Estimating ASHRAE Guideline 36 energy savings for multi-zone variable air volume systems using Spawn of EnergyPlus
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The work described in this report is responsive to the Office of Fossil Energy program ‘Energy Storage for Fossil Power Generation.’ This Phase I report has been prepared by Pintail Power LLC, with support from Nexant ECA, Electric Power Research Institute (EPRI) and Southern Company Services as a deliverable for the U.S. Department of Energy for NETL Award DE-FE-00320016. The Liquid Salt Combined Cycle™ (LSCC™) technology provides large-scale energy storage integrated with Fossil Electric Generating Units (FEGUs) to meet critical needs in the energy transition by providing: • the lowest cost large-scale storage for time-shifting of renewable energy, • superior fuel efficiency to reduce GHGs from dispatchable resources, • flexible capacity and ramping to balance variability of wind and solar resources, • essential grid stability services to assure reliability of a low-carbon grid. The LSCC approach: • employs equipment that has already been proven in utility service, • uses safe, non-toxic, non-degrading, perpetual-life storage medium, • leverages and repurposes existing FEGU assets, • expands the value stack of energy storage to reduce market, financing, and commodity risks. Pintail Power has developed the LSCC technology to meet the need for reliable, efficient, and cost-effective integration of Variable Renewable Energy (VRE) into a low-carbon electric grid by coupling proven thermal energy storage with proven gas turbines, steam turbines, and heat transfer equipment. This novel approach is intended to address the key issues facing the grid and operators of renewable and fossil generating units including: • Overgeneration and curtailment of renewables, • Need for fast ramping dispatchable resources, • Improved efficiency and flexibility of fossil units, • Additional peaking capacity to support electrification of transportation and heating, • Provision of reliability services to support high penetration of VRE, especially synchronous inertia and fast frequency response. A Technology Readiness assessment by EPRI confirmed that LSCC technology consists of commercially proven hardware used in industrial and utility applications. Although the novel LSCC approach has not yet been demonstrated as a complete system, interfaces between major components have been conservatively specified. A Phase III pilot is planned to demonstrate equipment integration and operation. The patented innovation is removal of the evaporator section from the exhaust heat recovery system, with the evaporation performed by stored energy in a separate steam generator. This arrangement couples renewable and fossil power generation via long-duration energy storage to deliver cost, performance, and operational synergies, including superior charging and discharging flexibility, reduced fuel consumption and lower CO 2 emissions compared to conventional Combined Cycle Power Plants, and low-cost, large-scale energy storage. The LSCC technology is composed of proven equipment integrated with gas turbine exhaust heat in a novel system. During charging, electric heaters raise the salt temperature as it flows from the Cold Salt Tank to the Hot Salt Tank. During discharging, hot salt produces steam from feedwater that is heated with gas turbine exhaust, which also superheats steam to drive a steam turbine. LSCC technology can be added to any combustion-turbine to integrate renewable energy, provide needed grid services, and increase the value of fossil electric generating units based on the technology’s following attributes: • Long-duration storage enables time-shifting of VRE to avoid curtailment and impairment of renewable assets. • Long storage duration combined with fast-charging capability increases arbitrage opportunities by storing more energy when the price is low and discharging more hours when the price is high. • Long storage duration allows resource adequacy to be supplied across multiple days to increase reliability and reduce risk. • The stored energy reduces fuel heat rate and GHG emissions, and increases merit, so the LSCC dispatches earlier and longer to increase the plant’s capacity factor and asset value. • The stored energy enables pre-heating and startup of the steam cycle, without operating the gas turbine, to enable fast startup and ramping when dispatched for discharge. • The steam turbine can operate without the gas turbine so it can provide valuable synchronous inertia during charging without consuming fuel. • Fast frequency response and regulation services can be provided during charging using solid-state heater and pump controls to vary the charge power input in response to grid signals. • The LSCC system can be configured for resilience including black start, islanded/micro-grid operation, and even self-recharging of storage using either gas turbine power or gas turbine exhaust heat. The commercialization plan is to add LSCC technology to existing simple cycle gas turbine power plants with the 50MW GE LM6000 