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Offshore wind resource characterization in the United States relies heavily on simulated winds from numerical weather prediction (NWP) models, given the lack of hub-height observations offshore. One such NWP data set used extensively by U.S. stakeholders is the Wind Integration National Dataset (WIND) Toolkit, a 7-year time-series data set produced in 2013 by the National Renewable Energy Laboratory. In this study, we present an update to that data set for offshore California that leverages recent advancements in NWP modeling capabilities and extends the period of record to a full 20 years. The data set predicts a significantly larger wind resource (0.25–1.75 m s-1 stronger), including in three Call Areas that the Bureau of Ocean Energy Management is considering for commercial activity. We conduct a set of yearlong simulations to study factors that contribute to this increase in the modeled wind resource. The largest impact arises from a change in the planetary boundary layer parameterization from the Yonsei University scheme to the Mellor-Yamada-Nakanishi-Niino scheme and their diverging wind profiles under stable stratification. Additionally, we conduct a refined wind resource assessment at the three Call Areas, characterizing distributions of wind speed, shear, veer, stability, frequency of wind droughts, and power production. We find that, depending on the attribute, the new data set can show substantial disagreement with the WIND Toolkit, thereby driving important changes in predicted power. [ABSTRACT FROM AUTHOR]

An accurate wind resource dataset is required for assessing the potential energy yield of floating offshore wind farms that are expected along the California outer continental shelf. The National Renewable Energy Laboratory has developed and disseminated an updated wind resource dataset offshore of California, using the Weather Research and Forecasting Model, referred to as the CA20 dataset. As compared to buoy lidar measurements that have become available recently, the CA20 dataset showed significant positive biases for 100-m wind speeds along Northern California wind energy lease areas. To investigate the meteorological drivers for the model errors, we first consider two 1-yr simulations run with two different planetary boundary layer (PBL) parameterizations: the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL scheme (the chosen configuration in the CA20 dataset) and the Yonsei University PBL scheme (which significantly reduces the bias in modeled winds). By comparing the 1-yr simulations to the concurrent lidar buoy observations, we find that errors are larger with the MYNN PBL scheme in warm seasons. We then dive deeper into the analysis by running simulations for short-term (3-day) case studies to evaluate the sensitivity of initial/boundary condition forcings on model results. By analyzing the short-term simulations, we find that during synoptic-scale northerly flows driven by the North Pacific high and inland thermal low, a coastal warm bias in the MYNN simulation is mainly responsible for the modeled wind speed bias by altering the boundary layer thermodynamics. The results of our analysis will help guide the creation of an updated version of the CA20 dataset. [ABSTRACT FROM AUTHOR]

For each geographical region, one of the biggest challenges in reaching a zero-carbon grid is identifying sources of electricity that match the seasonal profile of the load. Summer-dominant solar electricity generation can often be balanced by winter-dominant wind electricity generation. Together with long-duration storage, balanced solar and wind generation are well positioned to provide reliable renewable electricity. However, in some locations the wind may not complement solar energy so well. For example, currently California's wind turbines produce more electricity during summer than winter, raising the question of whether all future wind plants in California will exhibit the same seasonality. As a response to this question, in this paper, we analyzed the generation from existing California wind plants and simulated potential onshore wind resource for the whole state using a metric that reflects the relative wind resource in winter. Our results indicate that the seasonality of the wind can vary for very small spatial difference with more than half of California showing stronger wind resource in the winter compared with the summer despite the current observation of the opposite trend. This study differentiates the seasonality of potential wind resources to inform the creation of a reliable, 100%-renewable-driven grid. [ABSTRACT FROM AUTHOR]

