E3S Web Conf.
Volume 209, 2020ENERGY-21 – Sustainable Development & Smart Management
|Number of page(s)||7|
|Section||Session 1. Towards Intelligent Energy Systems|
|Published online||23 November 2020|
Development of Digital Twin for Load Center on the Example of Distribution Network of an Urban District
1 Melentiev Energy Systems Institute, Electric Power System Department, 664033 Irkutsk, Russia
2 Irkutsk Scientific Center of SB RAS, 664033 Irkutsk, Russia
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The paper proposes a concept of building a digital twin based on the reinforcement learning method. This concept allows implementing an accurate digital model of an electrical network with bidirectional automatic data exchange, used for modeling, optimization, and control. The core of such a model is an agent (potential digital twin). The agent, while constantly interacting with a physical object (electrical grid), searches for an optimal strategy for active network management, which involves short-term strategies capable of controlling the power supplied by generators and/ or consumed by the load to avoid overload or voltage problems. Such an agent can verify its training with the initial default policy, which can be considered as a teacher’s advice. The effectiveness of this approach is demonstrated on a test 77-node scheme and a real 17-node network diagram of the Akademgorodok microdistrict (Irkutsk) according to the data from smart electricity meters.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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