Digital twin based reinforcement learning for extracting network structures and load patterns in planning and operation of distribution systems

Author:

Hua Weiqi,Stephen BruceORCID,Wallom David C.H.ORCID

Funder

Engineering and Physical Sciences Research Council

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference41 articles.

1. Enabling the distribution system operation (DSO)transition,2021

2. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems;Grieves,2017

3. Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties;You;Appl Energy,2022

4. On the implementation of IoT-based digital twin for networked microgrids resiliency against cyber attacks;Saad;IEEE Trans Smart Grid,2020

5. Overcoming decision paralysis—A digital twin for decision making in energy system design;Granacher;Appl Energy,2022

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1. Daily Load Profiles Clustering of Regional Distribution Grid 6-10 kV;2023 IEEE XVI International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering (APEIE);2023-11-10

2. Power distribution system planning framework (A comprehensive review);Energy Strategy Reviews;2023-11

3. Lazy deep Q networks for unified rotor angle stability framework with unified time-scale of power systems with mass distributed energy storage;Engineering Applications of Artificial Intelligence;2023-11

4. Synergistic integration of digital twins and sustainable industrial internet of things for new generation energy investments;Journal of Advanced Research;2023-11

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