The Value of Storage and Demand Response for Renewble Integration

Edmunds, T; Lamont, Alan; Bulaevskaya, V.; Meyers, Carol; Mirocha, Jeffrey D.; Schmidt, Andréa; Simpson, Matthew; Smith, Steven Escar; Sotorrio, P; Top, Philip; Yao, Yiming

doi:10.2172/1466167

Cited by 6 publications

(17 citation statements)

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“…Potter et al ). Another application is real‐time wind power production forecasting, used both by wind project operators bidding into markets, and by independent utility operators and independent systems operators, both to integrate renewable generation and to make unit commitment and ancillary provisions decisions (Makarov et al ; Edmunds et al ). Despite the proliferation of mesoscale forecasting approaches, including several publically available models that support multiple resolution domain configurability, numerous physical processes parameterization options and compatibility with several analysis and forecast datasets for initialization and forcing, the distribution of wind speed and direction within the lowest few hundred meters above the surface has received relatively little scrutiny in relation to other model performance metrics.…”

Section: Introductionmentioning

confidence: 99%

“…Mesoscale forecasting has benefited from development of real‐time four‐dimensional data assimilation (Liu et al ) and ensemble forecast systems (e.g. Deppe et al ; Williams et al ; Edmunds et al ). WRF's LES capabilities have been enhanced via introduction of both advanced LES subgrid models (Mirocha et al ; Kirkil et al ), and development of mesoscale‐to‐LES downscaling algorithms for multi‐scale simulations (Muñoz‐Esparza et al ; Mirocha et al ; Talbot et al ; Liu et al ).…”

Section: Introductionmentioning

confidence: 99%

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Investigation of boundary-layer wind predictions during nocturnal low-level jet events using the Weather Research and Forecasting model

et al. 2015

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The accuracy of boundary-layer wind profiles occurring during nocturnal low-level jet (LLJ) events, and their sensitivities to variations of user-specifiable model configuration parameters within the Weather Research and Forecasting model, was investigated. Simulations were compared against data from a wind-profiling lidar, deployed to the Northern Great Plains during the U.S. Department of Energy-supported Weather Forecast Improvement Project. Two periods during the autumn of 2011 featuring LLJs of similar magnitudes and durations occurring during several consecutive nights were selected for analysis. Simulated wind speed and direction at 80 and 180 m above the surface, the former a typical wind turbine hub height, bulk vertical gradients between 40 and 120 m, a typical rotor span, and the maximum wind speeds occurring at 80 and 180 m, and their times of occurrence, were compared with the observations. Sensitivities of these parameters to the horizontal and vertical grid spacing, planetary boundary layer and land surface model physics options, and atmospheric forcing dataset, were assessed using ensembles encompassing changes of each of these configuration parameters. Each simulation captured the diurnal cycle of wind speed and stratification, producing LLJs during each overnight period; however, large discrepancies in relation to the observations were frequently observed, with each ensemble producing a wide range of distributions, reflecting highly variable representations of stratification during the weakly stable overnight conditions. Root mean square error and bias values computed over the LLJ cycle (late evening through the following morning) revealed that, while some configurations performed better or worse in different aspects and at different times, none exhibited definitively superior performance. The considerable root mean square error and bias values, even among the 'best' performing simulations, underscore the need for improved simulation capabilities for the prediction of near-surface winds during LLJ conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of boundary-layer wind predictions during nocturnal low-level jet events using the Weather Research and Forecasting model

et al. 2015

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“…Thus, the computational burden associated with solving such problems becomes prohibitive for even very powerful systems. In the California Energy Commission study [2], it was found that for only 8 scenarios, each dayahead stochastic unit commitment problem already required an average of 5 hours to solve, and no solutions at all were found for 20 or more scenarios. It was for this reason the original study was downscaled to include only 5 scenarios [2].…”

Section: A Model Description and Prior Computational Performancementioning

confidence: 99%

“…The increased penetration of intermittent renewable generation needed to meet this goal will substantially increase the variability and uncertainty in generation resources available to system operators. To assess the impact of such high renewable penetrations, the California Energy Commission funded a recently completed study at Lawrence Livermore National Laboratory to couple atmospheric models capable of producing renewable generation trajectories with a stochastic day-ahead unit commitment optimization model [2]. This stochastic day-ahead unit commitment model employs at its core a deterministic unit commitment planning model developed by the California Independent System Operator (ISO) for their study of market impacts under the 33% renewable portfolio standard [3].…”

Section: Introductionmentioning

confidence: 99%

An efficient approach for solving large stochastic unit commitment problems arising in a California ISO planning model

Parriani

Cong²,

Meyers

et al. 2014

2014 IEEE PES General Meeting | Conference &Amp; Exposition

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We describe our experience in obtaining significant computational improvements in the solution of large stochastic unit commitment problems. The model we use is a stochastic version of a planning model used by the California Independent System Operator, covering the entire WECC western regional grid. We solve daily hour-timestep stochastic unit commitment problems using a new progressive hedging approach that features linear subproblems and guided solves for finding feasible solutions. For stochastic problems with 5 scenarios, the algorithm produces near-optimal solutions with a 6 times improvement in serial solution time, and over 20 times improvement when run in parallel; for previously unsolvable stochastic problems, we obtain near-optimal solutions within a couple of hours. We note that although this algorithm is demonstrated for stochastic unit commitment problems, the algorithm itself is suitable for application to generic stochastic optimization problems.

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“…1 has been developed to estimate the value of technologies in the context of this high degree of uncertainty and variability [1]. The system builds upon and extends previous studies conducted by the California Independent System Operator (CAISO) and the California Energy Commission [2,3,4].…”

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confidence: 99%