Search Results for author: David Lubkeman

Found 11 papers, 0 papers with code

Multi-Feeder Restoration using Multi-Microgrid Formation and Management

no code implementations26 Nov 2023 Valliappan Muthukaruppan, Rongxing Hu, Ashwin Shirsat, Mesut Baran, Ning Lu, Wenyuan Tang, David Lubkeman

This papers highlights the benefit of coordinating resources on mulitple active distribution feeders during severe long duration outages through multi-microgrid formation.

energy management Management

Under-frequency Load Shedding for Power Reserve Management in Islanded Microgrids

no code implementations3 Sep 2023 Bei Xu, Victor Paduani, Qi Xiao, Lidong Song, David Lubkeman, Ning Lu

Furthermore, in comparison to sectionalizer-based UFLS, using smart meters or controllable loads for UFLS allows for a more accurate per-phase load shedding in a progressive manner.

Management

Optimal Control Design for Operating a Hybrid PV Plant with Robust Power Reserves for Fast Frequency Regulation Services

no code implementations7 Dec 2022 Victor Paduani, Qi Xiao, Bei Xu, David Lubkeman, Ning Lu

The controller's objective is to control the PV and BESS to follow power setpoints sent to the the hybrid system while maintaining desired power reserves and meeting system operational constraints.

Feeder Microgrid Management on an Active Distribution System during a Severe Outage

no code implementations23 Aug 2022 Valliappan Muthukaruppan, Ashwin Shirsat, Rongxing Hu, Victor Paduani, Bei Xu, Yiyan Li, Mesut Baran, Ning Lu, David Lubkeman, Wenyuan Tang

The management of such feeder-level microgrid has however many challenges, such as limited resources that can be deployed on the feeder quickly, and the limited real-time monitoring and control on the distribution system.

energy management Management

Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy

no code implementations23 Nov 2021 Si Zhang, Mingzhi Zhang, Rongxing Hu, David Lubkeman, Yunan Liu, Ning Lu

In Stage 1(individual training), while holding all the other agents inactive, we separately train each agent to obtain its own optimal VVC actions in the action space: {consume, generate, do-nothing}.

reinforcement-learning Reinforcement Learning (RL)

Novel Real-Time EMT-TS Modeling Architecture for Feeder Blackstart Simulations

no code implementations19 Nov 2021 Victor Paduani, Bei Xu, David Lubkeman, Ning Lu

This paper presents the development and benchmarking of a novel real-time electromagnetic-transient and transient-stability (EMT-TS) modeling architecture for distribution feeder restoration studies.

Benchmarking

A Novel Grid-forming Voltage Control Strategy for Supplying Unbalanced Microgrid Loads Using Inverter-based Resources

no code implementations18 Nov 2021 Bei Xu, Victor Paduani, Hui Yu, David Lubkeman, Ning Lu

Compared with the conventional rotating reference frame ($dq$) based control scheme, the proposed scheme shows better dynamic performance.

Scheduling

Hierarchical Multi-timescale Framework For Operation of Dynamic Community Microgrid

no code implementations19 Nov 2020 Ashwin Shirsat, Valliappan Muthukaruppan, Rongxing Hu, Ning Lu, Mesut Baran, David Lubkeman, Wenyuan Tang

The intermediate near real-time scheduling stage updates the DA schedule closer to the dispatch time, followed by the RT dispatch stage.

Scheduling

FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets

no code implementations3 Apr 2020 Ming Liang, Yao Meng, Jiyu Wang, David Lubkeman, Ning Lu

This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN.

Attribute

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