Solving dynamic distribution network reconfiguration using deep reinforcement learning

Ognjen Kundačina; Predrag M. Vidović; Milan R. Petković

doi:10.1007/s00202-021-01399-y

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Solving dynamic distribution network reconfiguration using deep reinforcement learning

Ognjen Kundačina Predrag M. Vidović Milan R. Petković

Year: 2021 Journal: Electrical Engineering Vol: 104 (3)Pages: 1487-1501 Publisher: Springer Science+Business Media

DOI: 10.1007/s00202-021-01399-y

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Keywords:

Control reconfiguration Reinforcement learning Robustness (evolution) Network topology Scalability Markov decision process Computer science Mathematical optimization Controller (irrigation) Engineering Distributed computing Control theory (sociology) Markov process Artificial intelligence Embedded system Mathematics Control (management)

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Optimal Power Flow Distribution

Physical Sciences → Engineering → Electrical and Electronic Engineering

Microgrid Control and Optimization

Physical Sciences → Engineering → Control and Systems Engineering

Smart Grid Energy Management

Physical Sciences → Engineering → Electrical and Electronic Engineering

Solving dynamic distribution network reconfiguration using deep reinforcement learning

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