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The Interconnections Seam Study examines the potential economic value of increasing electricity transfer between the Eastern and Western Interconnections using high-voltage direct-current (HVDC) transmission and cost-optimizing both generation and transmission resources across the United States, proposing, assessing, justifying, and illustrating a major infrastructure change involving two of the worlds largest power grids. The study conducted a multi-model analysis that used co-optimized generation and transmission expansion planning and production cost modeling. Four transmission designs under eight scenarios were developed and studied to estimate costs and potential benefits. The results show benefit-to-cost ratios that reach as high as 2.5, indicating significant value to increasing the transmission capacity between the interconnections under the cases considered, realized through sharing generation resources and flexibility across regions.

Summary Understanding the technical and economic challenges of achieving 100% renewable energy (RE) electric power systems is critical, given the increasing number of United States regional and state commitments toward this goal. Although no detailed study of a major utility of large interconnection under 100% RE system has been published, considerable literature explores the potential to greatly increase RE penetration. This literature, combined with real-world experience with increased RE deployment, points to two main challenges associated with achieving 100% RE across all timescales: (1) economically maintaining a balance of supply and demand and (2) designing technically reliable grids using largely inverter-based resources. The first challenge results in a highly nonlinear increase in costs as the system approaches 100% RE, in large part because of seasonal mismatches. The second challenge might require new inverter designs, depending on the mix of RE technologies. Analysis and experience to date point to no fundamental technical reasons why a 100% RE electric power system cannot be achieved, but the economic challenges indicate the need for advancements in several technologies and careful consideration of the suite of options that could be used to achieve equivalent carbon-reduction goals. Previous work also points to the need for analytic tool development, and techno-economic feasibility analysis must also consider the host of regulatory, market, and policy issues that might limit the ability to deploy mixes of resources that are suggested by least-cost modeling exercises.

Grid integration studies are key to understanding our ability to integrate variable generation resources into the power system and evaluating the associated costs and benefits. In these studies, it is important to understand the flexibility of the thermal power fleet, including how thermal plants operate at part load. Without a comprehensive understanding of thermal plant operation, we may over- or underestimate our ability to integrate variable generation resources and thus draw incomplete or inaccurate conclusions regarding their potential economic and environmental effects. The only public data source for understanding many elements of the operational characteristics of the thermal fleet is the U.S. Environmental Protection Agency Clean Air Markets database of historical power plant operation. However, though these data sets have been widely utilized, their use has proven to be difficult, and methods to clean and filter the data are not transparent. Here, we describe the database and a method to clean and filter it. We then use the cleaned database to demonstrate several characteristics of historical plant operation, including frequent part load operation. Finally, we provide a cleaned data set with heat rate curves and describe how to use it in general modeling activities and analysis.

An indisputable fact cannot be rebutted. It is supported by theory and experience. Over the past 25 years, wind and solar generation has undergone dramatic growth, resulting in a variety of experiences that model the integration of wind and solar into the planning and operation of modern electric power systems. In this article, we bring together examples from Europe, North America, and Australia to identify five indisputable facts about planning and operating modern power systems. Taken together, we hope these experiences can help build consensus among the engineering and public policy communities about the current state of wind and solar integration and also facilitate conversations about evolving future challenges.

This study explores how emissions from electricity generation in the Western Interconnection region of the U.S. might respond in circa 2030 to contrasting scenarios for fuel prices and greenhouse gas (GHG) emissions fees. We examine spatial and temporal variations in generation mix across the region and year using the PLEXOS unit commitment and dispatch model with a production cost model database adapted from the Western Electricity Coordinating Council. Emissions estimates are computed by combining the dispatch model results with unit-specific, emissions-load relationships. Wind energy displaces natural gas and coal in scenarios with relatively expensive natural gas or with GHG fees. Correspondingly, annual emissions of NOx, SO2, and CO2 are reduced by 20–40% in these cases. NOx emissions, which are a concern as a precursor of ground-level ozone, are relatively high and consistent across scenarios during summer, when peak electricity loads occur and wind resources in the region are comparatively weak. Accounting for the difference in start-up versus stabilized NOx emissions rates for natural gas plants had little impact on region-wide emissions estimates due to the dominant contribution from coal-fired plants, but would be more important in the vicinity of the natural gas units.

