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Real Time Line Power Flow Control Using Model Agnostic Controller
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DOI：10.1109/TPWRS.2023.3299386.png)
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摘要
En 中文
This article addresses the challenge of mitigating line overloading/congestion caused by renewable penetration and contingencies. Traditional methods of mitigating overloading involve generation rescheduling in tertiary dispatch every 5-30 minutes. This approach is generally based on forecasted load and generation, and not effective in real-time corrective actions. The article introduces a model-free Neuro Predictive Controller (NPC) that can map line power flows to the inputs of dispatching resources such as generators and/or battery energy storage systems. The NPC receives synchrophasor measurement of line power flows and generates control inputs (i.e., set points) to generators and/or battery energy storage systems. The controller does not require frequent update of the system model. The efficacy of the controller was tested on 16-machine 68-bus test system and the Indian grid for various scenarios, such as line outage, generator outage, variable renewable generation, and load change. The proposed controller demonstrated better performance as compared to the existing controllers. Upon successful implementation, the controller can regulate line flow in less than 60 seconds, allowing the lines to operate closer to the thermal limit and resolve limit violations due to contingencies and variable renewable generation in real-time.
Keyword:
Battery energy storage systems (BESS)
congestion management
neuro predictive controller (NPC)
power flow controller
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
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