The Large-scale Testbed (LTB) is a tightly integrated, closed-loop platform for rapid prototyping of power systems. ANDES, AMS, DiME, and AGVis are four major independent sub packages of LTB that serve as a dynamic simulator, market simulator, distributed messaging environment, and grid visualizer, respectively. Towards a one-stop solution, these LTB packages can serve for both individual and federated use.
AMS is included in the newly released v2.0.
LTB platform enables power system dynamic simualiton, market simulation, geographical visualization, real-time simualtion, and dispatch-dynamic co-simulaiton.
Getting trouble with dispatch in a dynamic simulation? The interoperability of ANDES for scheduling supports secured dispatch in a dynamic simulation.
Getting lost in the APIs of simulators? Easy-to-use ANDES and AMS can be your last stop to prototype new algorithms and models.
Looking for a geographical visualization? AGVis allows geographical visualization with multiple options.
Interested in a real-time closed-loop simulation? LTB has integrated simulators and communication environments.
Video list:
- Using ANDES for Damping Control Allocation using Wind Generation
- Using AMS for LMP-based cyber attack detection
- Using ANDES and AMS for dispatch-dynamic co-simulaiton
- Webnair: Large-scale Testbed as a Virtual Power Grid
LTB is currently under active development. Welcome to join the Discussion of LTB.
If you use LTB packages for research or consulting, we kindly request you to cite the following papers in your publication that uses LTB
F. Li, K. Tomsovic and H. Cui, "A Large-Scale Testbed as a Virtual Power Grid: For Closed-Loop Controls in Research and Testing," in IEEE Power and Energy Magazine, vol. 18, no. 2, pp. 60-68, March-April 2020, doi: 10.1109/MPE.2019.2959054.
H. Cui, F. Li and K. Tomsovic, "Hybrid Symbolic-Numeric Framework for Power System Modeling and Analysis," in IEEE Transactions on Power Systems, vol. 36, no. 2, pp. 1373-1384, March 2021, doi: 10.1109/TPWRS.2020.3017019.
Parsly, N., Wang, J., West, N., Zhang, Q., Cui, H., & Li, F. (2022). "DiME and AGVIS A Distributed Messaging Environment and Geographical Visualizer for Large-scale Power System Simulation". arXiv. https://doi.org/https://arxiv.org/abs/2211.11990v1
H. Cui et al., "Disturbance Propagation in Power Grids With High Converter Penetration," in Proceedings of the IEEE, doi: 10.1109/JPROC.2022.3173813.
W. Wang, X. Fang, H. Cui, F. Li, Y. Liu and T. J. Overbye, "Transmission-and-Distribution Dynamic Co-Simulation Framework for Distributed Energy Resource Frequency Response," in IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 482-495, Jan. 2022. doi: 10.1109/TSG.2021.3118292.
Y. Zhang et al., "Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning With Region-of-Interest Active Sampling," in IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 1942-1955, May 2022. doi: 10.1109/TPWRS.2021.3110881.
H. Cui, F. Li and X. Fang, "Effective Parallelism for Equation and Jacobian Evaluation in Large-Scale Power Flow Calculation," in IEEE Transactions on Power Systems, vol. 36, no. 5, pp. 4872-4875, Sept. 2021. doi: 10.1109/TPWRS.2021.3073591.
C. Lackner, D. Osipov, H. Cui and J. H. Chow, "A Privacy-Preserving Distributed Wide-Area Automatic Generation Control Scheme," in IEEE Access, vol. 8, pp. 212699-212708, 2020. doi: 10.1109/ACCESS.2020.3040883
H. Cui and Y. Zhang, "Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power Systems," 2022 IEEE Power & Energy Society General Meeting (PESGM), Denver, CO, USA, 2022, pp. 01-05. doi: 10.1109/PESGM48719.2022.9916967.
Y. Liu et al., "Transmission-Distribution Dynamic Co-simulation of Electric Vehicles Providing Grid Frequency Response," 2022 IEEE Power & Energy Society General Meeting (PESGM), Denver, CO, USA, 2022, pp. 1-5. doi: 10.1109/PESGM48719.2022.9917027.
J. Wang, F. Li, H. Cui and Q. Zhang, "Impacts of VSG Control on Frequency Response in Power Systems with High-Penetration Renewables," 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), Taiyuan, China, 2021, pp. 171-176. doi: 10.1109/EI252483.2021.9712880.
This work was supported in part by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program.
ANDES is authored by Hantao Cui, AMS is authored by Qiwei Zhang, DiME and AGVis are developed and maintained by Nicholas West and Nicholas Parsly.
LTB is licensed under the GPL v3 License.