Residential distributed photovoltaic (PV) deployment in the United States has experienced robust growth, and policy changes impacting the value of solar are likely to occur at the federal and state levels. To establish a credible baseline and evaluate impacts of potential new policies, this analysis employs multiple methods to forecast residential PV deployment in California, including a time-series forecasting model, a threshold heterogeneity diffusion model, a Bass diffusion model, and National Renewable Energy Laboratory's dSolar model. As a baseline, the residential PV market in California is modeled to peak in the early 2020s, with a peak annual installation of 1.5–2 GW across models. We then use the baseline results from the dSolar model and the threshold model to gauge the impact of the recent federal investment tax credit (ITC) extension, the newly approved California net energy metering (NEM) policy, and a hypothetical value-of-solar (VOS) compensation scheme. We find that the recent ITC extension may increase annual PV installations by 12%–18% (roughly 500 MW) for the California residential sector in 2019–2020. The new NEM policy only has a negligible effect in California due to the relatively small new charges (

The integration of large amounts of variable renewable generation can increase the demand on flexible resources in the power system. Conventional hydropower can be an important asset for managing variability and uncertainty in the power system, but multi-purpose reservoirs are often limited by non-power constraints. Previous large-scale variable generation integration studies have simulated the operation of the electric system under different penetration levels but often with simplified representations of hydropower to avoid complex non-power constraints. This paper illustrates the value of bridging the gap between power system models and detailed hydropower models with a demonstration case. The United States Western Interconnection is modeled with PLEXOS, and ten large reservoirs on the Columbia River are modeled with RiverWare. The results show the effect of detailed hydropower modeling on the power system and its benefits to the power system, such as the decrease in overall production cost and the reduction of variable generation curtailment.

This presentation provides an overview of new and ongoing NREL research that aims to improve our understanding of reliability and revenue sufficiency challenges through modeling tools within a markets framework.

This article describes selected results from a study examining some of the benefits and challenges of meeting up to 20% of U.S. electricity demand with solar technologies. The discussion in this article will focus on the "20% scenario," which is part of the larger ongoing study of the barriers to large scale deployment of solar energy. (The larger study being conducted by the U.S. Department of Energy's Solar Energy Technology Program with contributions from a large number of individuals in industry and academia is tentatively titled the Solar Vision study.) This is not intended to be a detailed integration study. Results to date, however, provide a preliminary examination of the issues associated with deploying solar energy on a large scale in the United States.

Personal exposure to fine particulate matter (PM2.5) is due to both indoor and outdoor sources. Contributions of sources to personal exposure can be quite different from those observed at ambient sampling locations. The primary goal of this study was to investigate the effectiveness of using trace organic speciation data to help identify sources influencing PM2.5 exposure concentrations. Sixty-four 24-h PM2.5 samples were obtained on seven different subjects in and around Boulder, CO. The exposure samples were analyzed for PM2.5 mass, elemental and organic carbon, organic tracer compounds, water-soluble metals, ammonia, and nitrate. This study is the first to measure a broad distribution of organic tracer compounds in PM2.5 personal samples. PM2.5 mass exposure concentrations averaged 8.4 μg m −3 . Organic carbon was the dominant constituent of the PM2.5 mass. Forty-four organic species and 19 water-soluble metals were quantifiable in more than half of the samples. Fifty-four organic species and 16 water-soluble metals had measurement signal-to-noise ratios larger than two after blank subtraction. The dataset was analyzed by Principal Component Analysis (PCA) to determine the factors that account for the greatest variance. Eight significant factors were identified; each factor was matched to its likely source based primarily on the marker species that loaded the factor. The results were consistent with the expectation that multiple marker species for the same source loaded the same factor. Meat cooking was an important source of variability. The factor that represents meat cooking was highly correlated with organic carbon concentrations ( r = 0.84). The correlation between ambient PM2.5 and PM2.5 exposure was relatively weak ( r = 0.15). Time participants spent performing various activities was generally not well correlated with PCA factor scores, likely because activity duration does not measure emissions intensity. The PCA results demonstrate that organic tracers can aid in identifying factors that influence personal exposures to PM2.5.

A Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM2.5 concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A circular block bootstrap is used to create replicate datasets, with the same receptor model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across the model results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability across results yet also be very biased. These findings are likely dependent on characteristics of the data.

In 2013, the California Independent System Operator published the "duck chart,"" which shows a significant drop in mid-day net load on a spring day as solar photovoltaics (PV) are added to the system. The chart raises concerns that the conventional power system will be unable to accommodate the ramp rate and range needed to fully utilize solar energy, particularly on days characterized by the duck shape. This could result in "overgeneration"" and curtailed renewable energy, increasing its costs and reducing its environmental benefits. This paper explores the duck chart in detail, examining how much PV might need to be curtailed if additional grid flexibility measures are not taken, and how curtailment rates can be decreased by changing grid operational practices. It finds that under business-as-usual types of assumptions and corresponding levels of grid flexibility in California, solar penetrations as low as 20 percent of annual energy could lead to marginal curtailment rates that exceed 30 percent. However, by allowing (or requiring) distributed PV and storage (including new installations that are part of the California storage mandate) to provide grid services, system flexibility could be greatly enhanced. Doing so could significantly reduce curtailment and allow much greater penetration of variable generationmore » resources in achieving a 50 percent renewable portfolio standard. Overall, the work described in this paper points to the need to fully integrate distributed resources into grid system planning and operations to allow maximum use of the solar resource.« less

Capacity expansion models (CEM) provide a high-level long-term view at the prospects of the evolving power system. In simulating the possibilities of long-term capacity expansion, it is important to maintain the viability of power system operation in the short-term (daily, hourly and sub-hourly) scales. Production-cost models (PCM) simulate routine power system operation on these shorter time scales using detailed load, transmission and generation fleet data by minimizing production costs and following reliability requirements. When based on CEM 'predictions' about generating unit retirements and buildup, PCM provide more detailed simulation for the short-term system operation and, consequently, may confirm the validity of capacity expansion predictions. Further, production cost model simulations of a system that is based on capacity expansion model solution are 'evolutionary' sound: the generator mix is the result of logical sequence of unit retirement and buildup resulting from policy and incentives. The above has motivated us to bridge CEM with PCM by building a capacity expansion - to - production cost model Linking Tool (CEPCoLT). The Linking Tool is built to onset capacity expansion model prescriptions onto production cost model inputs. NREL's ReEDS and Energy Examplar's PLEXOS are the capacity expansion and the production cost models, respectively. Via themore » Linking Tool, PLEXOS provides details of operation for the regionally-defined ReEDS scenarios.« less

Enhanced prospects for natural gas production raise questions about the balance of impacts on air quality, as increased emissions from production activities are considered alongside the reductions expected when natural gas is burned in place of other fossil fuels. This study explores how trends in natural gas production over the coming decades might affect emissions of greenhouse gases (GHG), volatile organic compounds (VOCs) and nitrogen oxides (NOx) for the United States and its Rocky Mountain region. The MARKAL (MARKet ALlocation) energy system optimization model is used with the U.S. Environmental Protection Agency's nine-region database to compare scenarios for natural gas supply and demand, constraints on the electricity generation mix, and GHG emissions fees. Through 2050, total energy system GHG emissions show little response to natural gas supply assumptions, due to offsetting changes across sectors. Policy-driven constraints or emissions fees are needed to achieve net reductions. In most scenarios, wind is a less expensive source of new electricity supplies in the Rocky Mountain region than natural gas. U.S. NOx emissions decline in all the scenarios considered. Increased VOC emissions from natural gas production offset part of the anticipated reductions from the transportation sector, especially in the Rocky Mountain region.

