scholarly journals The impact of wind field spatial heterogeneity and variability on short-term wind power forecast errors

2019 ◽  
Vol 11 (3) ◽  
pp. 033304 ◽  
Author(s):  
Mao Yang ◽  
Luobin Zhang ◽  
Yang Cui ◽  
Qiongqiong Yang ◽  
Binyang Huang
Keyword(s):  
Spatial Heterogeneity ◽  
Wind Power ◽  
Wind Field ◽  
Forecast Errors ◽  
Short Term ◽  
Wind Power Forecast ◽  
The Impact

scholarly journals Spatio-temporal analysis and modeling of short-term wind power forecast errors

Wind Energy ◽  
2011 ◽  
Vol 14 (1) ◽  
pp. 43-60 ◽  
Author(s):  
Julija Tastu ◽  
Pierre Pinson ◽  
Ewelina Kotwa ◽  
Henrik Madsen ◽  
Henrik Aa. Nielsen
Keyword(s):  
Wind Power ◽  
Temporal Analysis ◽  
Forecast Errors ◽  
Short Term ◽  
Wind Power Forecast ◽  
Spatio Temporal

Wind power forecast based on improved Long Short Term Memory network

Energy ◽  
2019 ◽  
Vol 189 ◽  
pp. 116300 ◽  
Author(s):  
Li Han ◽  
Huitian Jing ◽  
Rongchang Zhang ◽  
Zhiyu Gao
Keyword(s):  
Wind Power ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Forecast ◽  
Memory Network ◽  
Long Short Term Memory

Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch

2014 ◽  
Vol 5 (1) ◽  
pp. 511-520 ◽  
Author(s):  
Le Xie ◽  
Yingzhong Gu ◽  
Xinxin Zhu ◽  
Marc G. Genton
Keyword(s):  
Power System ◽  
Wind Power ◽  
Short Term ◽  
Look Ahead ◽  
Wind Power Forecast ◽  
Spatio Temporal

scholarly journals Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6532
Author(s):  
Vahab Rostampour ◽  
Thom S. Badings ◽  
Jacquelien M. A. Scherpen
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Power ◽  
Distribution System ◽  
Large Scale ◽  
Wind Power Generation ◽  
Forecast Errors ◽  
Monte Carlo Simulation Study ◽  
System Operator ◽  
The Impact

We present a Buildings-to-Grid (BtG) integration framework with intermittent wind-power generation and demand flexibility management provided by buildings. First, we extend the existing BtG models by introducing uncertain wind-power generation and reformulating the interactions between the Transmission System Operator (TSO), Distribution System Operators (DSO), and buildings. We then develop a unified BtG control framework to deal with forecast errors in the wind power, by considering ancillary services from both reserves and demand-side flexibility. The resulting framework is formulated as a finite-horizon stochastic model predictive control (MPC) problem, which is generally hard to solve due to the unknown distribution of the wind-power generation. To overcome this limitation, we present a tractable robust reformulation, together with probabilistic feasibility guarantees. We demonstrate that the proposed demand flexibility management can substitute the traditional reserve scheduling services in power systems with high levels of uncertain generation. Moreover, we show that this change does not jeopardize the stability of the grid or violate thermal comfort constraints of buildings. We finally provide a large-scale Monte Carlo simulation study to confirm the impact of achievements.

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