Treatment of uncertainty for next generation power systems: State-of-the-art in stochastic optimization

2016 ◽  
Vol 141 ◽  
pp. 233-245 ◽  
Author(s):  
Amru Alqurashi ◽  
Amir H. Etemadi ◽  
Amin Khodaei
Keyword(s):  
Power Systems ◽  
Stochastic Optimization ◽  
State Of The Art ◽  
Next Generation ◽  
Generation Power

An SET-Free, All-Digital Controlled Point-of-Load Regulator for Next-Generation Power Systems: ADC-POL

2011 ◽  
Vol 58 (6) ◽  
pp. 3011-3017 ◽  
Author(s):  
Philippe C. Adell ◽  
Tao Liu ◽  
Bert Vermeire ◽  
Bertan Bakkaloglu ◽  
David Aveline
Keyword(s):  
Power Systems ◽  
Next Generation ◽  
Generation Power

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

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