OPC UA security for protecting substation and control center data communication in the distribution domain of the smart grid

AbstractLithium-ion battery energy storage systems (Li-ion BESS), due to their capability in providing both active and reactive power services, act as a bridging technology for efficient implementation of active network management (ANM) schemes for land-based grid applications. Due to higher integration of intermittent renewable energy sources in the distribution system, transient instability may induce power quality issues, mainly in terms of voltage fluctuations. In such situations, ANM schemes in the power network are a possible solution to maintain operation limits defined by grid codes. However, to implement ANM schemes effectively, integration and control of highly flexible Li-ion BESS play an important role, considering their performance characteristics and economics. Hence, in this paper, an energy management system (EMS) has been developed for implementing the ANM scheme, particularly focusing on the integration design of Li-ion BESS and the controllers managing them. Developed ANM scheme has been utilized to mitigate MV network issues (i.e. voltage stability and adherence to reactive power window). The efficiency of Li-ion BESS integration methodology, performance of the EMS controllers to implement ANM scheme and the effect of such ANM schemes on integration of Li-ion BESS, i.e. control of its grid-side converter (considering operation states and characteristics of the Li-ion BESS) and their coordination with the grid side controllers have been validated by means of simulation studies in the Sundom smart grid network, Vaasa, Finland.

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Optimal Smart Grid Operation and Control Enhancement by Edge Computing

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) ◽

10.1109/smartgridcomm47815.2020.9302998 ◽

2020 ◽

Author(s):

Yuan Liao ◽

Jiangbiao He

Keyword(s):

Smart Grid ◽

Edge Computing ◽

Grid Operation ◽

And Control

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Modeling human-like longitudinal driver model for intelligent vehicles based on reinforcement learning

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407020983579 ◽

2021 ◽

pp. 095440702098357

Author(s):

Ju Xie ◽

Xing Xu ◽

Feng Wang ◽

Haobin Jiang

Keyword(s):

Reinforcement Learning ◽

Comprehensive Evaluation ◽

Path Following ◽

Intelligent Vehicles ◽

Driver Model ◽

Control Center ◽

Training Performance ◽

Learning Agents ◽

System A ◽

And Control

The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.

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