Improvement and Validation of the System Advisor Model

Freeman, Janine; DiOrio, Nicholas A.; Blair, Nate; Guittet, Darice; Gilman, Paul; Janzou, Steve

doi:10.2172/1495693

Cited by 6 publications

(3 citation statements)

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“…These user-friendly and budget-friendly tools combine wind resource models, such as the WIND Toolkit, with included or user-added power curves and loss assumptions to produce site-specific annual energy production estimates. While validations of SAM exist for solar technology using actual plant performance data (Freeman et al, 2013(Freeman et al, , 2019Rudié et al, 2014;Ezeanya et al, 2018), we were unable to determine similar validations of SAM for small wind systems. Similarly, no validations of Wind Report or GWA3 using small wind turbine production observations were able to be identified.…”

Section: Introductionmentioning

confidence: 76%

Validation of wind resource and energy production simulations for small wind turbines in the United States

et al. 2022

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Abstract. Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, MyWindTurbine.com, and Global Wind Atlas 3 (GWA3), which are seen to overestimate actual annual capacity factors by 2.5, 4.2, 11.5, and 7.3 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produces more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.

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Section: Introductionmentioning

confidence: 76%

Validation of wind resource and energy production simulations for small wind turbines in the United States

et al. 2022

View full text Add to dashboard Cite

show abstract

“…These user-friendly and budget-friendly tools combine wind resource models, such as the WIND Toolkit, with power curve databases and loss assumptions to produce site-specific annual energy production estimates. While validations of SAM exist for solar technology using actual plant performance data (Freeman et al, 2013;Rudié et al, 2014;Ezeanya et al, 2018;Freeman et al, 2019), we were unable to determine similar validations of SAM for small wind systems. Similarly, no validation of Wind Report using turbine production observations was able to be identified.…”

mentioning

confidence: 74%

Validation of Wind Resource and Energy Production Simulations for Small Wind Turbines in the United States

Sheridan

Phillips

Orrell

et al. 2021

Preprint

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Abstract. Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, and MyWindTurbine.com, which are seen to overestimate actual annual capacity factors by 2.5, 4.2, and 11.5 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produce more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.

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“…A Solar Multiple configuration equal to 2 and 9 hours of storage is selected, in order to produce electricity 24 hours a day, desirably only with the steam cycle during the day, but production at night can be complemented by power generation with the gas cycle, maintaining an output power between both turbines of 110 MW most of the time possible, following certain operating constraints. The methodology shown in Figure 2 consists of obtaining from the Solar Explorer [2] the TMY for each selected location, which is used as input in the System Advisor Model (SAM) software [3], where the solar field part of HYSOL is modeled, obtaining the mass flow vector of salt passing through the receiver, with hourly resolution for one year. Then, in the Engineering Equation Solver (EES) program [4] the Rankine and Brayton Cycles are modeled, determining their thermodynamic points through mass and energy balances, allowing to obtain the mass flows of salt flowing from or to them as a function of the operating power of each cycle.…”

Section: Methodsmentioning

confidence: 99%

Technical-Economic Feasibility Study of Hybrid CSP Plants With Gas in Chile

Cáceres,

Hernández,

Dinter

2024

SolarPACES Conf Proc

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The Chilean government has presented an accelerated decarbonization plan, proposing the closure of coal-fired power plants by 2025, to combat climate change. Therefore, it is important to migrate to new forms of cleaner generation that are also safe for the electricity system. It is in this context that the motivation arises to study the hybridization of two technologies usually opposed, such as CSP and Gas, to enhance the benefits of each one. The plant to be evaluated has a HYSOL configuration, which consists of a Solar Tower with thermal storage of salt to which is incorporated a gas cycle whose exhaust gases are used to heat the salt when necessary. For this study, SAM was used to model the solar field and EES was used to simulate the thermodynamic components of the Brayton and Rankine cycles. The study is carried out at 3 locations in the Antofagasta Region. Capacity factors close to 90% are obtained, which is higher than most of the Non-Conventional Renewable Energies. Similar CO2 emission factors are obtained in the 3 locations and around 0,12 Ton CO2/ MWh, which is substantially lower than other generators based on conventional fuels. The lowest LCOE occurs in L2 and has a value of 82,9 USD/MWh, being more cost effective than Open Cycle Gas and Nuclear technologies and could become competitive in the future with other technologies if the costs associated with the solar field are reduced as expected. It’s concluded that the proposed solution is technically and economically feasible.

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Improvement and Validation of the System Advisor Model

Cited by 6 publications

References 0 publications

Validation of wind resource and energy production simulations for small wind turbines in the United States

Validation of wind resource and energy production simulations for small wind turbines in the United States

Validation of Wind Resource and Energy Production Simulations for Small Wind Turbines in the United States

Technical-Economic Feasibility Study of Hybrid CSP Plants With Gas in Chile

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