Simulating Annual Variation in Load, Wind, and Solar by Representative Hour Selection

Blanford, Geoffrey J.; Merrick, James; Bistline, John; Young, D. T.

doi:10.5547/01956574.39.3.gbla

Cited by 58 publications

(28 citation statements)

References 26 publications

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“…Temporal and spatial resolution decisions in model construction may materially affect their ability to represent these technologies, which may understate deployment of specific assets and overstate others. For instance, capturing drivers and impacts of higher-than-anticipated cost reductions for solar and storage would increase deployment (ceteris paribus), but there are also questions about whether models are adequately representing endogenous value deflation at higher penetrations (i.e., declining economic value of added capacity), which would decrease deployment (Blanford et al, 2018).…”

Section: Effect Of Policy Scenarios On Individual Technologiesmentioning

confidence: 99%

Electric sector policy, technological change, and U.S. emissions reductions goals: Results from the EMF 32 model intercomparison project

Bistline¹,

Hodson²,

Rossmann³

et al. 2018

Energy Economics

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The Energy Modeling Forum (EMF) 32 study compares a range of coordinated scenarios to explore implications of U.S. climate policy options and technological change on the electric power sector. Harmonized policy scenarios (including mass-based emissions limits and various powersector-only carbon tax trajectories) across 16 models provide comparative assessments of potential impacts on electric sector investment and generation outcomes, emissions reductions, and economic implications. This paper compares results across these policy alternatives, including a variety of technological and natural gas price assumptions, and summarizes robust findings and areas of disagreement across participating models. Under a wide range of policy, technology, and market assumptions, model results suggest that future coal generation will decline relative to current levels while generation from natural gas, wind, and solar will increase, though the pace and extent of these changes vary by policy scenario, technological assumptions, region, and model. Climate policies can amplify trends already under way and make them less susceptible to future market changes. The model results provide useful insights to a range of stakeholders, but future research focused on intersectoral linkages in emission reductions (e.g., the role of electrification), effects of energy storage, and better coverage of bioenergy with carbon capture and storage (BECCS) can improve insights even further.

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Section: Effect Of Policy Scenarios On Individual Technologiesmentioning

confidence: 99%

Electric sector policy, technological change, and U.S. emissions reductions goals: Results from the EMF 32 model intercomparison project

Bistline¹,

Hodson²,

Rossmann³

et al. 2018

Energy Economics

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show abstract

“…The temporal resolution of the model dispatch refers to the number of such time slices. Numerous methods exist exogenously selecting individual hours or representative time steps from a full year of data; these include clustering techniques to select characteristic, aggregating similar hours, and using characteristic time blocks by day and/or season (Getman et al 2015;Blanford et al 2016;Nahmmacher et al 2016;Santen et al 2017). Temporal resolution is also reflected in the underlying VRE data used in these down-scaling methods, as well as in metrics calculated outside the optimization (but still endogenous to the model) to capture intra-time-slice VRE characteristics.…”

Section: Spatial and Temporal Resolutionmentioning

confidence: 99%

“…Table 2 summarizes many of these VRE attributes, which we compiled from various sources, including Milligan et al (2016) and Kroposki et al (2017). These attributes, to varying degrees, have economic implications for VRE, other electric sector investments, and system operations (Ueckerdt et al 2013;Blanford et al 2016). For example, VRE plant output is weather-driven and varies considerably from one location to another, while conventional 1 Some national-scale U.S. models also represent portions of the Canadian power system because of the synchronous interconnection ties across the two power systems.…”

Section: Introductionmentioning

confidence: 99%

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

Cole

Frew

Trieu

et al. 2017

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Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision makers. With the recent surge in variable renewable energy (VRE) generators-primarily wind and solar photovoltaics-the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. To assess current best practices, share methods and data, and identify future research needs for VRE representation in capacity expansion models, four capacity expansion modeling teams from the Electric Power Research Institute, the U.S. Energy Information Administration, the U.S. Environmental Protection Agency, and the National Renewable Energy Laboratory conducted two workshops of VRE modeling for national-scale capacity expansion models. The workshops covered a wide range of VRE topics, including transmission and VRE resource data, VRE capacity value, dispatch and operational modeling, distributed generation, and temporal and spatial resolution. The objectives of the workshops were both to better understand these topics and to improve the representation of VRE across the suite of models. Given these goals, each team incorporated model updates and performed additional analyses between the first and second workshops. This report summarizes the analyses and model "experiments" that were conducted as part of these workshops as well as the various methods for treating VRE among the four modeling teams. The report also reviews the findings and learnings from the two workshops. We emphasize the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making.

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“…The analysis uses the US Regional Economy, Greenhouse Gas, and Energy (US-REGEN) modeling framework to understand the relative importance of drivers to wind and solar penetration. US-REGEN is an economic capacity planning and dispatch model of the US electric sector and uses an innovative algorithm to capture the joint variation in load, wind, and solar output in a multidecadal capacity planning model (Blanford et al 2018). These unique features allow US-REGEN to evaluate how the cost of variable renewable technologies compares with their (declining) value, which can change in different locations, times, and deployment levels and can be difficult to capture in models with lower temporal resolutions (Cole et al 2017).…”

Section: Analytical Approachmentioning

confidence: 99%

“…All versions of US-REGEN represent the full spectrum of time-series variability to capture periods when, for instance, load is high and renewable output is low and periods when load is low but renewable output is high. Incomplete representations of this covariation can misvalue system resources (Merrick 2016, Blanford et al 2018. US-REGEN makes linked decisions about new generation investments and hourly system dispatch and co-optimizes transmission investment and trade.…”

Section: Analytical Approachmentioning

confidence: 99%

Economic drivers of wind and solar penetration in the US

Bistline

Young

2019

Environ. Res. Lett.

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Much has been made of the potential for wind and solar generation to supply cheap, low-emissions electricity, but considerable disagreement exists as to which combinations of many potential drivers will enable deep penetration of these technologies. Most existing analyses consider limited factors in isolation, such as investment costs or energy storage, and do not provide rigorous support for understanding which combinations of factors could underpin a leading role for wind and solar. This study addresses this gap by undertaking a systematic sensitivity analysis using a state-of-the-art energy-economic model to comprehensively evaluate the relative magnitudes of five key drivers that may influence future wind and solar deployment in the United States. We find future wind and solar capital costs and carbon policy are the dominant factors, causing the average wind and solar share to vary by 38 and 31 percentage points, respectively. Transmission and storage availability have much smaller effects, causing the average share to vary by no more than 15 and 5 percentage points, respectively. No single factor unilaterally determines wind and solar deployment. The variable renewable share of electricity generation never reaches 100% nationally in any scenario even with lowcost storage, as decreasing marginal returns at higher deployments eventually outpace cost reductions. Average wind and solar shares and ranges of possible outcomes are higher in this study relative to recent multi-model comparison studies due to lower renewable costs and the potential for more stringent policies. Understanding drivers and barriers to renewable deployment has important ramifications for technology developers, infrastructure, market design, and policymakers, and this research provides insights as to which combinations of drivers lead to the greatest share of economic wind and solar deployment and why.

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Simulating Annual Variation in Load, Wind, and Solar by Representative Hour Selection

Cited by 58 publications

References 26 publications

Electric sector policy, technological change, and U.S. emissions reductions goals: Results from the EMF 32 model intercomparison project

Electric sector policy, technological change, and U.S. emissions reductions goals: Results from the EMF 32 model intercomparison project

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

Economic drivers of wind and solar penetration in the US

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