SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
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This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
With the transition from our traditional electric grid to a cleaner grid with renewable power generation, there is a need to enable building loads to be flexible. Load shedding and shifting will be essential for flattening the “Duck” for decarbonization. This paper explored the trend in the timing of DR events as a reflection of the grid’s needs using recent four years of event data from 203 retail stores in 11 states. The events are becoming significantly shorter with 2-hour duration being the most popular; shifting to late afternoon and early evening is another trend beyond California. Benchmarking will be essential for accounting DF as a reliable grid resource. This paper addresses a challenging aspect of benchmarking – inaccuracies in counterfactual baseline methods can introduce significant DF metrics variations in addition to weather and building characteristics related factors. The conventional “10/10” with adjustment baseline method has inherent limitation by design for load shifting applications. Therefore, it is imperative to identify alternative methods. This study compared three hourly regression baseline methods with “10/10” methods using two groups of commercial buildings that participated in DR programs: (1) 121 big-box retail stores, and (2) 11 office buildings in CA. The 14-day hourly outdoor temperature regression method was found to produce least error in the tested datasets and is promising for load shifting. The paper also pointed out that commissioning issues can also be a significant barrier for achieving consistent DF performance, which building managers and utilities should be aware of.
The purpose of this project is to examine the energy impact of urban-scale traffic for the Los Angeles Basin by developing and implementing a scalable traffic assignment model. An energy optimization function will be posed and when integrated into the optimization code for travel assignment it can be mathematically proven to converge. The energy optimization function can then be compared to the typical travel time optimization that is traditionally used in traffic assignment models. The analysis will begin with static traffic assignment models with the routing for all origin and destinations computed in parallel on high performance computing facilities. Convergence of the numerical methods rely on the solution of convex programs (or extensions of these). This step will mostly consist of demonstrating the ability to parallelize the Frank Wolfe algorithm on various platforms.
As part of the Department of Energy response to the novel coronavirus pandemic of 2020, a modeling effort was sponsored by the DOE Office of Science. One task of this modeling effort at Sandia was to develop a model to predict medical resource needs given various patient arrival scenarios. Resources needed include personnel resources (nurses, ICU nurses, physicians, respiratory therapists), fixed resources (regular or ICU beds and ventilators), and consumable resources (masks, gowns, gloves, face shields, sedatives). This report documents the uncertainty analysis that was performed on the resource model. The uncertainty analysis involved sampling 26 input parameters to the model. The sampling was performed conditional on the patient arrival streams that also were inputs to the model. These patient arrival streams were derived from various epidemiology models and had a significant effect on the projected resource needs. In this report, we document the sampling approach, the parameter ranges used, and the computational workflow necessary to perform large-scale uncertainty studies for every county and state in the United States.
This project is aimed to design and modify the internal surfaces of porous composite cathode from currently commercially viable Solid Oxide Fuel Cells (SOFCs), using additive manufacturing process of Atomic Layer Deposition (ALD). The material systems being investigated are commercial composite electrodes complex three-dimensional topographies. In term of the chemistry of the ALD layer applied on the internal surface of the porous cathode, this project has employed commercially relevant electrolyte, electrocatalyst and noble metal materials set. Such materials are fully compatible with the commercial fuel cells, and this project has developed special nanostructure on the surface of the commercial composite cathodes. The formation of the designed nanoarchitecture on the surface of SOFC cathode has been achieved through precise control of ALD parameters and their effect on overall cell performance and resultant electrochemical reaction mechanism of cathodes has been investigated through full cell electrochemical performance testing and nanostructure characterization by transmission electron microscopy (TEM). Under the support of this award, following has been achieved: (1). For cathode materials