Capacity value assessments of wind power

Milligan, Michael; Frew, Bethany; Ibáñez, Eduardo; Kiviluoma, Juha; Holttinen, Hannele; Söder, Lennart

doi:10.1002/wene.226

Cited by 28 publications

(24 citation statements)

References 33 publications

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“…To estimate capacity values, we use ERCOT's current method to quantify real-world IAV consequences (Electric Reliability Council of Texas 2017b). However, many methods to calculate capacity value exist (Milligan et al 2017), and future research should explore the sensitivity of capacity value IAV to different calculation methods. Per ERCOT's method, we calculate winter and summer capacity values as generation coincident with the top 20 demand hours in the winter (December-February) and summer (June-August).…”

Section: Quantifying Iav Of Wind and Solar Generation And Capacity Vamentioning

confidence: 99%

Inter-annual variability of wind and solar electricity generation and capacity values in Texas

Kumler

Carreño

Craig

et al. 2019

Environ. Res. Lett.

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As power systems shift towards increasing wind and solar electricity generation, inter-annual variability (IAV) of wind and solar resource and generation will pose increasing challenges to power system planning and operations. To help gauge these challenges to the power system, we quantify IAV of wind and solar resource and electricity generation across the Electric Reliability Council of Texas (ERCOT) power system, then assess the IAV of wind and solar electricity generation during peak-load hours (i.e. IAV of wind and solar capacity values) for the current ERCOT wind and solar generator fleet. To do so, we leverage the long timespan of four reanalysis datasets with the high resolution of grid integration datasets. We find the IAV (quantified as the coefficient of variation) of wind generation ranges from 2.3%-11% across ERCOT, while the IAV of solar generation ranges from 1.7%-5% across ERCOT. We also find significant seasonal and regional variability in the IAV of wind and solar generation, highlighting the importance of considering multiple temporal and spatial scales when planning and operating the power system. In addition, the IAV of the current wind and solar fleets' capacity values (defined as generation during peak-load hours) are larger than the IAV of the same fleets' capacity factors. IAV of annual generation and capacity values of wind and solar could impact operations and planning in several ways, e.g. through annual emissions, meeting emission reduction targets, and investment needs to maintain capacity adequacy.

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Section: Quantifying Iav Of Wind and Solar Generation And Capacity Vamentioning

confidence: 99%

Inter-annual variability of wind and solar electricity generation and capacity values in Texas

Kumler

Carreño

Craig

et al. 2019

Environ. Res. Lett.

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“…Because of their variability and uncertainty, wind and solar generation poses new challenges to the planning process. For instance, planning traditionally focused on procuring capacity to meet demand in high demand periods, but increasing wind and solar penetrations are elevating other high stress periods in planning (Lew et al 2013, Milligan et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Compounding climate change impacts during high stress periods for a high wind and solar power system in Texas

Craig

Jaramillo

Hodge

et al. 2020

Environ. Res. Lett.

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Power system planning aims at ensuring that sufficient supply-and demand-side assets exist to meet electricity demand at all times. For a Texas electric power system with high wind and solar penetrations, we quantify how climate change will affect supply and demand during three types of high stress periods for the power grid: high demand hours, high net demand hours, and high system ramp hours. We specifically quantify effects on demand, reductions in available thermal capacity (i.e. thermal deratings), wind and solar generation, and net demand. We estimate each using meteorological variables from five climate change projections (2041-2050) assuming Representative Concentration Pathway 8.5 and from a reference period (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005). All five projections indicate that climate change will increase demand by up to 2 GWh during high demand hours (4% of demand in the reference period) and increase net demand by up to 3 GWh during high net demand periods (6% of net demand in the reference period). All five projections also indicate thermal deratings will increase during high demand and net demand periods by up to 2 GWh and high net demand ramps will increase by up to 2 GW. Overall, our results indicate compounding effects of climate change in Texas will necessitate greater investment in peak and flexible capacity. RECEIVED

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“…The limitations on solar's CC-due to variable cloud cover and the timing of sunlight versus the timing of peak power-system demand-are well understood [2], [8]- [14]. Probabilistic methods are widely accepted as an accurate way to calculate the CC of solar (and wind), with several approximation methods developed to reduce the large data and computational needs [15]- [17]. Also understood is the decline in solar's CC with increasing solar penetration on the grid, as the net peak (system demand minus generation supplied by variable resources such as solar) shifts into hours without strong sunlight [2], [18].…”

Section: Introductionmentioning

confidence: 99%

Drivers of the Resource Adequacy Contribution of Solar and Storage for Florida Municipal Utilities

Mills¹,

Rodriguez²

2019

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Solar's variable generation limits its contribution to reliably meeting peak demand, or its resource adequacy contribution. Energy storage is a leading option to increase solar's resource adequacy contribution, yet the contribution from different configurations of solar and storage is not widely understood. We develop methods for exploring the primary drivers of an estimate of the resource adequacy contribution of solar and storage, and we apply the methods to a case study in Florida, where demand peaks in winter and summer. We find that the portion of solar nameplate capacity that contributes to resource adequacy-its capacity credit-is less than 50% and that it declines with increasing solar penetration. The capacity credit of storage, even though it is fully controllable by the system operator, strongly depends on the duration of storage. The capacity credit of 1 hour of storage can be less than the capacity credit of solar. Achieving a 90% capacity credit requires at least 4-5 hours of storage when storage capacity is small relative to the system peak. As storage deployment increases to 20% of the peak demand, 9 hours-and sometimes more than 10 hours-of storage are needed to achieve a 90% capacity credit. Increased solar deployment at the system level can increase storage's capacity credit. Directly pairing solar and storage can also impact the capacity credit. Storage with a power rating similar to the solar inverter rating loses capacity credit when coupled with solar if its duration is more than 1-2 hours, because storage competes with solar for use of the inverter. On the other hand, there is no reduction in capacity credit when the storage is small relative to the solar inverter. The approach and tools developed here for exploratory analysis can be useful for many other utilities and regions grappling with similar preliminary questions, prior to evaluation of specific cases using more detailed and resourceintensive modeling.

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Capacity value assessments of wind power

Cited by 28 publications

References 33 publications

Inter-annual variability of wind and solar electricity generation and capacity values in Texas

Inter-annual variability of wind and solar electricity generation and capacity values in Texas

Compounding climate change impacts during high stress periods for a high wind and solar power system in Texas

Drivers of the Resource Adequacy Contribution of Solar and Storage for Florida Municipal Utilities

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