Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “demand modelling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 901 records · Page 50

TwInflation

The general structure of Hybrid Inflation remains a very well-motivated mechanism for lower-scale cosmic inflation in the face of improving constraints on the tensor-to-scalar ratio. However, as originally modeled, the “waterfall” field in this mechanism gives rise to a hierarchy problem (η–problem) for the inflaton after demanding standard effective field theory (EFT) control. We modify the hybrid mechanism and incorporate a discrete “twin” symmetry, thereby yielding a viable, natural and EFT-controlled model of non-supersymmetric low-scale inflation, “Twinflation”. Analogously to Twin Higgs models, the discrete exchange-symmetry with a “twin” sector reduces quadratic sensitivity in the inflationary potential to ultra-violet physics, at the root of the hierarchy problem. The observed phase of inflation takes place on a hilltop-like potential but without fine-tuning of the initial inflaton position in field-space. We also show that all parameters of the model can take natural values, below any associated EFT-cutoff mass scales and field values, thus ensuring straightforward theoretical control. We discuss the basic phenomenological considerations and constraints, as well as possible future directions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Numerical modeling and parametric study of a dual purpose underground thermal battery

We report integrating thermal energy storage with building energy systems can enable flexible building electric demands at buildings to help mitigate the mismatch between electricity supply and demand. A novel building heating and cooling system that integrates a dual-source heat pump with hybrid thermal storage named dual-purpose underground thermal battery (DPUTB) has been developed for reshaping building electric demands. The proposed DPUTB integrated geothermal heat pump system is an original innovation that enables Grid-interactive Energy Efficient buildings. This paper focuses on the study of the novel DPUTB. The DPUTB works as both a thermal storage tank (an inner tank) and a ground heat exchanger (an outer tank separated from the inner tank by the insulation material). High fidelity and computationally effective models are needed to predict the performance of the novel DPUTB. This study has developed a simplified dynamic model for the DPUTB according to heat transfer and energy conservation principles and validated it by using experimental data obtained from testing a small-scale DPUTB apparatus. A parametric study was conducted to identify a design that can achieve the target thermal storage performance of load shift and energy efficiency. The parametric study results show that the inner tank shell thermal conductivity and the phase change material's melting point are the two most influencing factors on the performance of the DPUTB. One single full-size DPUTB with the identified design could provide 1-ton cooling (3.51 kW) with the supply temperature lower than 11 °C for 4 hours in summer after being fully charged in 8 hours. The inner tank filled with phase change material is for cooling thermal storage as a latent tank in the design. However, its capacity can be as high as 60 MJ as a sensible water tank for heating storage in winter. In the future study, the DPUTB model will be incorporated into the dual-source heat pump system for evaluating the overall system performance of demand side management in the long term.

25 ENERGY STORAGE↗

Disjunctive optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems

This paper proposes a new optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems. The model optimizes both investment decisions (e.g., size, location, and time to install, retire and decommission facilities) and operation decisions (e.g., on/off status, operating capacity, and expected power output). It is also able to optimize reserve systems (or backup systems), as well as the main systems, to improve power systems reliability. An impact of operational strategies of generators (i.e., participating in electricity production vs. remaining as idle units during operation) on power systems reliability is considered. Probability of equipment failures and capacity failure states are used to rigorously estimate the power systems reliability depending on design and operation strategies. The optimization model is formulated with Generalized Disjunctive Programming (GDP), which is reformulated as a mixed-integer linear programming (MILP) model using the Hull relaxation. Two reliability-related penalties, such as downtime penalty and unmet demand penalty, are included in the objective function to maximize reliability while minimizing the total net present cost. Furthermore, a bilevel decomposition with tailored cuts is developed to reduce computational times of the multi-scale optimization model. The effectiveness of the proposed model is shown by comparing the results with the results obtained from the expansion planning models that do not explicitly consider reliability. In conclusion, we also show that the proposed bilevel decomposition is computationally efficient for solving large scale problems through 5-years and 10-years planning case studies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Towards a Techno-Economic Analysis of PCM Integrated Hybrid HVAC Systems