in solid oxide fuel cells (SOFCs), such as perovskite mixed conductor La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-x (LSCF), cation surface segregation and consequently losing conductivity and active sites for the oxygen reduction reaction (ORR) are problematic. To mitigate the cation segregation and enhance SOFC durability, further decorating the internal backbone surface using the desired electrocatalysts could be a promoting approach. Commonly, the cation segregation such as Sr is very volatile, so the effective surface decoration is ideally conformal. Nevertheless, the conformal surface coating would inevitably alter the ORR pathways that initially take place on the surface of the backbone. To reveal the impact of the conformal coating on both the catalytic activity and the conductivity of the cathode, the unary electrocatalyst of Pt or CoO x , was applied to the LSCF/SDC composite electrode of inherently functional SOFCs, respectively. Both ALD coating layers evolve strong interaction with the LSCF composite cathode. Upon operations, the Pt coating layer remains conformal on LSCF grain surfaces but turns into discrete particles on SDC grain surfaces. Meanwhile, CoO x conformal coating grows to be the discrete nanograins on both the LSCF and SDC grains. ALD coating of the cathode alone reduces the ohmic resistance up to 28 % for the entire cells. The increased conductivity induced by the ALD coating of Pt or CoO x is ascribed to different mechanisms. For the inherent functional SOFCs, the present study presents a novel and feasible approach to apply a conformal, dense coating layer on the surface of a mixed conductor, simultaneously increasing the conductivity and durability of the SOFC cathode. (2). High resistance of the oxygen electrode still significantly hinders the state-of-the-art Solid Oxide Fuel Cells (SOFCs). In particular, for an oxygen electrode consisting of mixed electronic and ionic conductors, such as perovskite lanthanum strontium cobalt ferrite (LSCF), it deteriorates due to its low chemical stability of the grain surface. Such degradation is often associated with the segregation of cations. To prevent the cation surface segregation and its resultant perovskite phase decomposition, we demonstrate a conformal ultra-thin (7-10 nm) surface heterogeneous coating layer consisting of subjacent discrete Pt nanoparticles capped with a superjacent fully dense conformal CoO x layer. The performance studies indicate the ALD coating reduces the cell series resistance by up to 40 %. The conformal CoO x layer consists of randomly orientated but single-layered nanograins, with high-density intergranular and surface grain boundaries serving as the electrochemical reaction sites and facilitating mass transport. The conformal coating layer appears to have successfully suppressed the Sr outward diffusion and confined the Sr enriched layer to a ~ 2 nm interface perovskite phase between the coating layer and the LSCF grain surface. Moreover, this ultra-thin Sr enriched perovskite layer presumably possesses high oxygen vacancy and high ionic conductivity and further imposes tensile strain to the LSCF grain surfaces. With the combination of a conformal CoO x nanoionics, Sr enriched layer, and its strained interface, the ALD coating induced surface layer is estimated to have a conductivity of ~ 1.27x10 4 S/cm, which is over two orders magnitude of that from LSCF at 750 ºC.
Predictive modeling of watershed to river basin scale biogeochemistry through the use of AI techniques and AI-derived model components and the use of AI to design a prediction system comprised of a hierarchy of multi-fidelity models, including AI driven model/component/parameterization selection.
The opaque envelope - the barrier that helps maintain comfortable indoor conditions regardless of prevailing outdoor conditions - is the single largest contributor to primary energy use in residential and commercial buildings. Residential and commercial buildings comprise 39% of total U.S. primary energy use [1]. The opaque envelope affects 25% of building energy use, or 10% of total U.S. primary energy use [2]. High-performance opaque envelope technologies, therefore, have substantial potential to reduce energy use in both new construction and existing buildings. Retrofits are particularly crucial to realizing the energy savings potential of the opaque envelope because nearly 85% of residential and 55% of commercial buildings that exist today will still exist in 2050 [1]. Building envelope performance is also important for occupant comfort, productivity, health, and well-being. Improving the energy performance of the opaque envelope in U.S. buildings is critical to achieving aggressive cli- mate goals that support a clean energy economy and an equitable energy future for all Americans. By reducing total building energy use and improving energy efficiency, envelope research and development (R&D) will have a significant impact on how low-carbon buildings can mitigate climate change.
