National Energy Modeling System Research Articles

Explaining and modeling the mediating role of energy consumption between financial development and carbon emissions

The effect of financial development on carbon emissions (CO2) has been examined mostly without taking into account the mediating function of energy consumption and how to integrate this effect into a national energy modeling system. This study will address three topics against this background. The first is whether empirical research should study and prioritize the direct or indirect impacts of financial development on CO2 through the mediation of energy consumption. The second is which empirical models should be utilized to assess these impacts. Third is how to include these impacts into a national energy modeling system at the level of policy formulation. The article utilizes the U.S. National Energy Modeling System for 1990M1 – 2021M2 using the bootstrap manifest structural equation modeling technique. The article has three findings. First, empirical study should concentrate more on the indirect rather than direct effects of financial development on CO2. Second, both types of impacts should be incorporated into a national energy modeling system via the distinct (indirect) effects of the components of aggregate financial development, namely financial market development (FMD) and financial institutional development (FID). Third, these effects should be investigated by at least six empirical models that estimate the FMD's and FID's effects on CO2 (i) via the single mediation of sectoral (nonrenewable and renewable) energy consumption, (ii) via the serial mediation of sectoral energy consumption and economic growth, (iii) via the serial mediation of aggregate energy consumption and growth, and (iv) via the serial mediation of energy consumption in one single sector and output in this sector.

Renewable energy in fisheries and aquaculture: Case studies from the United States

Fisheries and aquaculture are highly reliant on fossil fuels and must transition to renewable energy to reduce carbon emissions and meet global planetary heath goals. Here, we assessed total and renewable energy use in farmed catfish and wild-caught salmon, two of the largest seafood sectors in the United States (U.S.). Interviews were used to explore participants’ views of key barriers and opportunities to replace fossil fuel use. Modeled scenarios were used to assess changes in grid source energy and electricity costs for the farmed catfish sector using the U.S. EIA National Energy Modeling System. We found that renewable energy makes up 5% of direct energy use in catfish aquaculture in Mississippi and Alabama. Catfish industry interviewees indicate that cheap electricity costs and diurnal energy use are barriers to onsite implementation of renewables. Projected renewable energy use for the catfish sector could be as high as 41% of total direct energy use in 2050 under modeled scenarios, which would result in 86% lower CO2 emissions but 34% higher electricity costs. For wild-capture pink salmon from Prince William Sound, Alaska (AK) and sockeye salmon from Bristol Bay, AK, renewable energy makes up 2% and 0% of direct energy use, respectively. Wild-caught salmon industry interviewees identified the short duration of the fishing season as a barrier for onsite renewable energy, though there is a desire for more regional renewable energy technologies to lower electricity costs and increase reliability. Proposed renewable energy projects at utilities in fish processing towns in AK would only make modest improvements in the share of direct energy from renewables due to fuel use by fishing vessels. This is the first study to characterize current and potential renewable energy use among parts of the fisheries and aquaculture sectors in the U.S. We found that energy needs for fisheries and aquaculture are influenced by their often-remote location, production methods, and seasonal energy demands, which require context-specific solutions. There is the need for federal and state policies and incentives to shift energy sources used in these sectors to meet national and international climate change goals while supporting food security.

Post-RFS supports for cellulosic ethanol: Evaluation of economic and environmental impacts of alternative policies

Cellulosic ethanol is a sustainable and low-emission transportation fuel, but production has been low in the past years. Existing policies, including the Renewable Fuel Standard (RFS) and producers’ tax credit, have been driving the market for cellulosic ethanol. But they are expected to expire at the end of 2022 and their efficacy is compromised by multiple waivers and exemptions. This study models potential regulatory and financial policy instruments and evaluates their cost-effectiveness in supporting cellulosic ethanol in the post-RFS era using the National Energy Modeling System (NEMS), a hybrid computational general equilibrium model of the U.S. energy market. The analysis suggests that the high volumetric mandate and financial subsidy will increase cellulosic ethanol production from 20.78 million liters (ML) to 57.84–65.94 ML in 2050. When policies are bundled, they will boost production reaching 63.29–65.94 ML more quickly than a singular policy. Finally, cost-benefit analysis and sensitivity analysis were employed to study the tradeoff of economic cost and environmental benefits of the policies. The result finds that the high mandate scenario has the greatest net benefit, which is more cost-effective than other policy instruments.

