Wind Ramp Research Articles

Mitigation in the Very Short-term of Risk from Wind Ramps with Unforeseen Severity

This paper addresses a critical analysis of the impact of the wind ramp events with unforeseen magnitude in power systems at the very short term, modeling the response of the operational reserve against this type of phenomenon. A multi-objective approach is adopted, and the properties of the Pareto-optimal fronts are analyzed in cost versus risk, represented by a worst scenario of load curtailment. To complete this critical analysis, a study about the usage of the reserve in the event of wind power ramps is performed. A case study is used to compare the numerical results of the models based on stochastic programming and models that take a risk analysis view in the system with high level of wind power. Wind power uncertainty is represented by scenarios qualified by probabilities. The results show that the reliability reserve may not be adequate to accommodate unforeseen wind ramps and therefore the system may be at risk.

Impacts of synoptic circulation patterns on wind power ramp events in East Japan

This study presents an application of self-organizing maps (SOM) for the climatological/meteorological study of wind power ramp events. SOM constitutes an automatic data-mining clustering technique, which allows for summarizing of a high-dimensional data space in terms of a set of reference vectors. SOM is applied to analyze and establish the relationship between atmospheric synoptic patterns over Japan and wind power generation. SOM is employed on sea level pressure data derived from the JRA-55 reanalysis over the Tohoku region in Japan, whereby a two-dimensional lattice of weather patterns classified during the 1977–2013 period is obtained. Wind-power ramp events (defined as a 30% change in power in less than 6 h) mainly take place during the winter months in East Japan. Our SOM analysis for weather patterns in boreal winter extracts seven typical patterns that are linked to frequent occurrences of wind ramp events. The result of this study suggests that detailed classification of synoptic circulation patterns can be a useful tool for first-order approximations of both the probability of future wind power generation and its variability. Further research relating weather/climate variability and wind power generation is both necessary and valuable in East Asia.

An Optimized Swinging Door Algorithm for Identifying Wind Ramping Events

With the increasing penetration of renewable energy in recent years, wind power ramp events (WPREs) have started affecting the economic and reliable operation of power grids. In this paper, we develop an optimized swinging door algorithm (OpSDA) to improve the state of the art in WPREs detection. The swinging door algorithm (SDA) is utilized to segregate wind power data through a piecewise linear approximation. A dynamic programming algorithm is performed to optimize the segments by: 1)merging adjacent segments with the same ramp changing direction; 2)handling wind power bumps; and 3)postprocessing insignificant-ramps intervals. Measured wind power data from two case studies are utilized to evaluate the performance of the proposed OpSDA. Results show that the OpSDA provides 1)significantly better performance than the SDA and 2)equal-to-better performance compared to the L1-Ramp Detect with Sliding Window (L1-SW) method with significantly less computational time.

Variability in large‐scale wind power generation

AbstractThe paper demonstrates the characteristics of wind power variability and net load variability in multiple power systems based on real data from multiple years. Demonstrated characteristics include probability distribution for different ramp durations, seasonal and diurnal variability and low net load events. The comparison shows regions with low variability (Sweden, Spain and Germany), medium variability (Portugal, Ireland, Finland and Denmark) and regions with higher variability (Quebec, Bonneville Power Administration and Electric Reliability Council of Texas in North America; Gansu, Jilin and Liaoning in China; and Norway and offshore wind power in Denmark). For regions with low variability, the maximum 1 h wind ramps are below 10% of nominal capacity, and for regions with high variability, they may be close to 30%. Wind power variability is mainly explained by the extent of geographical spread, but also higher capacity factor causes higher variability. It was also shown how wind power ramps are autocorrelated and dependent on the operating output level. When wind power was concentrated in smaller area, there were outliers with high changes in wind output, which were not present in large areas with well‐dispersed wind power. Copyright © 2015 John Wiley & Sons, Ltd.

Wind ramp events at Suttu, Hokkaido

Wind ramp events at Suttu, Hokkaido

Evaluation of Wind Forecasts and Observation Impacts from Variational and Ensemble Data Assimilation for Wind Energy Applications

Abstract The U.S. Department of Energy Wind Forecast Improvement Project (WFIP) has recently been completed with the aim of 1) understanding the performance of different mesoscale data assimilation systems for lower-atmospheric wind prediction and 2) determining the observation impacts on wind forecasts within the different assimilation systems. Here an ensemble Kalman filter (EnKF) was tested against a three-dimensional variational data assimilation (3DVAR) technique. Forecasts lasting 24 hours were produced for a month-long period to determine the day-to-day performance of each system, as well as over 10 individual wind ramp cases. The observation impacts from surface mesonet and profiler/sodar wind observations aloft were also tested in each system for both the month-long run and the ramp forecasts. It was found that EnKF forecasts verified over a domain including Texas and Oklahoma were better than those of 3DVAR for the month-long experiment throughout the forecast window, presumably from the use of flow-dependent covariances in the EnKF. The assimilation of mesonet data improved both EnKF and 3DVAR early forecasts, but sodar/profiler data showed a degradation (EnKF) or had no effect (3DVAR), with the degradation apparently resulting from a lower-atmospheric wind bias. For the wind ramp forecasts, ensemble averaging appears to overwhelm any improvements flow-dependent assimilation may have on ramp forecasts, leading to better 3DVAR ramp prediction. This suggests that best member techniques within the EnKF may be necessary for improved performance over 3DVAR for forecasts of sharp features such as wind ramps. Observation impacts from mesonet and profiler/sodar observations generally improved EnKF ramp forecasts, but either had little effect on or degraded 3DVAR forecasts.

