An Improved Robust Optimization Algorithm for Short-Term Scheduling with Wind Power Integration

2018 ◽  
Author(s):  
Shu Xia ◽  
Xiaolin Ge ◽  
Guangdong Hao ◽  
Naidun Wang ◽  
Haojiang Han
Keyword(s):  
Robust Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Short Term ◽  
Wind Power Integration

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

Improving wind power integration by a novel short-term dispatch model based on free heat storage and exhaust heat recycling

Energy ◽  
2018 ◽  
Vol 160 ◽  
pp. 940-953 ◽  
Author(s):  
Jinda Wang ◽  
Zhigang Zhou ◽  
Jianing Zhao ◽  
Jinfu Zheng
Keyword(s):  
Wind Power ◽  
Heat Storage ◽  
Short Term ◽  
Wind Power Integration ◽  
Model Based ◽  
Exhaust Heat

Wavelet Neural Network Model for Short-Term Wind Power Forecasting Based on Particle Swarm Optimization Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 806-812 ◽  
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Prediction

The accuracy of short-term wind power forecast is important to the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network is proposed. At the same time in order to overcome the disadvantages of the wavelet neural network for only use error reverse transmission as a fixed rule, this paper puts forward using Particle Swarm Optimization algorithm to replace the traditional gradient descent method training wavelet neural network. Through the analysis of the measured data of a wind farm, Shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

scholarly journals Short-term wind power prediction based on GPR-BSO model

2021 ◽  
Vol 256 ◽  
pp. 02035
Author(s):  
Tao Chen ◽  
Xinjian Li ◽  
Zhemeng Zhang ◽  
Tongguang Yang ◽  
Shengtao He ◽  
...  
Keyword(s):  
Wind Power ◽  
Gaussian Process Regression ◽  
Optimization Method ◽  
Stable Operation ◽  
Local Optimum ◽  
Power Prediction ◽  
Short Term ◽  
Additional Information ◽  
Wind Power Integration ◽  
Wind Power Prediction

Wind power forecasting is a crucial part for the safe and stable operation of wind power integration, which is under the influence of different factors such as wind speed, wind direction, atmospheric pressure. These factors bring randomness and volatility to wind power which makes it less predictable. While, there are very limited studies on describing the uncertainty of wind power. Therefore, to providing additional information on the uncertainty and volatility, a kernel-based on Gaussian Process Regression (GPR) incorporating the hyper-parameters intelligent optimization method is proposed in this paper. Firstly, the hyper-parameters solution of GPR is formulated as a nonlinear optimization with constraints. Then, an intelligent algorithm named Brain-storming optimization (BSO) is adopted to obtain the optimal hyper-parameters of GPR. Furthermore, the performance is examined on short-term wind power data. Most importantly, the GPR incorporating BSO can avoid the hyper-parameters at local optimum.

Study on Short-Term Wind Power Prediction Model Based on ARMA Theory

2013 ◽  
Vol 448-453 ◽  
pp. 1875-1878 ◽  
Author(s):  
Wei Li ◽  
Hong Tu Zhang ◽  
Ting Ting An
Keyword(s):  
Wind Power ◽  
Wind Farm ◽  
Arma Model ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Integration ◽  
Proposed Model ◽  
Wind Power Prediction ◽  
High Prediction ◽  
New Research

At present, the difficulty of wind power integration has resulted in a large number of wind curtailment phenomena and wasted a lot of renewable energy. Due to the significant instability, anti-peak-regulation and intermittency of wind power, wind power integration needs an accurate prediction technique to be a basis. ARMA model has the advantage of high prediction accuracy in predicting short-term wind power. This paper puts forward the method for short-term wind power prediction using ARMA model and carries out empirical analysis using the data from a wind farm of Jilin province, which shows the science and operability of the proposed model. It provides a new research method for the wind power prediction.

Wind Power Integration: Connection and system operational aspects

2007 ◽  
Author(s):  
Fox ◽  
Jenkins ◽  
O'Malley ◽  
Bryans ◽  
Anaya-Lara ◽  
...  
Keyword(s):  
Wind Power ◽  
Wind Power Integration ◽  
Operational Aspects
Sign in / Sign up

Export Citation Format

Share Document