scholarly journals Coordination Scheduling of Wind-Hydropower Generation and Profit Allocation Based on Shapley Value Method

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Yi Quan ◽  
Min Jiang ◽  
Hongjun Sun ◽  
Xiaohui Yuan ◽  
Li He ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Shapley Value ◽  
Power Plants ◽  
Moving Average ◽  
Arima Model ◽  
Water Inflow ◽  
Spinning Reserve ◽  
Reservoir Volume ◽  
Two Parameters

The volatility of wind makes the forecasting of wind speed unreliable. The inaccurate forecast in wind speed always leads to generation imbalance and causes Wind Generating Companies’ (WGenCOs) losses in the intrahour market. In contrast to wind power, Hydrogenerating Companies (HGenCOs) can utilize the reservoir volume to settle the fluctuation of water inflow easily. When treated as a specialized Spinning Reserve (SR) unit for wind power, hydropower can help to settle the generation imbalance and obtain more profit in the power market for both power plants. In this paper, the author establishes a coordination scheduling model of wind-hydro alliance which covers the day-ahead market and the intrahour market. First, to evaluate the deviation of the wind-hydro generation in the intrahour market, an imbalance charge rule considering each period of schedule horizon is constructed. Second, the author introduces two parameters to control the resources that hydropower can use to coordinate with wind power. Finally, the author introduces the Shapley value method to allocate the profit of the alliance which comprises several independent entities fairly. For the simulation of uncertainties, the scenario-based approach is used to simulate the water inflow of a reservoir considering the Monte Carlo (MC) method. The wind speed for the intrahour market is forecasted with the Autoregressive Integrated Moving Average (ARIMA) model. Simulations are implemented, and the results show that when treated as an SR unit for wind power, hydropower can diminish the imbalance charges significantly and will improve the revenue of the wind-hydro alliance. Furthermore, the coordination operation also helps reduce the spillage of the reservoir and the curtailment of the wind power to achieve better utilization of renewable energy.

scholarly journals Optimization of a Small Wind Power Plant for Annual Wind Speed Distribution

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1587
Author(s):  
Krzysztof Wrobel ◽  
Krzysztof Tomczewski ◽  
Artur Sliwinski ◽  
Andrzej Tomczewski
Keyword(s):  
Wind Speed ◽  
Power Plant ◽  
Wind Power ◽  
Power Plants ◽  
Control Method ◽  
Wind Power Plant ◽  
Switched Reluctance ◽  
Wind Speeds ◽  
Wind Speed Distribution ◽  
And Control

This article presents a method to adjust the elements of a small wind power plant to the wind speed characterized by the highest annual level of energy. Tests were carried out on the basis of annual wind distributions at three locations. The standard range of wind speeds was reduced to that resulting from the annual wind speed distributions in these locations. The construction of the generators and the method of their excitation were adapted to the characteristics of the turbines. The results obtained for the designed power plants were compared with those obtained for a power plant with a commercial turbine adapted to a wind speed of 10 mps. The generator structure and control method were optimized using a genetic algorithm in the MATLAB program (Mathworks, Natick, MA, USA); magnetostatic calculations were carried out using the FEMM program; the simulations were conducted using a proprietary simulation program. The simulation results were verified by measurement for a switched reluctance machine of the same voltage, power, and design. Finally, the yields of the designed generators in various locations were determined.

Beneficial role of diurnal smoothing for grid integration of wind power

2021 ◽  
Author(s):  
Anasuya Gangopadhyay ◽  
Ashwin K Seshadri ◽  
Ralf Toumi
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Diurnal Cycle ◽  
Power Plants ◽  
Large Scale ◽  
Positive Impact ◽  
Forecast Error ◽  
Wind Generation ◽  
Grid Integration ◽  
Wind Maximum

