scholarly journals Data-driven stochastic optimization for power grids scheduling under high wind penetration

2021 ◽  
Author(s):  
Wei Xie ◽  
Yuan Yi ◽  
Zhi Zhou ◽  
Keqi Wang
Keyword(s):  
Stochastic Optimization ◽  
Power Grids ◽  
Data Driven ◽  
High Wind

Safety in Numbers: Online Security Analysis of Power Grids with High Wind Pentration

2012 ◽  
Vol 10 (2) ◽  
pp. 62-70 ◽  
Author(s):  
I. M. Dudurych ◽  
A. Rogers ◽  
R. Aherne ◽  
Lei Wang ◽  
F. Howell ◽  
...  
Keyword(s):  
Security Analysis ◽  
Power Grids ◽  
High Wind ◽  
Online Security ◽  
Safety In Numbers

scholarly journals Load frequency control of smart isolated power grids with high wind farm penetrations

2020 ◽  
Vol 14 (7) ◽  
pp. 1228-1238
Author(s):  
Reza Kazemi Golkhandan ◽  
Hossein Torkaman ◽  
Mohammad Reza Aghaebrahimi ◽  
Ali Keyhani
Keyword(s):  
Wind Farm ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Power Grids ◽  
High Wind ◽  
Load Frequency

scholarly journals Data‐driven online distributed disturbance location for large‐scale power grids

2019 ◽  
Vol 2 (3) ◽  
pp. 381-390
Author(s):  
Zekun Yang ◽  
Yu Chen ◽  
Ning Zhou ◽  
Aleksey Polunchenko ◽  
Yilu Liu
Keyword(s):  
Large Scale ◽  
Power Grids ◽  
Data Driven

scholarly journals A Review of Data-Driven Short-Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jiting Cao ◽  
Meng Zhang ◽  
Yang Li
Keyword(s):  
Power Systems ◽  
Voltage Stability ◽  
Power Transmission ◽  
Power Market ◽  
Power Grids ◽  
Data Driven ◽  
Stable Operation ◽  
Fast Recovery ◽  
Short Term ◽  
Air Conditioners

With the rapid growth of power market reform and power demand, the power transmission capacity of a power grid is approaching its limit and the secure and stable operation of power systems becomes increasingly important. In particular, in modern power grids, the proportion of dynamic loads with fast recovery characteristics such as air conditioners, refrigerators, and industrial motors is increasing. As well as there is an increasing proportion of different forms of renewable energy in power systems. Therefore, the short-term voltage stability (STVS) of power systems cannot be ignored. This article comprehensively sorts out the STVS problems of power systems from the perspective of data-driven methods and discusses existing challenges.

Smart4RES: Improved weather modelling and forecasting dedicated to renewable energy applications.

2021 ◽  
Author(s):  
George Kariniotakis ◽  
Simon Camal ◽  
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Large Eddy Simulation ◽  
Power Grids ◽  
Data Driven ◽  
Weather Variables ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Data Market ◽  
Modelling And Forecasting

<p>In this paper we present the research directions and innovative solutions developed in the European Horizon 2020 project Smart4RES (http://www.smart4res.eu) for better modelling and forecasting of weather variables necessary to optimise the integration of renewable energy (RES) production (i.e. wind, solar, run-of-the-river hydro) into power systems and electricity markets. Smart4RES gathers experts from several disciplines, from meteorology and renewable generation to market- and grid-integration. It aims to contribute to reach very high RES penetrations in power grids of 2030 and beyond, through thematic objectives including:<br>•    Improvement of weather and RES forecasting (+10-15% in performance),<br>•    Streamlined extraction of optimal value through new forecasting products, data market places, and novel business models,<br>•    New data-driven optimization and decision-aid tools for market and grid management applications.<br>•    Validation of new models in living labs and assessment of forecasting value vs costly remedies to hedge uncertainties (i.e. storage). </p><p>Smart4RES focuses both on improving forecasting models of weather (e.g. physical models, data assimilation, Large Eddy Simulation) and RES production (e.g. seamless models, highly resolved predictions), and on addressing applications in power grids. Developments in the project have been formalized in Use Cases that cover a large range of time frames, technologies and geographical scales. For example, use-cases on power grids refer to the provision of ancillary services to the upper-level grid (e.g., balancing power) and the local grid (e.g., voltage control and congestion management), where the accurate forecasts of variable generation are key for accurate decision-making. A grid state forecasting will quantify dynamically the flexibility potential of RES in distribution grids. Collaborative forecasting investigates the improvement associated to local data sharing between distributed RES plants. This data sharing paves the way to a data market where agents exchange measurements, predictions or other types of valuable data. Lastly, data-driven approaches will streamline decision-making by simplifying the model chain of bidding RES production, storage dispatch or predictive management electricity grids. They will also provide interpretable hindsight to decision-makers by integrating the decisions of experts (human-in-the-loop) and will be tested in realistic laboratory conditions (software-in-the-loop).</p><p>In this paper we focus on the work done for improving modelling and forecasting of weather variables; i.e. through innovative measuring set-ups (i.e. a network of sky cameras in Germany); through the development of seamless numerical weather prediction (NWP) approaches to be able to couple outputs of NWP models with different resolutions; through ultra high resolution NWPs based on Large Eddy Simulation. We present results using data from real world test cases considered in the project. Finally we assess how the new forecasting products may bring value to the applications. </p>

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