Output details
13 - Electrical and Electronic Engineering, Metallurgy and Materials
University of Strathclyde
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Article title
Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade
Type
D - Journal article
Title of journal
IEEE Transactions on Power Systems
Article number
-
Volume number
27
Issue number
1
First page of article
373
ISSN of journal
0885-8950
Year of publication
2012
URL
-
Number of additional authors
3
Additional information
The software developed during this research to simulate electricity markets has been publicly disseminated through industry-recognised open-source forums [http://rwl.github.com/pylon, sourceforge.net]. Development work related to this research has been undertaken on the Electric Power Research Institute’s (EPRI) Open DSS software [Roger Dugan, EPRI, Knoxville, Tennessee, USA, rdugan@epri.com]. Expertise resulting from this research was then used by the author in collaboration with SME Open-Grid-Systems [Alan McMorran, alan@opengrid.com] and an international standards working group [http://cimug.ucaiug.org].
Interdisciplinary
-
Cross-referral requested
-
Research group
E - Institute for Energy and Environment (InstEE)
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
