) to investigate the operating constraints that favor Miller cycle operation over the baseline strategy. Furthermore, utilizing high boost with conventional intake valve closing timing resulted in improved fuel consumption at the expense of sharp increases in peak cylinder pressures, engine-out NO x emissions, and reduced exhaust temperatures. Miller cycle without EGR at constant λ demonstrated LIVC strategies effectively reduce engine-out NO x emissions by up to 35%. However, Miller cycle associated with very aggressive LIVC timings led to fuel consumption penalties due to increased pumping work and exhaust enthalpy. LIVC strategies allowed for increased charge dilution at the baseline NO x constraint of 3.2 g/kWh, resulting in significant fuel consumption benefits over the baseline case without compromising exhaust temperatures or peak cylinder pressures. As Miller cycle implementation was shown to affect the boundary conditions dictating η TC , the LIVC and conventional IVC cases were studied at an equivalent η TC point representative of high boost operation. With high boost, LIVC yielded reduced NO x emissions, reduced peak cylinder pressures, and elevated exhaust temperatures compared to the conventional IVC case without compromising fuel consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Evaluating Domestic Hot Water Distribution System Options With Validated Analysis Models

A developing body of work is forming that collects data on domestic hot water consumption, water use behaviors, and energy efficiency of various distribution systems. A full distribution system developed in TRNSYS has been validated using field monitoring data and then exercised in a number of climates to understand climate impact on performance. This study builds upon previous analysis modelling work to evaluate differing distribution systems and the sensitivities of water heating energy and water use efficiency to variations of climate, load, distribution type, insulation and compact plumbing practices. Overall 124 different TRNSYS models were simulated. Of the configurations evaluated, distribution losses account for 13-29% of the total water heating energy use and water use efficiency ranges from 11-22%. The base case, an uninsulated trunk and branch system sees the most improvement in energy consumption by insulating and locating the water heater central to all fixtures. Demand recirculation systems are not projected to provide significant energy savings and in some cases increase energy consumption. Water use is most efficient with demand recirculation systems, followed by the insulated trunk and branch system with a central water heater. Compact plumbing practices and insulation have the most impact on energy consumption (2-6% for insulation and 3-4% per 10 gallons of enclosed volume reduced). The results of this work are useful in informing future development of water heating best practices guides as well as more accurate (and simulation time efficient) distribution models for annual whole house simulation programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Comparing Server Energy Use and Efficiency Using Small Sample Sizes

This report documents a demonstration that compared the energy consumption and efficiency of a limited sample size of server-type IT equipment from different manufacturers by measuring power at the server power supply power cords. The results are specific to the equipment and methods used. However, it is hoped that those responsible for IT equipment selection can used the methods described to choose models that optimize energy use efficiency. The demonstration was conducted in a data center at Lawrence Berkeley National Laboratory in Berkeley, California. It was performed with five servers of similar mechanical and electronic specifications; three from Intel and one each from Dell and Supermicro. Server IT equipment is constructed using commodity components, server manufacturer-designed assemblies, and control systems. Server compute efficiency is constrained by the commodity component specifications and integration requirements. The design freedom, outside of the commodity component constraints, provides room for the manufacturer to offer a product with competitive efficiency that meets market needs at a compelling price. A goal of the demonstration was to compare and quantify the server efficiency for three different brands. The efficiency is defined as the average compute rate (computations per unit of time) divided by the average energy consumption rate. The research team used an industry standard benchmark software package to provide a repeatable software load to obtain the compute rate and provide a variety of power consumption levels. Energy use when the servers were in an idle state (not providing computing work) were also measured. At high server compute loads, all brands, using the same key components (processors and memory), had similar results; therefore, from these results, it could not be concluded that one brand is more efficient than the other brands. The test results show that the power consumption variability caused by the key components as a group is similar to all other components as a group. However, some differences were observed. The Supermicro server used 27 percent more power at idle compared to the other brands. The Intel server had a power supply control feature called cold redundancy, and the data suggest that cold redundancy can provide energy savings at low power levels. Test and evaluation methods that might be used by others having limited resources for IT equipment evaluation are explained in the report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Recovery Act: Advanced Load Identification and Management for Buildings