In response to the growing interest in offshore wind energy development in California, the U.S. Bureau of Ocean Energy Management delineated three Call Areas for potential leasing. This study provides a comprehensive characterization and comparison of offshore wind power potential within the two Central California Call Areas (Diablo Canyon and Morro Bay) using 12‐ and 15‐MW turbines under different inter‐turbine spacing and wind farm size scenarios. Our analysis shows similar daily and seasonal patterns of wind power produced within the Call Areas, which peak in spring and during evening hours. Per‐turbine power production is higher in the Morro Bay Call Area due to slightly higher hub‐height wind speeds, whereas total power production is higher in the Diablo Canyon Call Area due to its larger size. Turbine type had a negligible impact on average power production per‐unit‐area because while larger turbines produce more power, they require greater inter‐turbine spacing. Combined power production from the two fully built out Call Areas could equal nearly a quarter of California's current annual electrical energy production. A commercial‐scale wind farm with a realized power output of 960 MW would require a footprint of at least half of the Morro Bay Call Area or at least a quarter of the Diablo Canyon Call Area. These results provide guidance on offshore wind development over the Central California Coast, and the framework demonstrated here could be applied to other wind data sets in other regions. [ABSTRACT FROM AUTHOR]

The accurate characterization of seasonal and inter-annual site-level wind energy variability is essential during wind project development. Understanding the features and probability of low-wind years is of particular interest to developers and financers. However, a dearth of long-term, hub-height wind observations makes these characterizations challenging, and thus techniques to improve these characterizations are of great value. To improve resource characterization, we explicitly link wind resource variability (at hub-height, and at specific sites) to regional and synoptic scale wind regimes. Our approach involves statistical clustering of high-resolution modeled wind data, and is applied to California for a period covering 1980–2015. With this approach, we investigate the unique meteorological patterns driving low and high wind years at five separate wind project sites. We also find wind regime changes over the 36-year period consistent with global warming: wind regimes associated with anomalously hot summer days increased at half a day per year and stagnant conditions increased at one-third days per year. Despite these changes, the average annual resource potential remained constant at all project sites. Additionally, we identify correlations between climate modes and wind regime frequency, a linkage valuable for resource characterization and forecasting. Our general approach can be applied in any location and may benefit many aspects of wind energy resource evaluation and forecasting. [ABSTRACT FROM AUTHOR]

ABSTRACT The California generation fleet manages the existing variability and uncertainty in the demand for electric power (load). When wind power is added, the dispatchable generators manage the variability and uncertainty of the net load (load minus wind power). The variability and uncertainty of the load and the net load are compared when 8790 MW of wind power are added to the California power system, a level expected when California achieves its 33% renewable portfolio standard, using a data set of 26,296 h of synchronous historic load and modeled historic wind power output. Variability was calculated as the rate of change in power generated by wind farms or consumed by the load from 1 h to the next (MW/h). Uncertainty was calculated as the 1 h ahead forecast error [MW] of the wind power or of the load. The data show that wind power adds no additional variability than is already present in the load variability. However, wind power adds additional uncertainty through increased forecast errors in the net load compared with the load. Forecast errors in the net load increase 18.7% for negative forecast errors (actual less than forecast) and 5.4% for positive forecast errors (actual greater than forecast). The increase in negative forecast errors occurs only during the afternoon hours when negative load forecasts and positive wind forecasts are strongly correlated. Managing the integration of wind power in the California power system should focus on reducing wind power forecast uncertainty for wind ramp ups during the afternoon hours. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Policymakers in the United States have asserted broad goals to reduce electricity rates by increasing market competition, increase the use of renewable sources Of electricity, and upgrade the US transmission system to accommodate intermittent renewable energy sources. When policy goals are not well integrated with local and national regulatory policies, periods of too little or too much electricity on the grid result. This article analyzes the restructuring of the California electricity market in the late 1990s and the 2012 periods of oversupply on the Bonneville Power Administration grid as case studies in regulatory failure, then looks to the current smart grid upgrades to the national transmission system to recommend more cooperative regulatory policy. [ABSTRACT FROM AUTHOR]

