The challenge of integrating offshore wind power in the U.S. electric grid. Part I: Wind forecast error

Archer, Cristina L.; Simão, Hugo P.; Kempton, Willett; Powell, Warren B.; Dvorak, Michael J.

doi:10.1016/j.renene.2016.11.047

Cited by 78 publications

(43 citation statements)

References 46 publications

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“…Wind forecasting is challenging due to low wind predictability. Even advanced forecasting models can generate vastly different forecasts due to the nonlinear characteristics of the atmospheric system . Both wind speed and wind direction are variables that are difficult to accurately simulate due to their large variability in time and space, to the effects of surface ruggedness, type of landscape, vegetation, and soil cover throughout the year .…”

Section: Literature Reviewmentioning

confidence: 99%

Wind farm generation forecast and optimal maintenance schedule model

et al. 2019

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Wind farms must periodically take their turbines offline in order to perform scheduled maintenance repairs. Given that these interruptions impact energy generation and that under Power Purchase Agreements productions shortfalls must be replaced by energy purchases in the spot market, the optimal time to begin maintenance work in a wind farm is a function of both the expected wind speeds and electricity spot prices. In this article, we develop a model to determine the optimal maintenance schedule of a wind farm based on forecasted wind speeds and energy prices. We analyze a window of opportunity in the most likely period of the year and perform weekly updates of expected wind speeds and energy price forecasts. Wind speeds are forecasted with an ARMAX model, where monthly dummies are used as exogenous variables to capture the seasonality of wind speeds, while spot prices are simulated under a standard dual stochastic programing model. The decision to defer maintenance to a future date is modeled in a probabilistic model and also under the real options approach. We test these models with actual data from a wind farm in the Brazilian Northeast and provide comparisons with current practice in order to determine the benefits of the model. The results suggest that this model may provide advantages over a stopping decision that randomly chooses a week to begin maintenance within the opportunity window and is close to the optimal stopping date considering perfect information on future wind speeds and electricity prices.

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Section: Literature Reviewmentioning

confidence: 99%

Wind farm generation forecast and optimal maintenance schedule model

et al. 2019

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“…It means that R 1 has the largest probability to be optimal when N 1 opt is 2, namely, λ 1 has the strongest relationship to WPFE when N 1 opt is 2 in Equation (2). For λ 2 , λ 3 , λ 4 , N 2 opt = 3, N 3 opt = 2 and N 4 opt = 2 respectively.…”

Section: Frequency Frequencymentioning

confidence: 99%

“…where, λ e is the weighted average of λ 1 , λ 2 , λ 3 , λ 4 . By using the mean of correlation coefficient R j opt as the weights of λ 1 , λ 2 , λ 3 , λ 4 , different factor influences λ e in different degree.…”

Section: Wpfe Estimation Based On Optimal Correlation Weightsmentioning

confidence: 99%

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Multi-Time Scale Rolling Economic Dispatch for Wind/Storage Power System Based on Forecast Error Feature Extraction

et al. 2018

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This paper looks at the ability to cope with the uncertainty of wind power and reduce the impact of wind power forecast error (WPFE) on the operation and dispatch of power system. Therefore, several factors which are related to WPFE will be studied. By statistical analysis of the historical data, an indicator of real-time error based on these factors is obtained to estimate WPFE. Based on the real-time estimation of WPFE, a multi-time scale rolling dispatch model for wind/storage power system is established. In the real-time error compensation section of this model, the previous dispatch plan of thermal power unit is revised according to the estimation of WPFE. As the regulating capacity of thermal power unit within a short time period is limited, the estimation of WPFE is further compensated by using battery energy storage system. This can not only decrease the risk caused by the wind power uncertainty and lessen wind spillage, but also reduce the total cost. Thereby providing a new method to describe and model wind power uncertainty, and providing economic, safe and energy-saving dispatch plan for power system. The analysis in case study verifies the effectiveness of the proposed model.

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“…Particularly, wind energy, as one of the clean renewable energy sources, is attracting more and more attention [1]. The newly installed capacity of wind power reaches 54.6 GW in 2016 [2].…”

Section: Introductionmentioning

confidence: 99%

Maximum Wind Power Tracking of Doubly Fed Wind Turbine System Based on Adaptive Gain Second-Order Sliding Mode

Sun

Han

Zhang³

2018

Journal of Control Science and Engineering

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This paper proposes an adaptive gain second-order sliding mode control strategy to track optimal electromagnetic torque and regulate reactive power of doubly fed wind turbine system. Firstly, wind turbine aerodynamic characteristics and doubly fed induction generator (DFIG) modeling are presented. Then, electromagnetic torque error and reactive power error are chosen as sliding variables, and fixed gain super-twisting sliding mode control scheme is designed. Considering that uncertainty upper bound is unknown and is hard to be estimated in actual doubly fed wind turbine system, a gain scheduled law is proposed to compel control parameters variation according to uncertainty upper bound real-time. Adaptive gain second-order sliding mode rotor voltage control method is constructed in detail and finite time stability of doubly fed wind turbine control system is strictly proved. The superiority and robustness of the proposed control scheme are finally evaluated on a 1.5 MW DFIG wind turbine system.

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The challenge of integrating offshore wind power in the U.S. electric grid. Part I: Wind forecast error

Cited by 78 publications

References 46 publications

Wind farm generation forecast and optimal maintenance schedule model

Wind farm generation forecast and optimal maintenance schedule model

Multi-Time Scale Rolling Economic Dispatch for Wind/Storage Power System Based on Forecast Error Feature Extraction

Maximum Wind Power Tracking of Doubly Fed Wind Turbine System Based on Adaptive Gain Second-Order Sliding Mode

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