Extending Macroeconomic Impacts Forecasting for NEMS

To comprehensively model the macroeconomic impacts that result from changes in long-term energy-economy forecasts, the United States (U.S.) Department of Energy’s National Energy Technology Laboratory (NETL) partnered with West Virginia University (WVU)’s Regional Research Institute to develop the NETL/WVU econometric input-output (ECIO) model. The NETL/WVU ECIO model is an impacts forecasting model that functions as an extension of the U.S. energy-economic models available from the U.S. Energy Information Administration’s National Energy Modeling System (NEMS) and the U.S. Environmental Protection Agency’s Market Allocation (MARKAL) model. The ECIO model integrates a macroeconomic econometric forecasting model and an input-output accounting framework along derived forecast scenarios detailing a baseline of the U.S. ener-gy-economy and an alternative forecast on how power generation resources can meet future levels of energy demand to generate estimates of the impacts to gross domestic product, employment, and labor income. This manuscript provides an overview of the model design, assumptions, and standard outputs.

The size, causes, and equity implications of the demand-response gap

Our analysis of policy options was motivated by an inexplicable under-investment in demand response (DR) in the U.S. state of Georgia. In addition to estimating the size of the DR gap, we identify its causes and consequences. By modifying parameters of the U.S. flagship National Energy Modeling System (NEMS), we generate a baseline DR forecast with a default 4% maximum on-peak demand reduction, an achievable case with a DR limit of 20%, and a technical scenario that also halved the cost of storage. The results document many benefits of DR including a demand-reduction induced price effect (DRIPE), which makes DR more equitable than many other clean-energy policies that shift costs to non-participants. Our modeling results, literature review, and focus group analysis enable identification of DR barriers and motivators related to financial costs, electricity rates, consumer bills, pollution emissions, public health, energy equity, and inclusion. Our results suggest that the DR gap is caused less by technology limitations than by the need for financing initiatives, market innovations, infrastructure modernization, and enablers of socio-economic inclusion. By studying a state that lags in DR implementation, other countries and sub-national entities where DR is under-utilized can learn from our findings and methods.

Effects of implausible power plant lifetime assumptions on US federal energy system projected costs, greenhouse gas emissions, air pollution, and water use

Abstract Climate change motivates rapid decarbonization of the energy sector, with a particular focus on decarbonizing and expanding the electricity system. Given multiple plausible pathways toward decarbonization, scenario-based analysis of potential energy futures is a common analytical approach in the field of macro-energy systems. Scenario analysis is particularly valuable for assessing multicriteria impacts and identifying potential unintended consequences, especially when policies focus on a specific indicator, like greenhouse gas emissions. In the United States (US), the federal government produces projections for the future US energy system using the National Energy Modeling System, published in an Annual Energy Outlook (AEO) that includes multiple economic and policy scenarios to inform decision making. Although AEO scenarios are not intended as predictions, they are widely used as contextual baselines for evaluating cost, pace, and environmental impacts of the energy transition. As the official energy system scenarios of the US, a wealthy, high-emissions country, AEO scenarios serve an anchoring function that influences perceptions of the relative implications of alternative energy transition strategies. This research shows that projected cost and environmental intensity of the US electricity system are surprisingly stable across AEO scenarios. Why? We show that by not incorporating business-as-usual license expiration and generator lifespans, AEO effectively assumes that power plants can run indefinitely, which overlooks major asset replacement costs and fleet turnover opportunities that would likely accelerate decarbonization. Assuming typical lifespans and licensed operating periods, roughly 700 gigawatts (GW) of fossil and nuclear electricity generation capacity beyond the 170 GW of retirements modeled in AEO2020’s reference case would be expected to close, with an estimated unmodeled replacement cost of 2019$1.3 trillion. This cost is similar to the projected cost impact of a $35/tonne carbon dioxide price. Macro-energy systems analysis can be improved with more attention to physical and infrastructural constraints in models.

