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Sigrin, B.

Publications and source records attributed to Sigrin, B..

Representation of the Solar Capacity Value in the ReEDS Capacity Expansion Model: Preprint

An important emerging issue is the estimation of renewables' contributions to reliably meeting system demand, or their capacity value. While the capacity value of thermal generation can be estimated easily, assessment of wind and solar requires a more nuanced approach due to resource variability. Reliability-based methods, particularly, effective load-carrying capacity (ELCC), are considered to be the most robust techniques for addressing this resource variability. The Regional Energy Deployment System (ReEDS) capacity expansion model and other long-term electricity capacity planning models require an approach to estimating CV for generalized PV and system configurations with low computational and data requirements. In this paper we validate treatment of solar photovoltaic (PV) capacity value by ReEDS capacity expansion model by comparing model results to literature for a range of energy penetration levels. Results from the ReEDS model are found to compare well with both comparisons--despite not being resolved at an hourly scale.

14 SOLAR ENERGY↗

Distributed Wind Diffusion Model Overview (Presentation)

Distributed wind market demand is driven by current and future wind price and performance, along with several non-price market factors like financing terms, retail electricity rates and rate structures, future wind incentives, and others. We developed a new distributed wind technology diffusion model for the contiguous United States that combines hourly wind speed data at 200m resolution with high resolution electricity load data for various consumer segments (e.g., residential, commercial, industrial), electricity rates and rate structures for utility service territories, incentive data, and high resolution tree cover. The model first calculates the economics of distributed wind at high spatial resolution for each market segment, and then uses a Bass diffusion framework to estimate the evolution of market demand over time. The model provides a fundamental new tool for characterizing how distributed wind market potential could be impacted by a range of future conditions, such as electricity price escalations, improvements in wind generator performance and installed cost, and new financing structures. This paper describes model methodology and presents sample results for distributed wind market potential in the contiguous U.S. through 2050.

17 WIND ENERGY↗