In response to the U.S. Department of Energy (DoE)’s goal of achieving market ready, net-zero energy residential and commercial buildings by 2020 and 2025, Eaton partnered with the Department of Energy’s National Renewable Energy Laboratory (NREL) and Georgia Institute of Technology to develop an intelligent load identification and management technology enabled by a novel “smart power strip” to provide critical intelligence and information to improve the capability and functionality of building load analysis and building power management systems. Buildings account for 41% of the energy consumption in the United States, significantly more than either transportation or industrial. Within the building sector, plug loads account for a significant portion of energy consumption. Plug load consumes 15-20% of building energy on average. As building managers implement aggressive energy conservation measures, the proportion of plug load energy can increase to as much as 50% of building energy leaving plug loads as the largest remaining single source of energy consumption. This project focused on addressing plug-in load control and management to further improve building energy efficiency accomplished through effective load identification. The execution of the project falls into the following three major aspects; An intelligent load modeling, identification and prediction technology was developed to automatically determine the type, energy consumption, power quality, operation status and performance status of plug-in loads, using electric waveforms at a power outlet level. This project demonstrated the effectiveness of the developed technology through a large set of plug-in loads measurements and testing; A novel “Smart Power Strip (SPS) / Receptacle” prototype was developed to act as a vehicle to demonstrate the feasibility of load identification technology as a low-cost, embedded solution; and Market environment for plug-in load control and management solutions, in particular, advanced power strips (APSs) was studied. The project evaluated the market potential for Smart Power Strips (SPSs) with load identification and the likely impact of a load identification feature on APS adoption and effectiveness. The project also identified other success factors required for widespread APS adoption and market acceptance. Even though the developed technology is applicable for both residential and commercial buildings, this project is focused on effective plug-in load control and management for commercial buildings, accomplished through effective load identification. The project has completed Smart Receptacle (SR) prototype development with integration of Load ID, Control/Management, WiFi communication, and Web Service. Twenty SR units were built, tested, and demonstrated in the Eaton lab; eight SR units were tested in the National Renewable Energy Lab (NREL) for one-month of field testing. Load ID algorithm testing for extended load sets was conducted within the Eaton facility and at local university campuses. This report is to summarize the major achievements, activities, and outcomes under the execution of the project.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Stockbridge Munsee Community Health and Wellness Center and the Mohican Family Center Renewable Energy and Energy Efficiency Feasibility Study

The results of the Renewable Energy and Energy Efficiency Feasibility Study of Stockbridge Munsee Community’s Health and Wellness Center (HWC) indicate that a variety of renewable energy options and energy conservation measures (ECMs) exist for the facility. A requirement of the Request for Proposal for this study was to assess renewable energy options that could offset 30 to 100 percent of the HWC’s energy use. This study identifies that a geothermal system is the most cost effective renewable energy option available to decrease the HWC’s energy consumption by 30 to 100 percent. Currently the HWC performs in the lowest 8 percent of buildings in its building category, as scored in the EPA portfolio manager benchmarking tool. Multiple ECM opportunities have been identified with paybacks of less than five years to yield an estimated 25-percent decrease in annual energy consumption. The ECMs within this payback period are estimated to save $26,800 per year with an implementation cost of just $4,650 (0.2 year payback). For the Mohican Family Center document: The results of the Renewable Energy and Energy Efficiency Feasibility Study of Stockbridge Munsee Community’s Mohican Family Center (MFC) indicate that a variety of renewable energy options and energy conservation measures (ECMs) exist for the facility. A requirement of the Request for Proposal for this study was to assess renewable energy options that could offset 30 to 100 percent of the MFC’s energy use. This study identifies that a geothermal system is the most cost effective renewable energy option available to decrease the MFC’s energy consumption by 30 to 100 percent. Currently the MFC performs better than 80 percent of buildings in its building category, as scored in the EPA portfolio manager benchmarking tool. Multiple ECM opportunities have been identified with short term paybacks to yield an estimated 13-percent decrease in energy consumption. The ECMs within this payback period are estimated to save $3,100 per year with an implementation cost of under $20,000.

