Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο: https://hdl.handle.net/20.500.14279/31359
Τίτλος: Decentralized control in active distribution grids via supervised and reinforcement learning
Συγγραφείς: Karagiannopoulos, Stavros 
Aristidou, Petros 
Hug, Gabriela 
Botterud, Audun 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Λέξεις-κλειδιά: Supervised learning;Reinforcement learning;Deep deterministic policy gradient;Decentralized control;Active distribution systems
Ημερομηνία Έκδοσης: Μαΐ-2024
Πηγή: Energy and AI, 2024, vol. 16, articl. no. 100342
Volume: 16
Περιοδικό: Energy and AI 
Περίληψη: While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.
URI: https://hdl.handle.net/20.500.14279/31359
ISSN: 26665468
DOI: 10.1016/j.egyai.2024.100342
Rights: Attribution-NonCommercial-ShareAlike 4.0 International
Type: Article
Affiliation: Cyprus University of Technology 
ETH Zurich 
Massachusetts Institute of Technology 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Άρθρα/Articles

Αρχεία σε αυτό το τεκμήριο:
Αρχείο Περιγραφή ΜέγεθοςΜορφότυπος
1-s2.0-S2666546824000089-main.pdf1.6 MBAdobe PDFΔείτε/ Ανοίξτε
CORE Recommender
Δείξε την πλήρη περιγραφή του τεκμηρίου

Page view(s)

87
Last Week
0
Last month
1
checked on 21 Νοε 2024

Download(s)

78
checked on 21 Νοε 2024

Google ScholarTM

Check

Altmetric


Αυτό το τεκμήριο προστατεύεται από άδεια Άδεια Creative Commons Creative Commons