Self-learning Control for Active Network Management

2021 ◽  
Author(s):  
Julio Perez-Olvera ◽  
Tim C. Green ◽  
Adria Junyent-Ferre
Keyword(s):  
Network Management ◽  
Learning Control ◽  
Active Network ◽  
Self Learning

Deep reinforcement learning enabled self-learning control for energy efficient driving

2019 ◽  
Vol 99 ◽  
pp. 67-81 ◽  
Author(s):  
Xuewei Qi ◽  
Yadan Luo ◽  
Guoyuan Wu ◽  
Kanok Boriboonsomsin ◽  
Matthew Barth
Keyword(s):  
Reinforcement Learning ◽  
Energy Efficient ◽  
Learning Control ◽  
Self Learning

scholarly journals Integration and control of lithium-ion BESSs for active network management in smart grids: Sundom smart grid backup feeding case

2021 ◽  
Author(s):  
Chethan Parthasarathy ◽  
Hossein Hafezi ◽  
Hannu Laaksonen
Keyword(s):  
Smart Grid ◽  
Network Management ◽  
Smart Grids ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Lithium Ion ◽  
Active Network ◽  
Li Ion ◽  
Grid Side ◽  
And Control

AbstractLithium-ion battery energy storage systems (Li-ion BESS), due to their capability in providing both active and reactive power services, act as a bridging technology for efficient implementation of active network management (ANM) schemes for land-based grid applications. Due to higher integration of intermittent renewable energy sources in the distribution system, transient instability may induce power quality issues, mainly in terms of voltage fluctuations. In such situations, ANM schemes in the power network are a possible solution to maintain operation limits defined by grid codes. However, to implement ANM schemes effectively, integration and control of highly flexible Li-ion BESS play an important role, considering their performance characteristics and economics. Hence, in this paper, an energy management system (EMS) has been developed for implementing the ANM scheme, particularly focusing on the integration design of Li-ion BESS and the controllers managing them. Developed ANM scheme has been utilized to mitigate MV network issues (i.e. voltage stability and adherence to reactive power window). The efficiency of Li-ion BESS integration methodology, performance of the EMS controllers to implement ANM scheme and the effect of such ANM schemes on integration of Li-ion BESS, i.e. control of its grid-side converter (considering operation states and characteristics of the Li-ion BESS) and their coordination with the grid side controllers have been validated by means of simulation studies in the Sundom smart grid network, Vaasa, Finland.

Framework for determining the degree of centralization in global production networks

2021 ◽  
Vol 75 (4) ◽  
pp. 537-551
Author(s):  
Günther Schuh ◽  
Andreas Gützlaff ◽  
Julian Ays ◽  
Tino X. Schlosser
Keyword(s):  
Complex Systems ◽  
Network Management ◽  
Environmental Conditions ◽  
Theoretical Framework ◽  
Active Network ◽  
Production Network ◽  
Production Networks ◽  
Global Production Networks

Over the last decades, global production networks have developed to high complex systems. To adapt quickly the dynamic environmental conditions, an active network management is required. The network management and the associated distribution of responsibilities in the production network is mostly grown historically. Further, the issue is only commonly considered in current approaches. Therefore, this paper presents a framework for determining the degree of centralization in global production networks under the aspect of increasing efficiency. Beyond the theoretical framework, a workshop procedure is presented in which the framework can be tested.

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