09 BIOMASS FUELS↗
DOE OSTI2015

The GREET Model Expansion for Well-to-Wheels Analysis of Heavy-Duty Vehicles

Heavy-duty vehicles (HDVs) account for a significant portion of the U.S. transportation sector’s fuel consumption, greenhouse gas (GHG) emissions, and air pollutant emissions. In our most recent efforts, we expanded the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREETTM) model to include life-cycle analysis of HDVs. In particular, the GREET expansion includes the fuel consumption, GHG emissions, and air pollutant emissions of a variety of conventional (i.e., diesel and/or gasoline) HDV types, including Class 8b combination long-haul freight trucks, Class 8b combination short-haul freight trucks, Class 8b dump trucks, Class 8a refuse trucks, Class 8a transit buses, Class 8a intercity buses, Class 6 school buses, Class 6 single-unit delivery trucks, Class 4 single-unit delivery trucks, and Class 2b heavy-duty pickup trucks and vans. These vehicle types were selected to represent the diversity in the U.S. HDV market, and specific weight classes and body types were chosen on the basis of their fuel consumption using the 2002 Vehicle Inventory and Use Survey (VIUS) database. VIUS was also used to estimate the fuel consumption and payload carried for most of the HDV types. In addition, fuel economy projections from the U.S. Energy Information Administration, transit databases, and the literature were examined. The U.S. Environmental Protection Agency’s latest Motor Vehicle Emission Simulator was employed to generate tailpipe air pollutant emissions of diesel and gasoline HDV types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Design of an Open Smart Energy Gateway for Smart Meter Data Management

With the widespread deployment of electronic interval meters, commonly known as smart meters, came the promise of real-time data on electric energy consumption. Recognizing an opportunity to provide consumers access to their near real-time energy consumption data directly from their installed smart meter, we designed a mechanism for capturing those data for consumer use via an open smart energy gateway (OpenSEG). By design, OpenSEG provides a clearly defined boundary for equipment and data ownership. OpenSEG is an open-source data management platform to enable better data management of smart meter data. Effectively, it is an information architecture designed to work with the ZigBee Smart Energy Profile 1.x (SEP 1.x). It was specifically designed to reduce cyber-security risks and provide secure information directly from smart meters to consumers in near real time, using display devices already owned by the consumers. OpenSEG stores 48 hours of recent consumption data in a circular cache using a format consistent with commonly available archived (not real-time) consumption data such as Green Button, which is based on the Energy Services Provider Interface (ESPI) data standard. It consists of a common XML format for energy usage information and a data exchange protocol to facilitate automated data transfer upon utility customer authorization. Included in the design is an application program interface by which users can acquire data from OpenSEG for further post processing. A sample data display application is included in the initial software product. The data display application demonstrates that OpenSEG can help electricity use data to be retrieved from a smart meter and ported to a wide variety of user-owned devices such as cell phones or a user-selected database. This system can be used for homes, multi-family buildings, or small commercial buildings in California.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Field Demonstration of Ground-Source Integrated Heat Pump - Final Report

Reducing energy consumption in buildings is key to reducing or limiting the negative environmental impacts from the building sector. According to the United States (U.S.) Energy Information Administration (EIA), in 2013, commercial buildings consumed 18.1 quads of primary energy, which was 18.6% of the total U.S. primary energy consumption. The primary energy consumption in the commercial sector is projected to increase by 2.8 quads from 2013 to 2040, the second largest increase after the industrial sector. Further space heating, space cooling, and ventilation (HVAC) services accounted for 31% of the energy consumption in commercial buildings. The technical objective of this project is to demonstrate the capability of the new GS-IHP system to reduce overall energy use for space heating, space cooling, and water heating by at least 45% vs. a conventional electric RTU and electric WH in a light commercial building application. This project supports the DOE-Building Technologies Office (BTO) goals of reducing HVAC energy use by 20% and water heating by 60% by 2030.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Generation and Use of Thermal Energy in the U.S. Industrial Sector and Opportunities to Reduce its Carbon Emissions