Estimating future industrial emissions of hazardous air pollutants in the United States using the National Energy Modeling System (NEMS)

Retrospective studies such as the National Air Toxics Assessment have focused on modeling hazardous air pollutant (HAP) concentrations and exposures in the near past; however, future projections of HAPs emissions are relatively unexplored compared to those for greenhouse gases or criteria pollutants, and HAPs are typically not included in energy systems optimization or integrated assessment modeling. In this study, we demonstrate a method that harnesses sector-based emissions data from the US environmentally extended input-output (USEEIO) model and scenario results from the National Energy Modeling System (NEMS) to project HAPs emissions from industrial sectors in United States, disaggregated to 9 regions. The results show that most HAPs have higher projected emissions in 2050 compared to the baseline year when assuming constant emissions intensities per unit of economic output, but lower emissions when trend values for pollution control and technology improvements are incorporated. Regional and temporal patterns are diverse among the 237 HAPs considered. Using formaldehyde as a prominent example, the oil and gas sector is the largest national contributor (49%-77%) to future formaldehyde emissions across scenarios. Overall, this method enables projections of industrial HAPs emissions, aligned with official NEMS scenarios, that can be used in assessments of future air quality and potential health impacts from HAPs.

Linking energy sector and air quality models through downscaling: Long-run siting of electricity generators to account for spatial variability and technological innovation

Modeling the air pollution implications of long-term energy transitions requires a downscaling process as an intermediate step between national-scale energy models and fine-scaled air quality models. Traditional “Grow-in-Place” (GIP) downscaling methods assume that future patterns of generator siting and emissions will be similar to those in the past. However, rapid technological change and shifting policy might yield very different future spatial patterns of power emissions. Here, we propose a “Site-and-Grow” (SAG) downscaling framework to couple the Electricity Market Module (EMM) of the National Energy Modeling System (NEMS) with the Community Multi-scale Air Quality (CMAQ) model to simulate future changes in emissions from power sector. The SAG framework consists of two steps. First, we downscale regional energy information to subregions using a modified generation expansion model under the assumption that economic fundamentals drive decisions at that scale. Second, we use GIS-based screening to locate potential sites for new power plants, and specify the final county-level placement using a multicriteria value function, assuming that land use and environmental constraints are most influential. The method is implemented in one EMM region (Carolinas and Virginia) as a case study. We compare spatial and temporal variability of downscaled emissions using both GIP and SAG methods, as well as emissions differences among four NEMS scenarios (base case, high natural gas consumption, high penetration of electric vehicles, and marine vessel electrification in ports). The results indicate that coal power plant emissions such as SO2 and NOx continue to dominate emissions from all other traditional power plants even in 2040, which suggests that emission changes will mainly be determined by where old coal plants are retired. An ANOVA (analysis of variance) comparison of four energy scenarios with two downscaling methods shows that the choice of downscaling method can contribute as much to emissions patterns as much as the choice of scenario.

Multi-input, Multi-output Hybrid Energy Systems

Summary Jurisdictions and industries are setting ambitious goals to decarbonize energy systems. Low-cost wind, solar, and natural gas and the resultant dynamic electric grid require energy technologies to adapt in order to meet key attributes for modern energy systems: resilience, reliability, security, affordability, flexibility, and sustainability. When considering energy sources independently and competitively, value-added synergies among energy technologies may be overlooked for meeting demanding, multidimensional requirements. This paper presents novel concepts for tightly coupled hybrid energy systems that leverage capabilities of diverse energy generators, including renewable, nuclear, and fossil with carbon capture, to provide power, heat, mobility, and other energy services. The paper also presents a framework for engineering-based modeling and analysis for complex optimization of energy generation, transmission, services, processes and products, and market interactions. New modeling capabilities are needed to adequately represent multi-input, multi-output tightly coupled hybrid energy systems that utilize multiple feedstocks to create multiple products and services in novel and synergistic ways through increased coordination of energy systems and tightly coupled hybrid system configurations.

