Abstract-This paper proposes a new simulation method that can fully assess the impacts of large-scale wind power on system operations from cost, reliability, and environmental perspectives. The method uses a time series of observed and predicted 15-min average wind speeds at foreseen onshore-and offshore-wind farm locations. A Unit Commitment and Economic Dispatch (UC-ED) tool is adapted to allow for frequent revisions of conventional generation unit schedules, using information on current wind energy output and forecasts for the next 36 h. This is deemed the most faithful way of simulating actual operations and short-term planning activities for a system with large wind power penetration. The problem formulation includes ramp-rate constraints for generation schedules and for reserve activation, and minimum up-time and down-time of conventional units. Results are shown for a realistic future scenario of the Dutch power system. It is shown that problems such as insufficient regulating and reserve power-which are typically associated with the variablility and limited predictability of wind power-can only be assessed in conjunction with the specifics of the conventional generation system that wind power is integrated into. For the thermal system with a large share of combined heat and power (CHP) investigated here, wind power forecasting does not provide significant benefits for optimal unit commitment and dispatch. Minimum load problems do occur, which result in wasted wind in amounts increasing with the wind power installed.Index Terms-Power system integration, unit commitment and economic dispatch, wind power, wind power forecast.

In metal forming, the workpieces are formed to the desired shapes or profiles. Especially in hot forming, the microstructure of workpiece changes during plastic deformation. Modern forming technologies allow to control the shape and the microstructure of formed product in a wide range and will gain increasing importance in future in the field of metal forming. In order to develop this forming technology which may be called “macroscopic microscopic materials processing”, theoretical predictions of plastic deformation as well as microstructural changes are indispensable. A new mathematical formulation to predict flow stress and microstructural change in hot forming will be presented in this paper. This model is based on an incremental formulation taking the dislocation density as a representative variable.

This paper presents a data-driven approach for estimating the degree of variability and predictability associated with large-scale wind energy production for a planned integration in a given geographical area, with an application to The Netherlands. A new method is presented for generating realistic time series of aggregated wind power realizations and forecasts. To this end, simultaneous wind speed time series-both actual and predictedat planned wind farm locations are needed, but not always available. A 1-year data set of 10-min averaged wind speeds measured at several weather stations is used. The measurements are first transformed from sensor height to hub height, then spatially interpolated using multivariate normal theory, and finally averaged over the market resolution time interval. Day-ahead wind speed forecast time series are created from the atmospheric model HiRLAM (High Resolution Limited Area Model). Actual and forecasted wind speeds are passed through multi-turbine power curves and summed up to create time series of actual and forecasted wind power. Two insights are derived from the developed data set: the degree of long-term variability and the degree of predictability when Dutch wind energy production is aggregated at the national or at the market participant level. For a 7.8 GW installed wind power scenario, at the system level, the imbalance energy requirements due to wind variations across 15-min intervals are ±14% of the total installed capacity, while the imbalance due to forecast errors vary between 53% for down-and 56% for up-regulation. When aggregating at the market participant level, the balancing energy requirements are 2-3% higher.

A dynamic model for the wind flow in wind farms is developed in this paper. The model is based on the spatial discretization of the linearized Navier-Stokes equation combined with the vortex cylinder theory. The spatial discretization of the model is performed using the finite difference method, which provides the state-space form of the dynamic wind farm model. The model provides an approximation of the behavior of the flow in the wind farm and obtains the wind speed in the vicinity of each wind turbine. Afterwards, the model is validated using measurement data of Energy research Center of the Netherlands' Wind turbine Test site in Wieringermeer in the Netherlands and by employing the outcomes of two other wind flow models. The end goal of this work is to present the wind farm flow model by ordinary differential equations, to be applied in wind farm control algorithms along with load and power optimizations.

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