<p>Smoothing of wind generation variability is important for grid integration of large-scale wind power plants. One approach to achieving smoothing is aggregating wind generation from plants that have uncorrelated or negatively correlated wind speed. It is well known that the wind speed correlation on average decays with increasing distance between plants, but the correlations may not be explained by distance alone. In India, the wind speed diurnal cycle plays a significant role in explaining the hourly correlation of wind speed between location pairs. This creates an opportunity of “diurnal smoothing”. At a given separation distance the hourly wind speeds correlation is reduced for those pairs that have a difference of +/- 12 hours in local time of wind maximum. This effect is more prominent for location pairs separated by 200 km or more and where the amplitude of the diurnal cycle is more than about  0.5 m/s. “Diurnal smoothing” also has a positive impact on the aggregate wind predictability and forecast error. “Diurnal smoothing” could also be important for other regions with diurnal wind speed cycles.</p>

Wind Speed Forecasting Based on FNN in Wind Farm

2014 ◽  
Vol 651-653 ◽  
pp. 1117-1122
Author(s):  
Zheng Ning Fu ◽  
Hong Wen Xie
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Fuzzy Neural Network ◽  
Negative Impact ◽  
Wind Speed Forecasting ◽  
Wind Power Plants ◽  
Fuzzy Neural

Wind speed forecasting plays a significant role to the operation of wind power plants and power systems. An accurate forecasting on wind power can effectively relieve or avoid the negative impact of wind power plants on power systems and enhance the competition of wind power plants in electric power market. Based on a fuzzy neural network (FNN), a method of wind speed forecasting is presented in this paper. By mining historical data as the learning stylebook, the fuzzy neural network (FNN) forecasts the wind speed. The simulation results show that this method can improve the accuracy of wind speed forecasting effectively.

Modeling of Wind Power Production Forecast Errors for Wind Integration Studies

2010 ◽  
Author(s):  
Jason J. Kemper ◽  
Mark F. Bielecki ◽  
Thomas L. Acker
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Power Plants ◽  
Moving Average ◽  
Forecast Errors ◽  
Production Forecast ◽  
Wind Integration ◽  
Forecast Performance ◽  
Auto Regressive ◽  
Statistical Trends

In wind integration studies, accurate representations of the wind power output from potential wind power plants and corresponding representations of wind power forecasts are needed, and typically used in a production cost simulation. Two methods for generating “synthetic” wind power forecasts that capture the statistical trends and characteristics found in commercial forecasting techniques are presented. These two methods are based on auto-regressive moving average (ARMA) models and the Markov random walk method. Statistical criteria are suggested for evaluation of wind power forecast performance, and both synthetic forecast methods proposed are evaluated quantitatively and qualitatively. The forecast performance is then compared with a commercial forecast used for an operational wind power plant in the Northwestern United States evaluated using the same statistical performance measures. These quantitative evaluation parameters are monitored during specific months of the year, during rapid ramping events, and at all times. The best ARMA based models failed to replicate the auto-regressive decay of forecast errors associated with commercial forecasts. A modification to the Markov method, consisting of adding a dimension to the state transition array, allowed the forecast time series to depend on multiple inputs. This improvement lowered the artificial variability in the original time series. The overall performance of this method was better than for the ARMA based models, and provides a suitable technique for use in creating a synthetic wind forecast for a wind integration study.

Short-term wind speed forecasting using ARIMA model

Energetika ◽  
2016 ◽  
Vol 62 (1-2) ◽  
Author(s):  
Ernesta Grigonytė ◽  
Eglė Butkevičiūtė
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Moving Average ◽  
Arima Model ◽  
Model Structure ◽  
Optimal Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Auto Correlation ◽  
Auto Correlation Function

