Distributed multi-step Q(λ) learning for Optimal Power Flow of large-scale power grids

T. Yu, J. Liu, Ka Wing Chan, J. J. Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


This paper presents a novel distributed multi-step Q(λ) learning algorithm (DQ(λ)L) based on multi-agent system for solving large-scale multi-objective OPF problem. It does not require any manipulation to the conventional mathematical Optimal Power Flow (OPF) model. Large-scale power system is first partitioned to subsystems and each subsystem is managed by an agent. Each agent adopts the standard multi-step Q(λ) learning algorithm to pursue its own objectives independently and approaches to the global optimal through cooperation and coordination among agents. The proposed DQ(λ)L has been thoroughly studied and tested on the IEEE 9-bus and 118-bus systems. Case studies demonstrated that DQ(λ)L is a feasible and effective for solving multi-objective OPF problem in large-scale complex power grid.
Original languageEnglish
Pages (from-to)614-620
Number of pages7
JournalInternational Journal of Electrical Power and Energy Systems
Issue number1
Publication statusPublished - 1 Nov 2012


  • Distributed Reinforcement Learning (DRL)
  • Multi-objective optimization
  • Optimal Power Flow (OPF)
  • Q(λ) learning

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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