The industrial sector was the third-largest source of direct U.S. greenhouse gas (GHG) emissions in 2014 behind electricity generation and transportation and accounted for roughly 20% of total emissions (EPA 2016). The Energy Information Administration (EIA) projects that total U.S. energy consumption will grow to about 108 exajoules (1 EJ = 10 18 J) or 102 quads (1 quad = 10 15 British thermal units) in 2025, with nearly all of the growth coming from the industrial sector (DOE 2015b). Energy consumption in the industrial sector is forecast to increase to 39.5 EJ (37.4 quads)—a 22% increase, exceeding 36% of total energy consumption in the United States. Therefore, it is imperative that industrial GHG emissions be considered in any strategy intent on achieving deep decarbonization of the energy sector as a whole. It is important to note that unlike the transportation sector and electrical grid, energy use by industry often involves direct conversion of primary energy sources to thermal and electrical energy at the point of consumption. About 52% of U.S. industrial direct GHG emissions are the result of fuel combustion (EPA 2016) to produce hot gases and steam for process heating, process reactions, and process evaporation, concentration, and drying. The heterogeneity and variations in scale of U.S. industry and the complexity of modern industrial firms’ global supply chains are among the sector’s unique challenges to minimizing its GHG emissions. A combination of varied strategies—such as energy efficiency, material efficiency, and switching to low-carbon fuels—can help reduce absolute industrial GHG emissions. This report provides a complement to process-efficiency improvement to consider how clean energy delivery and use by industry could reduce GHG emissions. Specifically, it considers the possibility of replacing fossil-fuel combustion in industry with nuclear (specifically small modular reactors [SMRs]), solar thermal (referred to herein as solar industrial process heat [SIPH]), and geothermal energy sources. The possibility of applying electrical heating and greater use of hydrogen is also considered, although these opportunities are not discussed in as much detail.

08 HYDROGEN↗
DOE OSTI2016

United States Data Center Energy Usage Report

This report estimates historical data center electricity consumption back to 2000, relying on previous studies and historical shipment data, and forecasts consumption out to 2020 based on new trends and the most recent data available. Figure ES-1 provides an estimate of total U.S. data center electricity use (servers, storage, network equipment, and infrastructure) from 2000-2020. In 2014, data centers in the U.S. consumed an estimated 70 billion kWh, representing about 1.8% of total U.S. electricity consumption. Current study results show data center electricity consumption increased by about 4% from 2010-2014, a large shift from the 24% percent increase estimated from 2005-2010 and the nearly 90% increase estimated from 2000-2005. Energy use is expected to continue slightly increasing in the near future, increasing 4% from 2014-2020, the same rate as the past five years. Based on current trend estimates, U.S. data centers are projected to consume approximately 73 billion kWh in 2020.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

The Building ENergy Demand (BEND) Model: A Comprehensive Composite Building Energy Modeling Framework

