Application of Modified Social Spider Algorithm on Unit Commitment Solution Considering the Uncertainty of Wind Power in Restructured Electricity Market

2021 ◽  
pp. 437-448
Author(s):  
Heidar Ali Shayanfar ◽  
Hossein Shayeghi ◽  
L. Bagherzadeh
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Unit Commitment ◽  
Social Spider ◽  
Social Spider Algorithm

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

scholarly journals Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

2021 ◽  
Vol 11 (10) ◽  
pp. 4438
Author(s):  
Satyendra Singh ◽  
Manoj Fozdar ◽  
Hasmat Malik ◽  
Maria del Valle Fernández Moreno ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Methods ◽  
Bidding Strategy ◽  
Wind Speed Profile ◽  
Gravitational Search ◽  
Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

scholarly journals Statistical metrics for assessing the quality of wind power scenarios for stochastic unit commitment

Wind Energy ◽  
2015 ◽  
Vol 19 (5) ◽  
pp. 873-893 ◽  
Author(s):  
Didem Sari ◽  
Youngrok Lee ◽  
Sarah Ryan ◽  
David Woodruff
Keyword(s):  
Wind Power ◽  
Unit Commitment ◽  
Stochastic Unit Commitment ◽  
Statistical Metrics
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