INTRODUCTION: This Report summarizes the research and development project performed by Sunvapor and the subrecipient National Renewable Energy Laboratory (NREL) during the period of 10/01/2018-9/30/2021. The work was aimed at the economic integration of solar steam with an industrial process, and in particular, the advantages that a novel type of thermal energy storage and collector design could bring to the system. The completed work includes experimental research on the storage material, design of the collector, engineering of a complete solar steam plant, and economic analysis. The plant was, in the end, not constructed, due to COVID-19 impacts to the project host. PURPOSE: The purpose of the project was to test the hypothesis that a levelized cost of heat (LCOH) of 2¢/kWh delivered to a steam-consuming process, including energy discharged from storage, could be feasibly achieved. PROJECT OBJECTIVES: For Budget Period 1 our objectives were to engineer a prototype industrial solar steam generation system, and design and test a Latent Heat Energy Storage (LHES) lab-scale system. For Budget Period 2 our objectives were to construct and test the solar steam prototype plant, and to build and factory-test a full LHES module. For Budget Period 3 our objective was to integrate the storage module with the solar steam generator, show successful long duration energy tests of the combined system, and use the results to project the LCOH of delivered steam to a second-generation plant. PROJECT OUTCOMES: Various phase change materials (PCMs) for the LHES were tested for compatibility with industrial steam uses as measured by phase change temperature, stability of latent heat of fusion, and their corrosive effects on steel (the heat exchanger material). Two of these materials (sodium formate and sodium/ potassium) showed potential to meet the technical criteria. The estimated cost of a complete LHES module with these materials did not meet economic targets. The developmental collector design builds from previous work to exploit the potential cost advantages of a spaceframe of lumber construction. Design improvements were achieved in assembly efficiency and structural performance. A complete engineering package was completed for the solar steam system without storage that included the developmental as well as commercial arrays. The plant was permitted for construction. The storage CAPEX for a reference plant was estimate to be greater than $\$ 57$/kWh. The LCOH of the system without storage and with developmental collectors of lumber construction was projected to be 2¢/kWh, assuming a FCR of 8.2% over thirty years. PROJECT MILESTONES: The Milestones fully achieved in the first Budget Period includes obtaining a Letter of Intent from an industrial steam-consuming project host, issuance of a construction permit, and corrosion resistance. The Milestone that was partly achieved was the degradation in the latent heat of fusion. Milestones associated with Budget Periods 2 and 3, related to the construction and operation of the plant were not achieved as a result of the host shutting down the plant due to COVID-19 impacts. CONCLUSIONS: Sunvapor was able to secure an agreement with a host to build a fully engineered and permitted solar steam plant that met the host’s economic goals. As a basis for comparison, the solar field design was comprised of eight commercial collector arrays and one developmental Green Parabolic Trough Collector (GPTC) array. The projected LCOH with a solar field entirely populated with a second generation GPTC met the goal of 2¢/kWh. The experimental program concerning the PCM indicated the feasibility of meeting their technical requirements for industrial steam uses. The cost of the complete LHES exceeded its $\$ 14$/kWh target, and therefore the inclusion of LHES could not be economically justified. Due to COVID-19 impacts on the host, the solar steam plant was not constructed.
Abstract not provided.
With increased penetration of subsidized variable renewable energy (VRE) resources and competition from low natural gas prices, existing light water reactor (LWR) nuclear power plants (NPPs) are struggling to remain economically competitive. This work examines the potential economic competitiveness of various thermal energy storage (TES) technologies when coupled directly or indirectly with a NPP. To highlight their relative economic competitiveness, we contrast several energy storage solutions in stochastic dispatch optimization. We leverage data from recent work analyzing a range of TES technologies with varying capital costs, performance, and technology readiness level (TRL) to establish our case. We explore inserting these technologies into an electricity market with existing nuclear generation and large projected variable renewable energy (VRE) penetration. Although these technologies' projected capital costs may make them unlikely candidates in their current state, this analysis demonstrates a high-fidelity techno-economic analysis of energy storage. Furthermore, as the projected cost of energy storage technologies evolves, this analysis sets a precedent for similar future investigations. One region with projected trends that may be unfavorable for existing nuclear capacity is the New York Independent System Operator (NYISO) market. New York state’s baseload generation has been historically provided by fossil-fired and nuclear assets. However, amid