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A Clean Energy Standard Analysis with the US-REGEN Model

Geoffrey J. Blanford, James H. Merrick, and David Young

Year: 2014
Volume: Volume 35
Number: Special Issue
DOI: 10.5547/01956574.35.SI1.8
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Abstract:
A clean energy standard (CES) is a potential policy alternative to reduce carbon emissions in the electric sector. We analyze this policy under a range of technological assumptions, expanding on the Energy Modeling Forum (EMF) 24 study scenarios, using a new modeling tool, US-REGEN. We describe three innovative features of the model: treatment of spatial and temporal variability of renewable resources, cost-of-service electric sector pricing, and explicit representation of energy end-use specific capital. We find that varying technology assumptions results in vastly different futures, with large contrasts in the distribution and scale of inter-regional financial flows, and in the generation mix. We explore regional differences in how the costs of CES credits are passed through with cost-of-service vs. competitive pricing. Finally, we compare the CES to an economy-wide emissions cap. We find that although the two policies result in a similar generation mix, price and electricity end-use results differ. Keywords: Clean energy standard, Market-based environmental policy, Greenhouse gas mitigation, Energy modelling, Electricity modeling



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

Geoffrey J. Blanford, James H. Merrick, John E.T. Bistline, and David T. Young

Year: 2018
Volume: Volume 39
Number: Number 3
DOI: 10.5547/01956574.39.3.gbla
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Abstract:
The spatial and temporal variability of renewable generation has important economic implications for electric sector investments and system operations. This study describes a method for selecting representative hours to preserve key distributional requirements for regional load, wind, and solar time series with a two-orders-of-magnitude reduction in dimensionality. We describe the implementation of this procedure in the US-REGEN model and compare impacts on energy system decisions with more common approaches. The results demonstrate how power sector modeling and capacity planning decisions are sensitive to the representation of intra-annual variation and how our proposed approach outperforms simple heuristic selection procedures with lower resolution. The representative hour approach preserves key properties of the joint underlying hourly distributions, whereas seasonal average approaches over-value wind and solar at higher penetration levels and under-value investment in dispatchable capacity by inaccurately capturing the corresponding residual load duration curves.





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