The massive integration of wind power into the power system increasingly calls for better short-term wind speed forecasting which helps transmission system operators to balance the power systems with less reserve capacities. The  time series analysis methods are often used to analyze the  wind speed variability. The  time series are defined as a sequence of observations ordered in time. Statistical methods described in this paper are based on the prediction of future wind speed data depending on the historical observations. This allows us to find a sufficiently good model for the wind speed prediction. The paper addresses a short-term wind speed forecasting ARIMA (Autoregressive Integrated Moving Average) model. This method was applied for a number of different prediction problems, including the short term wind speed forecasts. It is seen as an early time series methodology with well-known limitations in wind speed forecasting, mainly because of insufficient accuracies of the hourly forecasts for the second half of the day-ahead forecasting period. The authors attempt to find the maximum effectiveness of the model aiming to find: (1) how the identification of the optimal model structure improves the forecasting results and (2) what accuracy increase can be gained by reidentification of the structure for a new wind weather season. Both historical and synthetic wind speed data representing the sample locality in the Baltic region were used to run the model. The model structure is defined by rows p, d, q and length of retrospective data period. The structure parameters p (Autoregressive component, AR) and q (Moving Average component, MA) were determined by the Partial Auto-Correlation Function (PACF) and Auto-Correlation Function (ACF), respectively. The model’s forecasting accuracy is based on the root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). The results allowed to establish the optimal model structure and the length of the input/retrospective period. The  quantitative study revealed that identification of the  optimal model structure gives significant accuracy improvement against casual structures for 6–8 h forecast lead time, but a season-specific structure is not appropriate for the entire year period. Based on the conducted calculations, we propose to couple the ARIMA model with any more effective method into a hybrid model.

scholarly journals The Investigation of Wind Energy Potential in Order to Estimate Power Plants in Baladeh Mazandaran, Iran

2012 ◽  
pp. 29-33
Author(s):  
S. Asghar Gholamian ◽  
S. Bagher Soltani ◽  
R. Ilka
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Power Plants ◽  
Weather Station ◽  
Energy Potential ◽  
Wind Power Density ◽  
Synoptic Weather ◽  
Wind Potential

First step for achieving wind energy is to locate points with appropriate wind power density in a country. Wind data which are recorded in a synoptic weather station, are the best way to study the wind potential of an area. In this paper wind speed period of Baladeh synoptic weather station is studied, since it has the maximum average of wind speed among 15 stations of the MAZANDARAN Province. Weibull factors k and c are calculated for 40 months from September 2006 to December 2009 and wind power density is determined based on these data. The total average of factors k and c for a height for 50 m are 1.442 m/s and 5.1256 respectively. By using the average of factors, wind power density in 50 m height will be 147.40 watt/m2 which is categorized as weak potential in wind class. However by monthly investigation it is shown that with a 50 m wind, this station can be put in medium class in hot months of the year.

scholarly journals Wind power application research on the fusion of the determination and ensemble prediction

2017 ◽  
Vol 14 ◽  
pp. 227-230
Author(s):  
Shi Lan ◽  
Xu Lina ◽  
Hao Yuzhu
Keyword(s):  
Wind Speed ◽  
Numerical Model ◽  
Wind Power ◽  
Wind Farm ◽  
Bayesian Model Averaging ◽  
Arima Model ◽  
Model Averaging ◽  
Absolute Error ◽  
Ensemble Prediction ◽  
Probabilistic Forecasting

Abstract. The fused product of wind speed for the wind farm is designed through the use of wind speed products of ensemble prediction from the European Centre for Medium-Range Weather Forecasts (ECMWF) and professional numerical model products on wind power based on Mesoscale Model5 (MM5) and Beijing Rapid Update Cycle (BJ-RUC), which are suitable for short-term wind power forecasting and electric dispatch. The single-valued forecast is formed by calculating the different ensemble statistics of the Bayesian probabilistic forecasting representing the uncertainty of ECMWF ensemble prediction. Using autoregressive integrated moving average (ARIMA) model to improve the time resolution of the single-valued forecast, and based on the Bayesian model averaging (BMA) and the deterministic numerical model prediction, the optimal wind speed forecasting curve and the confidence interval are provided. The result shows that the fusion forecast has made obvious improvement to the accuracy relative to the existing numerical forecasting products. Compared with the 0–24 h existing deterministic forecast in the validation period, the mean absolute error (MAE) is decreased by 24.3 % and the correlation coefficient (R) is increased by 12.5 %. In comparison with the ECMWF ensemble forecast, the MAE is reduced by 11.7 %, and R is increased 14.5 %. Additionally, MAE did not increase with the prolongation of the forecast ahead.

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