economic pressures from subsidized VREs and low natural gas prices, the state has recently deactivated Indian Point nuclear power plant units 2 and 3. Furthermore, the state plans to meet its zero-emission generation target by 2040 by replacing fossil-fired capacity with significant investments in VRE resources like wind and solar photovoltaic (PV) and battery storage. Increased intermittent resource penetration lowers the baseload power requirement, adding further economic pressure to the state’s three remaining NPPs still in operation. With three NPPs still in operation in New York, this work analyzes potential economic benefits to NPPs on the New York grid when directly or indirectly coupled with various TES technologies. This work requires two modeling steps to analyze the potential economic benefits of various system configurations of the TES directly or indirectly coupled with nuclear. First, this analysis leverages capacity expansion modeling by experts at the Electric Power Research Institute (EPRI). Using their deterministic capacity expansion model, U.S. Regional Economy, Greenhouse Gas, and Energy (US-REGEN), EPRI analysts evaluated the capacity and generation evolution of the New York state energy market under four projection scenarios. These four projection scenarios were developed to represent the potential evolution of the capacity and generation in NYISO from 2015 to 2050 under various economic, technology, and policy constraints. The results from these capacity expansion models are then used as boundary conditions in the second modeling step. The second modeling step uses the Holistic Energy Resource Optimization Network (HERON) for a set of stochastic techno-economic analyses (STEAs) to investigate the potential increase in the economic viability of various configurations of the TES. With no current capacity expansion capabilities, HERON takes the data generated from US-REGEN for 2050 to generate synthetic load, solar, and wind data. Then HERON economically optimizes the capacity and dispatch of the various TES configurations. The potential economic benefit is the differential net present value (NPV) of the TES configurations from the no-TES baseline. As a stochastic techno-economic analysis package, HERON introduces uncertainty into the economic metrics, while US-REGEN trades resolution for reduced computational complexity. Using HERON also allows the modeling of direct thermal coupling, a feature not common in capacity and dispatch models. As expected, with high capital costs, the costs of introducing energy storage for all the technologies considered outweighed the potential economic benefit of this strategy for flexible plant operation. The benefit of this analysis is primarily in demonstrating a workflow that examines innovative solutions to increase NPP revenue via TES coupling. HERON’s stochastic capacity and dispatch optimization process used in this work has proven an effective tool in observing and evaluating the impact of introducing storage technologies in a grid energy system.
This research project set the groundwork for higher technology readiness level implementations of catalytic OCM pretreatment for NG engines. Future work to use OCM as a fuel pretreatment strategy for NG engines should consider catalyst durability and performance over a higher working temp range. Work should also develop robust and practical reactors that can be efficiently thermally integrated with a NG engine.
With the support of DOE's Vehicle Technologies Office and the DOE/DOT Joint Office (JO), NREL has applied the EVI-X modeling suite to conduct a National Electric Vehicle Infrastructure Needs Assessment. This report considers a 2030 scenario in which 50% of light-duty sales are electric (including plug-in hybrids), resulting in an on-road stock of 33 million vehicles. We consider the needs of vehicles used for typical daily driving, drivers without access to residential charging, corridor charging supporting long-distance travel, and ride-hailing electrification. We find that a cumulative capital investment of $82 billion in public and private charging infrastructure will be necessary in our baseline scenario (approximately 3x greater than our estimate of planned investments to date). This result is framed as a conservative estimate as the assumed costs include charging equipment and installation but exclude the cost of grid upgrades and distributed energy resources.
With the support of DOE's Vehicle Technologies Office and the DOE/DOT Joint Office (JO), NREL has applied the EVI-X modeling suite to conduct a National Electric Vehicle Infrastructure Needs Assessment. This report considers a 2030 scenario in which 50% of light-duty sales are electric (including plug-in hybrids), resulting in an on-road stock of 33 million vehicles. We consider the needs of vehicles used for typical daily driving, drivers without access to residential charging, corridor charging supporting long-distance travel, and ride-hailing electrification. We find that a cumulative capital investment of $82 billion in public and private charging infrastructure will be necessary in our baseline scenario (approximately 3x greater than our estimate of planned investments to date). This result is framed as a conservative estimate as the assumed costs include charging equipment and installation but exclude the cost of grid upgrades and distributed energy resources.
Abstract not provided.