Real-time AGC dispatch units considering wind power and ramping capacity of thermal units

The high penetration of wind energy sources in power systems has substantially increased the demand for faster-ramping thermal units participating in the frequency regulation service. To fulfill the automatic generation control (AGC) and compensate the influence of wind power fluctuations simultaneously, ramping capacity should be considered in the dispatch model of thermals. Meanwhile, conventional methods in this area do not take the impact of transmission loss into the dispatch model, or rely on offline network model and parameters, failing to reflect the real relationships between the wind farms and thermal generators. This paper proposes an online approach for AGC dispatch units considering the above issues. Firstly, the power loss sensitivity is online identified using recursive least square method based on the real-time data of phasor measurement units. It sets up power balance constraint and results in a more accurate dispatch model. Then, an improved multi-objective optimization model of dispatch is proposed and a connection is established between the thermal units with fast ramping capacity and the wind farms with rapid fluctuations. Genetic algorithm is used to solve the dispatch model. The proposed method is compared with conventional methods in simulation case in the IEEE 30-bus system. Finally, simulation results verify the validity and the feasibility of identification method and optimization model.

Support-Vector-Machine-Enhanced Markov Model for Short-Term Wind Power Forecast

Wind ramps introduce significant uncertainty into wind power generation. Reliable system operation, however, requires accurate detection and forecast of wind ramps, especially at high penetration levels. In this paper, to deal with the wind ramp dynamics, a support vector machine (SVM)-enhanced Markov model is developed for short-term wind power forecast, based on one key observation from the measurement data that wind ramps often occur with specific patterns. Specifically, using the historical data of the wind turbine power outputs recorded at an actual wind farm, data analytics-based finite-state Markov models are first developed to model the normal fluctuations of wind generation, while taking into account the diurnal nonstationarity and the seasonality of wind generation. Next, the forecast by the SVM is integrated cohesively into the finite-state Markov models. Based on the SVM-enhanced Markov model, both short-term distributional forecasts and point forecasts are then derived. Numerical test results, using real wind generation data traces, demonstrate the significantly improved accuracy of the proposed forecast approach.

Dual‐Doppler measurements of a wind ramp event at an Oklahoma wind plant

AbstractAn observational case study of a wind ramp event at Enel Green Power North America's wind plant in Oklahoma is presented. Using coordinated measurements collected by the Texas Tech University Ka‐band radars, dual‐Doppler‐synthesized wind fields are merged with data from a meteorological tower and 32 operational turbines to document the evolution and impact of the wind ramp on turbine behavior and performance over a 1 h period. During the event, average power output for turbines within the dual‐Doppler analysis domain increases from 18.3% of capacity to 98.9% of capacity, emphasizing the abrupt impact wind ramp events can have on the electrical grid. The presented measurements and analyses highlight the insights remote sensing technologies can offer towards documenting transient wind ramps and assisting modeling efforts used to forecast such events. Copyright © 2015 John Wiley & Sons, Ltd.

Increasing thermal plant flexibility in a high renewables power system

Thermal generation is a vital component of mature and reliable electricity markets. As the share of renewable electricity in such markets grows, so too do the challenges associated with its variability. Proposed solutions to these challenges typically focus on alternatives to primary generation, such as energy storage, demand side management, or increased interconnection. Less attention is given to the demands placed on conventional thermal generation or its potential for increased flexibility. However, for the foreseeable future, conventional plants will have to operate alongside new renewables and have an essential role in accommodating increasing supply-side variability. This paper explores the role that conventional generation has to play in managing variability through the sub-system case study of Northern Ireland, identifying the significance of specific plant characteristics for reliable system operation. Particular attention is given to the challenges of wind ramping and the need to avoid excessive wind curtailment. Potential for conflict is identified with the role for conventional plant in addressing these two challenges. Market specific strategies for using the existing fleet of generation to reduce the impact of renewable resource variability are proposed, and wider lessons from the approach taken are identified.

Enabling Convex Energy Bids for Flexible Ramp Product via Smart Local Automation

Electricity markets, for the purpose of dispatch, approximate non-linear generator dynamics by a constant ramp rate R MW/10 min. The scalar R enables generator serving entity (GSE) to submit convex energy bids. Given the high penetration of renewable energy resources in US utilities, particularly California and Mid-West Independent System Operators (ISO), hard-to-predict wind ramps within the operating hour necessitate frequent ramping of generators, also known as load-following. A generator’s non-linear energy conversion dynamics varies widely over the operating hour. Consequently, the assumption of a constant rate R is often violated under fast changing operating conditions. In this paper, we propose a smart local automation on a non-linear energy conversion model of a coal-fired steam turbine. The proposed controller enables an improved and a near-linear dynamic response, ensuring fulfillment of convex bids.