Thermal end uses dominate building energy consumption and are a major driver of peak demand. As heating is electrified, peak electrical power required will surge, prompting a need for innovative HVAC system designs and controls.These designs must incorporate novel technologies at the component level and new integration techniques at the system level. One such possibility involves the addition of thermal energy storage (TES) in heating and cooling equipment using a phase change material (PCM) heat exchanger. Here, thermal energy storage via phase change can be used to shift the HVAC system loads to times of lower electricity cost, reduced carbon intensity, and greater energy efficiency. Most of the current utilization of PCM in buildings involves passive components. By actively controlling when heat is stored and released from PCM, we can optimize the building HVAC system to cost-effectively meet consumer needs with the flexibility to draw on renewable energy resources when they are abundant and available. While this combination of technologies is promising in theory, simulation-based evaluation of a prototype can be difficult due to the modeling requirements at the component level and the large number of possible configurations and operating modes at the system level. To conduct this evaluation, we use the Modelica language for modeling and simulation because it enables users to represent the important physics of the problem, interchange and rearrange components in an efficient manner, and implement a range of control configurations. In this work, we considered three case studies: a portable building, a large commercial retail store, and a multifamily residential apartment unit. Each of these employs a different system design, ranging from a single package vertical unit incorporating PCM to a central plant with independent heat pump, evaporative cooling, and thermal energy storage components. This paper describes the technologies in question, presents modeling at the component and system levels, and demonstrates building energy and demand charge cost savings with local time-of-use tariffs in a hot-dry climate.

Helmns, Dre↗

Modeling the External Effects of Air Taxis in Reducing the Energy Consumption of Road Traffic

Air taxis are currently being demonstrated. Few studies have quantified their external effects in reducing on-road vehicle fuel consumption. The hypothesis of this paper is that air taxis may divert some drivers away from congested traffic corridors, improve traffic speed and fuel economy, and reduce congestion-induced energy consumption. A model is developed that links several key components: mode choice, the relationship between travel demand and traffic speeds, the relationship between traffic speeds and fuel economies, and the heterogenous value of travel time. It is applied to the route from downtown Los Angeles to Los Angeles International Airport, where at peak hours 38,200 vehicles attempt to use the route that has an hourly capacity of 17,200 vehicles. The model estimates that, with conservative assumptions and near-term technologies, diverting 3.2% of the traffic to air taxis could produce a 15% reduction in traffic vehicle fuel use. With optimistic assumptions and mature technologies, the study estimates that diverting 20% of traffic could reduce the traffic vehicle fuel use by about 74%. The key insight is that if a small share of congested travelers switched to air taxis, motivated by private benefits of time savings, significant external benefits for other road travelers (time savings and fuel savings) and to society (reduced energy use and emissions), would ensue creating a win-win-win outcome. Overall, these estimates (which are not intended as predictions because of the stated limitations) strongly suggest the need to consider the external energy effect in future cost-benefit analyses of air taxi technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Transient Modelling of HTGR Thermal Load Follow in Modelica

The ability of nuclear power plants to load follow electrical power demand is particularly important as we move to higher variable renewable (VRE) energy production on low carbon energy grids. High temperature gas reactors (HTGRs) are capable of load following by adjusting thermal output, but their high temperatures and thermal inertia significantly impact the power transients they are able to follow. To aid investigation into new integrated energy systems with advanced reactors, it is necessary to have an open-source transient model of a full HTGR primary and secondary. This paper introduces two new open-source models for a HTGR and corresponding balance of plant (BOP) written for the HYBRID library developed at Idaho National Laboratory. A transient thermal load follow test case was taken by the work from Brits et al. to benchmark our open-source model against transient operation results from other proprietary codes that exist. The control scheme for the two new models in combination is discussed and a comparison of the response in transient load follow between the two codes performed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

The Role of Groundwater Withdrawals on River Regulation: Example From the Columbia River Basin

