Correlated Equilibrium Q-learning for Multi-objective Reactive Power Optimization Considering Grid Side Carbon Emissions

DOI

10.2991/icicci-15.2015.49How to use a DOI?

Keywords

Keywords-multi-regional reactive power optimization; low-carbon electricity; correlated equilibrium; reinforcement learning

Abstract

Abstract—In order to meet the development trend of smart grid, the correlated equilibrium Q-learning (CEQ) algorithm is proposed for multi-regional reactive power optimization. Meanwhile, in response to the national strategy of low carbon environmental protection, CO2 emission is considered as one of the control objectives in reactive power optimization. In this paper, CEQ algorithm is adopted to allocate the control variables rationally, through the correlated equilibrium game among areas and information communication and sharing to achieve multi-regional reactive power optimization, which solves the limited information-sharing mechanisms and curse of dimensionality problem effectively. Simulation of the IEEE 9-bus system indicates that through the combine of pre-learning and online learning CEQ algorithm solves the multi-regional collaborative reactive power optimization quickly and rationally.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hong Hu
AU  - Wenmei Wu
AU  - Min Tan
AU  - Shaohua Xiao
AU  - Chuanjia Han
PY  - 2015/09
DA  - 2015/09
TI  - Correlated Equilibrium Q-learning for Multi-objective Reactive Power Optimization Considering Grid Side Carbon Emissions
BT  - Proceedings of the 2nd International Conference on Intelligent Computing and Cognitive Informatics
PB  - Atlantis Press
SP  - 230
EP  - 235
SN  - 1951-6851
UR  - https://doi.org/10.2991/icicci-15.2015.49
DO  - 10.2991/icicci-15.2015.49
ID  - Hu2015/09
ER  -