aero-derivative gas turbine as the reference design basis. A Techno-economic assessment of the reference design evaluated the benefits (Levelized Avoided Cost of Energy) and costs (Levelized Cost of Energy). The plant definition included all major systems and budgetary vendor quotes. Pintail Power and NexantECA developed the overall cost estimate for the LSCC plant up to the total plant cost level, following the DOE-NETL cost estimate guidelines at AACE Class 3 (-20%/+30%). This includes the equipment cost, bulk material, direct and indirect labor costs to arrive at the bare erected cost. Engineering costs are factored from the BEC and added to it to arrive at the EPC cost. Process and project contingencies were then factored from the EPC cost and rolled-up to yield the total plant cost of $\$$184 million for 1746 MWh of discharge electricity. • At $\$$105/kWh, the reference plant costs less than any of the Energy Storage Systems evaluated by PNNL in 2020 for the Energy Storage Grand Challenge. Operations and Maintenance cost estimates were scaled from combined cycle practice, assuming that the LSCC unit was co-located with and sharing some labor expense with other units, to arrive at $\$$2.2 million per year. Plant economics were evaluated using prices from the ERCOT Day-Ahead Market for calendar year 2019 (excluding the market disruptions from the COVID pandemic and the February 2020 deep freeze event). Assuming economic dispatch in the ERCOT Day-Ahead market, the reference plant capacity factor would have discharged for 2777 hours at 91.9 MW, a 31.66% capacity factor, with a marginal cost of $\$$25.59/MWh, and a LACE of $\$$82.41/MWh. Fixed charges were calculated according to EIA guidelines to arrive at an LCOE of $\$$83.48. The benefit-to-cost ratio of 0.99 suggests that the reference plant would have been cost-effective and competitive in the market. EPRI interviewed selected utilities to gauge the need for, applicability of and interest in the LSCC system. Several utilities are currently managing increased load growth along with the inclusion of increasing levels of renewable generation, putting pressure on conventional generation by requiring increased turndown requirements and ultimately lower capacity factors. All of the utilities interviewed have CO 2 reduction targets in the 2030-2050 timeframe that will severely limit the participation of fossil generation and require better utilization of carbon free generation. While there is limited opportunity for storage in the current markets, the utilities interviewed stated that there will be a substantial need for long duration energy storage in the future given the expected trends. Utilizing an energy storage system will generally be preferred over new gas capacity in some cases, with the capabilities of the LSCC system being a potential option for retrofit to existing simple cycle gas turbine units, allowing them to deliver greater participation in the market with lower carbon intensity. A technology gap assessment and technology maturation plan identified a pilot-scale demonstration as the final step before commercialization. Key gaps to be addressed during the Phase II FEED (Front-End Engineering Design) are component selection and design, commissioning procedures, and operational procedures and the control system for LSCC charging and discharging. The project team has been expanded to include Wood Group PLC as EPC. The proposed Phase II work leads to a pilot-scale engineering demonstration (TRL 6) to be conducted at Southern Company’s Plant Rowan, where the prototype system will perform “all the functions that will be required of the operational system.” The proposed pilot will facilitate commercialization (TRL-9) by scale-up to utility-scale systems integrated with peaking GTs or directly to facility scale systems using industrial GTs. The conceptual design for the pilot plant focuses on the novel integration aspects of LSCC technology. A slipstream of gas turbine exhaust will feed a waste heat recovery unit coupled to a molten salt steam generator heated by stored energy. The pilot is intended to demonstrate all key operating modes of the LSCC technology during charging, discharging and standby. The pilot equipment will be approximately one-seventh scale of the LM6000 commercial target and is expected to have commercial off-ramp potential for facility-scale applications.
The purpose of this section is to outline a method of cost analysis whereby a significantly better representation of “should achieve” costs may be attained for NOAK systems. Based on economists’ notions of producer theory, and grounded in the cost analysts’ and project managers’ tool called the Work Breakdown Structure (WBS), this section describes best practices in cost estimation. It then illustrates how the Code of Accounts (COA) structure, developed by the Economic Modeling Working Group (EMWG) of the Generation IV International Forum in “Cost Estimating Guidelines for Generation IV Nuclear Energy Systems [EMWG 2007] (hereafter “Gen IV Guidelines Document”), can be used to differentiate “should achieve” versus “did experience” costs.