The Building ENergy Demand (BEND) model was developed at Pacific Northwest National Laboratory (PNNL) to simulate climate-dependent hourly building energy demand with the ability to disaggregate down to or aggregate up to any geographic area of interest, including counties, states, electric utility service territories, and census regions. The BEND modeling framework is designed to study the effects of climate change, economics, technology advancement, and various mitigation policies on aggregate building energy demand. To make the process computationally feasible, BEND reduces granularity in building characteristics and weather forcing while maintaining enough detail to minimize systematic differences (biases) between BEND results and data from the Energy Information Administration (EIA). Based on the Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS) databases, BEND establishes a set of representative buildings with predefined sets of building types, sizes, and vintages. These representative buildings are defined for each of the four U.S. census regions and the five climate zones used by the EIA for building surveys (hereafter “EIA climate zones”). BEND’s representative buildings are based on a statistical analysis of the CBECS and RECS databases that infers detailed parameters such as construction type, thermal properties, internal loads, equipment and mechanical systems, and operation schedules from the high-level descriptors in those databases (building type, vintage, size, and location). The representative buildings are simplified and consolidated into a set of prototype buildings for the purpose of simulating building energy demand. Combining the prototype buildings with weather files from selected locations and years, BEND creates a set of simulation-ready building models to be run using the EnergyPlus simulation engine (EnergyPlus). The base spatial unit of each BEND simulation is a subregion that is the intersection of an International Energy Conservation Code climate zone, a census region, and an EIA climate zone; analysis for each spatial unit is influenced by the weather file BEND selects to represent the climate-similar area. The major simulation outputs of interest to BEND are hourly energy consumption values for each simulated year, possibly for multiple fuel types. Based on building-stock weights derived from the CBECS/RECS data, the hourly output streams are aggregated to a single, 8760-hour load profile for each of the major building categories—residential and commercial—with the possibility of segregating by fuel types. To correct for biases inherent in the use of a limited suite of representative buildings, BEND is calibrated against observational targets of total annual building energy consumption and hourly load profiles (including peak demand) obtained for the decision-relevant geographic scales of interest (e.g., total electric load data for individual balancing authorities available from EIA). Based on available data such as population distribution, BEND is able to aggregate both up and down scales, allowing the study of building energy demand over a range of spatial scales (e.g., states or counties within the Western Electricity Coordinating Council, Eastern Interconnection, or the entire U.S.) and/or subsets of building types (e.g., office buildings with particular size ranges).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Energy Optimization of Light and Heavy-Duty Vehicle Cohorts of Mixed Connectivity, Automation and Propulsion System Capabilities via Meshed V2V-V2I and Expanded Data Sharing (Final Scientific and Technical Report)

Vehicle connectivity and automated driving technologies individually have the potential to decrease energy consumption and/or increase safety on light, medium or heavy duty vehicles to varying degrees depending on the traffic infrastructure and specific driving scenarios. Due to advances in sensing, perception and computing power, research and development emphasis in the mobility sector has shifted away from connectivity. Prior research has shown that driving automation with the absence of connectivity can in certain circumstances increase energy consumption. The effectiveness of synergizing connectivity and driving automation technologies is the focus of this work, specifically applied to vehicle cohorts of mixed composition, light and heavy duty, and powertrains ranging from all electric to conventional internal combustion engine. The project team is led by Michigan Technological University (MTU) and partnered with AVL Mobility Technologies Inc. (AVL), Borg Warner (BW), Traffic Technology Services (TTS), American Center for Mobility (ACM) and Navistar (NAV). The main thrusts for the team are to develop a micro-traffic simulation environment with specific VD&PT system attributes and CAV capabilities, 2) field a vehicle test fleet of mixed classification, propulsion and CAV capacity, 3) develop artificial intelligence (AI) and machine learning (ML) based multi-agent optimization methods for various traffic infrastructures, 4) integrate the virtual environment and the optimization methods then deploy the system as a CAV hardware in the loop (HiL) for the vehicle test fleet and 5) conduct closed track and public road testing to validate simulation and demonstrated energy and mobility improvements at multiple scales. For a cohort of mixed vehicles, the team will demonstrate a reduction of energy consumption of 10-50% at intersection, arterial roadway and limited access highway scenarios through connectivity and automation in simulation and at a closed test track. The energy reduction objectives of the project are summarized in Table 1, indicating the infrastructure and over what distances are relevant considered. Single scenario energy reductions are not relevant and thus, the research team took the approach to vary parameters associated with the infrastructure, vehicle cohort composition and dynamic behavior to generate energy consumption distributions for both unconnected and connected scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Hardware Specification and Reference Design for the Low-Cost, Interoperable, User-Centric, Supervisory Controller Kit for Small and Medium Size Commercial Buildings