The Columbia River Basin (CRB) is heavily regulated by more than 250 dams on its river system while depending significantly on groundwater withdrawals in certain sub-basins. Neglecting groundwater withdrawals in hydrologic models of the basin could result in inaccurate predictions of its water budget and thus mislead water management decisions in the basin. This work aims to understand the impacts of groundwater pumping on the spatiotemporal patterns of modeling regulated streamflow in the CRB using a modified version of the Variable Infiltration Capacity (VIC) model integrated with a water management component that accounts for groundwater withdrawals, irrigation demands, and reservoir operation (VIC-GIRR). The VIC-GIRR simulations showed that considering additional groundwater withdrawals would alleviate the stress of irrigation water deficit in the Snake River Basin with an average reduction of 10 km 3 /year. Such a reduction in water deficit resulted in slight streamflow increase over the CRB with maximum increase up to 40% during dry period in certain locations. We also note that the implementation of groundwater withdrawal does not, however, improve the overall model performance in long-term averaged streamflow and storage predictions. Our results highlight the efforts needed to examine additional important processes in representing the interactions between water withdrawals and reservoir operations. Such efforts will aid in better simulation of multi-reservoir system and improve effectiveness for agricultural productivity, power generation, flood control, and navigation purposes.

54 ENVIRONMENTAL SCIENCES↗

Experimental validation of multiphysics model simulations of the thermal response of a cement clinker rotary kiln at laboratory scale

Abstract An increasing demand for buildings, transportation systems and civil infrastructure development has driven expansion of cement consumption world‐wide, producing a significant increase in related global energy demand. With approximately 7% of the world‐wide industrial energy consumption (10.7 exajoules [EJ]), the cement industry is the third most energy intensive industrial processes and a key component for concrete, the most consumed composite material in the global construction industry. In cement manufacturing, the cement kiln accounts for most of the energy consumption in the production process. As the heart of a cement plant, the cement kiln is where the kiln feed primarily containing calcium oxide (CaO), silica (SiO 2 ), alumina (Al 2 O 3 ), and iron (Fe 2 O 3 ) are thermally and chemically transformed into clinker minerals. The presented work developed a multiphysics model, designed and built a laboratory‐scale rotary cement clinker kiln, and produced cement clinker at laboratory‐scale. The model was developed to study the interaction between the various thermal, fluid dynamic and chemical interactions involved in the sintering process used to form Portland cement clinker in an effort to reduce energy use. The analytical model was validated through experimental testing using a unique laboratory‐scale rotary cement kiln developed during the investigation. Also demonstrated was the feasibility of producing clinker at laboratory scale. This modeling and lab scale tests were designed to better understand the clinker sintering process so that operational and quality decisions can be made to optimize energy consumption without compromising cement clinker quality. The computational fluid dynamics modeling was developed in COMSOL Multiphysics 6.0. The characteristics of the combustion fluid flow, concentration of species, temperature and heat transfer were studied for a turbulent flow of methane (CH 4 ) gas and oxygen (O 2 ). Theory suggests that heat transfer impacts the cement production process but the multiphysics model more accurately describes the convection, conduction, and radiant heat transfer in the kilning process and thus allows for a better understanding of the energy exchange driving the chemical reactions that produce Portland cement. Clinker minerals were formed because of appropriate burning conditions implemented during experimental model validation.

Tabares, Juan David↗

A data-driven linear formulation of the optimal demand response scheduling problem for an industrial air separation unit

Demand response (DR) has become a key element in balancing the power grid as the contribution of time-varying renewable power generation increases. Chemical plants are appealing candidates for DR programs as they offer large, concentrated and flexible loads. DR participation calls for frequent production rate changes over time scales that overlap with the dominant dynamics of the plant. Production scheduling should therefore consider the process dynamics explicitly. Here we present a data-driven approach for modelling the scheduling-relevant dynamics based on historical closed-loop operating data using autoregressive with extra inputs (ARX) models. We introduce a new, linear scheduling problem formulation based on the ARX representation, and demonstrate its implementation on an industrial air separation unit.

