Abstract
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.
Highlights
Innovative and structural changes in urban electric grids, their increasingly close interaction with the transport system, and the service sector determine the trends and related research on the development of concepts for the "smart neighborhood" with a subsequent transition to the "smart city" [1]
We show its application to a case of a real distribution electric system using Active Network Management (ANM) as a core of digital twin
Such advanced strategies imply that the system operator has to solve largescale optimal sequential decision-making problems under uncertainty [12]. We state these problems as Markov Decision Process (MDP), where the system dynamics describe the evolution of the electrical network and devices, while the action space encompasses the control actions that are available to the distribution system operator (DSO)
Summary
Innovative and structural changes in urban electric grids, their increasingly close interaction with the transport system, and the service sector determine the trends and related research on the development of concepts for the "smart neighborhood" with a subsequent transition to the "smart city" [1]. The digital twin of electrical networks is a mathematical model of electrical networks implemented based on special software It is capable of assessing the reliability of power supply to a smart neighborhood and identifying vulnerabilities in its electrical network, developing and visualizing various scenarios for the network development [2]. The paper proposes a concept of building digital twin based on reinforcement machine learning methods that allow implementing an accurate digital model of an electrical network with bidirectional automatic data exchange, used for modeling, optimization, and control. In this case, the data transmitted from the digital twin are control actions. Feedback signals that reflect the correctness of control actions are considered as a variant of state updates
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