7This paper investigates the impact of wind power on electricity prices using a production cost 8 model of the Independent System Operator -New England power system. Different scenarios in 9 terms of wind penetration, wind forecasts, and wind curtailment are modeled in order to analyze 10 the impact of wind power on electricity prices for different wind penetration levels and for 11 different levels of wind power visibility and controllability. The analysis concludes that 12 electricity price volatility increases even as electricity prices decrease with increasing wind 13 penetration levels. The impact of wind power on price volatility is larger in the shorter term (5-14 minutes compared to hour-to-hour). The results presented show that over-forecasting wind power 15 increases electricity prices while under-forecasting wind power reduces them. The modeling 16 results also show that controlling wind power by allowing curtailment reduces electricity prices, 17 and for higher wind penetrations it also reduces their volatility.18 19

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Summary A decade of research has shown that numerical weather prediction (NWP)‐modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high‐quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP‐modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP‐modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. Finally, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble‐based wind resource data sets.

Executive SummaryHydropower facilities are important assets for the electric power sector and represent a key source of flexibility for electric grids with high penetrations of variable generation. As variable renewable generation sources expand, understanding the capabilities and limitations of the flexibility from hydropower resources is important for grid planning. Appropriately modeling these resources, however, is difficult because of the wide variety of constraints these plants face that other generators do not. These constraints can be broadly categorized as environmental, operational, and regulatory. This report highlights several key issues incorporating these constraints when modeling hydropower operations in production cost and capacity expansion models. Many of these challenges involve a lack of data to adequately represent the constraints or issues of model complexity and run time. We present several potential methods for improving the accuracy of hydropower representation in these models to allow for a better understanding of hydropower's capabilities on the electric grid.