Designing, commissioning, and retrofitting HVAC control systems for energy efficiency is crucial, but the use of ad-hoc control sequences by designers and contractors, based on scattered information, results in diverse and sub-optimal sequences. ASHRAE Guideline 36 (G36) addresses the challenge by providing standardized, rule-based HVAC control sequences that prioritize energy efficiency. However, there is limited evaluation of their energy performance at the building level, with only a few studies primarily focused on HVAC airside systems in small-to-medium-sized commercial buildings. In this study, the energy performance of ASHRAE Guideline 36 control sequences was assessed using a large office building emulator in Chicago. The emulator features a central plant system with multiple chillers and boilers as well as multiple variable air volume (VAV) systems with terminal reheat. To achieve a high-fidelity representation, we developed a Spawn-of-EnergyPlus-based model for the large office building, maintaining the DOE prototype large office building setup but substituting the HVAC system with its Modelica counterpart. This substitution ensures that the building thermal load, HVAC system's dynamics, and detailed control sequences are all accurately represented. The study involved evaluating and implementing control strategies outlined in ASHRAE Guideline 36-2021 to replace conventional controls. These strategies include the demand-based supply air temperature and duct static pressure setpoint reset and the request logic for demand-based reset of chilled/hot water supply temperature setpoints and pipe static pressure setpoints. Energy performance was evaluated under various load conditions, including cooling, heating, and transitional seasons, both for individual control strategies and in combination. The results indicate that the collective control strategies retrofit yield greater energy savings than the sum of individual strategies, highlighting the synergistic benefits of incorporating both airside and plant-side control retrofits. Additionally, energy savings of up to 41% in the heating season, 18% in the shoulder season, and 20 % in the cooling season were observed compared to baseline control while maintaining the thermal comfort level.
This Quality Guidelines for Energy System Studies (QGESS) summarizes the methodology employed by the National Energy Technology Laboratory (NETL) in calculating plant costs in its techno-economic studies, such as the Cost and Performance Baseline for Fossil Energy Systems series of reports. It also outlines the approach used to calculate the cost of electricity (COE) or cost of product (COP) metrics by which NETL estimates the impact of technology options. These metrics and a clear understanding of the methodology used are essential in allowing different plant technologies to be compared on a similar basis. These guidelines were initially developed for power producing plants, although they can be applied to a variety of different revenue generating plants (e.g., coal to liquids, syngas generation, hydrogen) by substituting the industrial product quantity for the electrical quantity in the equations. The tables contained in this QGESS have been expanded upon relative to previous versions to include values to assume for several industries.
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Transmission electron microscopy (TEM) is arguably the most important tool for atomic-scale material characterization. A significant portion of the energy of transmitted electrons is transferred to the material under study through inelastic scattering, causing inadvertent damage via ionization, radiolysis, and heating. In particular, heat generation complicates TEM observations as the local temperature can affect material properties. Here, the heat generation due to electron irradiation is quantified using both top-down and bottom-up approaches: direct temperature measurements using nanowatt calorimeters as well as the quantification of energy loss due to inelastic scattering events using electron energy loss spectroscopy. Combining both techniques, a microscopic model is developed for beam-induced heating and to identify the primary electron-to-heat conversion mechanism to be associated with valence electrons. Building on these results, the model provides guidelines to estimate temperature rise for general materials with reasonable accuracy. This study extends the ability to quantify thermal impact on materials down to the atomic scale.
Autoclave processing is a commonly used state-of-the-art fiber-reinforced composite manufacturing technology, albeit high capital cost, long cycle times and high energy consumption. Alternatively, out-of-autoclave processing reduces the initial and operating costs while producing composite structures with similar quality as that of autoclave parts. Additive Manufacturing (AM) the scaled-up molds for out-of-autoclave process using carbon fiber (CF) reinforced composite offers design flexibility, enhanced mechanical, and thermal properties in addition to reduction in weight and cost. However, heating of these molds using an oven is still expensive and necessitates an energy-efficient heating process. In this study, resistive heating through heating elements embedded within fiber reinforced composite molds is used as an efficient heating mechanism. The goal is to design wire embeddings and determine the optimal heat flux density to achieve a target uniform temperature of 1000C across the mold surface. To this end, numerical analyses were performed to evaluate the temperature distribution across the composite mold surface for a given wire placement and mold configuration. Constant thermal properties of the 20 wt.% short CF reinforced acrylonitrile butadiene styrene (ABS) were used in the thermal analysis. Time taken to reach the steady state temperature was also estimated. Design guidelines for wire embeddings were included to enable efficient manufacturing of fiber-reinforced composites through out-of-autoclave molds.
This paper summarizes the methodology employed by the National Energy Technology Laboratory (NETL) in calculating power plant costs in its techno-economic studies, such as the Cost and Performance Baseline for Fossil Energy Systems series of reports. It also outlines the approach used to calculate the cost of electricity by which NETL evaluates electric power plants. These metrics and a clear understanding of the methodology used are essential in allowing different power plant technologies to be compared on a similar basis. These guidelines are tailored for power producing plants, although they can be applied to a variety of different revenue generating plants (e.g., coal to liquids, syngas generation, hydrogen).
