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Highlights • We analyze the economic value of a U.S windfarm using long-term LCOE information. • Long-term LCOE of wind energy is obtained from two decades of history. • Monte Carlo and Risk-Neutral valuation is used to valuate a reference windfarm. • Managerial flexibilities and generation cost uncertainty make windfarms more valuable. • Real Option accounts for the flexibility of decision-making with stochastic costs. Abstract Renewable energies are a natural replacement for conventional or fossil fuel energy generation. One of these sources, wind energy, has exhibited significant cost reductions during the last decades. Even though this historical cost reduction trend is well known in the industry and in the academia, information on this cost trend and volatility has been scarcely incorporated in previous works, thus a natural concern arises when assessing their influence on the economic valuation of wind farms. Moreover, wind projects are modular in size and have a short time-to-market, providing a valuable managerial flexibility to defer investment under non-attractive market conditions, such as high development cost scenarios. Traditional evaluation tools often consider those scenarios, wrongly assuming that wind-developers would move forward with the project under such conditions. Accordingly, this paper analyzes the effect of incorporating both generation cost history and investment flexibility in the valuation of wind farms in the United States using Real Options methodology. Taking two decades of cost data and a reference wind farm, this "uncertainty-flexibility" effect is incorporated and valued, finding that the value of the wind project is 14.2% larger than the one neglecting such effect – where cost stochasticity and managerial flexibility are ignored. Therefore, traditional tools for project valuation, such as discounted cash flow and scenario analysis can no longer solely be used, as they do not properly account for the cost reduction trend, uncertainty, and managerial flexibility that a wind farm could face. [ABSTRACT FROM AUTHOR]

This paper evaluates potential changes in the power system associated with sustained growth in wind generation in the United States to 35% of end-use demand by 2050; Wiser et al. (2016) evaluate societal benefits and other impacts for this same scenario. Under reference or central conditions, the analysis finds cumulative wind capacity of 404 gigawatts (GW) would be required to reach this level and drive 2050 incremental electricity rate and cumulative electric sector savings of 2% and 3% respectively, relative to a scenario with no new wind capacity additions. Greater savings are estimated under higher fossil fuel costs or with greater advancements in wind technologies. Conversely, incremental costs are found when fossil fuel costs are lower than central assumptions or wind technology improvements are more-limited. Through 2030, the primary generation sources displaced by new wind capacity include natural gas and coal-fired generation. By 2050, wind could displace other renewables. Incremental new transmission infrastructure totaling 29 million megawatt-miles is estimated to be needed by 2050. In conjunction with related societal benefits, this work demonstrates that 35% wind energy by 2050 is plausible, could support enduring benefits, and could result in long-term consumer savings, if nearer-term (pre-2030) cost barriers are overcome; at the same time, these opportunities are not anticipated to be realized in their full form under “business-as-usual” conditions. [ABSTRACT FROM AUTHOR]

We model scenarios of the U.S. electric sector in which wind generation reaches 10% of end-use electricity demand in 2020, 20% in 2030, and 35% in 2050. As shown in a companion paper, achieving these penetration levels would have significant implications for the wind industry and the broader electric sector. Compared to a baseline that assumes no new wind deployment, under the primary scenario modeled, achieving these penetrations imposes an incremental cost to electricity consumers of less than 1% through 2030. These cost implications, however, should be balanced against the variety of environmental and social implications of such a scenario. Relative to a baseline that assumes no new wind deployment, our analysis shows that the high-penetration wind scenario yields potential greenhouse-gas benefits of $85–$1,230 billion in present-value terms, with a central estimate of $400 billion. Air-pollution-related health benefits are estimated at $52–$272 billion, while annual electric-sector water withdrawals and consumption are lower by 15% and 23% in 2050, respectively. We also find that a high-wind-energy future would have implications for the diversity and risk of energy supply, local economic development, and land use and related local impacts on communities and ecosystems; however, these additional impacts may not greatly affect aggregate social welfare owing to their nature, in part, as resource transfers. [ABSTRACT FROM AUTHOR]

• Temporal and spatial features are used for wind speed forecasting. • A novel probabilistic deep learning model is proposed for wind speed forecasting. • The proposed model shows the best performance compared with other models. • Temporal and spatial uncertainty of the wind speed are estimated based on the model. Reliable and accurate probabilistic forecasting of wind speed is of vital importance for the utilization of wind energy and operation of power systems. In this paper, a probabilistic spatiotemporal deep learning model for wind speed forecasting is proposed. The underlying wind turbines are embedded into a grid space, which fully expresses the spatiotemporal variation process of the airflow. Thus, advanced image recognition methods can be employed to solve the spatiotemporal wind speed forecasting problem. The proposed model is based on a spatial–temporal neural network (STNN) and variational Bayesian inference. The proposed STNN combines the convolutional GRU model and 3D Convolutional Neural Network and uses an encoding-forecasting structure to generate the spatiotemporal predictions. Variational Bayesian inference is employed to obtain the approximated posterior parameter distribution of the model and determine the probability of the prediction. The proposed model is applied in two real-world case studies in United States. The experimental results demonstrate that the proposed model significantly outperforms other models in both forecast skill and forecast reliability. The uncertainty estimation is also shown and it demonstrates that the proposed model is able to provide effective uncertainty estimation in both the time level and space level. [ABSTRACT FROM AUTHOR]