42 ENGINEERING↗

Demand reduction and energy saving potential of thermal energy storage integrated heat pumps [Réduction de la demande et potentiel d'économie d'énergie des pompes à chaleur avec stockage d'énergie thermique intégré]

A heat pump (HP) moves heat from a low-temperature source to a high-temperature sink with an input of energy. Often, one temperature body fluctuates with time (e.g., diurnal ambient temperature), causing the HP efficiency to vary. Integrating thermal energy storage (TES) into a HP system adds a third temperature body, enabling the HP to be advantageously coupled to any two: the application, the ambient, or the TES at strategic times. Although TES integration with HPs is an important emerging technology to lower energy consumption and decrease energy demand during critical times, the favorable circumstances for TES integration are poorly understood. Here, this paper establishes the energy reduction and demand reduction potential of TES-integrated HPs with both analytical and numerical HP models. All possible temperature arrangements are considered for HP-TES systems with two fixed temperature bodies (application and TES) and one variable temperature (ambient). Results show that overall energy savings are most attainable when the TES temperature is near the application temperature, whereas a large temperature difference between the TES and the application leads to the most peak demand reduction. The potential for overall energy savings increases as the magnitude of ambient temperature fluctuations increases.

25 ENERGY STORAGE↗

Hydrological connectivity: a review and emerging strategies for integrating measurement, modeling, and management

This review synthesizes methods for measuring, modeling, and managing hydrologic connectivity, offering pathways to improve practices and address environmental challenges (e.g., climate change) and sustainability. As a key driver of water movement and nutrient cycling, hydrologic connectivity influences flood mitigation, water quality regulation, and biodiversity conservation. However, traditional field-based methods (e.g., dye tracing), indirect measurements (e.g., runoff analysis), and remote sensing techniques (e.g., InSAR) often struggle to capture the complexity of catchment-scale interactions. Similarly, modeling approaches—including process-based and percolation theory-based models, graph theory, and entropy-based metrics—face limitations in fully representing these interconnected processes. Both modeling and measurement techniques are constrained by inadequate spatial and temporal coverage, high data demands, computational complexity, and difficulties in representing subsurface connectivity. Subsequently, we critique current management practices that prioritize isolated variables (e.g., streamflow, sediment transport) over system-wide strategies and emphasize the need for adaptive, connectivity-based approaches in water resource planning and restoration. Moving forward, we highlight the importance of interdisciplinary collaboration, technological innovations (e.g., AI-driven modeling, real-time monitoring), and integrated frameworks to improve connectivity measurement, modeling, and adaptive management to restore fragmented hydrologic networks. This integrated approach sets the stage for transformative water resource management, fostering proactive policy development and stakeholder engagement.

Dwivedi, Dipankar↗

Stochastic Optimization and Uncertainty Quantification of Natrium-based Nuclear-Renewable Energy Systems for Flexible Power Applications in Deregulated Markets

Rapid integration of variable renewable energy sources (VRES) has made modeling and stochastic optimization of hybrid energy systems crucial for studying their long-term performance and viability. However, most studies have focused on just historical data, which may be unreliable for capturing short-term fluctuations, rare events, and long-term patterns of energy demand, price, and the variability of renewable energy sources. For this study, optimal synthetic time series models were developed using Wasserstein distance. The models were validated by comparing the key statistical measures against those of the historical data. They were then used to optimize the integrated Natrium-style advanced energy systems and their long-term (30 years) economics. The stochastic model performs bi-level optimization to find the optimal sizes for the balance of plant and thermal energy storage, while also optimizing energy dispatch to achieve the maximum net present value. In studies of two deregulated markets (California ISO and the Electric Reliability Council of Texas), the integrated Natrium-style system performed better in CAISO than in ERCOT, given higher and more consistent electricity prices during peak-demand periods. The potentially enlarged cost associated with the variable operation and maintenance of the TES system also plays a significant role in driving the system sizing, thus its impacts on the system are investigated in detail through comparison against a baseline case. The study also finds that the bi-level optimization results based on stochastic gradient descent closely match the grid search results. The uncertainty quantification of the stochastic signals provides further NPV-related insights and probability distributions for the case studies. The normal standard error of the mean of NPV for the case with and without TES VOM for CAISO were found to be 7.73M (plus-minus sign) 1.09M USD and 104.99M (plus-minus sign) 1.25M USD, respectively based on a 95% confidence. Given the relatively small NPV variance based on 150 samples, the analysis affords the most robust possible prediction of the techno-economic performance of the integrated Natrium-style energy systems.