Appending material flows to the National Energy Modeling System (NEMS) for projecting the physical economy of the United States

Appending material flows to the National Energy Modeling System (NEMS) for projecting the physical economy of the United States

Profitable Decarbonization through E-Mobility

This paper focuses on the interdependent relationship of power generation, transportation and CO2 emissions to evaluate the impact of electric vehicle deployment on power generation and CO2 emissions. The value of this evaluation is in the employment of a large-scale, bottom-up, national energy modeling system that encompasses the complex relationships of producing, transforming, transmitting and supplying energy to meet the useful demand characteristics with great technological detail. One of such models employed in this analysis is the BUEMS model. The BUEMS model provides evidence of win-win policy options that lead to profitable decarbonization using Turkey’s data in BUEMS. Specifically, the result shows that a ban on diesel fueled vehicles reduces lifetime emissions as well as lifetime costs. Furthermore, model results highlight the cost-effective emission reduction potential of e-buses in urban transportation. More insights from the results indicate that the marginal cost of emission reduction through e-bus transportation is much lower than that through other policy measures such as carbon taxation in transport. This paper highlights the crucial role the electricity sector plays in the sustainability of e-mobility and the value of related policy prescriptions.

EMF32 RESULTS FROM NEMS: REVENUE RECYCLING

We show results from the National Energy Modeling System generated during the Energy Modeling Forum 32 study — focusing on macroeconomic responses to different methods of recycling carbon tax revenue. We find that recycling such revenue directly to consumers in the form of lump sum payments results in smaller negative GDP impacts than using the revenues to reduce business taxes.

Sensitivity of natural gas deployment in the US power sector to future carbon policy expectations

One option for reducing carbon emissions in the power sector is replacement of coal-fired generation with less carbon-intensive natural gas combined cycle (NGCC) generation. In the United States, where there is abundant, low-cost natural gas supply, increased NGCC deployment could be a cost-effective emissions abatement opportunity at relatively modest carbon prices. However, under scenarios in which carbon prices rise and deeper emissions reductions are achieved, other technologies may be more cost-effective than NGCC in the future. In this analysis, using a US energy system model with foresight (a version of the National Energy Modeling System or “NEMS” model), we find that varying expectations about carbon prices after 2030 does not materially affect NGCC deployment prior to 2030, all else equal. An important implication of this result is that, under the set of natural gas and carbon price trajectories explored here, myopic behavior or other imperfect expectations about potential future carbon policy do not change the natural gas deployment path or lead to stranded natural gas generation infrastructure. We explain these results in terms of the underlying economic competition between available generation technologies and discuss the broader relevance to US climate change mitigation policy.

Exploring the impact of energy efficiency as a carbon mitigation strategy in the U.S.

As temperatures across the globe hit record highs and extreme climate events multiply, interest in least-cost CO2 mitigation pathways is growing. This paper examines the pros and cons of strengthening demand-side options in strategies to reduce carbon emissions from the U.S. electricity sector. To date, demand-side management in the U.S. power sector has received overly simplistic treatment in energy models. To help fill this gap, we develop a customized version of the National Energy Modeling System to assess a range of demand- and supply-side policy scenarios. This enables four research hypotheses to be tested, related to mitigation costs, investment in new natural gas plants, carbon leakage, and local air pollution.We conclude that the clean power transformation can be made more affordable by improving the efficiency of energy utilization. By downscaling the expansion of natural gas plants, energy efficiency can also avoid legacy impacts. While strong energy-efficiency policies lower overall CO2 emissions, coal plant retirements can be delayed, postponing associated local air quality benefits. Thus, we illustrate a limitation of single-pollutant policies while also demonstrating the value of co-optimizing demand- and supply-side carbon mitigation options.

OIL PRICE FORECASTS FOR THE LONG TERM: EXPERT OUTLOOKS, MODELS, OR BOTH?

Little is known about the accuracy of expert outlooks, so heavily relied upon by industry participants and policy makers, regarding the future path of oil prices. Using the regular publications by the Energy Information Administration (EIA), we examine the accuracy of annual recursive oil price forecasts generated by the National Energy Modeling System model of the Agency for forecast horizons of up to 15 years. Our results reveal that the EIA model outperforms the benchmark random walk model around the two ends of the forecast horizon spectrum. Additionally, at the longer horizons, simple econometric forecasting models often produce similar, if not better accuracy than the EIA model. Time varying such specifications generally also exhibit stability in their forecast performance. Finally, although combining forecasts does not change the overall patterns, some additional accuracy gains are obtained at intermediate horizons, and in some cases, forecast performance stability is also achieved.