In developing countries, agriculture generally represents a large fraction of GHG emissions reported in National Inventories, and emissions are typically estimated using Tier 1 IPCC guidelines. However, field data and locally adapted simulation models can improve the accuracy of IPCC estimations. In this report we aimed to quantify anthropogenic N2O emissions from croplands of Argentina through field measurements, model simulations and IPCC guidelines. Here we measured N 2 O emissions and their controlling factors in 62 plots of the Pampas Region with corn, soybean and wheat/soybean crops and in unmanaged grasslands. We accounted for gross emissions from crops and background emissions from unmanaged grasslands to calculate net anthropogenic emissions from crops as the difference between them. We calibrated and evaluated the DayCent model and then simulated different weather and management scenarios. Finally, we applied IPCC guidelines to estimate anthropogenic N 2 O emissions at the same plots. The DayCent model accurately simulated annual N 2 O emission for all crops as compared to measured data (RMSE = 1.4 g N ha -1 day -1 ). Measured and simulated emissions in soybean crops were higher than in corn and wheat/soybean crops. Gross N 2 O emissions ranged from 1.4 to 5.1 kg N ha -1 yr -1 for current environmental (soil and weather) and management (crops and fertilizer doses) conditions. Background emissions ranged between 1.1 and 1.3 kg N ha -1 yr -1 , and therefore net anthropogenic emissions ranged from 0.3 to 4.0 kg N ha-1 yr -1 . IPCC Tier 1 emission factors underestimated N2O releases from soybean, that were on average 4.87 times greater when estimated with DayCent and observations (0.53 vs 2.47 and 2.69 kg N ha -1 yr -1 , respectively). On the contrary, IPCC estimates for corn and wheat/soybean crops were similar to modeled and measured values. Our results suggest that N 2 O emissions from the vast 15 million ha of soybean croplands in the Pampas Region may be substantially underestimated.
The frequency measurement and rate of change of frequency (RoCoF) calculations are important for active power, frequency control, and frequency-related protection in power system operations. With the rapid decrease in inertia under modern power systems, however, multiple power variations introduced by variable renewable energy bring large RoCoF and introduce oscillations in frequency measurements. To shed light on this issue, this article proposes a frequency measurement and RoCoF analysis technology, as well as the sensitivity analysis for the real low-inertia power grid. First, a real-world event in an island power grid is analyzed as an example, and an event numerical model is estimated considering four uncertainties, including event RoCoF, oscillation magnitude, oscillation frequency, and underfrequency load-shedding threshold. Then, a sensitivity analysis is performed using the Sobol indices method and quasi-Monte Carlo simulations to identify the dominant uncertainties based on the event model. Here, the dominant uncertainties are compared to provide a guideline for the frequency estimation algorithm selection under different event cases. This algorithm is compared with an enhanced zero-crossing algorithm and gives a 3.41% better RoCoF error rate. Finally, an experiment is conducted using universal grid analyzers to validate the simulation results and the theoretical analysis.
Accurate quantification of photovoltaic (PV) system degradation rate (R D ) is essential for lifetime yield predictions. Although R D is a critical parameter, its estimation lacks a standardized methodology that can be applied on outdoor field data. The purpose of this paper is to investigate the impact of time period duration and missing data on R D by analyzing the performance of different techniques applied to synthetic PV system data at different linear R D patterns and known noise conditions. The analysis includes the application of different techniques to a 10-year synthetic dataset of a crystalline Silicon PV system, with emulated degradation levels and imputed missing data. Here, the analysis demonstrated that the accuracy of ordinary least squares (OLS), year-on-year (YOY), autoregressive integrated moving average (ARIMA) and robust principal component analysis (RPCA) techniques is affected by the evaluation duration with all techniques converging to lower R D deviations over the 10-year evaluation, apart from RPCA at high degradation levels. Moreover, the estimated R D is strongly affected by the amount of missing data. Filtering out the corrupted data yielded more accurate R D results for all techniques. It is proven that the application of a change-point detection stage is necessary and guidelines for accurate R D estimation are provided.