The paper analyzes the impact of integrating wind generation on the regulation and load following requirements of the California Independent System Operator (CAISO). These requirements are simulated and compared for the study cases with and without wind generation impacts included into the study for the years 2006 and 2010. Regulation and load following models were built based on hour-ahead and five-minute ahead load and wind generation forecasts. In 2006, the CAISO system peaked at 50270 MW. Wind generation (at the installed capacity of 2600 MW) had limited impact on the requirement of load following and regulation in the CAISO Balancing Authority. However, in 2010 (with an expected installed capacity of approximately 6700 MW), this impact will significantly increase. The results provide very useful information for the CAISO to adjust its scheduling and real-time dispatch systems to reliably accommodate future wind generation additions within the CAISO Balancing Authority. [ABSTRACT FROM AUTHOR]

Floating offshore wind is being considered in northern California as indicated by the Bureau of Ocean Energy Management's issuance of a lease consideration in the Humboldt Call Area. Humboldt County offers access to this enormous resource, but local electric load and transmission are limited. The potential impacts of offshore wind generators at three different scales were studied using a regional grid model of Humboldt County. Offshore wind generation was calculated using modeled wind speed data and 12-MW turbine specifications and integrated with projected load and historical generation. Offshore wind farms deployed in the Humboldt Call Area achieve annual capacity factors between 45% and 54% after losses and maintenance. Power output is variable between and within seasons, with full power output 30% of the time and no output approximately 20% of the time. Electricity from a 48-MW wind farm provides 22% of regional load with limited exports. A 144-MW wind farm serves 38% of local load, exporting 40% of its electricity with the extant 70-MW transmission capacity. A full build-out of 1836 MW would result in 88% curtailment with existing transmission. Across scenarios, offshore wind variability necessitates reliance on existing power plants to meet local demand in periods of low wind. [ABSTRACT FROM AUTHOR]

Deep decarbonization of electricity production is a societal challenge that can be achieved with high penetrations of variable renewable energy. We investigate the potential of energy storage technologies to reduce renewable curtailment and CO2 emissions in California and Texas under varying emissions taxes. We show that without energy storage, adding 60 GW of renewables to California achieves 72% CO2 reductions (relative to a zero-renewables case) with close to one third of renewables being curtailed. Some energy storage technologies, on the other hand, allow 90% CO2 reductions from the same renewable penetrations with as little as 9% renewable curtailment. In Texas, the same renewable-deployment level leads to 54% emissions reductions with close to 3% renewable curtailment. Energy storage can allow 57% emissions reductions with as little as 0.3% renewable curtailment. We also find that generator flexibility can reduce curtailment and the amount of energy storage that is needed for renewable integration. Existing studies on the economic feasibility of energy storage are system-specific without considering the decarbonisation of electricity production or impacts of GHG taxes. Here the authors applied an optimization model to investigate the economic viability of nice selected energy storage technologies in California and found that renewable curtailment and GHG reductions highly depend on capital costs of energy storage. [ABSTRACT FROM AUTHOR]

Current ammonia production technologies have a significant carbon footprint. In this study, we present a process synthesis and global optimization framework to discover the efficient utilization of renewable resources in ammonia production. Competing technologies are incorporated in a process superstructure where biomass, wind, and solar routes are compared with the natural gas‐based reference case. A deterministic global optimization‐based branch‐and‐bound algorithm is used to solve the resulting large‐scale nonconvex mixed‐integer nonlinear programming problem (MINLP). Case studies for Texas, California, and Iowa are conducted to examine the effects of different feedstock prices and availabilities. Results indicate that profitability of ammonia production is highly sensitive to feedstock and electricity prices, as well as greenhouse gas (GHG) restrictions. Under strict 75% GHG reductions, biomass to ammonia route is found to be competitive with natural gas route, whereas wind and solar to ammonia routes require further improvement to compete with those two routes. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16498 2019 [ABSTRACT FROM AUTHOR]