Optimal short-term scheduling of large-scale power systems

1981 ◽  
Author(s):  
D. Bertsekas ◽  
G. Lauer ◽  
N. Sandell ◽  
T. Posbergh
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Short Term

scholarly journals Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jia Ning ◽  
Guanghao Lu ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Hualei Wang
Keyword(s):  
Power Systems ◽  
Spatial Correlation ◽  
Data Storage ◽  
Power Plants ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Storage Condition ◽  
Support Vector ◽  
Forecast Method ◽  
Short Term

With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.

scholarly journals Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4349 ◽  
Author(s):  
Tian Shi ◽  
Fei Mei ◽  
Jixiang Lu ◽  
Jinjun Lu ◽  
Yi Pan ◽  
...  
Keyword(s):  
Phase Space ◽  
Power Systems ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Load Forecasting ◽  
Reconstruction Algorithm ◽  
Phase Space Reconstruction ◽  
Load Prediction ◽  
Short Term ◽  
New Energy

With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load.

Optimal short-term scheduling of large-scale power systems

1983 ◽  
Vol 28 (1) ◽  
pp. 1-11 ◽  
Author(s):  
D. Bertsekas ◽  
G. Lauer ◽  
N. Sandell ◽  
T. Posbergh
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Short Term

scholarly journals Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
E. Faghihnia ◽  
S. Salahshour ◽  
A. Ahmadian ◽  
N. Senu
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Learning Algorithm ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Power Forecasting

Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF) approach, trained by the polynomial model tree (POLYMOT) learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.

scholarly journals Short-Term Scheduling of Expected Output-Sensitive Cascaded Hydro Systems Considering the Provision of Reserve Services

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2477
Author(s):  
Ruhong Zhong ◽  
Chuntian Cheng ◽  
Shengli Liao ◽  
Zhipeng Zhao
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Solar Power ◽  
Short Term ◽  
Iterative Approximation ◽  
Peak Shaving ◽  
Renewable Power ◽  
Optimality Algorithm ◽  
Output Characteristics ◽  
Iterative Approximation Method

The integration of large-scale wind and solar power into the power grid brings new challenges to the security and stability of power systems because of the uncertainty and intermittence of wind and solar power. This sensitive expected output has real implications for short-term hydro scheduling (STHS), which provides reserve services to alleviate electrical perturbations from wind and solar power. This paper places an emphasis on the sensitive expected output characteristics of hydro plants and their effect on reserve services. The multi-step progressive optimality algorithm (MSPOA) is used for the STHS problem. An iterative approximation method is proposed to determine the forebay level and output under the condition of the sensitive expected output. Cascaded hydro plants in the Wujiang River are selected as an example. The results illustrate that the method can achieve good performance for peak shaving and reserve services. The proposed approach is both accurate and computationally acceptable so that the obtained hydropower schedules are in accordance with practical circumstances and the reserve can cope with renewable power fluctuations effectively.

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