Representation of Wind Energy Scenarios in the Stochastic Dual Dynamic Programming for Hydrothermal Systems Operation

2020 ◽  
Author(s):  
Marcos T.B. de Oliveira ◽  
Alexandre S. Fernandes ◽  
Elisa Oliveira ◽  
André L. M. Marcato ◽  
Lucas R. Conceição
Keyword(s):  
Dynamic Programming ◽  
Wind Power ◽  
Wind Farm ◽  
Hydrothermal Systems ◽  
Wind Power Generation ◽  
Medium Term ◽  
Stochastic Dual Dynamic Programming ◽  
Convergence Process ◽  
The Impact ◽  
Dual Dynamic Programming

This article presents a methodology for including wind power generation in the medium-term planning ofhydrothermal systems, where Stochastic Dual Dynamic Programming (SDDP) is widely applied in the literature to solve this class of problem. To assess the impact of the intermittent generation, wind power scenarios were generated through Weibull Distribution, which were applied to reduce the load, generating several demand scenarios. Thus, the aim of this paper is to improve the computational eort of the conventional SDDP with demand scenarios, where the main contribution of the work consists of applying the Immediate Cost Function (ICF) to accelerate the SDDP convergence process. The proposed methodology was analyzed using part of the Brazilian system, considering a wind farm.

scholarly journals A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2442 ◽  
Author(s):  
Jussi Ekström ◽  
Matti Koivisto ◽  
Ilkka Mellin ◽  
Robert Millar ◽  
Matti Lehtonen
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Statistical Modeling ◽  
Renewable Energy Sources ◽  
Measurement Data ◽  
Wind Power Generation ◽  
System Level ◽  
Wind Generation ◽  
The Impact

In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.

scholarly journals Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Amila T. Peiris ◽  
Jeevani Jayasinghe ◽  
Upaka Rathnayake
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Wind Power ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Wind Power Generation ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Squared Error ◽  
Ann Models

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.

Wind power generation forecast by coupling numerical weather prediction model and gradient boosting machines in Yahyalı wind power plant

2020 ◽  
pp. 0309524X2097211
Author(s):  
Cem Özen ◽  
Umur Dinç ◽  
Ali Deniz ◽  
Haldun Karan
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Hybrid Model ◽  
Spatial Resolution ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Wind Power Generation ◽  
Gradient Boosting

Forecasting of the wind speed and power generation for a wind farm has always been quite challenging and has importance in terms of balancing the electricity grid and preventing energy imbalance penalties. This study focuses on creating a hybrid model that uses both numerical weather prediction model and gradient boosting machines (GBM) for wind power generation forecast. Weather Research and Forecasting (WRF) model with a low spatial resolution is used to increase temporal resolutions of the computed new or existing variables whereas GBM is used for downscaling purposes. The results of the hybrid model have been compared with the outputs of a stand-alone WRF which is well configured in terms of physical schemes and has a high spatial resolution for Yahyalı wind farm over a complex terrain located in Turkey. Consequently, the superiority of the hybrid model in terms of both performance indicators and computational expense in detail is shown.

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