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

Journal article

Low voltage distribution networks deliver power to the last mile of the network, but are often legacy assets from a time when low carbon technologies, e.g., electrified heat, storage, and electric vehicles, were not envisaged. Furthermore, exploiting emerging data from distribution networks to provide decision support for adapting planning and operational strategies with system transitions presents a challenge. To overcome these challenges, this paper proposes a novel application of digital twins based reinforcement learning to improve decision making by a distribution system operator, with key metrics of predictability, responsiveness, interoperability, and automation. The power system states, i.e., network configurations, technological combinations, and load patterns, are captured via a convolutional neural network, chosen for its pattern recognition capability with high-dimensional inputs. The convolutional neural networks are iteratively trained through the fitted Q-iteration algorithm, as a batch mode reinforcement learning, to adapt the planning and operational decisions with the dynamic system transitions. Case studies demonstrate the effectiveness of the proposed model by reducing 50% of the investment cost when the system transitions towards the winter and maintaining the power loss and loss of load within 5% compared to the benchmark optimisation. Doubled power consumption was observed in winter under future energy scenarios due to the electrification of heat. The trained model can accurately adapt optimal decisions according to the system changes while reducing the computational time of solving optimisation problems, for a range of scales of distribution systems, demonstrating its potential for scalable deployment by a system operator.

Authors: Hua, W; Stephen, B; Wallom, DCH

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Applied Energy
• Volume: 342
• Article number: 121128
• Publication date: 2023-04-24
• Acceptance date: 2023-04-08
• DOI: 10.1016/j.apenergy.2023.121128
• EISSN: 1872-9118
• ISSN: 0306-2619
• Language: English