Water-Energy Prototype Model for the NEMS Modeling Platform: Thermoelectric Water Demand and Its Implications on Regional Electricity Market

A simplified energy-water prototype model has been developed at the National Energy Technology Laboratory (NETL) as a part of a larger effort to comprehensively model energy-water interactions. The NETL Water-Energy Model (NWEM) prototype passively couples a variety of data on water supply, water availability, and power plant water use with the National Energy Modeling System (NEMS) power generation forecasts. NWEM operates at a watershed level and its efficacy in resolving local water supply and water-use trade-offs was demonstrated using data from Sandia National Laboratory along with a water supply scenario projected by the World Resources Institute (WRI). The prototype model only passively utilized a forecast of power generation from an existing forecast; the model’s choices were limited to purchases or retrofitting to meet future water supply constraints. NETL is continuing to integrate the water sub-module into the NEMS framework, which will allow active interaction between the water market and power markets, extending the industry’s ability to re-dispatch its generating units with the price of water as one of the variable costs.

Modeling climate-driven changes in U.S. buildings energy demand

How climate change might impact energy demand is not well understood, yet energy forecasting requires that assumptions be specified. This paper reviews the literature on the relationship between global warming and the demand for space cooling in buildings. It then estimates two key parameters that link energy for space cooling to cooling degree days (CDDs) using data for nine U.S. Census divisions, which is the spatial resolution of the National Energy Modeling System (NEMS). The first parameter is the set point temperature for calculating CDDs; the second is the exponent for representing the relationship between changes in CDDs and changes in electricity consumption for space cooling. We find that the best-fitting CDDs have a set point of 67 °F (19.4 °C), for both residential and commercial buildings, rather than the conventional 65 °F (18.3 °C). Set points also vary by region, with warmer regions tending to have higher set points. When CDDs are based on the conventional set point, the best fitting exponent is 1.5 for both residential and commercial buildings, indicating that space cooling is more climate-sensitive than is specified in NEMS. As a result, the official projections of U.S. energy consumption would appear to underestimate the energy required for space cooling.

The economic effect of efficiency programs on energy consumers and producers

An increase in the efficiency of natural gas fired residential appliances allows users to realize the same level of service, heating water for example, while using less natural gas. In addition to this technological benefit to the residential sector, the reduced demand for natural gas depresses the price of natural gas, resulting in pecuniary gains to other energy consumers and pecuniary losses to energy producers. The question we address in this study is whether purely pecuniary effects, those that follow from the price changes elicited by lower usage of natural gas, should enter the debate concerning the implementation of energy efficiency programs. To that end, we explore the price and social welfare impacts of natural gas energy efficiency standards by evaluating the impacts of a specific efficiency standard using the National Energy Modeling System. Our analysis indicates that purely pecuniary losses to producers are largely offset by pecuniary benefits to consumers. Our analysis also provides useful insight into the sources of these benefits and losses. Although our results are based on a specific model and efficiency standard, we believe that the results generalize to other efficiency programs and would be reproduced using other energy models.

Peak Electricity and the Clean Power Plan

Key elements of EPA's Clean Power Plan rely on forecasted electricity sales from the National Energy Modeling System (NEMS), but NEMS has consistently over-projected electricity sales. An analysis of the model's bias as applied by EPA raises concerns about the stringency of the proposed emissions targets.

Demand response: A carbon-neutral resource?

Recent literature on demand response raises questions about the long-term capacity and carbon emissions impacts of expanding its deployment. To provide economy-wide insights into how demand response, capacity planning, and carbon emissions might interact in the future, we perform economic forecasts using a computational general equilibrium model based on the Energy Information Administration's National Energy Modeling System. We develop multiple scenarios of assumptions about the load-shifting and load reduction potential of demand response based on prior literature. The results of these scenarios suggest that demand response can defer large amounts of peak capacity construction. Contrary to expectations of increased carbon intensity, the results of our scenarios also suggest that demand response will have little impact on overall carbon emissions from electric power generation. This suggests that demand response can serve as a long-term, low-cost alternative for peak-hour load balancing without increasing carbon emissions.