Improving Wind Farm Dispatchability Using Model Predictive Control for Optimal Operation of Grid-Scale Energy Storage

This paper demonstrates the use of model-based predictive control for energy storage systems to improve the dispatchability of wind power plants. Large-scale wind penetration increases the variability of power flow on the grid, thus increasing reserve requirements. Large energy storage systems collocated with wind farms can improve dispatchability of the wind plant by storing energy during generation over-the-schedule and sourcing energy during generation under-the-schedule, essentially providing on-site reserves. Model predictive control (MPC) provides a natural framework for this application. By utilizing an accurate energy storage system model, control actions can be planned in the context of system power and state-of-charge limitations. MPC also enables the inclusion of predicted wind farm performance over a near-term horizon that allows control actions to be planned in anticipation of fast changes, such as wind ramps. This paper demonstrates that model-based predictive control can improve system performance compared with a standard non-predictive, non-model-based control approach. It is also demonstrated that secondary objectives, such as reducing the rate of change of the wind plant output (i.e., ramps), can be considered and successfully implemented within the MPC framework. Specifically, it is shown that scheduling error can be reduced by 81%, reserve requirements can be improved by up to 37%, and the number of ramp events can be reduced by 74%.

Measurements in support of wind farm simulations and power forecasts: The Crop/Wind-energy Experiments (CWEX)

The Midwest US currently is experiencing a large build-out of wind turbines in areas where the nocturnal low-level jet (NLLJ) is a prominent and frequently occurring feature. We describe shear characteristics of the NLLJ and their influence on wind power production. Reports of individual turbine power production and concurrent measurements of near-surface thermal stratification are used to turbine wake interactions and turbine interaction with the overlying atmosphere. Progress in forecasting conditions such as wind ramps and shear are discussed. Finally, the pressure perturbation introduced by a line of turbines produces surface flow convergence that may create a vertical velocity and hence a mesoscale influence on cloud formation by a wind farm.

Detection and Statistics of Wind Power Ramps

Ramps events are a significant source of uncertainty in wind power generation. Developing statistical models from historical data for wind power ramps is important for designing intelligent distribution and market mechanisms for a future electric grid. This requires robust detection schemes for identifying wind ramps in data. In this paper, we propose an optimal detection technique for identifying wind ramps for large time series. The technique relies on defining a family of scoring functions associated with any rule for defining ramps on an interval of the time series. A dynamic programming recursion is then used to find all such ramp events. Identified wind ramps are used to propose a new stochastic framework to characterize wind ramps. Extensive statistical analysis is performed based on this framework, characterizing ramping duration and rates as well as other key features needed for evaluating the impact of wind ramps in the operation of the power system. In particular, evaluation of new ancillary services and wind ramp forecasting can benefit from the proposed approach.

Variability and uncertainty of wind power in the California electric power system

ABSTRACTThe 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.

A Statistical Model for Wind Power on the Basis of Ramp Analysis

Due to stochastic nature of wind distribution, wind power output comes with unscheduled changes called ramp events. In this paper, a semi-analytical approach is considered to analyze the distribution of ramp events. A simple empirical equation is derived based on the probability of wind ramp events considering the stochastic nature of wind power distribution for the Indian state of Karnataka. The proposed functional relationship shows a satisfactory approximation of the actual distribution of ramp events and helps to estimate the statistical behaviour of wind output in terms of frequency of ramp events for pre-defined threshold power outputs.

Applying portfolio theory to the electricity sector: Energy versus power

Portfolio theory has found its way in numerous applications for optimizing the electricity generation mix of a particular region. Existing models, however, consider typically a single time period and correspondingly do not properly account for actual dispatch constraints and energy sources with a non-dispatchable, variable output. This paper presents a portfolio theory model that explicitly distinguishes between installed capacity (power), electricity generation (energy) and actual instantaneous power delivery. This way, the variability of wind power and ramp limits of conventional power plants are correctly included in the investment optimization. The model is written as a quadratically constrained programming problem and illustrated in a case study. The results show that the introduction of wind power can be motivated to lower the risk on generation cost, albeit to smaller levels than typically reported in the literature. This wind power deployment further requires the need for sufficiently rampable technologies, to deal with its fluctuating output.

Youth Football: Heat Stress and Injury Risk

Michael F. Bergeron, Ph.D., FACSM (Co-Chair)Douglas B. McKeag, M.D., FACSM (Co-Chair)Douglas J. Casa, Ph.D., ATC, FACSMPriscilla M. Clarkson, Ph.D., FACSMRandall W. Dick, FACSME. Randy Eichner, M.D., FACSMCraig A. Horswill, Ph.D., FACSMAnthony C. Luke, M.D., MPHFrederick Mueller, Ph.D., FACSMThayne A. Munce, Ph.D.William O. Roberts, M.D., FACSMThomas W. Rowland, M.D., FACSM