Commercial buildings are responsible for approximately 20 percent of the total United States energy consumption and greenhouse gas emissions. Over 85 percent of these buildings lack building automation systems to manage the various building systems they have. Many of these buildings are small (< 50,000 square feet), underserved, and use rooftop units (RTUs) for heating, ventilation, and air-conditioning needs. Because these buildings lack proper control systems, they have several operational deficiencies that lead to excess energy consumption. Studies have shown that managing the RTU’s heating and cooling setpoints, schedules, setbacks, and optimal start times can result in 20 to 25 percent reduction in electricity consumption in small and medium commercial buildings (SMBs). In addition, improving the demand flexibility of these buildings will result in additional cost savings for the building owner. To address the needs of the SMBs, the Department of Energy’s Building Technologies Office jointly funded Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) to design, develop, test, and validate a low-cost, interoperable, user-centric, retrofit supervisory controller kit (SC-SMB) that can be used to continuously optimize energy consumption and deliver demand flexibility of SMBs, including all-electric buildings, and provide a means for maximizing decarbonization benefits. The team also includes industry partners Edo and Intellimation. Previously, the team drafted an SC-SMB system specification document (Goodman et al. 2023). This document describes a reference design of a low-cost supervisory control system for SMBs, including the various hardware components, software system, and an example implementation of the reference design. The final release of the reference design is planned for December 2025. Section 2.0 of the report documents the relevant building types that the SC-SMB system is suitable for. Section 3.0 documents the various hardware components, their function, cost, whether they are off-the-shelve, support standard communication, etc., and the software system. An example deployment in a 10,000-sf building with six rooftop units, a hot water heater, solar, and storage is described in Section 4.0. The planned next steps are described in Section 5.0, and references are listed in Section 6.0.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Design Requirements and Software Specification for the Autonomous Energy Management Software System for Small Commercial Buildings

Commercial buildings are responsible for approximately 20 percent of the total United States energy consumption and greenhouse gas emissions. Over 85 percent of these buildings lack building automation systems. Many of these buildings are small (<50,000 square feet), underserved, and use rooftop units for heating, ventilation, and air-conditioning needs. Because these buildings lack proper energy management systems, they have several operational deficiencies that lead to excess energy consumption. Studies have shown that managing the rooftop units heating and cooling set points, schedules, setbacks, and optimal start can result in 20 to 25 percent reduction in electricity consumption in small commercial buildings. In addition, improving demand flexibility of these buildings will result additional cost savings for the building owner. Therefore, the Department of Energy’s Building Technologies Office approved a project to address the needs for small commercial buildings. The project is led by Pacific Northwest National Laboratory (PNNL) with Intellimation LLC as the cooperative research and development agreement partner. The primary goal of the project is to develop and validate an autonomous energy management software (AEMS) system that will continuously optimize small commercial building operations by minimizing energy consumption and cost, while providing a solution for maximizing decarbonization benefits from electrification of buildings. The work will leverage the vast experience of PNNL research and development staff who have over two decades of experience in developing and successfully transferring software technologies to the private sector. This solution will be jointly developed with Intellimation, a company that plans to use it to scale their building energy efficiency (EE) and grid services offering. Widespread deployment of the AEMS system will improve the EE and demand flexibility of the building commercial building stock. It should also support cities and states in meeting their climate change mitigation goals. This document describes the various EE and grid service features of the AEMS system, infrastructure and data required to implement those features, and how the features should be automated. It also details how the various features will be tested and validated, including field validation. The document also details what flexibility the users have and how they will be able to leverage those capabilities exercise those. The intent is to create an AEMS system that would support scalable deployment, requires minimal configuration, and is easy to maintain over its expected lifespan. The initial alpha release of AEMS system is planned for March 2023, and the beta release is planned for the summer of 2023. The final release is planned for March 2024. Section 2 of the report documents the relevant building types that AEMS is suitable for. Section 3 documents EE features that will be supported. It will also include the data requirements, hardware requirements, implementation details, and how EE features will be tested and validated. Grid service features will be documented in section 4, including data requirements, hardware requirements, implementation details, and how the services will be tested and validated. Planned next steps are described in section 5.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building

Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total energy usage. As a result, it is essential to explore ways to conserve energy and improve HVAC system efficiency. One such solution is the use of economizer controls, which can reduce cooling energy consumption by using the free-cooling effect. However, there are various types of economizer controls available, and their effectiveness may vary depending on the specific climate conditions. To investigate the cooling energy-saving potential of economizer controls, this study employs a dry-bulb temperature-based economizer control approach. The dry-bulb temperature-based control strategy uses the outdoor air temperature as an indicator of whether free cooling can be used instead of mechanical cooling. This study also introduces an artificial neural network (ANN) prediction model to optimize the control of the HVAC system, which can lead to additional cooling energy savings. To develop the ANN prediction model, the EnergyPlus program is used for simulation modeling, and the Python programming language is employed for model development. The results show that implementing a temperature-based economizer control strategy can lead to a reduction of 7.6% in annual cooling energy consumption. Moreover, by employing an ANN-based optimal control of discharge air temperature in air-handling units, an additional 22.1% of cooling energy savings can be achieved. In conclusion, the findings of this study demonstrate that the implementation of economizer controls, especially the dry-bulb temperature-based approach, can be an effective strategy for reducing cooling energy consumption in HVAC systems. Additionally, using ANN prediction models to optimize HVAC system controls can further increase energy savings, resulting in improved energy efficiency and reduced operating costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Operational Performance Characterization of a Heat Pump System Utilizing Recycled Water as Heat Sink and Heat Source in a Cool and Dry Climate

The wastewater leaving from homes and businesses contains abundant low-grade energy, which can be utilized through heat pump technology to heat and cool buildings. Although the energy in the wastewater has been successfully utilized to condition buildings in other countries, it is barely utilized in the United States, until recently. In 2013, the Denver Museum of Nature & Science at Denver, the United States implemented a unique heat pump system that utilizes recycled wastewater from a municipal water system to cool and heat its 13,000 m 2 new addition. This recycled water heat pump (RWHP) system uses seven 105 kW (cooling capacity) modular water-to-water heat pumps (WWHPs). Each WWHP uses R-410A refrigerant, has two compressors, and can independently provide either 52 °C hot water (HW) or 7 °C chilled water (CHW) to the building. This paper presents performance characterization results of this RWHP system based on the measured data from December 2014 through August 2015. The annual energy consumption of the RWHP system was also calculated and compared with that of a baseline Heating, Ventilation, and Air Conditioning (HVAC) system which meets the minimum energy efficiencies that are allowed by American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 90.1-2013. The performance analysis results indicate that recycled water temperatures were favorable for effective operation of heat pumps. As a result, on an annual basis, the RWHP system avoided 50% of source energy consumption (resulting from reduction in natural gas consumption although electricity consumption was increased slightly), reduced CO 2 emissions by 41%, and saved 34% in energy costs as compared with the baseline system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Battery Electric Vehicle Eco-Cooperative Adaptive Cruise Control in the Vicinity of Signalized Intersections

This study develops a connected eco-driving controller for battery electric vehicles (BEVs), the BEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The developed controller can assist BEVs while traversing signalized intersections with minimal energy consumption. The calculation of the optimal vehicle trajectory is formulated as an optimization problem under the constraints of (1) vehicle acceleration/deceleration behavior, defined by a vehicle dynamics model; (2) vehicle energy consumption behavior, defined by a BEV energy consumption model; and (3) the relationship between vehicle speed, location, and signal timing, defined by vehicle characteristics and signal phase and timing (SPaT) data shared under a connected vehicle environment. The optimal speed trajectory is computed in real-time by the proposed BEV eco-CACC-I controller, so that a BEV can follow the optimal speed while negotiating a signalized intersection. The proposed BEV controller was tested in a case study to investigate its performance under various speed limits, roadway grades, and signal timings. In addition, a comparison of the optimal speed trajectories for BEVs and internal combustion engine vehicles (ICEVs) was conducted to investigate the impact of vehicle engine types on eco-driving solutions. Lastly, the proposed controller was implemented in microscopic traffic simulation software to test its networkwide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed controller can effectively reduce stop-and-go traffic in the vicinity of signalized intersections and that the BEV Eco-CACC-I controller produces average savings of 9.3% in energy consumption and 3.9% in vehicle delays.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