25 ENERGY STORAGE↗

Toward Enhancing Wastewater Treatment with Resource Recovery in Integrated Assessment and Computable General Equilibrium Models

Sustainable water management is essential to increasing water availability and decreasing water pollution. The wastewater sector is expanding globally and beginning to incorporate technologies that recover nutrients from wastewater. Nutrient recovery increases energy consumption but may reduce the demand for nutrients from virgin sources. We estimate the increase in annual global energy consumption (1,100 million GJ) and greenhouse gas emissions (84 million t CO2e) for wastewater treatment in the year 2030 compared to today’s levels to meet sustainable development goals. To capture these trends, integrated assessment and computable general equilibrium models that address the energy-water nexus must evolve. We reviewed 16 of these models to assess how well they capture wastewater treatment plant energy consumption and GHG emissions. Only three models include biogas production from the wastewater organic content. Four explicitly represent energy demand for wastewater treatment, and eight include explicit representation of wastewater treatment plant greenhouse gas emissions. Of those eight models, six models quantify methane emissions from treatment, five include representation of emissions of nitrous oxide, and two include representation of emissions of carbon dioxide. Our review concludes with proposals to improve these models to better capture the energy-water nexus associated with the evolving wastewater treatment sector.

42 ENGINEERING↗

Optimizing Price-Informed Operation of a Battery Storage System in an Office Building

New prescriptive efficiency requirements amended to US model energy codes historically have been evaluated using an average, blended electricity rate, which obscures demand charges. Post 2019, new measures can be evaluated using a representative time-of-use (TOU) tariff yet more sophisticated analysis methods are needed to consider a variety of TOU tariffs and assess price-informed control. To address these needs, this study couples building prototype simulation model with varying-in-sophistication battery storage operating strategies for different electricity TOU tariffs. The analysis compares the impact of operating a battery storage system following simpler rule-of-thumb methods versus a semi-optimized priced-informed heuristic approach. The investigation demonstrates that the heuristic approach results in greater electricity cost savings and is practical to implement.

Lei, Xuechen↗

PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk

During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85% accuracy using a one-person offset. Furthermore, the accuracy of 15 min of the ahead prediction could reach 95% with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2% for 93% of the day. This OBMPC model could reduce the coil load by 52.44% and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9% to 26% difference in demand ventilation, a 0.3°C to 2.4°C difference in zone temperature, a 28.5% to 44.5% difference in outdoor airflow rate, and a 10.7% to 28.2% difference in coil load.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Leveraging ARM Data to Improve Models for Predictive Understanding of Energy and Security Challenges