High penetrations of wind and solar power will impact the operations of the conventional generators on the power system. Regional integration studies have shown that wind and solar may cause fossil-fueled generators to cycle on and off and load follow more frequently and potentially more rapidly. Increased cycling, deeper load following, and rapid ramping may result in wear and tear impacts on fossil-fueled generators that lead to increased capital and maintenance costs, increased equivalent forced outage rates, and degraded performance over time. Heat rates and emissions from fossil-fueled generators may be higher during cycling and ramping than during steady-state operation. Many wind and solar integration studies have not taken these increased cost and emissions impacts into account because data have not been available. This analysis considers the cost and emissions impacts of cycling and ramping of fossil-fueled generation to refine assessments of wind and solar impacts on the power system.

The Renewable Electricity Futures (RE Futures) Study investigated the challenges and impacts of achieving very high renewable electricity generation levels in the contiguous United States by 2050. The analysis focused on the sufficiency of the geographically diverse U.S. renewable resources to meet electricity demand over future decades, the hourly operational characteristics of the U.S. grid with high levels of variable wind and solar generation, and the potential implications of deploying high levels of renewables in the future. RE Futures focused on technical aspects of high penetration of renewable electricity; it did not focus on how to achieve such a future through policy or other measures. Given the inherent uncertainties involved with analyzing alternative long-term energy futures as well as the multiple pathways that might be taken to achieve higher levels of renewable electricity supply, RE Futures explored a range of scenarios to investigate and compare the impacts of renewable electricity penetration levels (30%-90%), future technology performance improvements, potential constraints to renewable electricity development, and future electricity demand growth assumptions. RE Futures was led by the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of Technology (MIT).

This document describes the use of production cost modeling in the SunShot Vision study, including methods used to create the SunShot Vision scenarios, their implementation in the Gridview model, and assumptions regarding transmission system and operation of each generator type. It also describes challenges and limitations of modeling solar generation technologies in production cost models, and suggests methods for improving their representation in current models.

This study explores how ozone concentrations in the Denver, CO area might have been different if plug-in hybrid electric vehicles (PHEVs) had replaced light duty gasoline vehicles in summer 2006. A unit commitment and dispatch model was used to estimate the charging patterns of PHEVs and dispatch power plants to meet electricity demand. Emission changes were estimated based on gasoline displacement and the emission characteristics of the power plants providing additional electricity. The Comprehensive Air Quality Model with extensions (CAMx) was used to simulate the effects of these emissions changes on ozone concentrations. Natural gas units provided most of the electricity used for charging PHEVs in the scenarios considered. With 100% PHEV penetration, nitrogen oxide (NO(x)) emissions were reduced by 27 tons per day (tpd) from a fleet of 1.7 million vehicles and were increased by 3 tpd from power plants; VOC emissions were reduced by 57 tpd. These emission changes reduced modeled peak 8-h average ozone concentrations by approximately 2-3 ppb on most days. Ozone concentration increases were modeled for small areas near central Denver. Future research is needed to forecast when significant PHEV penetration may occur and to anticipate characteristics of the corresponding power plant and vehicle fleets.

Positive matrix factorization (PMF) was applied to synthetic datasets that simulate personal exposures to airborne PM2.5 from 12 sources. Three differentfilter analysis scenarios using different analytical chemistry techniques were considered. The full suite scenario quantified elemental carbon, organic carbon, inorganic ions, trace elements, and trace organic species including carboxylic acids and organic compounds with -OH functionality. A second scenario excluded trace elements and a third assumed that derivatization steps to quantify polar organic compounds were not performed. Similar errors in source apportionment were seen with all three scenarios. In most cases, PMF failed to separate out factors corresponding to road dust and vegetative debris, two sources that made relatively uniform contributions to the synthetic exposures. Factors representing wood smoke, natural gas combustion, and meat cooking sources were difficult to identify due to a lack of unique tracers with concentrations reliably above the detection limits assumed in the study. Factors representing cigarette smoke, candle smoke, gasoline exhaust, and secondary aerosols were comparatively easy to identify. When contributions from a pair of sources, such as diesel and gasoline exhaust, were highly correlated in the synthetic datasets, a single factor corresponding to both sources was usually found.