Hierarchically correlated equilibrium Q-learning for multi-area decentralized collaborative reactive power optimization

A hierarchically correlated equilibrium Q-learning (HCEQ) algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective, is proposed here. Based on the multi-area decentralized collaborative framework, the controllable variables in each region...

Full description

Saved in:
Bibliographic Details
Published in:CSEE Journal of Power and Energy Systems 2016-09, Vol.2 (3), p.65-72
Main Authors: Tan, Min, Han, Chuanjia, Zhang, Xiaoshun, Guo, Lexin, Yu, Tao
Format: Article
Language:English
Subjects:
Algorithms
Collaboration
Computer simulation
Equilibrium
Game theory
Machine learning
Optimization
Reactive power
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A hierarchically correlated equilibrium Q-learning (HCEQ) algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective, is proposed here. Based on the multi-area decentralized collaborative framework, the controllable variables in each region are divided into several optimization layers, which is an effective method for solving the limitations posed by dimensionality. The HCEQ provides constant information on the interaction between the state-action value function matrices, as well as on the cooperative game equilibrium among agents in each region. After acquiring the optimal value function matrix in the pre-learning process, HCEQ is able to quickly achieve an optimal solution online. Simulation of the IEEE 57-bus system is performed, which demonstrates that the proposed algorithm can effectively solve multi-area decentralized collaborative reactive power optimization, with the desired global search capabilities and convergence speed.
ISSN:2096-0042
2096-0042
DOI:10.17775/CSEEJPES.2016.00037