Extreme weather and natural hazards can disrupt the energy sector, affecting demand, generation, transmission, distribution, consumption and operational planning at regional and national scales. These disruptions stem from a broad range of atmospheric phenomena, including winter storms, freezing rain, wet snow loading, severe convection, flooding and landslides, wildfires, prolonged heat, and drought. Many of these same phenomena can also affect national security through impacts to transportation and infrastructure. To support the U.S. Department of Energy (DOE) focus on energy resilience and national security, the Atmospheric Radiation Measurement (ARM) User Facility is uniquely positioned to contribute measurement data, analyses, and modeling frameworks that can significantly improve predictive understanding of these hazards to mitigate their effects. To explore this opportunity, ARM convened a two-part virtual workshop in November 2025. The workshop engaged interdisciplinary experts in atmospheric science, energy systems, modeling, and operations. The goal of the meeting was to engage with these interdisciplinary experts to address three questions: • What are examples of atmospheric processes that represent significant risks to energy security or national security and where are those risks greatest? • What measurements or measurement strategies would improve ARM’s capacity to address these issues? • How can ARM and users of the ARM facility better work with the Energy Exascale Earth System Model (E3SM) and multi-sector modeling communities to apply ARM data to improving E3SM simulations of these phenomena? Participants were asked to submit white papers ahead of the meeting to initiate thinking on these themes and to help organize discussions. Workshop sessions were then organized around themes identified in the white papers. First from the white papers and then through subsequent discussions, workshop participants identified many examples that address the three questions listed above. Participants called out energy system vulnerabilities to weather phenomena such as the impact of freezing rain, strong winds, and excessive heat on power grids. They also noted the effects that weather phenomena could have on energy demand or supply (e.g., through effects of extreme temperatures). They called out security vulnerabilities such as impacts to crops from aerosol-borne pathogens and risks to industry due to melting permafrost in the Arctic. In all, over a dozen meteorological phenomena were linked to energy or security vulnerabilities. For many of the identified phenomena, participants pointed out where ARM was well poised to address issues (e.g., through measurements of cloud microphysics to inform studies of freezing rain) but also noted needs for additional measurements or modified measurement strategies. For example, adaptive scanning of severe weather would be valuable for probing winter storms or severe convection. Participants pointed out the value in integrating external observations with ARM measurements and with applying artificial intelligence (AI) to ARM observation analysis and they advocated for using model simulations to help optimize measurement strategies through Observing System Simulation Experiments (OSSEs). It was clear from the workshop that there are many ways that ARM observations can be used to mitigate energy and security concerns, but meeting participants were also asked to identify what they considered to be the greatest opportunities by ranking issues pertaining to the three workshop questions. This was accomplished through a survey administered to participants between the two virtual sessions. The highest-priority phenomena identified were winter storms, severe convection, and arctic processes. Discussion in the second session, therefore, focused primarily on these three areas, which were most fully developed in exploring ARM opportunities. Nevertheless, it was also clear that ARM has opportunities to contribute to all the identified topics. This report describes the workshop, including input from discussion and white papers (Sections 2 and 3) and a list of priority recommendations (section 4). Many other ideas for ARM contributions are discussed in individual white papers (Appendix D).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Challenges and Opportunities for Electric Utility Modeling and Asset Valuation Frameworks: Case Study on Valuing New Pumped Storage Hydropower

Asset valuation by electric utilities is becoming increasingly difficult in the rapidly changing electric sector. Rapid deployment of variable generation and inverter-based storage systems along with uncertain demand growth, climate, policies, and other factors create a challenging environment for understanding the value proposition of a new potential asset. This report describes an effort between the Tennessee Valley Authority (TVA) and three U.S. Department of Energy laboratories to perform a detailed review of utility modeling and analysis practices for asset valuation and identify challenges and opportunities for advancing its methods into the future. It focuses on a case study of new potential pumped storage hydropower (PSH) because of growing interest in new PSH capacity to provide energy balancing, firm capacity, and a range of ancillary services. Staff from the DOE labs conducted systematic interviews about current practices in capacity expansion modeling, production-cost modeling, hydrological modeling, and transmission stability modeling while also discussing how scenario analysis is conducted and how models and data are integrated. The effort resulted in a set of model, integration, and scenario recommendations that could be valuable to TVA, other utilities, system operators, and other stakeholders conducting integrated grid analysis. Individual model recommendations suggest exploring computational tradeoffs with detail and resolution across spatiotemporal structure, supply- and demand-side details, transmission overlays, market interactions, and ancillary services. Automated processes to pass data between models and conduct larger scenario suites could also enhance valuation practices by enabling a more consistent study of asset value across a broader range of uncertain future grid conditions where PSH could be particularly valuable. TVA and other industry stakeholders can learn from and adapt applied research-grade methods developed by DOE laboratories and other research institutions to improve decision making and accelerate progress towards a reliable, economic, sustainable energy system.

13 HYDRO ENERGY↗