The design of breeding blankets represents the major challenge for fusion reactor engineering because of performance requirements and severe operating conditions in terms of heat load and neutron flux. Liquid metal alloys such as lead-lithium, due to their lithium content, can be used to breed tritium, one of the plasma fuel components, and owing to their high thermal conductivity, they may serve as coolants. On the other hand, there are technical issues related to the fact that the liquid metals are electrically conducting and interact with the plasma-confining magnetic field. Induced electric currents and generated electromagnetic forces affect velocity and pressure distribution in the blankets. Magnetohydrodynamic (MHD) flows for fusion applications have been often investigated in simplified geometries, such as pipes, ducts, bends, with focus on their fundamental features. These analyses are essential, since results remain valid as background for the development of blanket designs, even when a concept is dismissed. However, the conceptual study of fusion blankets requires to take into account the global multiple effects, that arise when the full system is considered. Progress made in fusion-related MHD research results from combined numerical and experimental activities. In this paper we review and summarize features of 2D and 3D MHD flows that are typical in liquid metal blankets, together with available correlations for MHD pressure losses. This knowledge can provide simple design MHD guidelines that support a preliminary estimate of MHD effects in a blanket concept, in terms of pressure drop and flow distribution.
This software package contains a simple calculator that can be used to estimate savings from implementing a retrofit of HVAC controls to ASHRAE Guideline 36 in a multi-zone office building. Implemented in Excel, the calculator has a user-interface front-end to configure parameters of the case study, such as location, building size, and existing control strategies, and a back-end containing results from a large number of pre-run annual energy simulations. The software package also contains the simulation models and associated pre- and post-processing scripts to generate these results.
Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.
The coal combusted at coal-fired power plants (CFPPs) contains several trace elements that impact human heath if released to the environment. Effective combinations of air pollution control devices (APCDs) and water pollution control devices (WPCDs) are critical to reducing these impacts. This work sampled pulverized CFPPs owned and operated by Louisville Gas & Electricity – Kentucky Utilities (LGE-KU) during baseload and cycling conditions. Next, an open-source and easy-to-implement trace element partitioning model was developed using publicly available datasets, literature studies of trace element partitioning, and sampling data to estimate trace element partitioning in APCDs to the gas, liquid, and solid phases exiting CFPPs. These estimates of the liquid phase trace element concentration in FGD wastewater were then used to estimate trace element behavior in WPCDs and evaluate treated wastewater effluent concentrations for compliance with the Effluent Limitation Guidelines (ELG) for the Steam Electric Generating Sector. Finally, cost estimates were developed for established and emerging wastewater treatment trains to identify the most cost-effective approaches for ELG compliance.
In recent years, acid mine drainage (AMD) has emerged as a promising unconventional source of rare earth elements (REEs) and other critical minerals (CMs) such as cobalt and manganese. In this regard, AMD provides a natural heap leaching effect that extracts and concentrates REE/CM from the host strata creating a partially enriched feedstock suitable for downstream extraction, separation, and recovery. While several prior studies have described processes and approaches for the valorization of AMD, very few have described the supply chain and infrastructure requirements as well as the associated economic assessment. To that end, this paper provides a fundamental economic assessment of REE/CM recovery from AMD using a network sourcing strategy in addition to a robust, flexible feedstock separations and refining facility. The methodology of this paper follows that of a typical techno-economic analysis with capital and operating costs estimated using AACE Class IV (FEL-2) guidelines. To demonstrate the range of possible outcomes, four pricing scenarios were modeled including contemporary prices (September, 2021) as well as the minimum and maximum prices over the last decade. In addition, five production scenarios were considered reflecting variations in the product suite, ranging from full elemental separation to magnet REE and CM production only (i.e., Pr, Nd, Tb, Dy, Y, Sc, Co, and Mn). The results of this analysis show that, with the exception of the minimum price scenario, all operational configurations have positive economic indicators with rates of return varying from 25% to 32% for the contemporary price scenario. The optimal configuration was determined to be production of Co, Mn, and all REEs except for mischmetal, which is not recovered. Sensitivity analysis and Monte Carlo simulation show that capital cost and HCl consumption are the two major factors influencing rate of return, thus indicating opportunities for future technology development and cost optimization. Implications of the study and a cooperative profit-sharing model for sourcing are also described.
Determining and explaining the presence of a gap at a magnon crossing point is a critical step to characterize the topological properties of a material. An inelastic neutron scattering study of a single crystal is a powerful experimental technique to probe the magnetic excitation spectra of topological materials. Here, we show that when the scattering intensity rapidly disperses in the vicinity of a crossing point, such as a Dirac point, the apparent topological gap size is extremely sensitive to experimental conditions including sample mosaic, resolution, and momentum integration range. In this work, we demonstrate these effects using comprehensive neutron scattering measurements of CrCl 3 . Our measurements confirm the gapless nature of the Dirac magnon in CrCl 3 , but also reveal an artificial, i.e., extrinsic, magnon gap unless the momentum integration range is carefully controlled. Our study provides an explanation of the discrepancies between spectroscopic and first-principles estimates of Dirac magnon gap sizes and provides guidelines for accurate measurement of topological magnon gaps.